Thermal performance prediction with high precision and low cost is always the need for designers of heat exchangers. Three typical design of experiments (DOE) known as Taguchi design method (TDM), Uniform design method (UDM), and Response surface method (RSM) are commonly used to reduce experimental cost. The radial basis function artificial neural network (RBF) based on different DOE is used to predict the thermal performance of two new parallel-flow shell and tube heat exchangers. The applicability and expense of ten different prediction methods (RBF + TDML9, RBF + TDML18, RBF + UDM, RBF + TDML9 + UDM, RBF + TDML18 + UDM, RBF + RSM, RBF + RSM + TDML9, RBF + RSM + TDML18, RBF + RSM + UDM, RSM) are discussed. The results show that the RBF + RSM is a very efficient method for the precise prediction of thermal-hydraulic performance: the minimum error is 2.17% for Nu and 5.30% for f . For RBF, it is not true that the more of train data, the more precision of the prediction. The parameter “spread” of RBF should be adjusted to optimize the prediction results. The prediction using RSM only can also obtain a good balance between precision and time cost with a maximum prediction error of 14.52%.

中文翻译：

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基于不同DOE的RBF两种新型换热器热工水力性能预测

高精度、低成本的热性能预测一直是换热器设计人员的需求。三种典型的实验设计 (DOE)，即田口设计法 (TDM)、均匀设计法 (UDM) 和响应面法 (RSM)，通常用于降低实验成本。基于不同DOE的径向基函数人工神经网络（RBF）用于预测两种新型平行流管壳式换热器的热性能。十种不同预测方法（RBF+TDML9、RBF+TDML18、RBF+UDM、RBF+TDML9+UDM、RBF+TDML18+UDM、RBF+RSM、RBF+RSM+TDML9、RBF+RSM+TDML18、 RBF + RSM + UDM、RSM) 进行了讨论。结果表明，RBF + RSM 是一种非常有效的热工水力性能精确预测方法：Nu 的最小误差为 2.17%，f 的最小误差为 5.30%。对于 RBF，并不是说训练数据越多，预测的精度就越高。应调整 RBF 的参数“spread”以优化预测结果。仅使用 RSM 的预测也可以在精度和时间成本之间取得良好的平衡，最大预测误差为 14.52%。