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首页> 外文期刊>International Journal of Heat and Mass Transfer >Prediction of oscillatory heat transfer coefficient for a thermoacoustic heat exchanger through artificial neural network technique
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Prediction of oscillatory heat transfer coefficient for a thermoacoustic heat exchanger through artificial neural network technique

机译：人工神经网络技术预测热声换热器的振荡传热系数

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摘要

Heat exchangers under oscillatory flow condition in thermoacoustic devices are quite different with the traditional ones in heat transfer and flow behavior of thermo-viscous fluid. As a result, one cannot directly apply the heat transfer correlations for the steady flow to design thermoacoustic heat exchangers, otherwise, significant deviation will arise. However, some correlations of heat transfer for the oscillatory flow have not been well established yet. This study involves the application of artificial neural network (ANN) as a new approach to predict oscillatory heat transfer coefficient of one thermoacoustic heat exchanger under some operating conditions. One ANN model for the oscillatory heat exchanger used in one standing wave thermoacoustic refrigerator has been developed based on the published experimental data. This proposed ANN model has three layers with the configuration of 2-10-1, namely one input layer with two neurons representing two operating parameters, oscillating frequency and mean pressure, one hidden layer with optimal ten hidden neurons and one output layer with one neuron representing the oscillatory heat transfer coefficient as response. Moreover, a statistical analysis has been provided for studying the influence strength of these two input parameters on the oscillatory heat transfer coefficient. This ANN model had been proven to be desirable in accuracy for predicting oscillatory heat transfer coefficient by comparing ANN model results with both experimental results and calculated results by several other correlations from the published literature at the same operating conditions. This research work provides a new and accurate modeling approach based on ANN technique for the research of thermoacoustic heat exchangers and solving heat transfer problems related with oscillatory flow condition. (C) 2018 Elsevier Ltd. All rights reserved.
机译：在热声装置中，在振荡流动条件下的热交换器与传统的热交换器在热粘性流体的传热和流动特性方面有很大的不同。结果，不能将稳态流动的传热相关性直接用于设计热声换热器，否则会产生明显的偏差。然而，对于振荡流的热传递的一些相关性还没有很好地建立。这项研究涉及人工神经网络（ANN）的应用，作为一种预测在某些工况下一个热声换热器的振荡传热系数的新方法。基于已发布的实验数据，开发了一种用于驻波热声制冷机的振荡热交换器的ANN模型。所提出的人工神经网络模型具有三层，其结构为2-10-1，即一个输入层，其中两个神经元代表振荡频率和平均压力，两个神经元代表两个工作参数，一个隐藏层具有最佳的十个隐藏神经元，一个输出层具有一个神经元。代表振荡传热系数作为响应。此外，已经提供了统计分析来研究这两个输入参数对振荡传热系数的影响强度。通过将ANN模型结果与实验结果和计算结果进行比较，在相同的操作条件下，通过比较ANN模型的结果与实验结果和计算结果，已证明该ANN模型在预测振荡传热系数的准确性方面是理想的。该研究工作为热声换热器的研究和解决与振荡流动条件相关的传热问题提供了一种基于ANN技术的新型准确建模方法。 （C）2018 Elsevier Ltd.保留所有权利。

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