Johns Hopkins Turbulence Databases


Publications and abstracts:

      ●  Yi Li, Eric Perlman, Minping Wan, Yunke Yang, Charles Meneveau, Randal Burns, Shiyi Chen, Alexander Szalay, and Gregory Eyink. A public turbulence database cluster and applications to study Lagrangian evolution of velocity increments in turbulence. Journal of Turbulence, 9:N31, 2008. [ DOI | arXiv ]
      ●  Huidan Yu, Kalin Kanov, Eric Perlman, Jason Graham, Edo Frederix, Randal Burns, Alexander Szalay, Gregory Eyink, and Charles Meneveau. Studying Lagrangian dynamics of turbulence using on-demand fluid particle tracking in a public turbulence database. Journal of Turbulence, 13:N12, 2012. [ DOI ]
      ●  Eric Perlman, Randal Burns, Yi Li, and Charles Meneveau. Data exploration of turbulence simulations using a database cluster. In Proceedings of the 2007 ACM/IEEE conference on Supercomputing - SC '07, page 1, New York, New York, USA, 2007. ACM Press. [ DOI ]
      ●  J. Graham, K. Kanov, X. I. A. Yang, M. Lee, N. Malaya, C. C. Lalescu, R. Burns, G. Eyink, A. Szalay, R. D. Moser, and C. Meneveau. A Web services accessible database of turbulent channel flow and its use for testing a new integral wall model for LES. Journal of Turbulence, 17(2):181-215, 2016. [ DOI ]
      ●  Xiaodan Wang, Eric Perlman, Randal Burns, Tanu Malik, Tamas Budavári, Charles Meneveau, and Alexander Szalay. JAWS: Job-Aware Workload Scheduling for the Exploration of Turbulence Simulations. In 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis, pages 1-11. IEEE, 2010. [ DOI ]
      ●  Kalin Kanov, Eric Perlman, Randal Burns, Yanif Ahmad, and Alexander Szalay. I/O streaming evaluation of batch queries for data-intensive computational turbulence. In Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis on - SC '11, page 1, New York, New York, USA, 2011. ACM Press. [ DOI ]
      ●  Kalin Kanov, Randal Burns, Greg Eyink, Charles Meneveau, and Alexander Szalay. Data-intensive spatial filtering in large numerical simulation datasets. In 2012 International Conference for High Performance Computing, Networking, Storage and Analysis, pages 1-9. IEEE, 2012. [ DOI ]
      ●  Zhao Wu, Jin Lee, Charles Meneveau, and Tamer Zaki. Application of a self-organizing map to identify the turbulent-boundary-layer interface in a transitional flow. Physical Review Fluids, 4(2):023902, 2019. [ DOI ]

Sample publications of Turbulence Database usage:

generated by bibbase.org
  2024 (44)
Multiscale and anisotropic characterization of images based on complexity: An application to turbulence. Granero-Belinchon, C.; Roux, S., G.; and Garnier, N., B. Physica D: Nonlinear Phenomena, 459: 134027. 2024.
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Deep learning for particle image velocimetry with attentional transformer and cross-correlation embedded. Yu, C.; Chang, Y.; Liang, X.; Liang, C.; and Xie, Z. Ocean Engineering, 292: 116522. 2024.
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Maximum likelihood filtering for particle tracking in turbulent flows. Kearney, G., M.; Laurent, K., M.; and Kearney, R., V. Experiments in Fluids, 65(2): 1-11. 2024.
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Particle image velocimetry combining unsupervised learning and optical flow model. Shan, L.; Lou, X.; Hong, B.; Xiong, J.; Jian, J.; and Kong, M. Optics Communications, 554: 130200. 2024.
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STUDY OF CARDIAC FLUID DYNAMICS IN THE RIGHT SIDE OF THE HEART WITH AI PIV. Bouchahda, N.; Ayari, R.; Majewski, W.; and Wei, R. Journal of Flow Visualization and Image Processing, 31. 2024.
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Comparing local energy cascade rates in isotropic turbulence using structure-function and filtering formulations. Yao, H.; Schnaubelt, M.; Szalay, A., S.; Zaki, T., A.; and Meneveau, C. Journal of Fluid Mechanics, 980: A42. 2024.
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Vorticity alignment with Lyapunov vectors and rate-of-strain eigenvectors. Encinas-Bartos, A.; and Haller, G. European Journal of Mechanics-B/Fluids. 2024.
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Theory of flow-induced covalent polymer mechanochemistry in dilute solutions. Rognin, E.; Willis-Fox, N.; and Daly, R. RSC Mechanochemistry, 1(1): 138-144. 2024.
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A fast, matrix-based method to perform omnidirectional pressure integration. Zigunov, F.; and Charonko, J., J. Measurement Science and Technology, 35(6): 65302. 2024.
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Meshless track assimilation (MTA) of 3D PTV data. Sperotto, P.; Watz, B.; and Hess, D. Measurement Science and Technology, 35(8): 86005. 2024.
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Scattering spectra models for physics. Cheng, S.; Morel, R.; Allys, E.; Ménard, B.; and Mallat, S. PNAS nexus, 3(4): pgae103. 2024.
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Neural ideal large eddy simulation: Modeling turbulence with neural stochastic differential equations. Boral, A.; Wan, Z., Y.; Zepeda-Núñez, L.; Lottes, J.; Wang, Q.; Chen, Y.; Anderson, J.; and Sha, F. Advances in Neural Information Processing Systems, 36. 2024.
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Koopman dynamic-oriented deep learning for invariant subspace identification and full-state prediction of complex systems. Wu, J.; Luo, M.; Xiao, D.; Pain, C., C.; and Khoo, B., C. Computer Methods in Applied Mechanics and Engineering, 429: 117071. 2024.
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Efficient survival strategy for zooplankton in turbulence. Mousavi, N.; Qiu, J.; Mehlig, B.; Zhao, L.; and Gustavsson, K. Physical Review Research, 6(2): L022034. 2024.
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A deep learning super-resolution model for turbulent image upscaling and its application to shock wave–boundary layer interaction. Sofos, F.; Drikakis, D.; Kokkinakis, I., W.; and Spottswood, S., M. Physics of Fluids, 36(2). 2024.
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Evaluation of seedless wavelet-based optical flow velocimetry for schlieren images. Chen, M.; Zhao, Z.; Hou, Y.; Zhu, J.; Sun, M.; and Zhou, B. Physics of Fluids, 36(7). 2024.
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Robust relation of streamwise velocity autocorrelation in atmospheric surface layers based on an autoregressive moving average model. Zhang, F.; Xie, J.; Chen, S., X.; and Zheng, X. Journal of Fluid Mechanics, 981: A20. 2024.
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Nonlocal contributions to the turbulent cascade in magnetohydrodynamic plasmas. Friedrich, J.; Wilbert, M.; and Marino, R. Physical Review E, 109(4): 45208. 2024.
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Multifacets of lossy compression for scientific data in the Joint-Laboratory of Extreme Scale Computing. Cappello, F.; Di, S.; Underwood, R.; Tao, D.; Calhoun, J.; Kazutomo, Y.; Sato, K.; Singh, A.; Giraud, L.; Agullo, E.; and others Future Generation Computer Systems. 2024.
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Leveraging arbitrary mobile sensor trajectories with shallow recurrent decoder networks for full-state reconstruction. Ebers, M., R.; Williams, J., P.; Steele, K., M.; and Kutz, J., N. IEEE Access. 2024.
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Vortex polarization and circulation statistics in isotropic turbulence. Moriconi, L.; Pereira, R., M.; and Valadão, V., J. Physical Review E, 109(4): 45106. 2024.
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A polynomial model with line-of-sight constraints for Lagrangian particle tracking under interface refraction. Zeng, X.; Qu, H.; He, C.; Liu, Y.; and Gan, L. Measurement Science and Technology, 35(6): 66011. 2024.
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Characterizing Complex Spatiotemporal Patterns from Entropy Measures. Barauna, L., O.; Sautter, R., A.; Rosa, R., R.; Rempel, E., L.; and Frery, A., C. Entropy, 26(6): 508. 2024.
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High-performance Effective Scientific Error-bounded Lossy Compression with Auto-tuned Multi-component Interpolation. Liu, J.; Di, S.; Zhao, K.; Liang, X.; Jin, S.; Jian, Z.; Huang, J.; Wu, S.; Chen, Z.; and Cappello, F. Proceedings of the ACM on Management of Data, 2(1): 1-27. 2024.
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Spectral energy transfer analysis of a forced homogeneous isotropic turbulence using triple decomposition of velocity gradient tensor. Fathali, M.; and Khoei, S. Journal of Turbulence, 25(1-3): 125-143. 2024.
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Large-eddy simulations of turbulent wake flows behind helical-and straight-bladed vertical axis wind turbines rotating at low tip speed ratios. Gharaati, M.; Xiao, S.; Mart\'\inez-Tossas, L., A.; Araya, D., B.; and Yang, D. Physical Review Fluids, 9(7): 74603. 2024.
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High-resolution velocity determination from particle images via neural networks with optical flow velocimetry regularization. Ji, K.; Hui, X.; and An, Q. Physics of Fluids, 36(3). 2024.
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On the scaling and critical layer in a turbulent boundary layer over a compliant surface. Lu, Y.; Xiang, T.; Zaki, T., A.; and Katz, J. Journal of Fluid Mechanics, 980: R2. 2024.
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Structure and role of the pressure Hessian in regions of strong vorticity in turbulence. Yang, P.; Xu, H.; Pumir, A.; and He, G., W. Journal of Fluid Mechanics, 983: R2. 2024.
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Turbulent flows are not uniformly multifractal. Mukherjee, S.; Murugan, S., D.; Mukherjee, R.; and Ray, S., S. Physical Review Letters, 132(18): 184002. 2024.
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Cross-correlation-based convolutional neural network with velocity regularization for high-resolution velocimetry of particle images. Ji, K.; An, Q.; and Hui, X. Physics of Fluids, 36(7). 2024.
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Uncovering wall-shear stress dynamics from neural-network enhanced fluid flow measurements. Lagemann, E.; Brunton, S., L.; and Lagemann, C. Proceedings of the Royal Society A, 480(2292): 20230798. 2024.
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Lagrangian modeling of a nonhomogeneous turbulent shear flow: Molding homogeneous and isotropic trajectories into a jet. Viggiano, B.; Basset, T.; Bourgoin, M.; Cal, R., B.; Chevillard, L.; Meneveau, C.; and Volk, R. Physical Review Fluids, 9(4): 44604. 2024.
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Characterization and evolution of local streamline geometry in an incompressible turbulent channel flow. Wu, G.; and Xu, C. Physics of Fluids, 36(2). 2024.
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Maximum likelihood filtering for particle tracking in turbulent flows. Kearney, G., M.; Laurent, K., M.; and Kearney, R., V. Experiments in Fluids, 65(2): 24. 2024.
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Hidden mechanism of dynamic large-eddy simulation models. Hu, X.; Vedula, K.; and Park, G., I. Physical Review Fluids, 9(7): 74607. 2024.
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New Method to Calculate Friction Velocity in Smooth Channel Flows Using Direct Numerical Simulation Data. Mishra, H.; and Venayagamoorthy, S., K. Journal of Hydraulic Engineering, 150(4): 4024019. 2024.
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Detecting the large-scale wall-attached structural inclination angles by a machine learning perspective in turbulent boundary layer. Li, X.; Hu, X.; Hu, L.; Li, P.; and Liu, W. Physics of Fluids, 36(3). 2024.
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Invariant data-driven subgrid stress modeling on anisotropic grids for large eddy simulation. Prakash, A.; Jansen, K., E.; and Evans, J., A. Computer Methods in Applied Mechanics and Engineering, 422: 116807. 2024.
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Extension of the law of the wall exploiting weak similarity of velocity fluctuations in turbulent channels. Hansen, C.; Sørensen, J., N.; Yang, X., I., A.; and Abkar, M. Physics of Fluids, 36(1). 2024.
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Forward and Inverse Energy Cascade in Fluid Turbulence Adhere to Kolmogorov’s Refined Similarity Hypothesis. Yao, H.; Yeung, P., K.; Zaki, T., A.; and Meneveau, C. Physical Review Letters, 132(16): 164001. 2024.
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Hybrid-attention-based Swin-Transformer super-resolution reconstruction for tomographic particle image velocimetry. Li, X.; Yang, Z.; and Yang, H. Physics of Fluids, 36(6). 2024.
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Data-driven model for Lagrangian evolution of velocity gradients in incompressible turbulent flows. Das, R.; and Girimaji, S., S. Journal of Fluid Mechanics, 984: A39. 2024.
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Detecting the high/low-speed large-scale structures by a machine learning perspective in turbulent boundary. Zhang, Y. In Journal of Physics: Conference Series, volume 2756, pages 12021, 2024.
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  2023 (57)
A sparse optical flow inspired method for 3D velocimetry. Lu, G.; Steinberg, A.; and Yano, M. Experiments in Fluids, 64(4): 66. 2023.
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Optimizing Dt for MP-STB in Particle Tracking Velocimetry. Fenelon, M., R.; Zhang, Y.; and Cattafesta, L., N. In AIAA SCITECH 2023 Forum, pages 634, 2023.
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Modeling the resuspension of small inertial particles in turbulent flow over a fractal-like multiscale rough surface. Hu, R.; Johnson, P., L.; and Meneveau, C. Physical Review Fluids, 8(2): 24304. 2023.
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A wall model learned from the periodic hill data and the law of the wall. Zhou, Z.; Yang, X., I., A.; Zhang, F.; and Yang, X. Physics of Fluids, 35(5). 2023.
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Assessment of implicit LES modelling for bypass transition of a boundary layer. Perrin, R.; and Lamballais, E. Computers & Fluids, 251: 105728. 2023.
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Mechanisms of mass transfer to small spheres sinking in turbulence. Lawson, J., M.; and Ganapathisubramani, B. Journal of Fluid Mechanics, 954: A15. 2023.
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Vorticity locking and pressure dynamics in finite-temperature superfluid turbulence. Laurie, J.; and Baggaley, A., W. Physical Review Fluids, 8(5): 54604. 2023.
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Conserving Local Magnetic Helicity in Numerical Simulations. Zenati, Y.; and Vishniac, E., T. The Astrophysical Journal, 948(1): 11. 2023.
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Creation of an isolated turbulent blob fed by vortex rings. Matsuzawa, T.; Mitchell, N., P.; Perrard, S.; and Irvine, W., T., M. Nature Physics,1-8. 2023.
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General attached eddies: Scaling laws and cascade self-similarity. Hu, R.; Dong, S.; and Vinuesa, R. Physical Review Fluids, 8(4): 44603. 2023.
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Stochastic particle advection velocimetry (SPAV): theory, simulations, and proof-of-concept experiments. Zhou, K.; Li, J.; Hong, J.; and Grauer, S., J. Measurement Science and Technology, 34(6): 65302. 2023.
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A unified understanding of scale-resolving simulations and near-wall modelling of turbulent flows using optimal finite-element projections. Pradhan, A.; and Duraisamy, K. Journal of Fluid Mechanics, 955: A6. 2023.
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Deep-learning-based image preprocessing for particle image velocimetry. Fan, Y.; Guo, C.; Han, Y.; Qiao, W.; Xu, P.; and Kuai, Y. Applied Ocean Research, 130: 103406. 2023.
