Optimization of Refrigeration Rate for a Thermoelectric Cooler in Restricted Space using Stochastic Algorithms
Jitendra Mohan Giri1, Pawan Kumar Singh Nain2 

1Jitendra Mohan Giri, Research Scholar, School of Mechanical Engineering, Galgotias University, Greater Noida, India.
2Pawan Kumar Singh Nain, Professor, School of Mechanical Engineering, Galgotias University, Greater Noida, India.

Manuscript received on 17 March 2019 | Revised Manuscript received on 22 March 2019 | Manuscript published on 30 July 2019 | PP: 2306-2311 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2701078219/19©BEIESP | DOI: 10.35940/ijrte.B2701.078219
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In the present study, a mathematical model of single stage thermoelectric cooler (TEC) is reported. This model is then employed to optimize the rate of refrigeration (ROR) which is one of the important performance measures of TEC. Two stochastic algorithms, namely, the genetic algorithm (GA) and simulated annealing (SA) are employed for optimizing the said performance of TEC for restricted space. The selected design variables are the geometric structural parameters of TEC elements and the input current. This study also includes the thermal resistance of hot side heat exchanger and electrical contact resistances into consideration. The results show that these design variables can be optimally set to maximize ROR within restricted space very significantly. The two algorithms for optimization attained almost the same values of design variables that lead to optimum ROR, though the GA could locate multi-modal optimum and hence can be used by the designer to choose among various options of design variables without compromising on the optimized value of ROR. .
Index Terms: Single-Stage Thermoelectric Cooler, Rate of Refrigeration, Genetic Algorithm, Simulated Annealing

Scope of the Article:
Discrete Optimization