Search Results - "Journal of fluid mechanics"

The flow physics of COVID-19 by Mittal, Rajat, Ni, Rui, Seo, Jung-Hee

“…Flow physics plays a key role in nearly every facet of the COVID-19 pandemic. This includes the generation and aerosolization of virus-laden respiratory…”
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Spectral proper orthogonal decomposition and its relationship to dynamic mode decomposition and resolvent analysis by Towne, Aaron, Schmidt, Oliver T., Colonius, Tim

“…We consider the frequency domain form of proper orthogonal decomposition (POD), called spectral proper orthogonal decomposition (SPOD). Spectral POD is derived…”
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Flow over an espresso cup: inferring 3-D velocity and pressure fields from tomographic background oriented Schlieren via physics-informed neural networks by Cai, Shengze, Wang, Zhicheng, Fuest, Frederik, Jeon, Young Jin, Gray, Callum, Karniadakis, George Em

“…Tomographic background oriented Schlieren (Tomo-BOS) imaging measures density or temperature fields in three dimensions using multiple camera BOS projections,…”
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Effects of ventilation on the indoor spread of COVID-19 by Bhagat, Rajesh K., Davies Wykes, M. S., Dalziel, Stuart B., Linden, P. F.

“…Although the relative importance of airborne transmission of the SARS-CoV-2 virus is controversial, increasing evidence suggests that understanding airflows is…”
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Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows by Fukami, Kai, Fukagata, Koji, Taira, Kunihiko

“…We present a new data reconstruction method with supervised machine learning techniques inspired by super resolution and inbetweening to recover…”
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Super-resolution reconstruction of turbulent flows with machine learning by Fukami, Kai, Fukagata, Koji, Taira, Kunihiko

“…We use machine learning to perform super-resolution analysis of grossly under-resolved turbulent flow field data to reconstruct the high-resolution flow field…”
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Nonlinear mode decomposition with convolutional neural networks for fluid dynamics by Murata, Takaaki, Fukami, Kai, Fukagata, Koji

“…We present a new nonlinear mode decomposition method to visualize decomposed flow fields, named the mode decomposing convolutional neural network autoencoder…”
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Artificial neural networks trained through deep reinforcement learning discover control strategies for active flow control by Rabault, Jean, Kuchta, Miroslav, Jensen, Atle, Réglade, Ulysse, Cerardi, Nicolas

“…We present the first application of an artificial neural network trained through a deep reinforcement learning agent to perform active flow control. It is…”
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Reynolds averaged turbulence modelling using deep neural networks with embedded invariance by Ling, Julia, Kurzawski, Andrew, Templeton, Jeremy

“…There exists significant demand for improved Reynolds-averaged Navier–Stokes (RANS) turbulence models that are informed by and can represent a richer set of…”
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Deep learning of vortex-induced vibrations by Raissi, Maziar, Wang, Zhicheng, Triantafyllou, Michael S., Karniadakis, George Em

“…Vortex-induced vibrations of bluff bodies occur when the vortex shedding frequency is close to the natural frequency of the structure. Of interest is the…”
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Convolutional-network models to predict wall-bounded turbulence from wall quantities by Guastoni, Luca, Güemes, Alejandro, Ianiro, Andrea, Discetti, Stefano, Schlatter, Philipp, Azizpour, Hossein, Vinuesa, Ricardo

“…Two models based on convolutional neural networks are trained to predict the two-dimensional instantaneous velocity-fluctuation fields at different wall-normal…”
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Spectral analysis of jet turbulence by Schmidt, Oliver T., Towne, Aaron, Rigas, Georgios, Colonius, Tim, Brès, Guillaume A.

“…Informed by large-eddy simulation (LES) data and resolvent analysis of the mean flow, we examine the structure of turbulence in jets in the subsonic, transonic…”
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Surfactant dynamics: hidden variables controlling fluid flows by Manikantan, Harishankar, Squires, Todd M.

“…Surfactants – molecules and particles that preferentially adsorb to fluid interfaces – play a ubiquitous role in the fluids of industry, of nature and of life…”
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A theoretical model for compressible bubble dynamics considering phase transition and migration by Zhang, A-Man, Li, Shi-Min, Xu, Run-Ze, Pei, Shao-Cong, Li, Shuai, Liu, Yun-Long

“…A novel theoretical model for bubble dynamics is established that simultaneously accounts for the liquid compressibility, phase transition, oscillation,…”
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Deep learning in fluid dynamics by Kutz, J. Nathan

“…It was only a matter of time before deep neural networks (DNNs) – deep learning – made their mark in turbulence modelling, or more broadly, in the general area…”
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Rheology of dense granular suspensions by Guazzelli, Élisabeth, Pouliquen, Olivier

“…Suspensions are composed of mixtures of particles and fluid and are omnipresent in natural phenomena and in industrial processes. The present paper addresses…”
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Coherent structures in wall-bounded turbulence by Jiménez, Javier

“…This article discusses the description of wall-bounded turbulence as a deterministic high-dimensional dynamical system of interacting coherent structures,…”
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Unsupervised deep learning for super-resolution reconstruction of turbulence by Kim, Hyojin, Kim, Junhyuk, Won, Sungjin, Lee, Changhoon

“…Recent attempts to use deep learning for super-resolution reconstruction of turbulent flows have used supervised learning, which requires paired data for…”
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Investigation of hydrodynamics of water impact and tail slamming of high-speed water entry with a novel immersed boundary method by Liu, Wen-Tao, Zhang, A-Man, Miao, Xu-Hong, Ming, Fu-Ren, Liu, Yun-Long

“…High-speed water entry is a transient hydrodynamic process that is accompanied by strongly compressible flow, free surface splash, cavity evolution and other…”
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Data-driven prediction of unsteady flow over a circular cylinder using deep learning by Lee, Sangseung, You, Donghyun

“…Unsteady flow fields over a circular cylinder are used for training and then prediction using four different deep learning networks: generative adversarial…”
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