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An end-to-end KNN-based PTV approach for high-resolution measurements and uncertainty quantification. Tirelli, I.; Ianiro, A.; and Discetti, S. Experimental Thermal and Fluid Science, 140: 110756. 2023.
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Accurate Near Wall Measurements in Wall Bounded Flows with wOFV via an Explicit No-Slip Boundary Condition. Jassal, G., R.; and Schmidt, B., E. In AIAA SCITECH 2023 Forum, pages 2444, 2023.
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Rational Boolean Stabilization of Subgrid Models for Large Eddy Simulation. Torres, E., E.; and Dahm, W., J. In AIAA SCITECH 2023 Forum, pages 2485, 2023.
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3D Lagrangian tracking of polydispersed bubbles at high image densities. Tan, S.; Zhong, S.; and Ni, R. Experiments in Fluids, 64(4): 85. 2023.
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A simple trick to improve the accuracy of PIV/PTV data. Tirelli, I.; Ianiro, A.; and Discetti, S. Experimental Thermal and Fluid Science, 145: 110872. 2023.
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Full-Volume 3D Fluid Flow Reconstruction With Light Field PIV. Ding, Y.; Li, Z.; Chen, Z.; Ji, Y.; Yu, J.; and Ye, J. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2023.
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Three-dimensional particle tracking algorithm based on the special ellipsoids. Lin, Y.; Zhang, Y.; Jin, Y.; Guan, K.; Ma, Q.; Cui, Y.; and Yang, B. Measurement, 216: 112883. 2023.
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Folding dynamics and its intermittency in turbulence. Qi, Y.; Meneveau, C.; Voth, G., A.; and Ni, R. Physical Review Letters, 130(15): 154001. 2023.
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Assessment and application of wavelet-based optical flow velocimetry (wOFV) to wall-bounded turbulent flows. Nicolas, A.; Zentgraf, F.; Linne, M.; Dreizler, A.; and Peterson, B. Experiments in fluids, 64(3): 50. 2023.
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Super-resolution reconstruction for the three-dimensional turbulence flows with a back-projection network. Yang, Z.; Yang, H.; and Yin, Z. Physics of Fluids, 35(5). 2023.
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Extension of the Smagorinsky Subgrid Stress Model to Anisotropic Filters. Prakash, A.; Jansen, K., E.; and Evans, J., A. In AIAA SCITECH 2023 Forum, pages 2486, 2023.
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Multiresolution convolutional autoencoders. Liu, Y.; Ponce, C.; Brunton, S., L.; and Kutz, J., N. Journal of Computational Physics, 474: 111801. 2023.
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Mapping the shape and dimension of three-dimensional Lagrangian coherent structures and invariant manifolds. Aksamit, N., O. Journal of Fluid Mechanics, 958: A11. 2023.
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Deep learning for fluid velocity field estimation: A review. Yu, C.; Bi, X.; and Fan, Y. Ocean Engineering, 271: 113693. 2023.
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Recurrent graph optimal transport for learning 3D flow motion in particle tracking. Liang, J.; Xu, C.; and Cai, S. Nature Machine Intelligence,1-13. 2023.
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Online measurement of granular velocity of rotary drums by a fast PIV deep network FPN-FlowNet. Duan, J.; Liu, X.; and Yin, Y. Measurement, 209: 112529. 2023.
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A transformer-based synthetic-inflow generator for spatially developing turbulent boundary layers. Yousif, M., Z.; Zhang, M.; Yu, L.; Vinuesa, R.; and Lim, H. Journal of Fluid Mechanics, 957: A6. 2023.
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Homogeneity constraints on the mixed moments of velocity gradient and pressure Hessian in incompressible turbulence. Zhou, Z.; and Yang, P. Physical Review Fluids, 8(2): 24601. 2023.
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Pressure Reconstruction from the Measured Pressure Gradient Using Gaussian Process Regression. You, Z.; Wang, Q.; and Liu, X. In AIAA SCITECH 2023 Forum, pages 414, 2023.
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Reconstructing turbulent velocity and pressure fields from under-resolved noisy particle tracks using physics-informed neural networks. Clark Di Leoni, P.; Agarwal, K.; Zaki, T., A.; Meneveau, C.; and Katz, J. Experiments in Fluids, 64(5): 95. 2023.
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Optical flow for particle images with optimization based on a priori knowledge of the flow. Benkovic, T.; Krawczynski, J.; and Druault, P. Measurement Science and Technology. 2023.
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A sparse optical flow inspired method for 3D velocimetry. Lu, G.; Steinberg, A.; and Yano, M. Experiments in Fluids, 64(4): 66. 2023.
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Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data. Pan, S.; Brunton, S., L.; and Kutz, J., N. Journal of Machine Learning Research, 24(41): 1-60. 2023.
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Comparative assessment for pressure field reconstruction based on physics-informed neural network. Fan, D.; Xu, Y.; Wang, H.; and Wang, J. Physics of Fluids, 35(7). 2023.
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Compensation of seeding bias for particle tracking velocimetry in turbulent flows. Barois, T.; Viggiano, B.; Basset, T.; Cal, R., B.; Volk, R.; Gibert, M.; and Bourgoin, M. Physical Review Fluids, 8(7): 74603. 2023.
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Divergence–curl correction for pressure field reconstruction from acceleration in turbulent flows. Lin, Y.; and Xu, H. Experiments in Fluids, 64(8): 137. 2023.
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Assessment of implicit LES modelling for bypass transition of a boundary layer. Perrin, R.; and Lamballais, E. Computers & Fluids, 251: 105728. 2023.
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Identifying dominant flow features from very-sparse Lagrangian data: a multiscale recurrence network-based approach. Iacobello, G.; and Rival, D., E. Experiments in fluids, 64(10): 157. 2023.
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Deep-learning-based image preprocessing for particle image velocimetry. Fan, Y.; Guo, C.; Han, Y.; Qiao, W.; Xu, P.; and Kuai, Y. Applied Ocean Research, 130: 103406. 2023.
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Entrainment, detrainment and enstrophy transport by small-scale vortex structures. Aligolzadeh, F.; Holzner, M.; and Dawson, J., R. Journal of Fluid Mechanics, 973: A5. 2023.
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An Enhanced Python-Based Open-Source Particle Image Velocimetry Software for Use with Central Processing Units. Shirinzad, A.; Jaber, K.; Xu, K.; and Sullivan, P., E. Fluids, 8(11): 285. 2023.
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Deep dual recurrence optical flow learning for time-resolved particle image velocimetry. Yu, C.; Fan, Y.; Bi, X.; Kuai, Y.; and Chang, Y. Physics of Fluids, 35(4). 2023.
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Laminar to turbulent transition in terms of information theory. Bahamonde, A., D.; Cornejo, P.; and Sepúlveda, H., H. Physica A: Statistical Mechanics and its Applications, 629: 129190. 2023.
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An unsupervised deep learning model for dense velocity field reconstruction in particle image velocimetry (PIV) measurements. Zhang, W.; Dong, X.; Sun, Z.; and Xu, S. Physics of Fluids, 35(7). 2023.
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A velocity decomposition-based 3D optical flow method for accurate Tomo-PIV measurement. Kang, M.; Yang, H.; Yin, Z.; Gao, Q.; and Liu, X. Experiments in Fluids, 64(7): 135. 2023.
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Predictive data-driven model based on generative adversarial network for premixed turbulence-combustion regimes. Grenga, T.; Nista, L.; Schumann, C.; Karimi, A., N.; Scialabba, G.; Attili, A.; and Pitsch, H. Combustion Science and Technology, 195(15): 3923-3946. 2023.
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Estimating turbulent kinetic energy with an acoustic Doppler current profiler. Schwab, L., E.; and Rehmann, C., R. Flow Measurement and Instrumentation, 94: 102435. 2023.
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Dynamics of the perceived velocity gradient tensor and its modelling. Yang, P.; Bodenschatz, E.; He, G., W.; Pumir, A.; and Xu, H. Physical Review Fluids, 8(9): 94604. 2023.
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A Swin-transformer-based model for efficient compression of turbulent flow data. Zhang, M.; Yousif, M., Z.; Yu, L.; and Lim, H. Physics of Fluids, 35(8). 2023.
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An attention-mechanism incorporated deep recurrent optical flow network for particle image velocimetry. Han, Y.; and Wang, Q. Physics of Fluids, 35(7). 2023.
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Entropy and fluctuation relations in isotropic turbulence. Yao, H.; Zaki, T., A.; and Meneveau, C. Journal of Fluid Mechanics, 973: R6. 2023.
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Computer and Physical Modeling for the Estimation of the Pos-sibility of Application of Convolutional Neural Networks in Close-Range Photogrammetry. Pinchukov, V., V.; Poroykov, A., Y.; Shmatko, E., V.; and Sivov, N., Y. Computer, 15(1): 71-82. 2023.
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Experimental investigation of pressure statistics in laboratory homogeneous isotropic turbulence. Lin, Y.; and Xu, H. Physics of Fluids, 35(6). 2023.
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Universal alignment in turbulent pair dispersion. Shnapp, R.; Brizzolara, S.; Neamtu-Halic, M., M.; Gambino, A.; and Holzner, M. Nature Communications, 14(1): 4195. 2023.
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  2022 (60)
Law of bounded dissipation and its consequences in turbulent wall flows. Chen, X.; and Sreenivasan, K., R. J. Fluid Mech, 933: 20. 2022.
Law of bounded dissipation and its consequences in turbulent wall flows [pdf]Paper   Law of bounded dissipation and its consequences in turbulent wall flows [link]Website   doi   link   bibtex   abstract  
Approach to the 4/3 law for turbulent pipe and channel flows examined through a reformulated scale-by-scale energy budget. Zimmerman, S., J.; Antonia, R., A.; Djenidi, L.; Philip, J.; and Klewicki, J., C. A28, Fluid Mech, 931: 87-109. 2022.
Approach to the 4/3 law for turbulent pipe and channel flows examined through a reformulated scale-by-scale energy budget [pdf]Paper   Approach to the 4/3 law for turbulent pipe and channel flows examined through a reformulated scale-by-scale energy budget [link]Website   doi   link   bibtex   abstract  
Adaptive Scale-Similar Closure for Large Eddy Simulations. Part 1: Subgrid Stress Closure. Stallcup, E., W.; Kshitij, A.; and Dahm, W., J. In AIAA SCITECH 2022 Forum, pages 595, 2022.
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Systematic generation of moment invariant bases for 2D and 3D tensor fields. Bujack, R.; Zhang, X.; Suk, T.; and Rogers, D. Pattern Recognition, 123: 108313. 2022.
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The Onsager theory of wall-bounded turbulence and Taylor’s momentum anomaly. Eyink, G., L.; Kumar, S.; and Quan, H. Philosophical Transactions of the Royal Society A, 380(2218): 20210079. 2022.
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Single inertial particle statistics in turbulent flows from Lagrangian velocity models. Friedrich, J.; Viggiano, B.; Bourgoin, M.; Cal, R., B.; and Chevillard, L. Physical Review Fluids, 7(1): 14303. 2022.
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Scale interactions and anisotropy in Rayleigh–Taylor turbulence. Zhao, D.; Betti, R.; and Aluie, H. Journal of Fluid Mechanics, 930. 2022.
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Reinforcement Learning for Load-balanced Parallel Particle Tracing. Xu, J.; Guo, H.; Shen, H.; Raj, M.; Wurster, S., W.; and Peterka, T. IEEE Transactions on Visualization and Computer Graphics. 2022.
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Systematic generation of moment invariant bases for 2D and 3D tensor fields. Bujack, R.; Zhang, X.; Suk, T.; and Rogers, D. Pattern Recognition, 123: 108313. 2022.
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Correlation and decomposition concepts for identifying and disentangling flow structures: Framework and insights into turbulence organization. Mukherjee, S.; Mascini, M.; and Portela, L., M. Physics of Fluids, 34(1): 15119. 2022.
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Multifractality in a nested velocity gradient model for intermittent turbulence. Luo, Y.; Shi, Y.; and Meneveau, C. Physical Review Fluids, 7(1): 14609. 2022.
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Analysis of spatiotemporal inner-outer large-scale interactions in turbulent channel flow by multivariate empirical mode decomposition. Mäteling, E.; and Schröder, W. Physical Review Fluids, 7(3): 34603. 2022.
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Motion-Induced Noise Modeling of Towed Magnetic Antenna. Huang, Z.; and Jiang, Y. IEEE Transactions on Antennas and Propagation. 2022.
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Helicity distributions and transfer in turbulent channel flows with streamwise rotation. Yu, C.; Hu, R.; Yan, Z.; and Li, X. Journal of Fluid Mechanics, 940: A18. 2022.
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Origin of enhanced skin friction at the onset of boundary-layer transition. Wang, M.; Eyink, G., L.; and Zaki, T., A. Journal of Fluid Mechanics, 941: 2022. 2022.
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On the enhancement of boundary layer skin friction by turbulence: an angular momentum approach. Elnahhas, A.; and Johnson, P., L. Journal of Fluid Mechanics, 940: A36. 2022.
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Objective momentum barriers in wall turbulence. Aksamit, N., O.; and Haller, G. Journal of Fluid Mechanics, 941: A3. 2022.
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Tomographic long-distance μPIV to investigate the small scales of turbulence in a jet at high Reynolds number. Fiscaletti, D.; Ragni, D.; Overmars, E., F., J.; Westerweel, J.; and Elsinga, G., E. Experiments in Fluids, 63(1): 1-16. 2022.
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Nonparametric inference for diffusion processes in systems with smooth evolution. Sarnitsky, G.; and Heinz, S. Physica A: Statistical Mechanics and its Applications,127386. 2022.
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Fine scale reconstruction (VIC#) by implementing additional constraints and coarse-grid approximation into VIC+. Jeon, Y., J.; Müller, M.; and Michaelis, D. Experiments in Fluids, 63(4): 1-24. 2022.
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Pressure from data-driven-estimated velocity fields using snapshot PIV and fast probes. Chen, J.; Raiola, M.; and Discetti, S. Experimental Thermal and Fluid Science,110647. 2022.
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Probabilistic Characterization of Sweep and Ejection Events in Turbulent Flows and its Implications on Sediment Transport. Wu, K.; Tsai, C., W.; and Wu, M. Water Resources Research,e2021WR030417. 2022.
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A Lagrangian relaxation towards equilibrium wall model for large eddy simulation. Fowler, M.; Zaki, T., A.; and Meneveau, C. Journal of Fluid Mechanics, 934: A44. 2022.
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Surfing on Turbulence: A Strategy for Planktonic Navigation. Monthiller, R.; Loisy, A.; Koehl, M., A., R.; Favier, B.; and Eloy, C. Physical Review Letters, 129(6): 64502. 2022.
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Perturbative model for the second-order velocity structure function tensor in turbulent shear flows. Kumar, S.; Meneveau, C.; and Eyink, G. Physical review fluids, 7(6): 64601. 2022.
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Eddy-Viscous Modeling and the Topology of Extreme Circulation Events in Three-Dimensional Turbulence. Apolinário, G., B.; Moriconi, L.; Pereira, R., M.; and Valadão, V., J. Physics Letters A,128360. 2022.
