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physics.flu-dyn2023
Multi-Fidelity Data Assimilation For Physics Inspired Machine Learning In Uncertainty Quantification Of Fluid Turbulence Simulations
Minghan Chu, Weicheng Qian
Reliable prediction of turbulent flows is an important necessity across different fields of science and engineering. In Computational Fluid Dynamics (CFD) simulations, the most com…
physics.flu-dyn2023
Combination of Multi-Fidelity Data Sources For Uncertainty Quantification: A Lightweight CNN Approach
Minghan Chu, Weicheng Qian
Reynolds Averaged Navier Stokes (RANS) modelling is notorious for introducing the model-form uncertainty due to the Boussinesq turbulent viscosity hypothesis. Recently, the eigensp…
physics.flu-dyn2022
Machine learning based uncertainty quantification of turbulence model for airfoils
Minghan Chu, Weicheng Qian
Reynolds-averaged Navier-Stokes (RANS)-based transition modeling is widely used in aerospace applications but suffers inaccuracies due to the Boussinesq turbulent viscosity hypothe…