Bayesian neural networks correct RANS turbulence models via kinetic energy source terms and anisotropy tensors, improving velocity predictions on training flows but showing reduced accuracy and under-coverage on unseen separated flows.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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physics.flu-dyn 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
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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Bayesian neural network correction of RANS turbulence models with uncertainty quantification in separated flows
Bayesian neural networks correct RANS turbulence models via kinetic energy source terms and anisotropy tensors, improving velocity predictions on training flows but showing reduced accuracy and under-coverage on unseen separated flows.
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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.