叶尖迷宫密封泄漏率预测及结构参数优化

IF 0.6 4区 综合性期刊 Q3 MULTIDISCIPLINARY SCIENCES
Haiyin Guo, Yuqin Ma, Wei Xu, Yatao Zhao, Zedu Yang, Yi Xu, Fei Li, Yatao Li
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引用次数: 0

摘要

为了研究叶尖迷宫密封(BTLS)结构参数对泄漏流动特性的影响,采用有限元方法计算了叶尖泄漏率(BTLR)与齿宽、齿高、齿距和齿数等4个结构参数的关系。以有限元结果为样本,采用支持向量回归(SVR)、反向传播(BP)神经网络和极限学习机(ELM)建立了BTLR与4个结构参数之间关系的预测模型。比较分析了三种预测模型的精度和适用性。结果表明,与其他算法相比,SVR算法对BTLR的预测具有更高的精度和稳定性。其检验集的均方误差为0.00059637,决定系数为0.99253。然后将SVR结果作为遗传算法的样本,寻找结构参数与最小BTLR的组合。将得到的结构参数组合起来进行仿真建模计算。结果表明:叶片尖端区域流体速度显著降低,速度过渡平缓;仿真结果与优化结果相差0.01%。该方法创新性地将机器学习算法应用于BTLR的预测,改善了仅使用有限元方法时速度慢、成本高的问题。这为计算BTLR提供了一种新的方法。此外,还对BTLS的结构参数进行了优化,以减小BTLR。这一思想拓展了机器学习算法的应用领域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of Leakage Rate and Optimization of Structural Parameter of Blade Tip Labyrinth Seal
To study the influence of the structural parameters of blade tip labyrinth seal (BTLS) on leakage flow characteristics, finite element method was used to calculate the relationship between blade tip leakage rate (BTLR) and four structural parameters such as tooth width, tooth height, tooth pitch and tooth number. With the finite element results as samples, support vector regression (SVR), back propagation (BP) neural network and extreme learning machine (ELM) were used to establish the prediction model of the relationship between BTLR and four structural parameters. The accuracy and applicability of three prediction models were compared and analyzed. The results showed that SVR algorithm has higher prediction accuracy and stability compared with other algorithms for the prediction of BTLR. The mean square error and determination coefficient of its test set are 0.00059637 and 0.99253 respectively. After that, SVR results were taken as samples of genetic algorithm to find the combination of structural parameters with the minimum BTLR. The obtained structural parameters were combined for simulation modeling calculation. Its results showed that the fluid velocity in the blade tip region is significantly reduced and the velocity transition is gentle. The difference between simulation and optimization was 0.01%. This method innovatively applies machine learning algorithm to the prediction of BTLR, and improves the problem of low speed and high cost when only using finite element method. It provides a new way to calculate BTLR. In addition, the structural parameters of BTLS are optimized to reduce BTLR. This idea expands the field of application of machine learning algorithms.
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来源期刊
Chiang Mai Journal of Science
Chiang Mai Journal of Science MULTIDISCIPLINARY SCIENCES-
CiteScore
1.00
自引率
25.00%
发文量
103
审稿时长
3 months
期刊介绍: The Chiang Mai Journal of Science is an international English language peer-reviewed journal which is published in open access electronic format 6 times a year in January, March, May, July, September and November by the Faculty of Science, Chiang Mai University. Manuscripts in most areas of science are welcomed except in areas such as agriculture, engineering and medical science which are outside the scope of the Journal. Currently, we focus on manuscripts in biology, chemistry, physics, materials science and environmental science. Papers in mathematics statistics and computer science are also included but should be of an applied nature rather than purely theoretical. Manuscripts describing experiments on humans or animals are required to provide proof that all experiments have been carried out according to the ethical regulations of the respective institutional and/or governmental authorities and this should be clearly stated in the manuscript itself. The Editor reserves the right to reject manuscripts that fail to do so.
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