文章基本信息

标题：Performance Predictions of Air-Cooled Condensers Having Circular and Elliptic Cross-Sections with Artificial Neural Networks
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作者：Fatih Selimefendigil ; Hakan Öztop
期刊名称：Journal of Thermal Engineering
电子版ISSN：2148-7847
出版年度：2019
卷号：5
期号：3
页码：105-114
DOI：10.18186/thermal.539967
出版社：Yildiz Technical University
摘要：In this study, mathematical models of air cooled condensers with circular and elliptic cross-sections were developed and performances were evaluated with artificial neural networks. Air velocity, orientation angle and ambient temperature were used as the input to the neural network structure while heat transfer rate to the air was used as the output. The data sets were generated from high fidelity, computationally inefficient expensive three dimensional computational fluid dynamics simulations. It was observed that artificial neural network model replaces computational fluid dynamics model and based on the mathematical model with artificial neural network, elliptic condensers perform better in terms of heat transfer compared to circular ones.
关键词：Refrigeration; Elliptic; Computational Fluid Dynamics; Artificial Neural Network