Optimization of Microjet Location Using Surrogate Model Coupled with Particle Swarm Optimization Algorithm

Mohammad Owais Qidwai, Irfan Anjum Badruddin, Noor Zaman Khan, Mohammad Anas Khan and Saad Alshahrani
Additional contact information
Mohammad Owais Qidwai: Department of Mechanical Engineering, Delhi Skill and Entrepreneurship University, Okhla Campus-1, Okhla Industrial Estate, Phase-III, New Delhi 110020, India
Irfan Anjum Badruddin: Mechanical Engineering Department, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia
Noor Zaman Khan: Department of Mechanical Engineering, National Institute of Technology Srinagar, Hazratbal, Srinagar 190006, Jammu and Kashmir, India
Mohammad Anas Khan: Department of Mechanical Engineering, Jamia Millia Islamia, New Delhi 110025, India
Saad Alshahrani: Mechanical Engineering Department, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia

Mathematics, 2021, vol. 9, issue 17, 1-19

Abstract: This study aimed to present the design methodology of microjet heat sinks with unequal jet spacing, using a machine learning technique which alleviates hot spots in heat sinks with non-uniform heat flux conditions. Latin hypercube sampling was used to obtain 30 design sample points on which three-dimensional Computational Fluid Dynamics (CFD) solutions were calculated, which were used to train the machine learning model. Radial Basis Neural Network (RBNN) was used as a surrogate model coupled with Particle Swarm Optimization (PSO) to obtain the optimized location of jets. The RBNN provides continuous space for searching the optimum values. At the predicted optimum values from the coupled model, the CFD solution was calculated for comparison. The percentage error for the target function was 0.56%, whereas for the accompanied function it was 1.3%. The coupled algorithm has variable inputs at user discretion, including gaussian spread, number of search particles, and number of iterations. The sensitivity of each variable was obtained. Analysis of Variance (ANOVA) was performed to investigate the effect of the input variable on thermal resistance. ANOVA results revealed that gaussian spread is the dominant variable affecting the thermal resistance.

Keywords: optimization; surrogate model; Radial Basis Neural Network; Particle Swarm Optimization; thermal resistance; Computational Fluid Dynamics (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/9/17/2167/pdf (application/pdf)
https://www.mdpi.com/2227-7390/9/17/2167/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:9:y:2021:i:17:p:2167-:d:629386

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().