Výsledky vyhledávání - "Whitmore, Michael"

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Multi-agent reinforcement learning for wall modeling in LES of flow over periodic hills

Autor: Zhou, Di, Whitmore, Michael P., Griffin, Kevin P., Bae, H. Jane

We develop a wall model for large-eddy simulation (LES) that takes into account various pressure-gradient effects using multi-agent reinforcement learning (MARL). The model is trained using low-Reynolds-number flow over periodic hills with agents dis

Externí odkaz: http://arxiv.org/abs/2211.16427

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Non-Boussinesq subgrid-scale model with dynamic tensorial coefficients

Autor: Agrawal, Rahul, Whitmore, Michael P., Griffin, Kevin P., Bose, Sanjeeb T., Moin, Parviz

Publikováno v: Phys. Rev. Fluids 7 (7), 2022

A major drawback of Boussinesq-type subgrid-scale stress models used in large-eddy simulations is the inherent assumption of alignment between large-scale strain rates and filtered subgrid-stresses. A priori analyses using direct numerical simulation

Externí odkaz: http://arxiv.org/abs/2202.05502

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Requirements and sensitivity analysis of RANS-free wall-modeled LES

Autor: Whitmore, Michael, Lozano-Durán, Adrián, Moin, Parviz

Publikováno v: Annual Research Briefs 2020, Center for Turbulence Research, Stanford University

We study the sensitivity of wall model input variables to the modeling choices of the outer LES. This work is motivated by sensitivities observed in dynamic slip wall models. These dynamic wall models use variables from the near-wall LES solution as

Externí odkaz: http://arxiv.org/abs/2012.14900

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Large-Eddy Simulation of Flow over Boeing Gaussian Bump Using Multi-Agent Reinforcement Learning Wall Model

Autor: Zhou, Di, Whitmore, Michael P., Griffin, Kevin P., Bae, H. Jane

We develop a wall model for large-eddy simulation (LES) that takes into account various pressure-gradient effects using multi-agent reinforcement learning. The model is trained using low-Reynolds-number flow over periodic hills with agents distribute

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::62effaf480fbcc14daa260bd3c1dd5af

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