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Optical Flow Velocimetry using a Quasi-Optimal Basis with Implicit Regularization. Jassal, G., R.; Dobrosotskaya, J., A.; and Schmidt, B., E. In AIAA AVIATION 2022 Forum, pages 3336, 2022.
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Determining velocity from tagging velocimetry images using optical flow. Gevelber, T., S.; Schmidt, B., E.; Mustafa, M., A.; Shekhtman, D.; and Parziale, N., J. Experiments in Fluids, 63(6): 1-15. 2022.
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Origin of enhanced skin friction at the onset of boundary-layer transition. Wang, M.; Eyink, G., L.; and Zaki, T., A. Journal of Fluid Mechanics, 941: A32. 2022.
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Extracting discrete hierarchies of Townsend's wall-attached eddies. Hu, R.; Zheng, X.; and Dong, S. Physics of Fluids, 34(6): 61701. 2022.
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Transport of condensing droplets in Taylor-Green vortex flow in the presence of thermal noise. Nath, A., V., S.; Roy, A.; Govindarajan, R.; and Ravichandran, S. Physical Review E, 105(3): 35101. 2022.
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One neural network approach for the surrogate turbulence model in transonic flows. Zhu, L.; Sun, X.; Liu, Y.; and Zhang, W. Acta Mechanica Sinica, 38(3): 1-14. 2022.
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Predictive data-driven model based on generative adversarial network for premixed turbulence-combustion regimes. Grenga, T.; Nista, L.; Schumann, C.; Karimi, A., N.; Scialabba, G.; Attili, A.; and Pitsch, H. Combustion Science and Technology,1-24. 2022.
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Invariant data-driven subgrid stress modeling in the strain-rate eigenframe for large eddy simulation. Prakash, A.; Jansen, K., E.; and Evans, J., A. Computer Methods in Applied Mechanics and Engineering, 399: 115457. 2022.
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The effect of large-scale forcing on small-scale dynamics of incompressible turbulence. Das, R.; and Girimaji, S., S. Journal of Fluid Mechanics, 941. 2022.
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Large Eddy Simulation of Helical-and Straight-Bladed Vertical Axis Wind Turbines in Boundary Layer Turbulence. Gharaati, M.; Xiao, S.; Wei, N., J.; Martinez-Tossas, L., A.; Dabiri, J., O.; and Yang, D. Journal of Renewable and Sustainable Energy. 2022.
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Nonresonant particle acceleration in strong turbulence: Comparison to kinetic and MHD simulations. Bresci, V.; Lemoine, M.; Gremillet, L.; Comisso, L.; Sironi, L.; and Demidem, C. Physical Review D, 106(2): 23028. 2022.
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The effect of perspective error on 2D PIV Measurements of homogeneous isotropic turbulence. Lee, H.; Park, H., J.; Kim, M.; Han, J.; and Hwang, W. Experiments in Fluids, 63(8): 1-17. 2022.
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Robust training approach of neural networks for fluid flow state estimations. Nakamura, T.; and Fukagata, K. International Journal of Heat and Fluid Flow, 96: 108997. 2022.
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Role of the hierarchy of coherent structures in the transport of heavy small particles in turbulent channel flow. Motoori, Y.; Wong, C.; and Goto, S. Journal of Fluid Mechanics, 942: A3. 2022.
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Measurement error of tracer-based velocimetry in single-phase turbulent flows with inhomogeneous refractive indices. Li, H.; Fischer, A.; Avila, M.; and Xu, D. Experimental Thermal and Fluid Science, 136: 110681. 2022.
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Time-resolved particle image velocimetry algorithm based on deep learning. Guo, C.; Fan, Y.; Yu, C.; Han, Y.; and Bi, X. IEEE Transactions on Instrumentation and Measurement, 71: 1-13. 2022.
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A new single formula for the law of the wall and its application to wall-modeled large-eddy simulation. Zhang, F.; Zhou, Z.; Zhang, H.; and Yang, X. European Journal of Mechanics-B/Fluids, 94: 350-365. 2022.
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Assimilation and extension of particle image velocimetry data of turbulent Rayleigh–Bénard convection using direct numerical simulations. Bauer, C.; Schiepel, D.; and Wagner, C. Experiments in Fluids, 63(1): 1-17. 2022.
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Towards the Suitability of Information Entropy as an LES Quality Indicator. Engelmann, L.; Ihme, M.; Wlokas, I.; and Kempf, A. Flow, Turbulence and Combustion, 108(2): 353-385. 2022.
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Reinforcement Learning for Load-balanced Parallel Particle Tracing. Xu, J.; Guo, H.; Shen, H.; Raj, M.; Wurster, S., W.; and Peterka, T. IEEE Transactions on Visualization and Computer Graphics. 2022.
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The effect of inlet turbulence on the quiescent core of turbulent channel flow. Asadi, M.; Kamruzzaman, M.; and Hearst, R., J. Journal of Fluid Mechanics, 935: A37. 2022.
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CNN-Based Fluid Motion Estimation Using Correlation Coefficient and Multiscale Cost Volume. Chen, J.; Duan, H.; Song, Y.; Tang, M.; and Cai, Z. Electronics, 11(24): 4159. 2022.
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A comparative study of experiments with numerical simulations of free-stream turbulence transition. Mamidala, S., B.; Weingärtner, A.; and Fransson, J., H., M. Journal of Fluid Mechanics, 951: A46. 2022.
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A Statistical Approach to Quantify Taylor Microscale for Turbulent Flow Surrogate Model. Ross, M.; Matulis, J.; and Bindra, H. In International Conference on Nuclear Engineering, volume 86502, pages V015T16A045, 2022.
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Introducing JFM Notebooks. Meneveau, C.; and Colm-cille, P., C. Journal of Fluid Mechanics, 952: E1. 2022.
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Super-resolution generative adversarial networks of randomly-seeded fields. Güemes, A.; Sanmiguel Vila, C.; and Discetti, S. Nature Machine Intelligence, 4(12): 1165-1173. 2022.
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FlowSRNet: A multi-scale integration network for super-resolution reconstruction of fluid flows. Bi, X.; Liu, A.; Fan, Y.; Yu, C.; and Zhang, Z. Physics of Fluids, 34(12): 127104. 2022.
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Unsupervised learning on particle image velocimetry with embedded cross-correlation and divergence-free constraint. Chong, Y.; Liang, J.; Chen, T.; Xu, C.; and Pan, C. IET Cyber-Systems and Robotics, 4(3): 200-211. 2022.
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Scaling laws for partially developed turbulence. Hsu, A.; Kaufman, R.; and Glimm, J. Frontiers in Applied Mathematics and Statistics, 7: 91. 2022.
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Kinematic training of convolutional neural networks for particle image velocimetry. Manickathan, L.; Mucignat, C.; and Lunati, I. Measurement Science and Technology, 33(12): 124006. 2022.
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First-principles Fermi acceleration in magnetized turbulence. Lemoine, M. Physical Review Letters, 129(21): 215101. 2022.
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Deposition velocity of inertial particles driven by wall-normal external force in turbulent channel flow. Chen, P.; Chen, S.; Wu, T.; Ruan, X.; and Li, S. Physical Review Fluids, 7(10): 104301. 2022.
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Physics-informed Machine Learning for Modeling Turbulence in Supernovae. Karpov, P., I.; Huang, C.; Sitdikov, I.; Fryer, C., L.; Woosley, S.; and Pilania, G. The Astrophysical Journal, 940(1): 26. 2022.
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Study of cardiac fluid dynamics in the right side of the heart with AI PIV. Majewski, W.; Bouchahda, N.; Ayari, R.; and Wei, R. Journal of Flow Visualization and Image Processing. 2022.
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Learning dominant physical processes with data-driven balance models. Callaham, J., L.; Koch, J., V.; Brunton, B., W.; Kutz, J., N.; and Brunton, S., L. Nature Communications, 12(1): 1-10. 12 2021.
Learning dominant physical processes with data-driven balance models [pdf]Paper   Learning dominant physical processes with data-driven balance models [link]Website   doi   link   bibtex   abstract  
NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations. Jin, X.; Cai, S.; Li, H.; and Karniadakis, G., E. Journal of Computational Physics, 426: 109951. 2 2021.
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Effects of the quiescent core in turbulent channel flow on transport and clustering of inertial particles. Jie, Y.; Andersson, H., I.; and Zhao, L. International Journal of Multiphase Flow,103627. 3 2021.
Effects of the quiescent core in turbulent channel flow on transport and clustering of inertial particles [link]Website   doi   link   bibtex  
Optimal clipping of the gradient model for subgrid stress closure. Prakash, A.; Jansen, K., E.; and Evans, J., A. In AIAA Scitech 2021 Forum, pages 1-16, 2021. American Institute of Aeronautics and Astronautics Inc, AIAA
Optimal clipping of the gradient model for subgrid stress closure [link]Website   doi   link   bibtex   abstract  
A study of inner-outer interactions in turbulent channel flows by interactive POD. Wang, H.; and Gao, Q. Theoretical and Applied Mechanics Letters,100222. 2 2021.
A study of inner-outer interactions in turbulent channel flows by interactive POD [pdf]Paper   doi   link   bibtex  
Turbulent length scales and budgets of Reynolds stress-transport for open-channel flows; friction Reynolds numbers R = 150, 400 and 1020. Ahmed, U.; Apsley, D.; Stallard, T.; Stansby, P.; and Afgan, I. Journal of Hydraulic Research, 59(1): 36-50. 1 2021.
Turbulent length scales and budgets of Reynolds stress-transport for open-channel flows; friction Reynolds numbers R <sub>eτ</sub> = 150, 400 and 1020 [pdf]Paper   Turbulent length scales and budgets of Reynolds stress-transport for open-channel flows; friction Reynolds numbers R <sub>eτ</sub> = 150, 400 and 1020 [link]Website   doi   link   bibtex   abstract  
Construction of urban turbulent flow database with wavelet-based compression: A study with large-eddy simulation of flow and dispersion in block-arrayed building group model. Jia, H.; and Kikumoto, H. Journal of Wind Engineering and Industrial Aerodynamics, 208: 104433. 1 2021.
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Beyond Taylor’s hypothesis: a novel volumetric reconstruction of velocity and density fields for variable-density and shear flows. Fratantonio, D.; Lai, C., C., K.; Charonko, J.; and Prestridge, K. Experiments in Fluids, 62(4): 84. 4 2021.
Beyond Taylor’s hypothesis: a novel volumetric reconstruction of velocity and density fields for variable-density and shear flows [pdf]Paper   Beyond Taylor’s hypothesis: a novel volumetric reconstruction of velocity and density fields for variable-density and shear flows [link]Website   doi   link   bibtex   abstract  
Vision-based correspondence using relaxation algorithms for particle tracking velocimetry. Benkovic, T.; Krawczynski, J.; and Druault, P. Measurement Science and Technology, 32(2): 25303. 4 2021.
Vision-based correspondence using relaxation algorithms for particle tracking velocimetry [link]Website   doi   link   bibtex  
A perspective on machine learning methods in turbulence modeling. Beck, A.; and Kurz, M. GAMM-Mitteilungen, 44(1): e202100002. 3 2021.
A perspective on machine learning methods in turbulence modeling [pdf]Paper   A perspective on machine learning methods in turbulence modeling [link]Website   doi   link   bibtex   abstract  
Advanced Rendering of Line Data with Ambient Occlusion and Transparency. Gross, D.; and Gumhold, S. IEEE Transactions on Visualization and Computer Graphics, 27(2): 614-624. 2 2021.
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Model-based multi-sensor fusion for reconstructing wall-bounded turbulence. Wang, M.; Krishna, C., V.; Luhar, M.; and Hemati, M., S. Theoretical and Computational Fluid Dynamics, 35(5): 683-707. 10 2021.
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Multiresolution classification of turbulence features in image data through machine learning. Pulido, J.; da Silva, R., D.; Livescu, D.; and Hamann, B. Computers & Fluids, 214: 104770. 1 2021.
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Pressure reconstruction of a planar turbulent flow field within a multiply connected domain with arbitrary boundary shapes. Liu, X.; and Moreto, J., R. Phys. Fluids, 33: 101703. 2021.
Pressure reconstruction of a planar turbulent flow field within a multiply connected domain with arbitrary boundary shapes [pdf]Paper   Pressure reconstruction of a planar turbulent flow field within a multiply connected domain with arbitrary boundary shapes [link]Website   doi   link   bibtex  
Two-point stress-strain-rate correlation structure and non-local eddy viscosity in turbulent flows. Clark, P.; Leoni, D.; Zaki, T., A.; Karniadakis, G.; Meneveau, C.; Clark, P.; Zaki, T., A.; Karniadakis, G.; and Meneveau, C. J. Fluid Mech, 914: 6. 2021.
Two-point stress-strain-rate correlation structure and non-local eddy viscosity in turbulent flows [pdf]Paper   Two-point stress-strain-rate correlation structure and non-local eddy viscosity in turbulent flows [link]Website   doi   link   bibtex   abstract  
DPZ: Improving Lossy Compression Ratio with Information Retrieval on Scientific Data; DPZ: Improving Lossy Compression Ratio with Information Retrieval on Scientific Data. Zhang, J.; Chen, J.; Zhuo, X.; Moon, A.; and Woo Son, S. 2021 IEEE International Conference on Cluster Computing (CLUSTER). 2021.
DPZ: Improving Lossy Compression Ratio with Information Retrieval on Scientific Data; DPZ: Improving Lossy Compression Ratio with Information Retrieval on Scientific Data [pdf]Paper   doi   link   bibtex   abstract  
LightPIVNet: An Effective Convolutional Neural Network for Particle Image Velocimetry. Yu, C.; Bi, X.; Fan, Y.; Han, Y.; and Kuai, Y. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 70: 2021. 2021.
LightPIVNet: An Effective Convolutional Neural Network for Particle Image Velocimetry [pdf]Paper   LightPIVNet: An Effective Convolutional Neural Network for Particle Image Velocimetry [link]Website   doi   link   bibtex   abstract  
Using Neural Networks for Two Dimensional Scientific Data Compression; Using Neural Networks for Two Dimensional Scientific Data Compression. Hayne, L.; Clyne, J.; and Li, S. . 2021.
Using Neural Networks for Two Dimensional Scientific Data Compression; Using Neural Networks for Two Dimensional Scientific Data Compression [pdf]Paper   doi   link   bibtex   abstract  
Unsupervised deep learning for super-resolution reconstruction of turbulence. Kim, H.; Kim, J.; Won, S.; and Lee, C. J. Fluid Mech, 910: 29. 2021.
Unsupervised deep learning for super-resolution reconstruction of turbulence [pdf]Paper   Unsupervised deep learning for super-resolution reconstruction of turbulence [link]Website   doi   link   bibtex   abstract  
Deep particle image velocimetry supervised learning under light conditions. Yu, C.; Fan, Y.; Bi, X.; Han, Y.; and Kuai, Y. Flow Measurement and Instrumentation, 80: 102000. 2021.
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Generation and Parameterization of Forced Isotropic Turbulent Flow Using Autoencoders and Generative Adversarial Networks. Nandal, T.; Tyagi, P.; and Singh, R., K. In ASME International Mechanical Engineering Congress and Exposition, volume 85666, pages V010T10A062, 2021.
