https://sam.ensam.eu:443 The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material. Thu, 12 Mar 2026 13:56:59 GMT 2026-03-12T13:56:59Z http://hdl.handle.net/10985/15458 Optimization of cavitating flows simulation with data driven approach: from data assimilation to machine learning ZHANG, Xinlei; GOMEZ, Thomas; XIAO, HENG; COUTIER-DELGOSHA, Olivier This paper investigates the application of data-driven approach to the optimization of cavitating flow simulations. An evaluation of the performance of commonly used RANS models (k-e, k-w and k-w SST) is presented by comparison with high fidelity data (DNS solution and X-ray experimental measurements). An ensemble based variational method is introduced and used to reconstruct the inlet velocity and calibrate the empirical parameters in the turbulence model and the cavitation model. Machine learning approach is discussed to construct a discrepancy function of the Reynolds stresses to address the RANS model-form uncertainty. Mon, 01 Jan 2018 00:00:00 GMT http://hdl.handle.net/10985/15458 2018-01-01T00:00:00Z ZHANG, Xinlei GOMEZ, Thomas XIAO, HENG COUTIER-DELGOSHA, Olivier This paper investigates the application of data-driven approach to the optimization of cavitating flow simulations. An evaluation of the performance of commonly used RANS models (k-e, k-w and k-w SST) is presented by comparison with high fidelity data (DNS solution and X-ray experimental measurements). An ensemble based variational method is introduced and used to reconstruct the inlet velocity and calibrate the empirical parameters in the turbulence model and the cavitation model. Machine learning approach is discussed to construct a discrepancy function of the Reynolds stresses to address the RANS model-form uncertainty. http://hdl.handle.net/10985/15477 Bayesian optimisation of RANS simulation with ensemble-based variational method in convergent-divergent channel ZHANG, Xinlei; GOMEZ, Thomas; COUTIER-DELGOSHA, Olivier This paper investigates the applicability of a hybrid data assimilation approach, namely ensemble-based variational method (EnVar), to optimise Reynolds Averaged Navier-Stokes (RANS) simulations in convergent-divergent channel from the perspective of Bayesian inference. Concretely, the ensemble-based variational method is applied to infer the inlet velocity and turbulence model corrections by assimilating Direct Numerical Simulation (DNS) results or limited experimental data. The approach is first adopted to infer the inlet velocity profile for the WallTurb Bump and Venturi geometry. The improvement can be achieved near the inlet region for the bump, but for Venturi in light of the view field limited in adverse pressure gradient region, the observation space is not sensitive to the perturbation of inlet condition. In a second step, the model corrections in k − ω SST model are investigated by assimilating the limited sparse experimental data. With the inferred model corrections, the predictions on both velocity and turbulent kinetic energy (TKE) get improved. The results indicate that the ensemble-based variational method is efficient in inferring unknown quantities of both low dimension (D=20) and high dimension (D=2400) with small ensemble size robustly and non-intrusively. This approach could prove very useful for Bayesian inference or optimisation in CFD problems. Tue, 01 Jan 2019 00:00:00 GMT http://hdl.handle.net/10985/15477 2019-01-01T00:00:00Z ZHANG, Xinlei GOMEZ, Thomas COUTIER-DELGOSHA, Olivier This paper investigates the applicability of a hybrid data assimilation approach, namely ensemble-based variational method (EnVar), to optimise Reynolds Averaged Navier-Stokes (RANS) simulations in convergent-divergent channel from the perspective of Bayesian inference. Concretely, the ensemble-based variational method is applied to infer the inlet velocity and turbulence model corrections by assimilating Direct Numerical Simulation (DNS) results or limited experimental data. The approach is first adopted to infer the inlet velocity profile for the WallTurb Bump and Venturi geometry. The improvement can be achieved near the inlet region for the bump, but for Venturi in light of the view field limited in adverse pressure gradient region, the observation space is not sensitive to the perturbation of inlet condition. In a second step, the model corrections in k − ω SST model are investigated by assimilating the limited sparse experimental data. With the inferred model corrections, the predictions on both velocity and turbulent kinetic energy (TKE) get improved. The results indicate that the ensemble-based variational method is efficient in inferring unknown quantities of both low dimension (D=20) and high dimension (D=2400) with small ensemble size robustly and non-intrusively. This approach could prove very useful for Bayesian inference or optimisation in CFD problems.