Mean mesh adaptation for efficient CFD simulations with operating conditions variability

When numerically solving partial differential equations, for a given problem and operating condition producing a steady-state, mesh adaptation has proven its efficiency to automatically build a discretization achieving a prescribed error level at low cost. However, with continuously varying operating conditions, such as those encountered in uncertainty quantification, adapting a mesh for each condition and controlling the error level becomes complex and computationally expensive. To enable more effective error and cost control, this work proposes a novel approach to mesh adaptation. The method consists in building a single adapted mesh that aims to minimize the average error for a continuous set of operating conditions. In the proposed implementation, this single mesh is built iteratively, informed by an estimate of the local average interpolation error. The proposed method leverages the iterative nature of mesh adaptation by re-sampling Monte Carlo quadratures to obtain accurate average error estimates over a reduced set of sample conditions, ensuring a low computational cost. This approach is especially effective for localized flow features whose positions change only slightly with operating conditions, such as moving shocks in supersonic flows, as the refinement is confined to smaller areas of the computational domain. The study focuses on evaluating the method’s average error convergence, robustness, and computational cost in comparison to state-of-the-art adaptation techniques. Additionally, the sensitivity of the approach to the choice and size of the quadrature, as well as to the error estimation method, is assessed. For this purpose, the methodology is applied to a one-dimensional variable-step solution of the Burgers equation and a two-dimensional Euler scramjet flow with a variable inlet Mach number. The results show that Mean Mesh Adaptation (MMA) achieves error convergence comparable to specific mesh adaptation while reducing the evaluation cost by up to a factor of five (in the scramjet case). This efficiency gain stems from the reduced dependence on the number of sampled conditions, thanks to robust Monte Carlo re-sampling, as well as the shared computational expense of mesh construction across multiple evaluations. Therefore, the proposed method enables computational efficiency while maintaining error control across varying operating conditions within a prescribed parameter variation range.