Artificial Neural Network to Predict Pressure Drops in Heat Sinks
Artificial Neural Network to Predict Pressure Drops in Heat Sinks
复制标题
人工神经网络预测散热器中的压降
DOI：
10.11159/ffhmt22.202
复制
发表时间：
2022
期刊：
影响因子：
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通讯作者：
Sarabi, Soroush
中科院分区：
文献类型：
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作者：
Mengesha, Betelhiem N.;Shaeri, Mohammad R.;Sarabi, Soroush
在本研究中，使用人工神经网络 (ANN) 预测风冷散热器 (HS) 上的压降 ( )。开发了具有两个隐藏层的多层前馈 ANN 架构。采用反向传播算法来训练网络，并通过均方根误差来评价网络的准确性。用于训练神经网络的输入数据是通过使用计算流体动力学 (CFD) 方法对散热器通道内的空气进行三维模拟来准备的。本研究中开发的基于 ANN 的模型可以在基于 CFD 的数据范围内进行高精度预测。本研究表明，开发基于人工神经网络的高精度模型克服了基于物理的相关性的局限性，因为它们的准确性在很大程度上取决于识别和实现影响热流体现象物理的关键变量。
In this study, pressure drop ( ) across air-cooled heat sinks (HSs) are predicted using an artificial neural network (ANN). A multilayer feed-forward ANN architecture with two hidden layers is developed. Backpropagation algorithm is used for training the network, and the accuracy of the network is evaluated by the root mean square error. The input data for training the neural network is prepared through three-dimensional simulation of air inside the channels of heat sinks using a computational fluid dynamics (CFD) approach. The developed ANN-based model in this study predicts with a high accuracy and within of the CFD-based data. The present study suggests that developing an ANN-based model with a high level of accuracy overcomes the limitations of physics-based correlations that their accuracy strongly depends on identifying and implementing key variables that affect the physics of a thermo-fluid phenomenon.
影响因子：
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作者：
Chen, Si;Ren, Yaxing;Yu, James
通讯作者：
Yu, James
DOI：
10.1109/semi-therm.2018.8357380
发表时间：
2018
期刊：
2018 34th Thermal Measurement, Modeling & Management Symposium (SEMI-THERM)
影响因子：
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作者：
M. Shaeri;R. Bonner
通讯作者：
R. Bonner