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Turbulent length scales and budgets of Reynolds stress-transport for open-channel flows; friction Reynolds numbers R e τ= 150, 400 and 1020. Ahmed, U.; Apsley, D.; Stallard, T.; Stansby, P.; and Afgan, I. Journal of Hydraulic Research, 59(1): 36-50. 2021.
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A robust single-pixel particle image velocimetry based on fully convolutional networks with cross-correlation embedded. Gao, Q.; Lin, H.; Tu, H.; Zhu, H.; Wei, R.; Zhang, G.; and Shao, X. Physics of Fluids, 33(12): 127125. 2021.
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A scaling improved inner-outer decomposition of near-wall turbulent motions ARTICLES YOU MAY BE INTERESTED IN A scaling improved inner-outer decomposition of near-wall turbulent motions. Wang, L.; Hu, R.; and Zheng, X. Phys. Fluids, 33: 45120. 2021.
A scaling improved inner-outer decomposition of near-wall turbulent motions ARTICLES YOU MAY BE INTERESTED IN A scaling improved inner-outer decomposition of near-wall turbulent motions [pdf]Paper   A scaling improved inner-outer decomposition of near-wall turbulent motions ARTICLES YOU MAY BE INTERESTED IN A scaling improved inner-outer decomposition of near-wall turbulent motions [link]Website   doi   link   bibtex   abstract  
Time-resolved particle image velocimetry algorithm based on deep learning. Guo, C.; Fan, Y.; Yu, C.; Han, Y.; and Bi, X. . 2021.
Time-resolved particle image velocimetry algorithm based on deep learning [pdf]Paper   Time-resolved particle image velocimetry algorithm based on deep learning [link]Website   doi   link   bibtex   abstract  
DeepPTV: Particle Tracking Velocimetry for Complex Flow Motion via Deep Neural Networks; DeepPTV: Particle Tracking Velocimetry for Complex Flow Motion via Deep Neural Networks. Liang, J.; Cai, S.; Xu, C.; Chen, T.; and Chu, J. IEEE Transactions on Instrumentation and Measurement, PP. 2021.
DeepPTV: Particle Tracking Velocimetry for Complex Flow Motion via Deep Neural Networks; DeepPTV: Particle Tracking Velocimetry for Complex Flow Motion via Deep Neural Networks [pdf]Paper   DeepPTV: Particle Tracking Velocimetry for Complex Flow Motion via Deep Neural Networks; DeepPTV: Particle Tracking Velocimetry for Complex Flow Motion via Deep Neural Networks [link]Website   doi   link   bibtex   abstract  
Generation and Parameterization of Forced Isotropic Turbulent Flow Using Autoencoders and Generative Adversarial Networks. Kanishk, T., N.; Tyagi, P.; and Singh, R., K. In ASME 2021 International Mechanical Engineering Congress and Exposition, 2021.
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On closures for reduced order models—A spectrum of first-principle to machine-learned avenues. Ahmed, S., E.; Pawar, S.; San, O.; Rasheed, A.; Iliescu, T.; and Noack, B., R. Physics of Fluids, 33(9): 91301. 2021.
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A scaling improved inner-outer decomposition of near-wall turbulent motions ARTICLES YOU MAY BE INTERESTED IN A scaling improved inner-outer decomposition of near-wall turbulent motions. Wang, L.; Hu, R.; and Zheng, X. Phys. Fluids, 33: 45120. 2021.
A scaling improved inner-outer decomposition of near-wall turbulent motions ARTICLES YOU MAY BE INTERESTED IN A scaling improved inner-outer decomposition of near-wall turbulent motions [pdf]Paper   A scaling improved inner-outer decomposition of near-wall turbulent motions ARTICLES YOU MAY BE INTERESTED IN A scaling improved inner-outer decomposition of near-wall turbulent motions [link]Website   doi   link   bibtex   abstract  
Time-resolved particle image velocimetry algorithm based on deep learning. Guo, C.; Fan, Y.; Yu, C.; Han, Y.; and Bi, X. . 2021.
Time-resolved particle image velocimetry algorithm based on deep learning [pdf]Paper   Time-resolved particle image velocimetry algorithm based on deep learning [link]Website   doi   link   bibtex   abstract  
Geometry of turbulent dissipation and the Navier–Stokes regularity problem. Rafner, J.; Grujić, Z.; Bach, C.; Bærentzen, J., A.; Gervang, B.; Jia, R.; Leinweber, S.; Misztal, M.; and Sherson, J. Scientific Reports, 11(1): 1-9. 2021.
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Third-order structure function in the logarithmic layer of boundary-layer turbulence. Xie, J.; De Silva, C.; Baidya, R.; Yang, X., I., A.; and Hu, R. Physical Review Fluids, 6(7): 74602. 2021.
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Wall model based on neural networks for LES of turbulent flows over periodic hills. Zhou, Z.; He, G.; and Yang, X. Physical Review Fluids, 6(5): 54610. 2021.
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Compressive neural representations of volumetric scalar fields. Lu, Y.; Jiang, K.; Levine, J., A.; and Berger, M. In Computer Graphics Forum, volume 40, pages 135-146, 2021.
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Local vortex line topology and geometry in turbulence. Sharma, B.; Das, R.; and Girimaji, S., S. Journal of Fluid Mechanics, 924. 2021.
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Logarithmic energy profile of the streamwise velocity for wall-attached eddies along the spanwise direction in turbulent boundary layer. Li, X.; Wang, G.; and Zheng, X. Physics of Fluids, 33(10): 105119. 2021.
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Effects of the quiescent core in turbulent channel flow on transport and clustering of inertial particles. Jie, Y.; Andersson, H., I.; and Zhao, L. International Journal of Multiphase Flow, 140: 103627. 2021.
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On the relationships between different vortex identification methods based on local trace criterion. Liu, Y.; Zhong, W.; and Tang, Y. Physics of Fluids, 33(10): 105116. 2021.
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Unsupervised Recurrent All-Pairs Field Transforms for Particle Image Velocimetry. Lagemann, C.; Klaas, M.; and Schröder, W. In 14th International Symposium on Particle Image Velocimetry, volume 1, 2021.
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Optimal Clipping of the Gradient Model for Subgrid Stress Closure. Prakash, A.; Jansen, K., E.; and Evans, J., A. In AIAA Scitech 2021 Forum, pages 1665, 2021.
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Large-to-small scale frequency modulation analysis in wall-bounded turbulence via visibility networks. Iacobello, G.; Ridolfi, L.; and Scarsoglio, S. Journal of Fluid Mechanics, 918. 2021.
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Snapshot space–time holographic 3D particle tracking velocimetry. Chen, N.; Wang, C.; and Heidrich, W. Laser & Photonics Reviews, 15(8): 2100008. 2021.
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Particle acceleration in strong MHD turbulence. Lemoine, M. Physical Review D, 104(6): 63020. 2021.
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The effect of nonlinear drag on the rise velocity of bubbles in turbulence. Ruth, D., J.; Vernet, M.; Perrard, S.; and Deike, L. Journal of Fluid Mechanics, 924. 2021.
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A single-camera, 3D scanning velocimetry system for quantifying active particle aggregations. Fu, M., K.; Houghton, I., A.; and Dabiri, J., O. Experiments in Fluids, 62(8): 1-17. 2021.
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Learning dominant physical processes with data-driven balance models. Callaham, J., L.; Koch, J., V.; Brunton, B., W.; Kutz, J., N.; and Brunton, S., L. Nature communications, 12(1): 1-10. 2021.
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NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations. Jin, X.; Cai, S.; Li, H.; and Karniadakis, G., E. Journal of Computational Physics, 426: 109951. 2021.
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As a matter of tension: Kinetic energy spectra in MHD turbulence. Grete, P.; O’Shea, B., W.; and Beckwith, K. The Astrophysical Journal, 909(2): 148. 2021.
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Modelling Lagrangian velocity and acceleration in turbulent flows as infinitely differentiable stochastic processes. Viggiano, B.; Friedrich, J.; Volk, R.; Bourgoin, M.; Cal, R., B.; and Chevillard, L. Journal of Fluid Mechanics, 900. 2020.
Modelling Lagrangian velocity and acceleration in turbulent flows as infinitely differentiable stochastic processes [link]Website   doi   link   bibtex   abstract   11 downloads  
Interactive spatio-temporal exploration of massive time-Varying rectilinear scalar volumes based on a variable bit-rate sparse representation over learned dictionaries. Díaz, J.; Marton, F.; and Gobbetti, E. Computers and Graphics (Pergamon), 88: 45-56. 5 2020.
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ENFORCING HARD PHYSICAL CONSTRAINTS IN CNNS THROUGH DIFFERENTIABLE PDE LAYER. Kashinath, K.; and Marcus, P. Technical Report 2 2020.
ENFORCING HARD PHYSICAL CONSTRAINTS IN CNNS THROUGH DIFFERENTIABLE PDE LAYER [pdf]Paper   link   bibtex   abstract  
Speed-direction description of turbulent flows. Olshanskii, M., A. Physics of Fluids, 32(11): 115128. 11 2020.
Speed-direction description of turbulent flows [pdf]Paper   Speed-direction description of turbulent flows [link]Website   doi   link   bibtex   abstract  
A take on wake modeling of turbines based on deep learning. Ziaei, D.; and Goudarzi, N. In American Society of Mechanical Engineers, Power Division (Publication) POWER, volume 2020-August, 10 2020. American Society of Mechanical Engineers (ASME)
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Flows over periodic hills of parameterized geometries: A dataset for data-driven turbulence modeling from direct simulations. Xiao, H.; Wu, J., L.; Laizet, S.; and Duan, L. Computers and Fluids, 200: 104431. 3 2020.
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Developing particle image velocimetry software based on a deep neural network. Majewski, W.; Wei, R.; and Kumar, V. Journal of Flow Visualization and Image Processing, 27(4): 359-376. 2020.
Developing particle image velocimetry software based on a deep neural network [link]Website   doi   link   bibtex   abstract   12 downloads  
Dual channels of helicity cascade in turbulent flows. Yan, Z.; Li, X.; Yu, C.; Wang, J.; and Chen, S. Journal of Fluid Mechanics, 894. 2020.
Dual channels of helicity cascade in turbulent flows [link]Website   doi   link   bibtex   abstract  
Data compression for turbulence databases using spatiotemporal subsampling and local resimulation. Wu, Z.; Zaki, T., A.; and Meneveau, C. Physical Review Fluids, 5(6): 064607. 6 2020.
Data compression for turbulence databases using spatiotemporal subsampling and local resimulation [link]Website   doi   link   bibtex   abstract   5 downloads  
Enforcing temporal consistency in physically constrained flow field reconstruction with FlowFit by use of virtual tracer particles. Ehlers, F.; Schröder, A.; and Gesemann, S. Measurement Science and Technology, 31(9): 16. 9 2020.
Enforcing temporal consistency in physically constrained flow field reconstruction with FlowFit by use of virtual tracer particles [pdf]Paper   Enforcing temporal consistency in physically constrained flow field reconstruction with FlowFit by use of virtual tracer particles [link]Website   doi   link   bibtex   abstract   14 downloads  
As a matter of tension - kinetic energy spectra in MHD turbulence. Grete, P.; O’Shea, B., W.; and Beckwith, K. 9 2020.
As a matter of tension - kinetic energy spectra in MHD turbulence [link]Website   doi   link   bibtex   abstract  
Unsupervised deep learning for super-resolution reconstruction of turbulence. Kim, H.; Kim, J.; Won, S.; and Lee, C. Journal of Fluid Mechanics, 910. 2020.
Unsupervised deep learning for super-resolution reconstruction of turbulence [link]Website   doi   link   bibtex   abstract  
Two-point stress-strain rate correlation structure and non-local eddy viscosity in turbulent flows. Clark Di Leoni, P.; Zaki, T., A.; Karniadakis, G.; and Meneveau, C. 6 2020.
Two-point stress-strain rate correlation structure and non-local eddy viscosity in turbulent flows [pdf]Paper   Two-point stress-strain rate correlation structure and non-local eddy viscosity in turbulent flows [link]Website   doi   link   bibtex   abstract  
Stochastic Lagrangian dynamics of vorticity. Part 1. General theory for viscous, incompressible fluids. Eyink, G., L.; Gupta, A.; and Zaki, T., A. Journal of Fluid Mechanics, 901. 10 2020.
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Stochastic Lagrangian dynamics of vorticity. Part 2. Application to near-wall channel-flow turbulence. Eyink, G., L.; Gupta, A.; and Zaki, T., A. Journal of Fluid Mechanics, 901. 10 2020.
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Reconstructing the time evolution of wall-bounded turbulent flows from non-time-resolved PIV measurements. Krishna, C., V.; Wang, M.; Hemati, M., S.; and Luhar, M. Physical Review Fluids, 5(5): 54604. 4 2020.
Reconstructing the time evolution of wall-bounded turbulent flows from non-time-resolved PIV measurements [link]Website   doi   link   bibtex  
High-Reynolds-number fractal signature of nascent turbulence during transition. Wu, Z.; Zaki, T., A.; and Meneveau, C. Proceedings of the National Academy of Sciences, 117(7): 3461-3468. 4 2020.
High-Reynolds-number fractal signature of nascent turbulence during transition [link]Website   doi   link   bibtex   abstract   6 downloads  
Geometric constraints on energy transfer in the turbulent cascade. Ballouz, J., G.; and Ouellette, N., T. Physical Review Fluids, 5(3): 34603. 4 2020.
Geometric constraints on energy transfer in the turbulent cascade [link]Website   doi   link   bibtex   abstract  
Double-frame tomographic PTV at high seeding densities. Cornic, P.; Leclaire, B.; Champagnat, F.; Besnerais, G., L.; Cheminet, A.; Illoul, C.; and Losfeld, G. Experiments in Fluids, 61(2): 23. 4 2020.
Double-frame tomographic PTV at high seeding densities [link]Website   doi   link   bibtex   abstract  
Effects of Atwood and Reynolds numbers on the evolution of buoyancy-driven homogeneous variable-density turbulence. Aslangil, D.; Livescu, D.; and Banerjee, A. Journal of Fluid Mechanics, 895: A12. 4 2020.
Effects of Atwood and Reynolds numbers on the evolution of buoyancy-driven homogeneous variable-density turbulence [link]Website   doi   link   bibtex  
Developing particle image velocimetry software based on a deep neural network. Majewski, W.; Wei, R.; and Kumar, V. Journal of Flow Visualization and Image Processing, 27(4): 359-376. 2020.
Developing particle image velocimetry software based on a deep neural network [link]Website   doi   link   bibtex   12 downloads  
Interactive spatio-temporal exploration of massive time-Varying rectilinear scalar volumes based on a variable bit-rate sparse representation over learned dictionaries. Díaz, J.; Marton, F.; and Gobbetti, E. Computers & Graphics, 88: 45-56. 4 2020.
Interactive spatio-temporal exploration of massive time-Varying rectilinear scalar volumes based on a variable bit-rate sparse representation over learned dictionaries [link]Website   doi   link   bibtex   abstract   7 downloads  
A fractional subgrid-scale model for turbulent flows: Theoretical formulation and a priori study. Samiee, M.; Akhavan-Safaei, A.; and Zayernouri, M. Physics of Fluids, 32(5): 55102. 4 2020.
