Multi-objective optimization of Stirling engine systems using Front-based Yin-Yang-Pair Optimization. (1st February 2017)

• Record Type: Journal Article
• Authors: Punnathanam, Varun; Kotecha, Prakash
• Is Part Of: Energy conversion and management. Volume 133(2017)
• Page Start: 332
• Page End: 348
• Publication Date: 2017-02-01
• Subjects: Multi-objective optimization -- Stochastic optimization -- Front-based Yin-Yang-Pair Optimization -- Yin-Yang-Pair Optimization -- Stirling engine systems; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2016.10.035
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: Efficient multi-objective optimization algorithm F-YYPO demonstrated. Three Stirling engine applications with a total of eight cases. Improvements in the objective function values of up to 30%. Superior to the popularly used gamultiobj of MATLAB. F-YYPO has extremely low time complexity. Abstract: In this work, we demonstrate the performance of Front-based Yin-Yang-Pair Optimization (F-YYPO) to solve multi-objective problems related to Stirling engine systems. The performance of F-YYPO is compared with that of (i) a recently proposed multi-objective optimization algorithm (Multi-Objective Grey Wolf Optimizer) and (ii) an algorithm popularly employed in literature due to its easy accessibility (MATLAB's inbuilt multi-objective Genetic Algorithm function: gamultiobj ). We consider three Stirling engine based optimization problems: (i) the solar-dish Stirling engine system which considers objectives of output power, thermal efficiency and rate of entropy generation; (ii) Stirling engine thermal model which considers the associated irreversibility of the cycle with objectives of output power, thermal efficiency and pressure drop; and finally (iii) an experimentally validated polytropic finite speed thermodynamics based Stirling engine model also with objectives of output power and pressure drop. We observe F-YYPO to be significantly more effective as compared to its competitors in solving the problems, while requiring only a fraction of the computational time required by the other algorithms.