Non‐intrusive reduced genetic algorithm for near‐real time flow optimal control,International Journal for Numerical Methods in Fluids

当前位置： X-MOL 学术 › Int. J. Numer. Methods Fluids › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Non‐intrusive reduced genetic algorithm for near‐real time flow optimal control
International Journal for Numerical Methods in Fluids ( IF 1.7 ) Pub Date : 2020-03-08 , DOI: 10.1002/fld.4820
Mourad Oulghelou ₁ , Cyrille Allery ₁

Affiliation

Most genetic algorithms (GAs) used in the literature to solve control problems are time consuming and involve important storage memory requirements. In fact, the search in GAs is iteratively performed on a population of chromosomes (control parameters). As a result, the cost functional needs to be evaluated through solving the high fidelity model or by performing the experimental protocol for each chromosome and for many generations. To overcome this issue, a non‐intrusive reduced real‐coded genetic algorithm (RGA) for near real‐time optimal control is designed. This algorithm uses precalculated parametrized solution snapshots stored in the POD (proper orthogonal decomposition) reduced form, to predict the solution snapshots for chromosomes over generations. The method used for this purpose is a economic reduced version of the Bi‐CITSGM method (Bi‐calibrated interpolation on the tangent space of the Grassmann manifold) designed specially for nonlinear parametrized solution snapshots interpolation. This approach is proposed in such a way to accelerate the usual Bi‐CITSGM process by bringing this last to a significantly lower dimension. Thus, the whole optimization process by RGA can be performed in near real‐time. The potential of RGA in terms of accuracy and central processing unit time is demonstrated on control problems of the flow past a cylinder and flow in a lid‐driven cavity when the Reynolds number value varies.

中文翻译：

用于近实时流量最优控制的非侵入式简化遗传算法

文献中用于解决控制问题的大多数遗传算法（GA）都很耗时，并且涉及重要的存储内存需求。实际上，GA中的搜索是对一组染色体（控制参数）迭代执行的。结果，需要通过求解高保真度模型或通过对每个染色体和许多世代执行实验方案来评估成本功能。为了克服这个问题，设计了一种非侵入式的简化实时编码遗传算法（RGA），用于近实时最优控制。该算法使用以POD（适当的正交分解）约简形式存储的预先计算的参数化解决方案快照，以预测染色体在世代中的解决方案快照。用于此目的的方法是Bi-CITSGM方法（格拉斯曼流形切线空间上的双向校准插值）的经济简化版本，专门为非线性参数化解决方案快照插值而设计。提出这种方法的方式是通过将最后的CITSGM的尺寸大大降低来加快通常的Bi-CITSGM过程。因此，可以几乎实时地执行RGA的整个优化过程。当雷诺数值发生变化时，RGA在精度和中央处理单元时间方面的潜力已通过流过圆柱体的流和盖驱动腔中的流的控制问题得到了证明。提出这种方法的方式是通过将最后的CITSGM的尺寸大大降低来加快通常的Bi-CITSGM过程。因此，可以几乎实时地执行RGA的整个优化过程。当雷诺数值发生变化时，RGA在精度和中央处理单元时间方面的潜力已通过流过圆柱体的流和盖驱动腔中的流的控制问题得到了证明。提出这种方法的目的是通过将最后的CITSGM降低到明显更低的尺寸来加快通常的Bi-CITSGM过程。因此，可以几乎实时地执行RGA的整个优化过程。当雷诺数值发生变化时，RGA在精度和中央处理单元时间方面的潜力已通过流过圆柱体的流和盖驱动腔中的流的控制问题得到了证明。

更新日期：2020-03-08

点击分享查看原文

点击收藏

阅读更多本刊最新论文本刊介绍/投稿指南11