Adaptive Time Windows for Discrete Adjoint Topology Optimization of Unsteady Flows

Abstract

Rather than prescribing an evaluation interval a priori, the proposed framework characterizes each evolving unsteady flow using a sequence of time windows. A consecutive-window convergence criterion is introduced to automatically identify a representative time window within its fully developed stage. The objective evaluation and discrete adjoint analysis are then carried out consistently over the identified representative time window. The framework is implemented using a regularized lattice Boltzmann method-based large-eddy simulation (LBM-LES) solver together with a partial bounce-back fluid-solid model. The consecutive-window convergence criterion is first validated using the backward-facing step flow. Cylinder-flow applications are then employed to investigate the influence of different flow regimes on the proposed framework. The wake-flow recovery problem verifies its effectiveness for unsteady topology optimization, while U-bend optimization further demonstrates its capability to identify and reorganize complex vortical structures.