Sparse regression yields explicit invariant polynomial SGS closures for LES on anisotropic grids that achieve neural-network accuracy with simpler forms and lower computational cost.
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Bayesian PINNs with Hamiltonian Monte Carlo sampling deliver the most consistent uncertainty estimates for turbulent flow inverse problems, while repulsive deep ensembles provide a faster but slightly less calibrated alternative.
The SST k-ω model most accurately predicts mean velocities, TKE, shear stress, CRZ, CVC, temperature distribution, and species concentrations among the tested RANS models.
citing papers explorer
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Discovery of Sparse Invariant Subgrid-Scale Closures via Dissipation-Controlled Training for Large Eddy Simulation on Anisotropic Grids
Sparse regression yields explicit invariant polynomial SGS closures for LES on anisotropic grids that achieve neural-network accuracy with simpler forms and lower computational cost.
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Uncertainty Quantification in PINNs for Turbulent Flows: Bayesian Inference and Repulsive Ensembles
Bayesian PINNs with Hamiltonian Monte Carlo sampling deliver the most consistent uncertainty estimates for turbulent flow inverse problems, while repulsive deep ensembles provide a faster but slightly less calibrated alternative.
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Performance Evaluation of RANS-Based Turbulence Models in Predicting Turbulent Non-Premixed Swirling Combustion within a Realistic Can Combustor
The SST k-ω model most accurately predicts mean velocities, TKE, shear stress, CRZ, CVC, temperature distribution, and species concentrations among the tested RANS models.