Customized data-driven RANS closures for bi-fidelity LES-RANS optimization
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Publication:2128496
DOI10.1016/j.jcp.2021.110153OpenAlexW3015596437MaRDI QIDQ2128496
Yu Zhang, Javier F. Gómez, Martin Schmelzer, Richard P. Dwight, Zhong-Hua Han, Stefan Hickel
Publication date: 22 April 2022
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2004.03003
turbulence modellinglarge-eddy simulationReynolds-averaged Navier-Stokesalgebraic stress modelmulti-fidelity optimization
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- Flow over periodic hills -- numerical and experimental study in a wide range of Reynolds numbers
- A conservative immersed interface method for Large-Eddy Simulation of incompressible flows
- Implicit large-eddy simulation applied to turbulent channel flow with periodic constrictions
- Efficient global optimization of expensive black-box functions
- A novel evolutionary algorithm applied to algebraic modifications of the RANS stress-strain relationship
- Data-driven modelling of the Reynolds stress tensor using random forests with invariance
- A paradigm for data-driven predictive modeling using field inversion and machine learning
- An adaptive local deconvolution method for implicit LES
- A more general effective-viscosity hypothesis
- Some Recent Developments in Turbulence Closure Modeling
- Efficient Implementation of Nonlinear Deconvolution Methods for Implicit Large-Eddy Simulation
- Predicting the output from a complex computer code when fast approximations are available
- Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
- Turbulence Modeling in the Age of Data
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