Machine Learning for Prediction of Heat Pipe Effectiveness

被引：10

作者：

Nair, Anish ^{[1
]}

Ramkumar, P. ^{[1
]}

Mahadevan, Sivasubramanian ^{[2
]}

Prakash, Chander ^{[3
]}

Dixit, Saurav ^{[4
,5
]}

Murali, Gunasekaran ^{[4
]}

Vatin, Nikolai Ivanovich ^{[4
]}

Epifantsev, Kirill ^{[6
]}

Kumar, Kaushal ^{[7
]}

机构：

[1] Kalasalingam Acad Res & Educ, Mech Engn, Krishnankoil 626126, India

[2] Kalasalingam Acad Res & Educ, Automobile Engn, Krishnankoil 626126, India

[3] Lovely Profess Univ, Sch Mech Engn, Phagwara 144411, India

[4] Peter Great St Petersburg Polytech Univ, St Petersburg 195251, Russia

[5] Uttaranchal Univ, Div Res & Innovat, Dehra Dun 248007, Uttarakhand, India

[6] St Petersburg Univ Aerosp Instrumentat, St Petersburg 190000, Russia

[7] KR Mangalam Univ, Dept Mech Engn, Gurgaon 122103, India

来源：

ENERGIES | 2022年 / 15卷 / 09期

关键词：

heat pipe; exchanger; machine learning; effectiveness; THERMAL PERFORMANCE; EXCHANGER; OPTIMIZATION; FLOW;

D O I：

10.3390/en15093276

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

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 degrees to 90 degrees, values of temperature varied from 50 degrees C to 70 degrees 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.

引用

页数：14

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