Robust preconditioned one-shot methods and direct-adjoint-looping for optimizing Reynolds-averaged turbulent flows. (30th April 2022) - Nabi, S.; Grover, P.; Caulfield, C.P.
Abstract: We compare the performance of direct-adjoint-looping (DAL) and one-shot methods in a design optimization task involving turbulent flow modeled using Reynolds-Averaged-Navier–Stokes equations. Two preconditioned variants of the one-shot algorithm are proposed and tested. The role of an approximate Hessian as a preconditioner for the one-shot method iterations is highlighted. We find that the preconditioned one-shot methods can solve the PDE-constrained optimization problem with the cost of computation comparable (about fourfold) to that of the simulation run alone. This cost is substantially less than that of DAL, which requires O ( 10 ) direct-adjoint loops to converge. The optimization results arising from the one-shot method can be used for optimal sensor/actuator placement tasks, or to provide a reference trajectory to be used for online feedback control applications. Highlights: We study the boundary optimization of turbulent flows using one-shot and DAL methods. Curvature-based (OC) and BFGS-based (OB) are the two one-shot variants we used. OB algorithm is found to be more robust than the OC and requires less supervision. One-shot method is significantly faster than direct-adjoint-looping (DAL) method. Two case-studies validate and demonstrate the numerical algorithms.