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Experimental analysis and ANN prediction on performances of finned oval-tube heat exchanger under different air inlet angles with limited experimental data
Open Physics ( IF 1.9 ) Pub Date : 2020-12-17 , DOI: 10.1515/phys-2020-0212 Xueping Du 1, 2 , Zhijie Chen 1 , Qi Meng 1 , Yang Song 1
Open Physics ( IF 1.9 ) Pub Date : 2020-12-17 , DOI: 10.1515/phys-2020-0212 Xueping Du 1, 2 , Zhijie Chen 1 , Qi Meng 1 , Yang Song 1
Affiliation
Abstract A high accuracy of experimental correlations on the heat transfer and flow friction is always expected to calculate the unknown cases according to the limited experimental data from a heat exchanger experiment. However, certain errors will occur during the data processing by the traditional methods to obtain the experimental correlations for the heat transfer and friction. A dimensionless experimental correlation equation including angles is proposed to make the correlation have a wide range of applicability. Then, the artificial neural networks (ANNs) are used to predict the heat transfer and flow friction performances of a finned oval-tube heat exchanger under four different air inlet angles with limited experimental data. The comparison results of ANN prediction with experimental correlations show that the errors from the ANN prediction are smaller than those from the classical correlations. The data of the four air inlet angles fitted separately have higher precisions than those fitted together. It is demonstrated that the ANN approach is more useful than experimental correlations to predict the heat transfer and flow resistance characteristics for unknown cases of heat exchangers. The results can provide theoretical support for the application of the ANN used in the finned oval-tube heat exchanger performance prediction.
中文翻译:
有限实验数据下不同进气角度下翅片椭圆管换热器性能的实验分析及人工神经网络预测
摘要 传热和流动摩擦的实验相关性的高精度总是被期望根据有限的换热器实验数据计算未知情况。然而,传统方法在数据处理过程中会出现一定的误差,以获得传热和摩擦的实验相关性。提出了一种包含角度的无量纲实验相关方程,使相关性具有广泛的适用性。然后,人工神经网络 (ANNs) 用于预测翅片椭圆管换热器在四种不同进气角度下的传热和流动摩擦性能,并使用有限的实验数据。ANN 预测与实验相关性的比较结果表明,ANN 预测的误差小于经典相关性的误差。四个进风角分别拟合的数据比一起拟合的数据精度更高。结果表明,ANN 方法比实验相关性更有用,可以预测未知换热器情况下的传热和流动阻力特性。研究结果可为人工神经网络在翅片椭圆管换热器性能预测中的应用提供理论支持。结果表明,ANN 方法比实验相关性更有用,可以预测未知换热器情况下的传热和流动阻力特性。研究结果可为人工神经网络在翅片椭圆管换热器性能预测中的应用提供理论支持。结果表明,ANN 方法比实验相关性更有用,可以预测未知换热器情况下的传热和流动阻力特性。研究结果可为人工神经网络在翅片椭圆管换热器性能预测中的应用提供理论支持。
更新日期:2020-12-17
中文翻译:
有限实验数据下不同进气角度下翅片椭圆管换热器性能的实验分析及人工神经网络预测
摘要 传热和流动摩擦的实验相关性的高精度总是被期望根据有限的换热器实验数据计算未知情况。然而,传统方法在数据处理过程中会出现一定的误差,以获得传热和摩擦的实验相关性。提出了一种包含角度的无量纲实验相关方程,使相关性具有广泛的适用性。然后,人工神经网络 (ANNs) 用于预测翅片椭圆管换热器在四种不同进气角度下的传热和流动摩擦性能,并使用有限的实验数据。ANN 预测与实验相关性的比较结果表明,ANN 预测的误差小于经典相关性的误差。四个进风角分别拟合的数据比一起拟合的数据精度更高。结果表明,ANN 方法比实验相关性更有用,可以预测未知换热器情况下的传热和流动阻力特性。研究结果可为人工神经网络在翅片椭圆管换热器性能预测中的应用提供理论支持。结果表明,ANN 方法比实验相关性更有用,可以预测未知换热器情况下的传热和流动阻力特性。研究结果可为人工神经网络在翅片椭圆管换热器性能预测中的应用提供理论支持。结果表明,ANN 方法比实验相关性更有用,可以预测未知换热器情况下的传热和流动阻力特性。研究结果可为人工神经网络在翅片椭圆管换热器性能预测中的应用提供理论支持。