Optimization of stirling engine systems using single phase multi-group teaching learning based optimization and genetic algorithm

Abstract

In this work, Single Phase Multi-Group Teaching Learning Based Optimization (SPMGTLO) is used to optimize various objectives of three different models of Stirling heat engines, viz. (i) finite-time thermodynamic model, (ii) Stirling engine thermal model with associated irreversibility, and (iii) model based on polytropic finite-speed-based thermodynamics. The performance of SPMGTLO on solving these problems is compared with the inbuilt genetic algorithm (GA) function of MATLAB. It is observed that SPMGTLO performs better than GA in all the eight cases arising from the three models.