A fractional subgrid-scale model for turbulent flows: Theoretical formulation and a priori study [link]Website   doi   link   bibtex   abstract   10 downloads  
Wall-attached and wall-detached eddies in wall-bounded turbulent flows. Hu, R.; Yang, X., I., A.; and Zheng, X. Journal of Fluid Mechanics, 885: A30. 4 2020.
Wall-attached and wall-detached eddies in wall-bounded turbulent flows [link]Website   doi   link   bibtex   6 downloads  
Introducing OpenLPT: new method of removing ghost particles and high-concentration particle shadow tracking. Tan, S.; Salibindla, A.; Masuk, A., U., M.; and Ni, R. Experiments in Fluids, 61(2): 47. 4 2020.
Introducing OpenLPT: new method of removing ghost particles and high-concentration particle shadow tracking [link]Website   doi   link   bibtex   abstract   5 downloads  
The particle stress in dilute suspensions of inertialess spheroids in turbulent channel flow. Wang, Z.; and Zhao, L. Physics of Fluids, 32(1): 13302. 4 2020.
The particle stress in dilute suspensions of inertialess spheroids in turbulent channel flow [link]Website   doi   link   bibtex   abstract   5 downloads  
Pressure power spectrum in high-Reynolds number wall-bounded flows. Xu, H., H., A.; Towne, A.; Yang, X., I., A.; and Marusic, I. International Journal of Heat and Fluid Flow, 84: 108620. 4 2020.
Pressure power spectrum in high-Reynolds number wall-bounded flows [link]Website   doi   link   bibtex   5 downloads  
A Comparison of Rendering Techniques for 3D Line Sets with Transparency. Kern, M.; Neuhauser, C.; Maack, T.; Han, M.; Usher, W.; and Westermann, R. IEEE Transactions on Visualization and Computer Graphics,1. 4 2020.
A Comparison of Rendering Techniques for 3D Line Sets with Transparency [link]Website   doi   link   bibtex   abstract   5 downloads  
Turbulent length scales and budgets of Reynolds stress-transport for open-channel flows; friction Reynolds numbers (R e τ ) = 150, 400 and 1020. Ahmed, U.; Apsley, D.; Stallard, T.; Stansby, P.; and Afgan, I. Journal of Hydraulic Research,1-15. 4 2020.
Turbulent length scales and budgets of Reynolds stress-transport for open-channel flows; friction Reynolds numbers (R e τ ) = 150, 400 and 1020 [link]Website   doi   link   bibtex   abstract  
Enforcing temporal consistency in physically constrained flow field reconstruction with FlowFit by use of virtual tracer particles. Ehlers, F.; Schröder, A.; and Gesemann, S. Measurement Science and Technology, 31(9): 94013. 4 2020.
Enforcing temporal consistency in physically constrained flow field reconstruction with FlowFit by use of virtual tracer particles [link]Website   doi   link   bibtex   abstract   14 downloads  
Adaptive ensemble PTV. Raiola, M.; Lopez-Nuñez, E.; Cafiero, G.; and Discetti, S. Measurement Science and Technology, 31(8): 85301. 4 2020.
Adaptive ensemble PTV [link]Website   doi   link   bibtex   abstract   5 downloads  
A Priori Sub-grid Modelling Using Artificial Neural Networks. Prat, A.; Sautory, T.; and Navarro-Martinez, S. International Journal of Computational Fluid Dynamics, 34(6): 397-417. 4 2020.
A Priori Sub-grid Modelling Using Artificial Neural Networks [link]Website   doi   link   bibtex   abstract   6 downloads  
Robust principal component analysis for modal decomposition of corrupt fluid flows. Scherl, I.; Strom, B.; Shang, J., K.; Williams, O.; Polagye, B., L.; and Brunton, S., L. Physical Review Fluids, 5(5): 54401. 4 2020.
Robust principal component analysis for modal decomposition of corrupt fluid flows [link]Website   doi   link   bibtex   abstract   5 downloads  
Nanoflare Theory and Stochastic Reconnection. Jafari, A.; Vishniac, E., T.; and Xu, S. Research Notes of the AAS, 4(6): 89. 4 2020.
Nanoflare Theory and Stochastic Reconnection [link]Website   doi   link   bibtex   5 downloads  
A Model Reduction Method Using Resolvent Modes to Preserve Forcing Sensitivity. Myhre, N.; Prazenica, R., J.; Balas, M.; and Gnanamanickam, E., P. In AIAA AVIATION 2020 FORUM, 4 2020. American Institute of Aeronautics and Astronautics
A Model Reduction Method Using Resolvent Modes to Preserve Forcing Sensitivity [link]Website   doi   link   bibtex   8 downloads  
Modelling Lagrangian velocity and acceleration in turbulent flows as infinitely differentiable stochastic processes. Viggiano, B.; Friedrich, J.; Volk, R.; Bourgoin, M.; Cal, R., B.; and Chevillard, L. Journal of Fluid Mechanics, 900: A27. 4 2020.
Modelling Lagrangian velocity and acceleration in turbulent flows as infinitely differentiable stochastic processes [link]Website   doi   link   bibtex   11 downloads  
Dense particle tracking using a learned predictive model. Mallery, K.; Shao, S.; and Hong, J. Experiments in Fluids, 61(10): 223. 4 2020.
Dense particle tracking using a learned predictive model [link]Website   doi   link   bibtex   abstract   5 downloads  
Spectral energy analysis of bulk three-dimensional active nematic turbulence. Krajnik, Ž.; Kos, Ž.; and Ravnik, M. Soft Matter, 16(39): 9059-9068. 2020.
Spectral energy analysis of bulk three-dimensional active nematic turbulence [link]Website   doi   link   bibtex   abstract   5 downloads  
Shallow neural networks for fluid flow reconstruction with limited sensors. Erichson, N., B.; Mathelin, L.; Yao, Z.; Brunton, S., L.; Mahoney, M., W.; and Kutz, J., N. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 476(2238): 20200097. 4 2020.
Shallow neural networks for fluid flow reconstruction with limited sensors [link]Website   doi   link   bibtex   abstract   6 downloads  
Error propagation from the PIV-based pressure gradient to the integrated pressure by the omnidirectional integration method. Liu, X.; and Moreto, J., R. Measurement Science and Technology, 31(5): 55301. 4 2020.
Error propagation from the PIV-based pressure gradient to the integrated pressure by the omnidirectional integration method [link]Website   doi   link   bibtex   abstract   5 downloads  
Learning Similarity Metrics for Numerical Simulations. Kohl, G.; Um, K.; and Thuerey, N. Technical Report 11 2020.
Learning Similarity Metrics for Numerical Simulations [pdf]Paper   Learning Similarity Metrics for Numerical Simulations [link]Website   link   bibtex   abstract  
Drilling dataset exploration, processing and interpretation using volve field data. Tunkiel, A., T.; Wiktorski, T.; and Sui, D. In Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE, volume 11, 12 2020. American Society of Mechanical Engineers (ASME)
doi   link   bibtex   abstract  
  2019 (44)
Droplet-turbulence interactions and quasi-equilibrium dynamics in turbulent emulsions. Mukherjee, S.; Safdari, A.; Shardt, O.; Kenjeres, S.; and den Akker, H., E., A., V. . 4 2019.
Droplet-turbulence interactions and quasi-equilibrium dynamics in turbulent emulsions [link]Website   link   bibtex   abstract   3 downloads  
Velocity probability distribution scaling in wall-bounded flows at high Reynolds numbers. Ge, M.; Yang, X., I., A.; and Marusic, I. Physical Review Fluids, 4(3): 34101. 4 2019.
Velocity probability distribution scaling in wall-bounded flows at high Reynolds numbers [link]Website   doi   link   bibtex   5 downloads  
A uniform momentum zone–vortical fissure model of the turbulent boundary layer. Bautista, J., C., C.; Ebadi, A.; White, C., M.; Chini, G., P.; and Klewicki, J., C. Journal of Fluid Mechanics, 858: 609-633. 4 2019.
A uniform momentum zone–vortical fissure model of the turbulent boundary layer [link]Website   doi   link   bibtex   abstract   8 downloads  
GPU-based, parallel-line, omni-directional integration of measured pressure gradient field to obtain the 3D pressure distribution. Wang, J.; Zhang, C.; and Katz, J. Experiments in Fluids, 60(4): 58. 4 2019.
GPU-based, parallel-line, omni-directional integration of measured pressure gradient field to obtain the 3D pressure distribution [link]Website   doi   link   bibtex   5 downloads  
A quantitative study of track initialization of the four-frame best estimate algorithm for three-dimensional Lagrangian particle tracking. Clark, A.; Machicoane, N.; and Aliseda, A. Measurement Science and Technology, 30(4): 45302. 4 2019.
A quantitative study of track initialization of the four-frame best estimate algorithm for three-dimensional Lagrangian particle tracking [link]Website   doi   link   bibtex   8 downloads  
Shallow Learning for Fluid Flow Reconstruction with Limited Sensors and Limited Data. Erichson, N., B.; Mathelin, L.; Yao, Z.; Brunton, S., L.; Mahoney, M., W.; and Kutz, J., N. . 4 2019.
Shallow Learning for Fluid Flow Reconstruction with Limited Sensors and Limited Data [link]Website   link   bibtex   abstract   3 downloads  
Scale-dependent alignment, tumbling and stretching of slender rods in isotropic turbulence. Pujara, N.; Voth, G., A.; and Variano, E., A. Journal of Fluid Mechanics, 860: 465-486. 4 2019.
Scale-dependent alignment, tumbling and stretching of slender rods in isotropic turbulence [link]Website   doi   link   bibtex   abstract   5 downloads  
TTHRESH: Tensor Compression for Multidimensional Visual Data. Ballester-Ripoll, R.; Lindstrom, P.; and Pajarola, R. IEEE Transactions on Visualization and Computer Graphics,1. 4 2019.
TTHRESH: Tensor Compression for Multidimensional Visual Data [link]Website   doi   link   bibtex   abstract   8 downloads  
Beyond Kolmogorov cascades. Dubrulle, B. Journal of Fluid Mechanics, 867: P1. 4 2019.
Beyond Kolmogorov cascades [link]Website   doi   link   bibtex   abstract   5 downloads  
Blade-Resolved, Single-Turbine Simulations Under Atmospheric Flow. Lawson, M., J.; Melvin, J.; Ananthan, S.; Gruchalla, K., M.; Rood, J., S.; and Sprague, M., A. 4 2019.
Blade-Resolved, Single-Turbine Simulations Under Atmospheric Flow [link]Website   doi   link   bibtex   8 downloads  
Intermittency and Structure(s) of and/in Turbulence. Tsinober, A. 2019.
Intermittency and Structure(s) of and/in Turbulence [link]Website   doi   link   bibtex   5 downloads  
Using deformable particles for single particle measurements of velocity gradient tensors. Hejazi, B.; Krellenstein, M.; and Voth, G., A. . 4 2019.
Using deformable particles for single particle measurements of velocity gradient tensors [link]Website   link   bibtex   abstract   3 downloads  
A Voxel-Based Rendering Pipeline for Large 3D Line Sets. Kanzler, M.; Rautenhaus, M.; and Westermann, R. IEEE Transactions on Visualization and Computer Graphics, 25(7): 2378-2391. 4 2019.
A Voxel-Based Rendering Pipeline for Large 3D Line Sets [link]Website   doi   link   bibtex   abstract   5 downloads  
Robust Principal Component Analysis for Background Estimation of Particle Image Velocimetry Data. Baghaie, A. In 2019 IEEE Long Island Systems, Applications and Technology Conference (LISAT), pages 1-6, 4 2019. IEEE
Robust Principal Component Analysis for Background Estimation of Particle Image Velocimetry Data [link]Website   doi   link   bibtex   8 downloads  
A framework for GPU‐accelerated exploration of massive time‐varying rectilinear scalar volumes. Marton, F.; Agus, M.; and Gobbetti, E. Computer Graphics Forum, 38(3): 53-66. 4 2019.
A framework for GPU‐accelerated exploration of massive time‐varying rectilinear scalar volumes [link]Website   doi   link   bibtex   11 downloads  
Local approach to the study of energy transfers in incompressible magnetohydrodynamic turbulence. Kuzzay, D.; Alexandrova, O.; and Matteini, L. Physical Review E, 99(5): 53202. 4 2019.
Local approach to the study of energy transfers in incompressible magnetohydrodynamic turbulence [link]Website   doi   link   bibtex   8 downloads  
Dual channels of helicity cascade in turbulent flows. Yan, Z.; Li, X.; Yu, C.; and Chen, S. . 4 2019.
Dual channels of helicity cascade in turbulent flows [link]Website   link   bibtex   abstract   3 downloads  
Emergence of skewed non-Gaussian distributions of velocity increments in isotropic turbulence. Sosa-Correa, W.; Pereira, R., M.; Macêdo, A., M., S.; Raposo, E., P.; Salazar, D., S., P.; and Vasconcelos, G., L. Physical Review Fluids, 4(6): 64602. 4 2019.
Emergence of skewed non-Gaussian distributions of velocity increments in isotropic turbulence [link]Website   doi   link   bibtex   5 downloads  
Velocity and pressure visualization of three-dimensional flow in porous textiles. Lee, J.; Yang, B.; Cho, J.; and Song, S. Textile Research Journal, 89(23-24): 5041-5052. 4 2019.
Velocity and pressure visualization of three-dimensional flow in porous textiles [link]Website   doi   link   bibtex   abstract   5 downloads  
Dense motion estimation of particle images via a convolutional neural network. Cai, S.; Zhou, S.; Xu, C.; and Gao, Q. Experiments in Fluids, 60(4): 73. 4 2019.
Dense motion estimation of particle images via a convolutional neural network [link]Website   doi   link   bibtex   5 downloads  
On the inherent bias of swirling strength in defining vortical structure. Bernard, P., S. Physics of Fluids, 31(3): 35107. 4 2019.
On the inherent bias of swirling strength in defining vortical structure [link]Website   doi   link   bibtex   abstract   8 downloads  
Graphical Processing Unit-Accelerated Open-Source Particle Image Velocimetry Software for High Performance Computing Systems. Dallas, C.; Wu, M.; Chou, V.; Liberzon, A.; and Sullivan, P., E. Journal of Fluids Engineering, 141(11). 4 2019.
Graphical Processing Unit-Accelerated Open-Source Particle Image Velocimetry Software for High Performance Computing Systems [link]Website   doi   link   bibtex   abstract   5 downloads  
Stochastic modeling of subgrid-scale effects on particle motion in forced isotropic turbulence. Shen, H.; Wu, Y.; Zhou, M.; Zhang, H.; and Yue, G. Chinese Journal of Chemical Engineering. 4 2019.
Stochastic modeling of subgrid-scale effects on particle motion in forced isotropic turbulence [link]Website   doi   link   bibtex   abstract   7 downloads  
PIV/BOS synthetic image generation in variable density environments for error analysis and experiment design. Rajendran, L., K.; Bane, S., P., M.; and Vlachos, P., P. Measurement Science and Technology, 30(8): 85302. 4 2019.
PIV/BOS synthetic image generation in variable density environments for error analysis and experiment design [link]Website   doi   link   bibtex   8 downloads  
Accurate and Efficient Autonomic Closure for Turbulent Flows. Kshitij, A. 2019.
