CFD-driven symbolic identification of algebraic Reynolds-stress models
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Publication:2135797
DOI10.1016/J.JCP.2022.111037OpenAlexW3155710335MaRDI QIDQ2135797
Ismaïl Ben Hassan Saïdi, Paola Cinnella, Martin Schmelzer, Francesco Grasso
Publication date: 9 May 2022
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2104.09187
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Cites Work
- Unnamed Item
- Discovering governing equations from data by sparse identification of nonlinear dynamical systems
- Flow over periodic hills -- numerical and experimental study in a wide range of Reynolds numbers
- Constrained global optimization of expensive black box functions using radial basis functions
- RANS turbulence model development using CFD-driven machine learning
- Near-wall turbulence closure modeling without ``damping functions
- A novel evolutionary algorithm applied to algebraic modifications of the RANS stress-strain relationship
- On the realizability of Reynolds stress turbulence closures
- Data-driven RANS closures for wind turbine wakes under neutral conditions
- Customized data-driven RANS closures for bi-fidelity LES-RANS optimization
- Data-driven RANS closures for three-dimensional flows around bluff bodies
- A paradigm for data-driven predictive modeling using field inversion and machine learning
- Presentation of anisotropy properties of turbulence, invariants versus eigenvalue approaches
- Instability of streaks in wall turbulence with adverse pressure gradient
- Explicit algebraic Reynolds stress and non-linear eddy-viscosity models
- Progress in the development of a Reynolds-stress turbulence closure
- A more general effective-viscosity hypothesis
- On explicit algebraic stress models for complex turbulent flows
- An explicit algebraic Reynolds stress model for incompressible and compressible turbulent flows
- Data-Driven Science and Engineering
- Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
- Turbulence Modeling in the Age of Data
- Two calculation procedures for steady, three-dimensional flows with recirculation
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