基于数据驱动的可控变形叶型优化方法  

Data-driven design method of controllable morphing blade profile

作　　者：龙嘉明 潘天宇 李宸璋 郑孟宗 李秋实[1,4] LONG Jiaming;PAN Tianyu;LI Chenzhang;ZHENG Mengzong;LI Qiushi(Research Institute of Aero-Engine,Beihang University,Beijing 100191,China;Advanced Jet Propulsion Creativity Center,Aero Engine Academy of China,Beijing 101300,China;School of Energy and Power Engineering,Beihang University,Beijing 100191,China;Key Laboratory of Fluid and Power Machinery,Xihua University,Chengdu 610039,China)

机构地区：[1]北京航空航天大学航空发动机研究院,北京100191 [2]中国航空发动机研究院先进航空动力创新工作站,北京101300 [3]北京航空航天大学能源与动力工程学院,北京100191 [4]西华大学流体与动力机械重点实验室,成都610039

出　　处：《航空动力学报》2023年第7期1703-1714,共12页Journal of Aerospace Power

基　　金：国家自然科学基金(51976005);国家科技重大专项(2017-Ⅱ-0005-0018);先进航空动力创新工作站(依托中国航空发动机研究院设立)(HKCX2020-02-013);航空发动机及燃气轮机基础科学中心项目(P2022-B-Ⅱ-004-001)。

摘　　要：重点研究了综合考虑变形代价及气动收益的可控变形叶型优化设计方法。利用机器学习算法构建叶型几何与关键气动参数之间的预测模型,量化变形代价及气动收益,并搭建贝叶斯优化框架进行寻优。结果表明:基于机器学习的预测及优化框架能够准确预测风扇变形后的气动性能,且在考虑变形代价的条件下对叶型变形收益边界进行评估。主要结论是利用机器学习算法结合叶斯寻优框架可以获得兼顾变形代价以及气动收益的变形方案。相比于单纯的气动优化方案,此方案可以在保证气动性能提升的同时,使叶片最大应力降低14.17%,压电片驱动能耗降低67.45%。The optimization design method of controllable morphing blade profile considering both morphing cost and aerodynamic benefits was discussed.A machine learning algorithm was used to build a prediction model to predict key aerodynamic parameters of morphing blade profiles.The morphing cost and aerodynamic benefits were quantified,and a Bayesian optimization framework was built for optimization.Results showed that the prediction and optimization framework based on machine learning can accurately predict the aerodynamic performance of the fan after morphing,and evaluate the profit boundary of the blade profile morphing considering the morphing cost.The main conclusion indicated that using machine learning algorithm and Bayesian optimization framework can obtain a morphing scheme taking into account both morphing cost and aerodynamic benefits.This scheme can reduce the maximum stress of blade by 14.17%and the energy consumption of piezoelectric actuator by 67.45%while ensuring the improvement of aerodynamic performance compared with the scheme only considering aerodynamic benefits.

关 键 词：超声风扇 可控变形 智能材料 机器学习 贝叶斯优化 

分 类 号：V235.16[航空宇航科学与技术—航空宇航推进理论与工程]