热交换器
数学
同心的
均方误差
随机森林
体积流量
均方根
机器学习
人工智能
模拟
计算机科学
工程类
统计
机械工程
热力学
物理
几何学
电气工程
作者
Anish Nair,P. Ramkumar,M. Sivasubramanian,Chander Prakash,Saurav Dixit,G. Murali,Nikolai Vatin,Kirill Epifantsev,Kaushal Kumar
出处
期刊:Energies
[MDPI AG]
日期:2022-04-29
卷期号:15 (9): 3276-3276
被引量:8
摘要
This paper details the selection of machine learning models for predicting the effectiveness of a heat pipe system in a concentric tube exchanger. Heat exchanger experiments with methanol as the working fluid were conducted. The value of the angle varied from 0° to 90°, values of temperature varied from 50 °C to 70 °C, and the flow rate varied from 40 to 120 litres per min. Multiple experiments were conducted at different combinations of the input parameters and the effectiveness was measured for each trial. Multiple machine learning algorithms were taken into consideration for prediction. Experimental data were divided into subsets and the performance of the machine learning model was analysed for each of the subsets. For the overall analysis, which included all the three parameters, the random forest algorithm returned the best results with a mean average error of 1.176 and root-mean-square-error of 1.542.
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