摘要
针对航空发动机密封性能的提升需求,基于圆周密封工况分析,建立了以泄漏量最小、密封环最大温升和最大变形最小为优化目标,以主、辅助密封带宽度,搭接头角度和长度及卸荷槽宽度为设计变量的多目标优化模型。采用拉丁超立方抽样方法得到了代表性样本库,通过热流固耦合分析确定对应目标函数值。利用RBF神经网络建立了高拟合精度的设计变量与目标函数映射关系代理模型,并结合第二代非劣排序遗传算法(NSGA-II),得到了考虑目标函数重要度的一组最优解。结果表明:与优化前相比,圆周密封泄漏量降低了17.69%,最大温升降低了11.88%,最大变形降低了38.10%,最大应力降低了31.02%。
Aiming at the demand of aero-engine sealing performances improvement,based on the analysis of the working conditions of circumferential seal,a multi-objective optimization model with the minimum leak age,the minimum maximum temperature rise and the minimum maximum deformation of the sealing ring as the optimization objectives,and the width of the main and auxiliary sealing belts,the angle and length of the lap joint and the width of the unloading grooves as the design variables was established.A representative sample data base was obtained using the Latin hypercube sampling method,and the corresponding objective function values were determined by thermal-fluid-structure coupling analysis.The surrogate models of mapping relationship be tween design variables and objective functions with high fitting accuracy were established by RBF neural network method,and a set of optimal solutions considering the importance of the objective functions were obtained by combining with the Non-dominated Sorting Genetic Algorithm II(NSGA-II).The results show that the leakage of circumferential seal is reduced by 17.69%,the maximum temperature rise is decreased by 11.88%,the maxi mum deformation is reduced by 38.10%,and the maximum stress is reduced by 31.02%by optimization.
作者
闫玉涛
马洪旺
张立静
胡广阳
YAN Yu-tao;MA Hong-wang;ZHANG Li-jing;HU Guang-yang(School of Mechanical Engineering and Automation,Northeastern University,Shenyang 110819,China;Key Laboratory of Power Transmission Technology on Aero-engine,Aero Engine Corporation of China,Shenyang 110015,China)
出处
《推进技术》
EI
CAS
CSCD
北大核心
2023年第9期169-176,共8页
Journal of Propulsion Technology
关键词
圆周密封
热流固耦合
神经网络
遗传算法
多目标优化
Circumferential seal
Thermal-fluid-structure coupling
Neural network
Genetic algo rithm
Multi-objective optimization