In this research, configuration optimization was conducted for FDSHX (fan duct surface heat exchanger) that is a new type of heat exchanger adopted in aero-engine heat management in recent years. Firstly, a method of Taguchi-ANFIS (Taguchi-Adaptive Neuro-Fuzzy Inference System), which can reduce training data volume utilizing orthogonal experimental matrix, was proposed to construct a rapid response mathematical model between three configuration parameters, including fin pitch, fin thickness, and oil passages number, and two design indicators, including heat transfer capacity and operating weight, based on the data prepared by an experimental validated FDSHX heat transfer capacity calculation method using heat transfer unit simulation. Secondly, a hybrid method of NSGA II-IWO (Non-dominated Sorting Genetic Algorithm II-Invasive Weed Optimization), which can simultaneously obtain the strong abilities of exploration and escaping from trap into local optima, was proposed to drive the constructed response mathematical model to enhance heat transfer through adjusting the three configuration parameters. Optimization comparison was performed between NSGA II-IWO and four classic optimization algorithms including GA (Genetic Algorithm), IWO (Invasive Weed Optimization), CA (Cultural Algorithm), and HS (Harmony Search). This research provides an integrated solution to help mechanical engineers improve the applicability and reasonableness of FDSHX design.

中文翻译：

---

非支配排序遗传算法II与侵入杂草优化的混合风道表面换热器配置优化设计


本研究针对近年来航空发动机热管理中采用的新型换热器FDSHX（风扇管道表面换热器）进行了配置优化。首先，提出了一种利用正交实验矩阵减少训练数据量的Taguchi-ANFIS（Taguchi-Adaptive Neuro-Fuzzy Inference System）方法，构建了翅片节距、翅片厚度三个配置参数之间的快速响应数学模型。和油道数量，以及两个设计指标，包括传热能力和操作重量，基于使用传热单元模拟经实验验证的 FDSHX 传热能力计算方法所准备的数据。其次，提出了一种同时获得强大探索能力和跳出局部最优能力的NSGA II-IWO（非支配排序遗传算法II-入侵杂草优化）混合方法来驱动构建的响应数学模型通过调整三个配置参数来增强传热。将NSGA II-IWO与GA（遗传算法）、IWO（入侵杂草优化）、CA（文化算法）、HS（和谐搜索）四种经典优化算法进行优化比较。本研究提供了一个综合解决方案，帮助机械工程师提高FDSHX设计的适用性和合理性。