Flow enhancement from wall pressure observations: A compressible continuous adjoint data assimilation model

Abstract

This study establishes a compressible continuous adjoint data assimilation (C2ADA) approach for reproducing a complete mean flow from sparse wall pressure observations. The model-form error induced by the Boussinesq approximation is corrected by the addition of a spatially varying additive forcing term. The linear part of the eddy viscosity, computed using the conventional Reynolds-averaged Navier–Stokes model, is incorporated for ensuring the well-posedness of the optimization. The model is derived theoretically to minimize discrepancies between the wall pressure measurements and the numerical predictions of the primary-adjoint system, thereby enabling determination of the optimal contribution of the Reynolds force vector. The effects of divergence schemes and turbulence models are investigated by examining flow over a 30P30N airfoil. The C2ADA model, employing two distinct schemes, demonstrates significant improvements in velocity estimation, but the first-order scheme introduces excessive dissipation, resulting in an under-prediction of spanwise vorticity. The C2ADA model combined with different eddy-viscosity models uniquely recovers the Reynolds force vectors and obtains mean fields that outperform those achieved solely through conventional eddy viscosity models. The practicability of the C2ADA model for capturing complex flow phenomena is confirmed by applying it to study three-dimensional flow over a 65° delta wing. Despite limited wall pressure observations, the C2ADA model has shown a notable improvement in accurately estimating the intensity and location of both the primary and secondary vortices. Recovery errors in the apex region are significantly diminished by incorporating a paucity of observations account for the effect of inboard vortex. The study broadens the applicability of continuous adjoint-based approaches for modeling compressible flow, as our C2ADA approach is easily implemented in existing computational fluid dynamics solvers and has significantly higher computational efficiency than other approaches.