9 citations · 13 across the 14 of their papers we have counts for
14 papers
Physics-Guided Machine Learning for Uncertainty Quantification in Turbulence Models
Minghan Chu, Weicheng Qian
Predicting the evolution of turbulent flows is central across science and engineering. Most studies rely on simulations with turbulence models, whose empirical simplifications intr…
Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification
Minghan Chu, Weicheng Qian
Turbulence Models represent the workhorse for simulations used in engineering design and analysis. Despite their low computational cost and robustness, these models suffer from sub…
Convolutional Neural Networks For Turbulent Model Uncertainty Quantification
Minghan Chu, Weicheng Qian
Complex turbulent flow simulations are an integral aspect of the engineering design process. The mainstay of these simulations is represented by eddy viscosity based turbulence mod…
Physics Based & Machine Learning Methods For Uncertainty Estimation In Turbulence Modeling
Minghan Chu
Turbulent flows play an important role in many scientific and technological design problems. Both Sub-Grid Scale (SGS) models in Large Eddy Simulations (LES) and Reynolds Averaged…
Physics Constrained Deep Learning For Turbulence Model Uncertainty Quantification
Minghan Chu, Weicheng Qian
Engineering design and scientific analysis rely upon computer simulations of turbulent fluid flows using turbulence models. These turbulence models are empirical and approximate, l…
A Deep Learning Approach For Epistemic Uncertainty Quantification Of Turbulent Flow Simulations
Minghan Chu, Weicheng Qian
Simulations of complex turbulent flow are part and parcel of the engineering design process. Eddy viscosity based turbulence models represent the workhorse for these simulations. T…
Uncertainty Quantification in Computational Fluid Dynamics: Physics and Machine Learning Based Approaches
Minghan Chu
Turbulent flow has been extensively studied using computational fluid dynamics (CFD) simulations since turbulent flow regime is so frequently encountered in both academic and engin…
Multi-fidelity Deep Learning-based methodology for epistemic uncertainty quantification of turbulence models
Minghan Chu, Weicheng Qian
Computational Fluid Dynamics (CFD) simulations using turbulence models are commonly used in engineering design. Of the different turbulence modeling approaches that are available,…
Uncertainty Quantification For Turbulent Flows with Machine Learning
Minghan Chu, Weicheng Qian
Turbulent flows are of central importance across applications in science and engineering problems. For design and analysis, scientists and engineers use Computational Fluid Dynamic…
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…
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…
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…
Model form uncertainty quantification of Reynolds-averaged Navier-Stokes modeling of flows over a SD7003 airfoil
Minghan Chu, Xiaohua Wu, David E. Rival
It is well known that the Boussinesq turbulent viscosity hypothesis can yield inaccurate predictions when complex f low features are involved, e.g. laminar-turbulent transition. Th…
Quantification of Reynolds-averaged-Navier-Stokes model form uncertainty in transitional boundary layer and airfoil flows
Minghan Chu, Xiaohua Wu, David E. Rival
It is well known that Boussinesq turbulent-viscosity hypothesis can introduce uncertainty in predictions for complex flow features such as separation, reattachment, and laminar-tur…