Comparison of Perceptron and Radial Basis Function Neural Networks in Modeling Heat Exchangers with Rectangular Helical Channels

Document Type : Research Article

Author

Department of Chemical Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran

Abstract

In this research, computational fluid dynamics method was used to investigate the effect of geometrical parameters of rectangular spiral channels on heat transfer coefficient. Two artificial neural networks including perceptron (MLP) and radial basis function (RBF) models were used to model the heat transfer in helical channels. The model inputs included the Reynolds number and geometric parameters of the channels, and output was the Nusselt number. 135 data were generated by Computational Fluid Dynamics (CFD) simulation and after validation were used for training and evaluation of neural network models. The results of the research showed that the accuracy of MLP was slightly higher than RBF, however, both models were acceptable. Due to the high and acceptable accuracy of these two models, they can be well used in future research and applications. In this research, the main innovation is comparing two different methods for modelling the heat exchanger with a rectangular helical channel. This research shows that the use of perceptron neural network and radial basis function can both be effective in improving the performance and efficiency of the heat exchanger. This research can be used as a guide to choose the appropriate method for modeling heat exchangers and help to improve technologies related to this field.

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References

[1] V. Irabatti, Y. Patil, S. Kore, V. Barangule, A. Kothe, Comprehensive review of spiral heat exchanger for diverse applications, Materials Today: Proceedings, 72 (2023) 1328-1334.
[2] S. Soltanian, R. Beigzadeh, Computational fluid dynamics and fuzzy logic for modeling conical spiral heat exchangers, Chemical Engineering & Technology, 46(4) (2023) 747-755.
[3] R. Beigzadeh, S. Eiamsa-ard, Fuzzy logic to thermal and friction characteristics of turbulent air-flow over diamond-shaped turbulators, International Communications in Heat and Mass Transfer, 120 (2021) 105001.
[4] M. Rastegarmoghaddam, M. Rajabi, S.D. Nikkhouy Tanha, Use of Artificial Intelligence to Identify Adhesive Joints Defects by Using Ultrasonic, Amirkabir Journal of Mechanical Engineering, 54(2) (2022) 377-390.
[5] M. Sridharan, Applications of artificial intelligence techniques in heat exchanger systems, in:  Advanced Analytic and Control Techniques for Thermal Systems with Heat Exchangers, Elsevier, 2020, pp. 325-334.
[6] M. Mohanraj, S. Jayaraj, C. Muraleedharan, Applications of artificial neural networks for thermal analysis of heat exchangers–a review, International Journal of Thermal Sciences, 90 (2015) 150-172.
[7] E. Reynoso-Jardón, A. Tlatelpa-Becerro, R. Rico-Martínez, M. Calderón-Ramírez, G. Urquiza, Artificial neural networks (ANN) to predict overall heat transfer coefficient and pressure drop on a simulated heat exchanger, International Journal of Applied Engineering Research, 14(13) (2019) 3097-3103.
[8] J. Shieh, H. Chen, L. Ferng, Application of a fuzzy logic controller in temperature control of a pilot high-temperature short-time heat exchanger, Food Control, 3(2) (1992) 91-96.
[9] C. Yu, Y. Wang, H. Zhang, B. Gao, Y. He, Thermal-hydraulic performance prediction of two new heat exchangers using RBF based on different DOE, Open Physics, 19(1) (2021) 285-304.
[10] P. Ramkumar, C. Vivek, S. Ramasamy, A. Kajavali, M. Sivasubramanian, Experimental and numerical study using ANFIS-neuro fuzzy model on heat pipe heat exchanger, Materials Today: Proceedings, 62 (2022) 2152-2162.
[11] B. Shilpa, V. Leela, An artificial intelligence model for heat and mass transfer in an inclined cylindrical annulus with heat generation/absorption and chemical reaction, International Communications in Heat and Mass Transfer, 147 (2023) 106956.
[12] H. Jin, M. Wang, H. Xiang, X. Liu, C. Wang, D. Fu, A PSO-RBF prediction method on flow corrosion of heat exchanger using the industrial operations data, Process Safety and Environmental Protection, 183 (2024) 11-23.
[13] Y. Ji, Z. Yang, J. Ran, H. Li, Multi-objective parameter optimization of turbine impeller based on RBF neural network and NSGA-II genetic algorithm, Energy Reports, 7 (2021) 584-593.
[14] B. Wu, Dynamic performance simulation analysis method of split shaft gas turbine based on RBF neural network, Energy Reports, 7 (2021) 947-958.
[15] D. Taler, J. Taler, Simple heat transfer correlations for turbulent tube flow, in:  E3S Web of conferences, EDP Sciences, 2017, pp. 02008.
[16] A.H. Fath, F. Madanifar, M. Abbasi, Implementation of multilayer perceptron (MLP) and radial basis function (RBF) neural networks to predict solution gas-oil ratio of crude oil systems, Petroleum, 6(1) (2020) 80-91.
Amirkabir Journal of Mechanical Engineering
Volume 56, Issue 5
2024
Pages 699-716

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