High Reynolds number airfoil turbulence modeling method based on machine learning technique
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Publication:2670056
DOI10.1016/j.compfluid.2021.105298OpenAlexW3216676539MaRDI QIDQ2670056
Xuxiang Sun, Wei-Wei Zhang, Wenbo Cao, Yilang Liu, Linyang Zhu
Publication date: 10 March 2022
Published in: Computers and Fluids (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2111.13469
Uses Software
Cites Work
- Machine learning strategies for systems with invariance properties
- RANS turbulence model development using CFD-driven machine learning
- A turbulent eddy-viscosity surrogate modeling framework for Reynolds-averaged Navier-Stokes simulations
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Conditioning and accurate solutions of Reynolds average Navier–Stokes equations with data-driven turbulence closures
- Some Recent Developments in Turbulence Closure Modeling
- Deep learning of vortex-induced vibrations
- Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
- Reynolds-averaged Navier–Stokes equations with explicit data-driven Reynolds stress closure can be ill-conditioned
- Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
- Deep learning in fluid dynamics
- Random forests
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