我的訂單 | 我的收藏 | 加入收藏 | 返回首頁
暖通空調>期刊目次>2022年>第8期

基于參數靈敏度和神經網絡的貫流風機風道設計

Duct design of cross-flow fan based on parameter sensitivity and neural network

曹 睿 余杰彬 黃 鑫
珠海格力電器股份有限公司

摘要:

對貫流風機流動機理進行了定性分析,得到了影響其風量的關鍵結構參數。采用參數靈敏度分析法,得到了各個參數對風量的影響程度和正負相關性。以關鍵結構參數為徑向基(RBF)網絡的輸入參數,風量為輸出參數,按影響程度分配參數權重,構建了RBF神經網絡,并對貫流風機的風量進行了預測、評估。結果表明:影響貫流風機風量的關鍵結構參數包括蝸喉傾斜度、蝸喉間隙、蝸舌間隙、吸氣角、蝸舌長度、擴壓角、進風口寬度;影響程度由強到弱依次為進風口寬度、蝸舌長度、擴壓角、蝸喉傾斜度、蝸舌間隙、吸氣角、蝸喉間隙,其中蝸喉間隙、吸氣角、擴壓角和進風口寬度與風量呈正相關,蝸喉傾斜度、蝸舌間隙和蝸舌長度與風量呈負相關;構建的RBF神經網絡預測值與實際值最大相對誤差為4.56%,平均相對誤差為2.2%;使用該神經網絡,可以對貫流風機風量進行快速評估。

關鍵詞:貫流風機;風量;參數靈敏度;神經網絡;徑向基;評估

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-flowfan;airvolume;parametersensitivity;neuralnetwork;radialbasisfunction(RBF);evaluation

    你還沒注冊?或者沒有登錄?這篇期刊要求至少是本站的注冊會員才能閱讀!

    如果你還沒注冊,請趕緊點此注冊吧!

    如果你已經注冊但還沒登錄,請趕緊點此登錄吧!

99香蕉国产线看观看这里有精品