A New Global Gas Dynamics Performance Optimization Method for Refrigeration Centrifugal Compressor Stage Based on the Immune Algorithm

Lu Liang, Wuqi Gong, Yitong Liu, Fang Wang
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Abstract

This study proposes a new global gas dynamics optimization method which was applied to a multi-objective optimization task of centrifugal compressor performance with the aim of determining the improvement probability for achieving high efficiency across a wide operating range. Initially, the original Nondominated Neighbor Immune Algorithm (NNIA) was extended to solve constrained multi-objective optimization problems for the first time, which mainly incorporated a procedure for handling inequality and equality constraints without additional parameters. Subsequently, an adaptive topological Back-propagation Multilayer Feed-forward Artificial Neural Network (BP-MLFANN) was trained using the modified NNIA to quickly evaluate the fitness value of the centrifugal compressor stage performance during the optimization. The feasibility of the method was validated using the first stage of a refrigeration centrifugal compressor. The results indicated a substantial enhancement in the stage efficiency of the optimized impeller at the Near-stall, Design, and Near-choke operating points, with increasement of 1.8%, 1.9%, and 4%, respectively, as compared to the baseline stage. The flow field analysis shows that the impact loss at impeller leading edge and flow separation in the passage reduced greatly, the mixing process between the leakage flow and mainstream in the channel is significantly weakened, thus the flow field becomes more uniform after optimization. The new global gas dynamics optimization method provides a reference for the development of efficient and rapid optimization techniques for centrifugal compressor.
基于免疫算法的新型制冷离心压缩机级全局气体动力学性能优化方法
本研究提出了一种新的全局气体动力学优化方法,并将其应用于离心式压缩机性能的多目标优化任务,目的是确定在宽工作范围内实现高效率的改进概率。最初,原始的非支配邻域免疫算法(NNIA)首次被扩展用于解决受约束的多目标优化问题,该算法主要包含一个处理不等式和等式约束的程序,无需额外参数。随后,利用改进的 NNIA 训练了一个自适应拓扑反向传播多层前馈人工神经网络(BP-MLFANN),以在优化过程中快速评估离心压缩机级性能的适配值。使用制冷离心压缩机的第一级验证了该方法的可行性。结果表明,优化后的叶轮在近滞流、设计和近窒息运行点的级效率大幅提高,与基准级相比分别提高了 1.8%、1.9% 和 4%。流场分析表明,叶轮前缘的冲击损失和通道内的流动分离大大减少,泄漏流与主流在通道内的混合过程明显减弱,因此优化后的流场更加均匀。新的全局气体动力学优化方法为开发高效、快速的离心式压缩机优化技术提供了参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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