AI 中文总结
该研究提出自适应时间窗口框架，结合LBM-LES求解器与流固模型，经后台阶流、圆柱流等验证，可用于非定常流离散伴随拓扑优化，能识别重组复杂涡结构。
AI 中文摘要
本文提出的框架未预先设定评估区间，而是采用一系列时间窗口来表征每个演化的非定常流。引入连续窗口收敛准则，自动识别完全发展阶段内的代表性时间窗口，随后在该识别出的代表性时间窗口上一致地执行目标评估与离散伴随分析。该框架基于正则化格子玻尔兹曼方法的大涡模拟（LBM-LES）求解器，并结合部分反弹流固模型实现。首先通过后台阶流验证连续窗口收敛准则，再采用圆柱流应用研究不同流态对该框架的影响；尾流恢复问题验证其在非定常拓扑优化中的有效性，U型弯优化进一步证明其识别与重组复杂涡结构的能力。
英文摘要
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.
Comments36 pages, 19 figures