Accurate and Efficient Autonomic Closure for Turbulent Flows [link]Website   link   bibtex   3 downloads  
Singular value decomposition of noisy data: noise filtering. Epps, B., P.; and Krivitzky, E., M. Experiments in Fluids, 60(8): 126. 4 2019.
Singular value decomposition of noisy data: noise filtering [link]Website   doi   link   bibtex   5 downloads  
Regularized inverse holographic volume reconstruction for 3D particle tracking. Mallery, K.; and Hong, J. Optics Express, 27(13): 18069. 4 2019.
Regularized inverse holographic volume reconstruction for 3D particle tracking [link]Website   doi   link   bibtex   abstract   5 downloads  
A Declarative Grammar of Flexible Volume Visualization Pipelines. Shih, M.; Rozhon, C.; and Ma, K. IEEE Transactions on Visualization and Computer Graphics, 25(1): 1050-1059. 4 2019.
A Declarative Grammar of Flexible Volume Visualization Pipelines [link]Website   doi   link   bibtex   5 downloads  
On the Reynolds number dependence of velocity-gradient structure and dynamics. Das, R.; and Girimaji, S., S. Journal of Fluid Mechanics, 861: 163-179. 4 2019.
On the Reynolds number dependence of velocity-gradient structure and dynamics [link]Website   doi   link   bibtex   abstract   5 downloads  
Lagrangian statistics of pressure fluctuation events in homogeneous isotropic turbulence. Bappy, M.; Carrica, P., M.; and Buscaglia, G., C. . 4 2019.
Lagrangian statistics of pressure fluctuation events in homogeneous isotropic turbulence [link]Website   link   bibtex   abstract  
Identifying the Wall Signature of Large-Scale Motions with Extended POD. Güemes, A.; Vaquero, A.; Flores, O.; Discetti, S.; and Ianiro, A. In Örlü, R.; Talamelli, A.; Peinke, J.; and Oberlack, M., editor(s), 8th iTi Conference on Turbulence, pages 75-80, 4 2019. Springer, Cham
Identifying the Wall Signature of Large-Scale Motions with Extended POD [link]Website   doi   link   bibtex   7 downloads  
Robust Principal Component Analysis for Particle Image Velocimetry. Scherl, I.; Strom, B.; Shang, J., K.; Williams, O.; Polagye, B., L.; and Brunton, S., L. . 4 2019.
Robust Principal Component Analysis for Particle Image Velocimetry [link]Website   link   bibtex   abstract   3 downloads  
Multilevel Techniques for Compression and Reduction of Scientific Data-Quantitative Control of Accuracy in Derived Quantities. Ainsworth, M.; Tugluk, O.; Whitney, B.; and Klasky, S. SIAM Journal on Scientific Computing, 41(4): A2146-A2171. 4 2019.
Multilevel Techniques for Compression and Reduction of Scientific Data-Quantitative Control of Accuracy in Derived Quantities [link]Website   doi   link   bibtex   abstract  
High Spatial Resolution 3D Fluid Velocimetry by Tomographic Particle Flow Velocimetry. Kumashiro, K.; Steinberg, A., M.; and Yano, M. In AIAA Scitech 2019 Forum, 4 2019. American Institute of Aeronautics and Astronautics
High Spatial Resolution 3D Fluid Velocimetry by Tomographic Particle Flow Velocimetry [link]Website   doi   link   bibtex   5 downloads  
Kolmogorov-type theory of compressible turbulence and inviscid limit of the Navier-Stokes equations in R3. Chen, G., G.; and Glimm, J. Physica D: Nonlinear Phenomena,132138. 4 2019.
Kolmogorov-type theory of compressible turbulence and inviscid limit of the Navier-Stokes equations in R3 [link]Website   doi   link   bibtex   abstract   5 downloads  
A scanning particle tracking velocimetry technique for high-Reynolds number turbulent flows. Kozul, M.; Koothur, V.; Worth, N., A.; and Dawson, J., R. Experiments in Fluids, 60(8): 137. 4 2019.
A scanning particle tracking velocimetry technique for high-Reynolds number turbulent flows [link]Website   doi   link   bibtex   5 downloads  
Pressure from 2D snapshot PIV. der Kindere, J., W., V.; Laskari, A.; Ganapathisubramani, B.; and de Kat, R. Experiments in Fluids, 60(2): 32. 4 2019.
Pressure from 2D snapshot PIV [link]Website   doi   link   bibtex  
On Visualizing Continuous Turbulence Scales. Liu, X.; Mishra, M.; Skote, M.; and Fu, C. Computer Graphics Forum, 38(1): 300-315. 4 2019.
On Visualizing Continuous Turbulence Scales [link]Website   doi   link   bibtex   5 downloads  
Application of a self-organizing map to identify the turbulent-boundary-layer interface in a transitional flow. Wu, Z.; Lee, J.; Meneveau, C.; and Zaki, T. Physical Review Fluids, 4(2): 23902. 4 2019.
Application of a self-organizing map to identify the turbulent-boundary-layer interface in a transitional flow [link]Website   doi   link   bibtex   abstract   6 downloads  
Influence of the quiescent core on tracer spheroidal particle dynamics in turbulent channel flow. Jie, Y.; Xu, C.; Dawson, J., R.; Andersson, H., I.; and Zhao, L. Journal of Turbulence, 20(7): 424-438. 4 2019.
Influence of the quiescent core on tracer spheroidal particle dynamics in turbulent channel flow [link]Website   doi   link   bibtex   abstract   8 downloads  
Sensing the turbulent large-scale motions with their wall signature. Güemes, A.; Discetti, S.; and Ianiro, A. Physics of Fluids, 31(12): 125112. 4 2019.
Sensing the turbulent large-scale motions with their wall signature [link]Website   doi   link   bibtex   abstract   5 downloads  
Mean dynamics and transition to turbulence in oscillatory channel flow. Ebadi, A.; White, C., M.; Pond, I.; and Dubief, Y. Journal of Fluid Mechanics, 880: 864-889. 4 2019.
Mean dynamics and transition to turbulence in oscillatory channel flow [link]Website   doi   link   bibtex   abstract   5 downloads  
Turbulence at the Lee bound: maximally non-normal vortex filaments and the decay of a local dissipation rate. Keylock, C., J. Journal of Fluid Mechanics, 881: 283-312. 4 2019.
Turbulence at the Lee bound: maximally non-normal vortex filaments and the decay of a local dissipation rate [link]Website   doi   link   bibtex   abstract   5 downloads  
Predictive large-eddy-simulation wall modeling via physics-informed neural networks. Yang, X., I., A.; Zafar, S.; Wang, J.; and Xiao, H. Physical Review Fluids, 4(3): 34602. 4 2019.
Predictive large-eddy-simulation wall modeling via physics-informed neural networks [link]Website   doi   link   bibtex   abstract   5 downloads  
  2018 (40)
Artificial Neural Networks in Fluid Dynamics: A Novel Approach to the Navier-Stokes Equations. McCracken, M. . 4 2018.
Artificial Neural Networks in Fluid Dynamics: A Novel Approach to the Navier-Stokes Equations [link]Website   doi   link   bibtex   abstract   5 downloads  
Techniques for 3D-PIV. Raffel, M.; Willert, C., E.; Scarano, F.; Kähler, C., J.; Wereley, S., T.; and Kompenhans, J. 2018.
Techniques for 3D-PIV [link]Website   doi   link   bibtex   5 downloads  
Hierarchical models for financial markets and turbulence. Correa, W., O., S. 4 2018.
Hierarchical models for financial markets and turbulence [link]Website   link   bibtex   abstract   3 downloads  
Estimation of time-resolved 3D pressure fields in an impinging jet flow from dense Lagrangian particle tracking. Huhn, F.; Schröder, A.; Schanz, D.; Gesemann, S.; and Manovski, P. In Rösgen, T., editor(s), 18th International Symposium on Flow Visualization (ISFV18), 4 2018. ETH Zurich
Estimation of time-resolved 3D pressure fields in an impinging jet flow from dense Lagrangian particle tracking [link]Website   doi   link   bibtex   5 downloads  
Three-dimensional remeshed smoothed particle hydrodynamics for the simulation of isotropic turbulence. Obeidat, A.; and Bordas, S., P., A. International Journal for Numerical Methods in Fluids, 86(1): 1-19. 4 2018.
Three-dimensional remeshed smoothed particle hydrodynamics for the simulation of isotropic turbulence [link]Website   doi   link   bibtex   abstract   5 downloads  
Dependence of small-scale energetics on large scales in turbulent flows. Howland, M., F.; and Yang, X., I., A. Journal of Fluid Mechanics, 852: 641-662. 4 2018.
Dependence of small-scale energetics on large scales in turbulent flows [link]Website   doi   link   bibtex   abstract   5 downloads  
Vorticity, backscatter and counter-gradient transport predictions using two-level simulation of turbulent flows. Ranjan, R.; and Menon, S. Journal of Turbulence, 19(4): 334-364. 4 2018.
Vorticity, backscatter and counter-gradient transport predictions using two-level simulation of turbulent flows [link]Website   doi   link   bibtex   abstract   5 downloads  
Autonomic Closure for Turbulent Flows Using Approximate Bayesian Computation. Doronina, O.; Christopher, J.; Towery, C., A., Z.; Hamlington, P.; and Dahm, W., J., A. In 2018 AIAA Aerospace Sciences Meeting, 4 2018. American Institute of Aeronautics and Astronautics
Autonomic Closure for Turbulent Flows Using Approximate Bayesian Computation [link]Website   doi   link   bibtex   5 downloads  
The spanwise spectra in wall-bounded turbulence. Wang, H.; Wang, S.; and He, G. Acta Mechanica Sinica, 34(3): 452-461. 4 2018.
The spanwise spectra in wall-bounded turbulence [link]Website   doi   link   bibtex   5 downloads  
Estimation of time-resolved turbulent fields through correlation of non-time-resolved field measurements and time-resolved point measurements. Discetti, S.; Raiola, M.; and Ianiro, A. Experimental Thermal and Fluid Science, 93: 119-130. 4 2018.
Estimation of time-resolved turbulent fields through correlation of non-time-resolved field measurements and time-resolved point measurements [link]Website   doi   link   bibtex   abstract   5 downloads  
3D Fluid Flow Estimation with Integrated Particle Reconstruction. Lasinger, K.; Vogel, C.; Pock, T.; and Schindler, K. In 40th German Conference on Pattern Recognition, GCPR 2018, pages 315-332, 4 2018.
3D Fluid Flow Estimation with Integrated Particle Reconstruction [link]Website   doi   link   bibtex   abstract   5 downloads  
Theories and applications of second-order correlation of longitudinal velocity increments at three points in isotropic turbulence. Wu, J., Z.; Fang, L.; Shao, L.; and Lu, L., P. Physics Letters A, 382(25): 1665-1671. 4 2018.
Theories and applications of second-order correlation of longitudinal velocity increments at three points in isotropic turbulence [link]Website   doi   link   bibtex   abstract   5 downloads  
Exact result for mixed triple two-point correlations of velocity and velocity gradients in isotropic turbulence. Kopyev, A., V.; and Zybin, K., P. Journal of Turbulence, 19(9): 717-730. 4 2018.
Exact result for mixed triple two-point correlations of velocity and velocity gradients in isotropic turbulence [link]Website   doi   link   bibtex   abstract   5 downloads  
Tensor geometry in the turbulent cascade. Ballouz, J., G.; and Ouellette, N., T. Journal of Fluid Mechanics, 835: 1048-1064. 4 2018.
Tensor geometry in the turbulent cascade [link]Website   doi   link   bibtex   abstract   9 downloads  
Modeling three-dimensional scalar mixing with forced one-dimensional turbulence. Giddey, V.; Meyer, D., W.; and Jenny, P. Physics of Fluids, 30(12): 125103. 4 2018.
Modeling three-dimensional scalar mixing with forced one-dimensional turbulence [link]Website   doi   link   bibtex   abstract   5 downloads  
Visibility graph analysis of wall turbulence time-series. Iacobello, G.; Scarsoglio, S.; and Ridolfi, L. Physics Letters A, 382(1): 1-11. 4 2018.
Visibility graph analysis of wall turbulence time-series [link]Website   doi   link   bibtex   abstract   5 downloads  
Tracer particle dispersion around elementary flow patterns. Goudar, M., V.; and Elsinga, G., E. Journal of Fluid Mechanics, 843: 872-897. 4 2018.
Tracer particle dispersion around elementary flow patterns [link]Website   doi   link   bibtex   abstract   5 downloads  
A Deep Learning based Approach to Reduced Order Modeling for Turbulent Flow Control using LSTM Neural Networks. Mohan, A., T.; and Gaitonde, D., V. . 4 2018.
A Deep Learning based Approach to Reduced Order Modeling for Turbulent Flow Control using LSTM Neural Networks [link]Website   link   bibtex   abstract   3 downloads  
Estimating large-scale structures in wall turbulence using linear models. Illingworth, S., J.; Monty, J., P.; and Marusic, I. Journal of Fluid Mechanics, 842: 146-162. 4 2018.
Estimating large-scale structures in wall turbulence using linear models [link]Website   doi   link   bibtex   abstract   4 downloads  
Inhomogeneous growth of fluctuations of concentration of inertial particles in channel turbulence. Fouxon, I.; Schmidt, L.; Ditlevsen, P.; van Reeuwijk, M.; and Holzner, M. Physical Review Fluids, 3(6): 64301. 4 2018.
Inhomogeneous growth of fluctuations of concentration of inertial particles in channel turbulence [link]Website   doi   link   bibtex   3 downloads  
From Deep to Physics-Informed Learning of Turbulence: Diagnostics. King, R.; Hennigh, O.; Mohan, A.; and Chertkov, M. . 4 2018.
From Deep to Physics-Informed Learning of Turbulence: Diagnostics [link]Website   link   bibtex   abstract   2 downloads  
Multilevel techniques for compression and reduction of scientific data—the univariate case. Ainsworth, M.; Tugluk, O.; Whitney, B.; and Klasky, S. Computing and Visualization in Science, 19(5-6): 65-76. 4 2018.
Multilevel techniques for compression and reduction of scientific data—the univariate case [link]Website   doi   link   bibtex   8 downloads  
Extracting the spectrum of a flow by spatial filtering. Sadek, M.; and Aluie, H. Physical Review Fluids, 3(12): 124610. 4 2018.
Extracting the spectrum of a flow by spatial filtering [link]Website   doi   link   bibtex   6 downloads  
Dot-Tracking Methodology for Background Oriented Schlieren (BOS). Rajendran, L., K.; Bane, S., P., M.; and Vlachos, P., P. . 4 2018.
Dot-Tracking Methodology for Background Oriented Schlieren (BOS) [link]Website   link   bibtex   abstract   4 downloads  
Interactive visual exploration of line clusters. Kanzler, M.; and Westermann, R. In Beck, F.; Dachsbacher, C.; and Sadlo, F., editor(s), EG VMV '18 Proceedings of the Conference on Vision, Modeling, and Visualization, pages 155-163, 2018. Eurographics Association
Interactive visual exploration of line clusters [link]Website   doi   link   bibtex   3 downloads  
Geometry and scaling laws of excursion and iso-sets of enstrophy and dissipation in isotropic turbulence. Elsas, J., H.; Szalay, A., S.; and Meneveau, C. Journal of Turbulence, 19(4): 297-321. 4 2018.
