Next Article in Journal
Research on PCBN Tool Dry Cutting GCr15
Previous Article in Journal
Monitoring the Oil of Wind-Turbine Gearboxes: Main Degradation Indicators and Detection Methods
Article Menu

Export Article

Open AccessArticle
Machines 2018, 6(2), 26; https://doi.org/10.3390/machines6020026

Optimization of Microchannel Heat Sinks Using Prey-Predator Algorithm and Artificial Neural Networks

1
Department of Basic Sciences, College of Science and Theoretical Studies, Saudi Electronic University, Riyadh 11673, Kingdom of Saudi Arabia
2
Department of Mechanical Engineering, College of Engineering, Prince Mohammad Bin Fahd University, P.O. Box 1664, Al Khobar 31952, Kingdom of Saudi Arabia
3
Department of Mathematical Sciences, University of Zululand, Private Bag X1001, KwaDlangezwa 3886, South Africa
*
Author to whom correspondence should be addressed.
Received: 27 March 2018 / Revised: 23 May 2018 / Accepted: 28 May 2018 / Published: 11 June 2018
Full-Text   |   PDF [4510 KB, uploaded 11 June 2018]   |  

Abstract

A rectangular microchannel heat sink is modeled by employing thermal resistance and pressure drop networks. The available correlations for both thermal resistance and pressure drop are utilized in optimization. A multi-objective optimization technique, the prey–predator algorithm, is employed with the objective to find the optimal values for the heat sink performance parameters, i.e., thermal resistance and the pumping power of the heat sink. Additionally, a radial basis function neural network is used to investigate a relationship between these parameters. Full training based on the prey–predator algorithm with the sum of the squared error function is used to achieve the best performance of the model. The analysis of variance method is also employed to test the performance of this model. This study shows that the multi-objective function based on the prey–predator algorithm and the neural networks is suitable for finding the optimal values for the microchannel heat sink parameters. The minimum values of the multi-objective function are found to be “pumping power = 2.79344” and “total thermal resistance = 0.134133”. View Full-Text
Keywords: radial basis function neural network; prey–predator algorithm; microchannel heat sink; thermal resistance; pressure drop radial basis function neural network; prey–predator algorithm; microchannel heat sink; thermal resistance; pressure drop
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Hamadneh, N.; Khan, W.; Tilahun, S. Optimization of Microchannel Heat Sinks Using Prey-Predator Algorithm and Artificial Neural Networks. Machines 2018, 6, 26.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Machines EISSN 2075-1702 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top