基于参数灵敏度和神经网络的贯流风机风道设计
摘要：对贯流风机流动机理进行了定性分析，得到了影响其风量的关键结构参数。采用参数灵敏度分析法，得到了各个参数对风量的影响程度和正负相关性。以关键结构参数为径向基(RBF)网络的输入参数，风量为输出参数，按影响程度分配参数权重，构建了RBF神经网络，并对贯流风机的风量进行了预测、评估。结果表明：影响贯流风机风量的关键结构参数包括蜗喉倾斜度、蜗喉间隙、蜗舌间隙、吸气角、蜗舌长度、扩压角、进风口宽度；影响程度由强到弱依次为进风口宽度、蜗舌长度、扩压角、蜗喉倾斜度、蜗舌间隙、吸气角、蜗喉间隙，其中蜗喉间隙、吸气角、扩压角和进风口宽度与风量呈正相关，蜗喉倾斜度、蜗舌间隙和蜗舌长度与风量呈负相关；构建的RBF神经网络预测值与实际值最大相对误差为4.56%,平均相对误差为2.2%;使用该神经网络，可以对贯流风机风量进行快速评估。
关键词：贯流风机风量参数灵敏度神经网络径向基评估
尊敬的用户，本篇文章需要2元，点击支付交费后阅读
限时优惠福利：领取VIP会员
全年期刊、VIP视频免费!
全年期刊、VIP视频免费!
参考文献[1] ECK B.通风机[M].李强荣，邓贤贵，虞鸿余登，译.北京：机械工业出版社，1982:117- 126.
[2] 万淑敏，赵京华，张振林.横流风机的工作机理研究[J].天津商学院学报，1996(4):1- 5.
[3] 唐勇，梁亮，庞佑霞，等.贯流风机结构参数对性能的影响研究[J].机械科学与技术，2011,30(11):1934- 1937,1945.
[4] 张师帅，罗亮.空调用贯流风机叶轮几何建模的参数化[J].风机技术，2006(5):14- 16.
[5] 陈太聪，韩大建，苏成，等.参数灵敏度分析的神经网络方法及其工程应用[J].计算力学学报，2004,21(6):752- 756.
[6] 唐文勇，周佳，朱荣成.基于人工神经网络方法的舰船参数灵敏度分析[J].舰船科学技术，2006,28(6):111- 114.
[7] 丁俊杰.基于全局灵敏度分析和径向基神经网络的智能轮胎三向力估测方法研究[D].镇江：江苏大学，2020:6- 7.
[2] 万淑敏，赵京华，张振林.横流风机的工作机理研究[J].天津商学院学报，1996(4):1- 5.
[3] 唐勇，梁亮，庞佑霞，等.贯流风机结构参数对性能的影响研究[J].机械科学与技术，2011,30(11):1934- 1937,1945.
[4] 张师帅，罗亮.空调用贯流风机叶轮几何建模的参数化[J].风机技术，2006(5):14- 16.
[5] 陈太聪，韩大建，苏成，等.参数灵敏度分析的神经网络方法及其工程应用[J].计算力学学报，2004,21(6):752- 756.
[6] 唐文勇，周佳，朱荣成.基于人工神经网络方法的舰船参数灵敏度分析[J].舰船科学技术，2006,28(6):111- 114.
[7] 丁俊杰.基于全局灵敏度分析和径向基神经网络的智能轮胎三向力估测方法研究[D].镇江：江苏大学，2020:6- 7.
Duct design of cross-flow fan based on parameter sensitivity and neural network
Abstract: The flow mechanism of cross-flow fan is analysed qualitatively, and the key structural parameters affecting its air volume are obtained. Using the parameter sensitivity analysis method, the influence degree and positive and negative correlation of each parameter on air volume are obtained. Taking the key structural parameters as the input parameters of radial basis function(RBF) network and the air volume as the output parameter, the RBF neural network is constructed by distributing the parameter weights according to the degree of influence, and the air volume of cross-flow fan is predicted and evaluated. The results show that the key structural parameters affecting the air volume of the cross-flow fan include the inclination of the volute throat, the gap between the volute throats, the gap between the volute tongues, the suction angle, the length of the volute tongue, the diffuser angle, and the width of the air inlet. The influence degree from strong to weak is the width of the air inlet, the length of the volute tongue, the diffuser angle, the inclination of the volute throat, the gap between the volute tongues, the suction angle, the gap between the volute throats, among which the gap between the volute throats, the suction angle, the diffuser angle and the width of the air inlet are positively correlated with air volume, and the inclination of the volute throat, the gap between the volute tongues and the length of the volute tongue are negatively correlated with air volume. The maximum relative error between the predicted value and the actual value of RBF neural network is 4.56%, and the average relative error is 2.2%. Using this neural network, the air volume of cross-flow fan can be quickly evaluated.
Keywords: cross-flow fan; air volume; parameter sensitivity; neural network; radial basis function(RBF); evaluation;
517
0
0