基于BP神经网络和NSGA-Ⅱ的离心压气机机匣处理槽参数优化

陈金萍,叶顺宏,李颂

车用发动机 ›› 2023, Vol. 0 ›› Issue (1) : 62-68.

车用发动机 ›› 2023, Vol. 0 ›› Issue (1) : 62-68. DOI: 10.3969/j.issn.1001-2222.2023.01.010
栏目

基于BP神经网络和NSGA-Ⅱ的离心压气机机匣处理槽参数优化

  • 陈金萍1,叶顺宏2,李颂3
作者信息 +

Parameters Optimization of Centrifugal Compressor Casing Slot Based on BP Neural Network and NSGA-Ⅱ

  • CHEN Jinping1,YE Shunhong2,LI Song3
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摘要

为获取离心压气机机匣处理槽的最优结构参数,针对某型离心压气机机匣处理的槽参数展开了优化工作,通过已有模拟数据建立BP神经网络预测模型,利用遗传算法(NSGA-Ⅱ)对槽的结构参数进行了多目标寻优工作。结果表明:优化后的结构参数为开槽宽度4 mm,槽中心位置为靠近叶轮前缘距离导风轮中段1.5 mm处。经过模拟分析,优化值对应的槽处理结构位置相对靠近叶轮前缘,使低速区域相对前移,改善了主通道内的流动情况,喘振边界在高转速下明显向小流量偏移,进一步拓宽了压气机稳定工作范围。

Abstract

In order to obtain the optimal structural parameters of a centrifugal compressor casing treatment slot, the optimization of centrifugal compressor casing slot parameter was carried out. A BP neural network prediction model was established based on the existing simulation data, and a multi-objective optimization of the slot structural parameters was carried out by using the genetic algorithm (NSGA-Ⅱ). The results show that the optimized structural parameters are the slot width of 4 mm and the slot center position of 1.5 mm from the middle section of air guide wheel near the leading edge of impeller. According to the simulation analysis, the slot treatment structure corresponding to the optimized value is relatively close to the leading edge of impeller, which makes the low-speed region relatively forward and improves the flow situation of main channel so as to shift the surge boundary obviously to the small flow rate at high speed, the stable working range of compressor widens.

关键词

参数优化 / 离心压气机 / BP神经网络 / 遗传算法 / 数值模拟

Key words

parameter optimization / centrifugal compressor / BP neural network / genetic algorithm / numerical simulation

引用本文

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陈金萍,叶顺宏,李颂. 基于BP神经网络和NSGA-Ⅱ的离心压气机机匣处理槽参数优化[J]. 车用发动机. 2023, 0(1): 62-68 https://doi.org/10.3969/j.issn.1001-2222.2023.01.010
CHEN Jinping,YE Shunhong,LI Song. Parameters Optimization of Centrifugal Compressor Casing Slot Based on BP Neural Network and NSGA-Ⅱ[J]. Vehicle Engine. 2023, 0(1): 62-68 https://doi.org/10.3969/j.issn.1001-2222.2023.01.010

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