Application of Artificial Neural Networks in Predicting the Thermal Performance of Heat Pipes

Thomas Siqueira Pereira, Pedro Leineker Ochoski Machado, Barbara Dora Ross Veitia, Felipe Mercês Biglia, Paulo Henrique Dias dos Santos, Yara de Souza Tadano, Hugo Valadares Siqueira and Thiago Antonini Alves ()
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Thomas Siqueira Pereira: Graduate Program in Mechanical Engineering, Federal University of Technology—Parana, Ponta Grossa 84017-220, Brazil
Pedro Leineker Ochoski Machado: Graduate Program in Mechanical and Materials Engineering, Federal University of Technology—Parana, Curitiba 81280-340, Brazil
Barbara Dora Ross Veitia: Graduate Program in Industrial Engineering, Federal University of Technology—Parana, Ponta Grossa 84017-220, Brazil
Felipe Mercês Biglia: Graduate Program in Mechanical and Materials Engineering, Federal University of Technology—Parana, Curitiba 81280-340, Brazil
Paulo Henrique Dias dos Santos: Mechanical Engineering Department, Federal University of Paraiba, Joao Pessoa 58051-900, Brazil
Yara de Souza Tadano: Graduate Program in Mechanical Engineering, Federal University of Technology—Parana, Ponta Grossa 84017-220, Brazil
Hugo Valadares Siqueira: Graduate Program in Industrial Engineering, Federal University of Technology—Parana, Ponta Grossa 84017-220, Brazil
Thiago Antonini Alves: Graduate Program in Mechanical Engineering, Federal University of Technology—Parana, Ponta Grossa 84017-220, Brazil

Energies, 2024, vol. 17, issue 21, 1-25

Abstract: The loss of energy by heat is a common problem in almost all areas of industry, and heat pipes are essential to increase efficiency and reduce energy waste. However, in many cases, they have complex theoretical equations with high percentages of error, limiting their development and causing dependence on empirical methods that generate a waste of time and material, resulting in significant expenses and reducing the viability of their use. Thus, Artificial Neural Networks (ANNs) can be an excellent option to facilitate the construction and development of heat pipes without knowledge of the complex theory behind the problem. This investigation uses experimental data from previous studies to evaluate the ability of three different ANNs to predict the thermal performance of heat pipes with different capillary structures, each of them in various configurations of the slope, filling ratio, and heat load. The goal is to investigate results in as many different scenarios as possible to clearly understand the networks’ capacity for modeling heat pipes and their operating parameters. We chose two classic ANNs (the most used, Multilayer Perceptron (MLP) network, and the Radial Basis Function (RBF) network) and the Extreme Learning Machine (ELM), which has not yet been applied to heat pipes studies. The ELM is an Unorganized Machine with a fast training process and a simple codification. The ANN results were very close to the experimental ones, showing that ANNs can successfully simulate the thermal performance of heat pipes. Based on the RMSE (error metric being reduced during the training step), the ELM presented the best results (RMSE = 0.384), followed by MLP (RMSE = 0.409), proving their capacity to generalize the problem. These results show the importance of applying different ANNs to evaluate the system deeply. Using ANNs in developing heat pipes is an excellent option for accelerating and improving the project phase, reducing material loss, time, and other resources.

Keywords: artificial neural networks; machine learning; heat pipes; thermal performance (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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