ABSTRACTThis 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 bo...

中文翻译：

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在收敛-发散信道中使用基于集成的变分方法对 RANS 仿真进行贝叶斯优化

摘要本文研究了混合数据同化方法的适用性，即基于集合的变分方法 (EnVar)，从贝叶斯推理的角度优化收敛-发散通道中的雷诺平均纳维-斯托克斯 (RANS) 模拟。具体而言，基于集合的变分方法通过同化直接数值模拟 (DNS) 结果或有限的实验数据来推断入口速度和湍流模型校正。该方法首先用于推断 WallTurb Bump 和文丘里几何形状的入口速度分布。改进可以在凸起的入口区域附近实现，但对于文丘里管，鉴于在不利压力梯度区域中的视场受限，观察空间对入口条件的扰动不敏感。第二步，通过同化有限的稀疏实验数据来研究 k-ω SST 模型中的模型校正。通过推断的模型修正，对博...