Geometry and scaling laws of excursion and iso-sets of enstrophy and dissipation in isotropic turbulence [link]Website   doi   link   bibtex   abstract   3 downloads  
Predicting viscous-range velocity gradient dynamics in large-eddy simulations of turbulence. Johnson, P., L.; and Meneveau, C. Journal of Fluid Mechanics, 837: 80-114. 4 2018.
Predicting viscous-range velocity gradient dynamics in large-eddy simulations of turbulence [link]Website   doi   link   bibtex   abstract  
Theoretical Study of Fully Developed Turbulent Flow in a Channel, Using Prandtl’s Mixing Length Model. Antonialli, L., A.; Silveira-Neto, A.; Mecânica, F., D., E.; Uberlândia, U., F., D.; and Gerais, M. Journal of Applied Mathematics and Physics, 06(04): 677-692. 4 2018.
Theoretical Study of Fully Developed Turbulent Flow in a Channel, Using Prandtl’s Mixing Length Model [link]Website   doi   link   bibtex   abstract  
Fluid particle dynamics and the non-local origin of the Reynolds shear stress. Bernard, P., S.; and Erinin, M., A. Journal of Fluid Mechanics, 847: 520-551. 4 2018.
Fluid particle dynamics and the non-local origin of the Reynolds shear stress [link]Website   doi   link   bibtex   abstract  
Hypothesis Testing For Nonlinear Phenomena In The Geosciences Using Synthetic, Surrogate Data. Keylock, C., J. Earth and Space Science, 6(1): 2018EA000435. 4 2018.
Hypothesis Testing For Nonlinear Phenomena In The Geosciences Using Synthetic, Surrogate Data [link]Website   doi   link   bibtex   3 downloads  
High-End Volume Visualization. Shih, M. 2018.
High-End Volume Visualization [link]Website   link   bibtex   2 downloads  
Comparison of four large-eddy simulation research codes and effects of model coefficient and inflow turbulence in actuator-line-based wind turbine modeling. Martínez-Tossas, L., A.; Churchfield, M., J.; Yilmaz, A., E.; Sarlak, H.; Johnson, P., L.; Sørensen, J., N.; Meyers, J.; and Meneveau, C. Journal of Renewable and Sustainable Energy, 10(3): 33301. 4 2018.
Comparison of four large-eddy simulation research codes and effects of model coefficient and inflow turbulence in actuator-line-based wind turbine modeling [link]Website   doi   link   bibtex   abstract   3 downloads  
Towards Adaptive Grids for Atmospheric Boundary-Layer Simulations. van Hooft, J., A.; Popinet, S.; van Heerwaarden, C., C.; van der Linden, S., J., A., A.; de Roode, S., R.; and van de Wiel, B., J., H., H. Boundary-Layer Meteorology, 167(3): 421-443. 4 2018.
Towards Adaptive Grids for Atmospheric Boundary-Layer Simulations [link]Website   doi   link   bibtex   abstract   3 downloads  
Spurious noise in direct noise computation with a finite volume method for automotive applications. Dawi, A., H.; and Akkermans, R., A., D. International Journal of Heat and Fluid Flow, 72: 243-256. 4 2018.
Spurious noise in direct noise computation with a finite volume method for automotive applications [link]Website   doi   link   bibtex   abstract   8 downloads  
Multiscale analysis of the invariants of the velocity gradient tensor in isotropic turbulence. Danish, M.; and Meneveau, C. Physical Review Fluids, 3(4): 44604. 4 2018.
Multiscale analysis of the invariants of the velocity gradient tensor in isotropic turbulence [link]Website   doi   link   bibtex   4 downloads  
Building a scientific workflow framework to enable real-time machine learning and visualization. Li, F.; and Song, F. Concurrency and Computation: Practice and Experience, 31(16): e4703. 4 2018.
Building a scientific workflow framework to enable real-time machine learning and visualization [link]Website   doi   link   bibtex   3 downloads  
Generalization of Turbulent Pair Dispersion to Large Initial Separations. Shnapp, R.; and Liberzon, A. Physical Review Letters, 120(24): 244502. 4 2018.
Generalization of Turbulent Pair Dispersion to Large Initial Separations [link]Website   doi   link   bibtex   3 downloads  
Renormalization of viscosity in wavelet-based model of turbulence. Altaisky, M., V.; Hnatich, M.; and Kaputkina, N., E. Physical Review E, 98(3): 33116. 4 2018.
Renormalization of viscosity in wavelet-based model of turbulence [link]Website   doi   link   bibtex  
Experimental test of the crossover between the inertial and the dissipative range in a turbulent swirling flow. Debue, P.; Kuzzay, D.; Saw, E.; Daviaud, F.; Dubrulle, B.; Canet, L.; Rossetto, V.; and Wschebor, N. Physical Review Fluids, 3(2): 24602. 4 2018.
Experimental test of the crossover between the inertial and the dissipative range in a turbulent swirling flow [link]Website   doi   link   bibtex   abstract   3 downloads  
Remote visual analysis of large turbulence databases at multiple scales. Pulido, J.; Livescu, D.; Kanov, K.; Burns, R.; Canada, C.; Ahrens, J.; and Hamann, B. Journal of Parallel and Distributed Computing, 120: 115-126. 4 2018.
Remote visual analysis of large turbulence databases at multiple scales [link]Website   doi   link   bibtex   abstract   9 downloads  
  2017 (15)
Volumetric Flow Estimation for Incompressible Fluids Using the Stationary Stokes Equations. Lasinger, K.; Vogel, C.; and Schindler, K. In 2017 IEEE International Conference on Computer Vision (ICCV), pages 2584-2592, 4 2017. IEEE
Volumetric Flow Estimation for Incompressible Fluids Using the Stationary Stokes Equations [link]Website   doi   link   bibtex   abstract   3 downloads  
Towards a generalised dual-mesh hybrid LES/RANS framework with improved consistency. Tunstall, R.; Laurence, D.; Prosser, R.; and Skillen, A. Computers and Fluids, 157: 73-83. 4 2017.
Towards a generalised dual-mesh hybrid LES/RANS framework with improved consistency [link]Website   doi   link   bibtex   abstract   3 downloads  
In situ video encoding of floating-point volume data using special-purpose hardware for a posteriori rendering and analysis. Leaf, N.; Miller, B.; and Ma, K., L. In 2017 IEEE 7th Symposium on Large Data Analysis and Visualization, LDAV 2017, volume 2017-Decem, pages 64-73, 4 2017. IEEE
In situ video encoding of floating-point volume data using special-purpose hardware for a posteriori rendering and analysis [link]Website   doi   link   bibtex   abstract   3 downloads  
Deformation of a compliant wall in a turbulent channel flow. Zhang, C.; Wang, J.; Blake, W.; and Katz, J. Journal of Fluid Mechanics, 823: 345-390. 4 2017.
Deformation of a compliant wall in a turbulent channel flow [link]Website   doi   link   bibtex   abstract   3 downloads  
Analysis of geometrical and statistical features of Lagrangian stretching in turbulent channel flow using a database task-parallel particle tracking algorithm. Johnson, P., L.; Hamilton, S., S.; Burns, R.; and Meneveau, C. Physical Review Fluids, 2(1): 14605. 4 2017.
Analysis of geometrical and statistical features of Lagrangian stretching in turbulent channel flow using a database task-parallel particle tracking algorithm [link]Website   doi   link   bibtex   3 downloads  
Weighted divergence correction scheme and its fast implementation. Wang, C., Y.; Gao, Q.; Wei, R., J.; Li, T.; and Wang, J., J. Experiments in Fluids, 58(5): 44. 4 2017.
Weighted divergence correction scheme and its fast implementation [link]Website   doi   link   bibtex   abstract   3 downloads  
Uncertainty quantification in LES of channel flow. Safta, C.; Blaylock, M.; Templeton, J.; Domino, S.; Sargsyan, K.; and Najm, H. International Journal for Numerical Methods in Fluids, 83(4): 376-401. 4 2017.
Uncertainty quantification in LES of channel flow [link]Website   doi   link   bibtex   abstract   3 downloads  
Spatiotemporal velocity-velocity correlation function in fully developed turbulence. Canet, L.; Rossetto, V.; Wschebor, N.; and Balarac, G. Physical Review E, 95(2): 23107. 4 2017.
Spatiotemporal velocity-velocity correlation function in fully developed turbulence [link]Website   doi   link   bibtex   abstract   6 downloads  
Structure of the velocity gradient tensor in turbulent shear flows. Pumir, A. Physical Review Fluids, 2(7): 74602. 4 2017.
Structure of the velocity gradient tensor in turbulent shear flows [link]Website   doi   link   bibtex   abstract   3 downloads  
Synthetic velocity gradient tensors and the identification of statistically significant aspects of the structure of turbulence. Keylock, C., J. Physical Review Fluids, 2(8): 84607. 4 2017.
Synthetic velocity gradient tensors and the identification of statistically significant aspects of the structure of turbulence [link]Website   doi   link   bibtex   3 downloads  
Inhomogeneous preferential concentration of inertial particles in turbulent channel flow. Schmidt, L.; Fouxon, I.; Ditlevsen, P.; and Holzner, M. . 4 2017.
Inhomogeneous preferential concentration of inertial particles in turbulent channel flow [link]Website   link   bibtex   abstract   2 downloads  
POD-based background removal for particle image velocimetry. Mendez, M., A.; Raiola, M.; Masullo, A.; Discetti, S.; Ianiro, A.; Theunissen, R.; and Buchlin, J., M. Experimental Thermal and Fluid Science, 80: 181-192. 4 2017.
POD-based background removal for particle image velocimetry [link]Website   doi   link   bibtex   abstract   4 downloads  
Evaluation of the topological characteristics of the turbulent flow in a ‘box of turbulence’ through 2D time-resolved particle image velocimetry. Lian, H.; Soulopoulos, N.; and Hardalupas, Y. Experiments in Fluids, 58(9): 118. 4 2017.
Evaluation of the topological characteristics of the turbulent flow in a ‘box of turbulence’ through 2D time-resolved particle image velocimetry [link]Website   doi   link   bibtex   abstract   3 downloads  
Nonlinear effects in buoyancy-driven variable-density turbulence. Rao, P.; Caulfield, C., P.; and Gibbon, J., D. Journal of Fluid Mechanics, 810: 362-377. 4 2017.
Nonlinear effects in buoyancy-driven variable-density turbulence [link]Website   doi   link   bibtex   abstract   2 downloads  
A Lagrangian fluctuation - dissipation relation for scalar turbulence . Part II . Wall-bounded flows. Drivas, T., D.; and Eyink, G., L. Journal of Fluid Mechanics, 829: 236-279. 4 2017.
A Lagrangian fluctuation - dissipation relation for scalar turbulence . Part II . Wall-bounded flows [link]Website   doi   link   bibtex   abstract   2 downloads  
  2016 (16)
Multiscale analysis of the topological invariants in the logarithmic region of turbulent channels at a friction Reynolds number of 932. Lozano-Durán, A.; Holzner, M.; and Jiménez, J. Journal of Fluid Mechanics, 803: 356-394. 4 2016.
Multiscale analysis of the topological invariants in the logarithmic region of turbulent channels at a friction Reynolds number of 932 [link]Website   doi   link   bibtex   abstract   3 downloads  
The anisotropic structure of turbulence and its energy spectrum. Elsinga, G., E.; and Marusic, I. Physics of Fluids, 28(1): 11701. 4 2016.
The anisotropic structure of turbulence and its energy spectrum [link]Website   doi   link   bibtex   abstract   2 downloads  
Instantaneous Pressure Measurements from Large-Scale Tomo-PTV with HFSB Tracers past a Surface-Mounted Finite Cylinder. Schneiders, J.; Caridi, G., C., A.; Sciacchitano, A.; and Scarano, F. In 54th AIAA Aerospace Sciences Meeting, 4 2016. American Institute of Aeronautics and Astronautics
Instantaneous Pressure Measurements from Large-Scale Tomo-PTV with HFSB Tracers past a Surface-Mounted Finite Cylinder [link]Website   doi   link   bibtex   abstract   2 downloads  
Complex Networks Unveiling Spatial Patterns in Turbulence. Scarsoglio, S.; Iacobello, G.; and Ridolfi, L. International Journal of Bifurcation and Chaos, 26(13): 1650223. 4 2016.
Complex Networks Unveiling Spatial Patterns in Turbulence [link]Website   doi   link   bibtex   abstract   2 downloads  
A rapid non-iterative proper orthogonal decomposition based outlier detection and correction for PIV data. Higham, J., E.; Brevis, W.; and Keylock, C., J. Measurement Science and Technology, 27(12): 125303. 4 2016.
A rapid non-iterative proper orthogonal decomposition based outlier detection and correction for PIV data [link]Website   doi   link   bibtex   abstract   2 downloads  
Full-field pressure from snapshot and time-resolved volumetric PIV. Laskari, A.; de Kat, R.; and Ganapathisubramani, B. Experiments in Fluids, 57(3): 1-14. 4 2016.
Full-field pressure from snapshot and time-resolved volumetric PIV [link]Website   doi   link   bibtex   1 download  
An irrotation correction on pressure gradient and orthogonal-path integration for PIV-based pressure reconstruction. Wang, Z.; Gao, Q.; Wang, C.; Wei, R.; and Wang, J. Experiments in Fluids, 57(6): 104. 4 2016.
An irrotation correction on pressure gradient and orthogonal-path integration for PIV-based pressure reconstruction [link]Website   doi   link   bibtex  
A Two-length Scale Turbulence Model for Single-phase Multi-fluid Mixing. Schwarzkopf, J., D.; Livescu, D.; Baltzer, J., R.; Gore, R., A.; and Ristorcelli, J., R. Flow, Turbulence and Combustion, 96(1): 1-43. 4 2016.
A Two-length Scale Turbulence Model for Single-phase Multi-fluid Mixing [link]Website   doi   link   bibtex   abstract   3 downloads  
Compression and heuristic caching for GPU particle tracing in turbulent vector fields. Treib, M.; Bürger, K.; Wu, J.; and Westermann, R. Communications in Computer and Information Science, 598: 144-165. 2016.
Compression and heuristic caching for GPU particle tracing in turbulent vector fields [link]Website   doi   link   bibtex   abstract  
Line density control in screen-space via balanced line hierarchies. Kanzler, M.; Ferstl, F.; and Westermann, R. Computers and Graphics, 61: 29-39. 4 2016.
Line density control in screen-space via balanced line hierarchies [link]Website   doi   link   bibtex   abstract  
A closure for Lagrangian velocity gradient evolution in turbulence using recent-deformation mapping of initially Gaussian fields. Johnson, P., L.; and Meneveau, C. Journal of Fluid Mechanics, 804: 387-419. 4 2016.
A closure for Lagrangian velocity gradient evolution in turbulence using recent-deformation mapping of initially Gaussian fields [link]Website   doi   link   bibtex   abstract  
Main results of the 4th International PIV Challenge. Kähler, C., J.; Astarita, T.; Vlachos, P., P.; Sakakibara, J.; Hain, R.; Discetti, S.; Foy, R., L.; and Cierpka, C. Experiments in Fluids, 57(6): 97. 4 2016.
Main results of the 4th International PIV Challenge [link]Website   doi   link   bibtex   abstract   3 downloads  
Adaptive vector validation in image velocimetry to minimise the influence of outlier clusters. Masullo, A.; and Theunissen, R. Experiments in Fluids, 57(3): 1-21. 4 2016.
Adaptive vector validation in image velocimetry to minimise the influence of outlier clusters [link]Website   doi   link   bibtex   abstract  
Small-scale anisotropy in turbulent boundary layers. Pumir, A.; Xu, H.; and Siggia, E., D. Journal of Fluid Mechanics, 804: 5-23. 4 2016.
Small-scale anisotropy in turbulent boundary layers [link]Website   doi   link   bibtex   abstract   1 download  
Angular dynamics of a small particle in turbulence. Candelier, F.; Einarsson, J.; and Mehlig, B. Physical Review Letters, 117(20): 204501. 4 2016.
Angular dynamics of a small particle in turbulence [link]Website   doi   link   bibtex   abstract  
A study on the numerical dissipation of the Spectral Difference method for freely decaying and wall-bounded turbulence. Chapelier, J., B.; Lodato, G.; and Jameson, A. Computers and Fluids, 139: 261-280. 4 2016.
A study on the numerical dissipation of the Spectral Difference method for freely decaying and wall-bounded turbulence [link]Website   doi   link   bibtex   abstract  
  2015 (11)
On PIV random error minimization with optimal POD-based low-order reconstruction. Raiola, M.; Discetti, S.; and Ianiro, A. Experiments in Fluids, 56(4): 75. 4 2015.
On PIV random error minimization with optimal POD-based low-order reconstruction [link]Website   doi   link   bibtex   abstract  
Calibration and Forward Uncertainty Propagation for Large-eddy Simulations of Engineering Flows. Templeton, J., A.; Blaylock, M., L.; Domino, S., P.; Hewson, J., C.; Kumar, P., R.; Ling, J.; Najm, H., N.; Ruiz, A.; Safta, C.; Sargsyan, K.; Stewart, A.; and Wagner, G. 4 2015.
Calibration and Forward Uncertainty Propagation for Large-eddy Simulations of Engineering Flows [link]Website   doi   link   bibtex   1 download  
Local and nonlocal dynamics in superfluid turbulence. Sherwin-Robson, L., K.; Barenghi, C., F.; and Baggaley, A., W. Physical Review B, 91(10): 104517. 4 2015.
Local and nonlocal dynamics in superfluid turbulence [link]Website   doi   link   bibtex   abstract   6 downloads  
Large-deviation joint statistics of the finite-time Lyapunov spectrum in isotropic turbulence. Johnson, P., L.; and Meneveau, C. Physics of Fluids, 27(8): 85110. 4 2015.
Large-deviation joint statistics of the finite-time Lyapunov spectrum in isotropic turbulence [link]Website   doi   link   bibtex  
On velocity gradient dynamics and turbulent structure. Lawson, J., M.; and Dawson, J., R. Journal of Fluid Mechanics, 780: 60-98. 4 2015.
On velocity gradient dynamics and turbulent structure [link]Website   doi   link   bibtex   abstract  
Active Pointillistic Pattern Search. Ma, Y.; Sutherland, D., J.; Garnett, R.; and Schneider, J. In AISTATS, pages 672-680, 4 2015.
Active Pointillistic Pattern Search [pdf]Website   link   bibtex   abstract   1 download  
Inertial-Range Reconnection in Magnetohydrodynamic Turbulence and in the Solar Wind. Lalescu, C., C.; Shi, Y., K.; Eyink, G., L.; Drivas, T., D.; Vishniac, E., T.; and Lazarian, A. Physical Review Letters, 115(2): 25001. 4 2015.
Inertial-Range Reconnection in Magnetohydrodynamic Turbulence and in the Solar Wind [link]Website   doi   link   bibtex   abstract  
Short-time evolution of Lagrangian velocity gradient correlations in isotropic turbulence. Fang, L.; Bos, W., J., T.; and Jin, G., D. Physics of Fluids, 27(12): 125102. 4 2015.
Short-time evolution of Lagrangian velocity gradient correlations in isotropic turbulence [link]Website   doi   link   bibtex   abstract  
Shape-dependence of particle rotation in isotropic turbulence. Byron, M.; Einarsson, J.; Gustavsson, K.; Voth, G.; Mehlig, B.; and Variano, E. Physics of Fluids, 27(3): 35101. 4 2015.
Shape-dependence of particle rotation in isotropic turbulence [link]Website   doi   link   bibtex   abstract  
Investigation of the influence of the subgrid-scale stress on non-intrusive spatial pressure measurement using an isotropic turbulence database. Liu, X.; Siddle-mitchel, S.; Rybarczyk, R.; and Katz, J. 32nd AIAA Aerodynamic Measurement Technology and Ground Testing Conference,1-11. 4 2015.
Investigation of the influence of the subgrid-scale stress on non-intrusive spatial pressure measurement using an isotropic turbulence database [link]Website   doi   link   bibtex  
Complexity Phenomena and ROMA of the Earth’s Magnetospheric Cusp, Hydrodynamic Turbulence, and the Cosmic Web. Chang, T.; chin Wu, C.; Echim, M.; Lamy, H.; Vogelsberger, M.; Hernquist, L.; and Sijacki, D. Pure and Applied Geophysics, 172(7): 2025-2043. 4 2015.
Complexity Phenomena and ROMA of the Earth’s Magnetospheric Cusp, Hydrodynamic Turbulence, and the Cosmic Web [link]Website   doi   link   bibtex  
  2014 (7)
Tumbling of Small Axisymmetric Particles in Random and Turbulent Flows. Gustavsson, K.; Einarsson, J.; and Mehlig, B. Physical Review Letters, 112(1): 14501. 4 2014.
Tumbling of Small Axisymmetric Particles in Random and Turbulent Flows [link]Website   doi   link   bibtex   abstract  
Kolmogorov spectrum consistent optimization for multi-scale flow decomposition. Mishra, M.; Liu, X.; Skote, M.; and Fu, C., W. Physics of Fluids, 26(5): 55106. 4 2014.
Kolmogorov spectrum consistent optimization for multi-scale flow decomposition [link]Website   doi   link   bibtex   abstract  
Time-reversal-symmetry breaking in turbulence. Jucha, J.; Xu, H.; Pumir, A.; and Bodenschatz, E. Physical Review Letters, 113(5): 54501. 4 2014.
Time-reversal-symmetry breaking in turbulence [link]Website   doi   link   bibtex   abstract   1 download  
Flight-crash events in turbulence. Xu, H.; Pumir, A.; Falkovich, G.; Bodenschatz, E.; Shats, M.; Xia, H.; Francois, N.; and Boffetta, G. Proceedings of the National Academy of Sciences, 111(21): 7558-7563. 4 2014.
Flight-crash events in turbulence [link]Website   doi   link   bibtex   abstract  
Long-range μPIV to resolve the small scales in a jet at high Reynolds number. Fiscaletti, D.; Westerweel, J.; and Elsinga, G., E. Experiments in Fluids, 55(9): 1-15. 4 2014.
Long-range μPIV to resolve the small scales in a jet at high Reynolds number [link]Website   doi   link   bibtex   abstract  
Asymptotic results for backwards two-particle dispersion in a turbulent flow. Benveniste, D.; and Drivas, T., D. Physical Review E, 89(4): 41003. 4 2014.
Asymptotic results for backwards two-particle dispersion in a turbulent flow [link]Website   doi   link   bibtex   abstract  
Redistribution of kinetic energy in turbulent flows. Pumir, A.; Xu, H.; Boffetta, G.; Falkovich, G.; and Bodenschatz, E. Physical Review X, 4(4): 41006. 4 2014.
Redistribution of kinetic energy in turbulent flows [link]Website   doi   link   bibtex   abstract  
  2013 (6)
Flux-freezing breakdown in high-conductivity magnetohydrodynamic turbulence. Eyink, G.; Vishniac, E.; Lalescu, C.; Aluie, H.; Kanov, K.; Bürger, K.; Burns, R.; Meneveau, C.; and Szalay, A. Nature, 497(7450): 466-469. 4 2013.
Flux-freezing breakdown in high-conductivity magnetohydrodynamic turbulence [link]Website   doi   link   bibtex   abstract   3 downloads  
Fluctuation dynamos and their Faraday rotation signatures. Bhat, P.; and Subramanian, K. Monthly Notices of the Royal Astronomical Society, 429(3): 2469-2481. 4 2013.
Fluctuation dynamos and their Faraday rotation signatures [link]Website   doi   link   bibtex  
Invariants of the reduced velocity gradient tensor in turbulent flows. Cardesa, J., A., I.; Mistry, D.; Gan, L.; and Dawson, J., A., R. Journal of Fluid Mechanics, 716: 597-615. 4 2013.
Invariants of the reduced velocity gradient tensor in turbulent flows [link]Website   doi   link   bibtex   abstract  
Vortex-corner interactions in a cavity shear layer elucidated by time-resolved measurements of the pressure field. Liu, X.; and Katz, J. Journal of Fluid Mechanics, 728: 417-457. 4 2013.
Vortex-corner interactions in a cavity shear layer elucidated by time-resolved measurements of the pressure field [link]Website   doi   link   bibtex   abstract  
Accurate estimate of turbulent dissipation rate using PIV data. Xu, D.; and Chen, J. Experimental Thermal and Fluid Science, 44: 662-672. 4 2013.
Accurate estimate of turbulent dissipation rate using PIV data [link]Website   doi   link   bibtex   abstract  
Vorticity statistics and the time scales of turbulent strain. Moriconi, L.; and Pereira, R., M. Physical Review E, 88(1): 13005. 4 2013.
Vorticity statistics and the time scales of turbulent strain [link]Website   doi   link   bibtex   abstract  
  2012 (5)
On coarse-grained simulations of turbulent material mixing. Grinstein, F., F.; Gowardhan, A., A.; Ristorcelli, J., R.; and Wachtor, A., J. Physica Scripta, 86(5): 58203. 4 2012.
On coarse-grained simulations of turbulent material mixing [link]Website   doi   link   bibtex   abstract  
A classification scheme for turbulence based on the velocity-intermittency structure with an application to near-wall flow and with implications for bed load transport. Keylock, C., J.; Nishimura, K.; and Peinke, J. Journal of Geophysical Research: Earth Surface, 117(1): n/a-n/a. 4 2012.
A classification scheme for turbulence based on the velocity-intermittency structure with an application to near-wall flow and with implications for bed load transport [link]Website   doi   link   bibtex   abstract  
Pulsed, high-power LED illumination for tomographic particle image velocimetry. Buchmann, N., A.; Willert, C., E.; and Soria, J. Experiments in Fluids, 53(5): 1545-1560. 4 2012.
Pulsed, high-power LED illumination for tomographic particle image velocimetry [link]Website   doi   link   bibtex   abstract  
Turbulence visualization at the terascale on desktop PCs. Treib, M.; Burger, K.; Reichl, F.; Meneveau, C.; Szalay, A.; and Westermann, R. IEEE Transactions on Visualization and Computer Graphics, 18(12): 2169-2177. 4 2012.
Turbulence visualization at the terascale on desktop PCs [link]Website   doi   link   bibtex   abstract  
Detecting singular patterns in 2D vector fields using weighted Laurent polynomial. Liu, W.; and Ribeiro, E. Pattern Recognition, 45(11): 3912-3925. 4 2012.
Detecting singular patterns in 2D vector fields using weighted Laurent polynomial [link]Website   doi   link   bibtex   abstract  
  2011 (6)
Rank-Ordered Multifractal Analysis (ROMA) of probability distributions in fluid turbulence. Wu, C., C.; and Chang, T. Nonlinear Processes in Geophysics, 18(2): 261-268. 4 2011.
Rank-Ordered Multifractal Analysis (ROMA) of probability distributions in fluid turbulence [link]Website   doi   link   bibtex   abstract  
Group Anomaly Detection using Flexible Genre Models. Xiong, L.; Poczos, B.; and Schneider, J. In Advances in Neural Information Processing Systems, pages 1071-1079, 2011. Neural Information Processing Systems
Group Anomaly Detection using Flexible Genre Models [link]Website   doi   link   bibtex   abstract  
Stochastic flux freezing and magnetic dynamo. Eyink, G., L. Physical Review E, 83(5): 56405. 4 2011.
Stochastic flux freezing and magnetic dynamo [link]Website   doi   link   bibtex   abstract  
A method for characterising the sensitivity of turbulent flow fields to the structure of inlet turbulence. Keylock, C., J.; Tokyay, T., E.; and Constantinescu, G. Journal of Turbulence, 12: N45. 4 2011.
A method for characterising the sensitivity of turbulent flow fields to the structure of inlet turbulence [link]Website   doi   link   bibtex   abstract  
Simulations of Richtmyer-Meshkov instabilities in planar shock-tube experiments. Grinstein, F., F.; Gowardhan, A., A.; and Wachtor, A., J. Physics of Fluids, 23(3): 34106. 4 2011.
Simulations of Richtmyer-Meshkov instabilities in planar shock-tube experiments [link]Website   doi   link   bibtex   abstract  
Assessment of the modulated gradient model in decaying isotropic turbulence. Lu, H. Theoretical and Applied Mechanics Letters, 1(4): 41004. 2011.
Assessment of the modulated gradient model in decaying isotropic turbulence [link]Website   doi   link   bibtex  
  2010 (4)
Viscous tilting and production of vorticity in homogeneous turbulence. Holzner, M.; Guala, M.; Lüthi, B.; Liberzon, A.; Nikitin, N.; Kinzelbach, W.; and Tsinober, A. Physics of Fluids, 22(6): 1-4. 4 2010.
Viscous tilting and production of vorticity in homogeneous turbulence [link]Website   doi   link   bibtex   abstract  
Scaling of conditional lagrangian time correlation functions of velocity and pressure gradient magnitudes in isotropic turbulence. Yu, H.; and Meneveau, C. Flow, Turbulence and Combustion, 85(3-4): 457-472. 4 2010.
Scaling of conditional lagrangian time correlation functions of velocity and pressure gradient magnitudes in isotropic turbulence [link]Website   doi   link   bibtex   abstract  
Scale and rotation invariant detection of singular patterns in vector flow fields. Liu, W.; and Ribeiro, E. 2010.
Scale and rotation invariant detection of singular patterns in vector flow fields [link]Website   doi   link   bibtex   abstract  
A new two-scale model for large eddy simulation of wall-bounded flows. Gungor, A., G.; and Menon, S. 4 2010.
A new two-scale model for large eddy simulation of wall-bounded flows [link]Website   doi   link   bibtex   abstract   1 download  
  2009 (2)
Expanding the Q-R space to three dimensions. Lthi, B.; Holzner, M.; and Tsinober, A. Journal of Fluid Mechanics, 641: 497-507. 4 2009.
Expanding the Q-R space to three dimensions [link]Website   doi   link   bibtex   abstract  
Matrix exponential-based closures for the turbulent subgrid-scale stress tensor. Li, Y.; Chevillard, L.; Eyink, G.; and Meneveau, C. Physical Review E, 79(1): 16305. 4 2009.
Matrix exponential-based closures for the turbulent subgrid-scale stress tensor [link]Website   doi   link   bibtex   abstract  
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