Multi-stopping criterion multi-feature-based multi-objective cohort intelligence algorithm for thermoacoustic engine optimisation
by Mukundraj V. Patil; Satish Kumar; Anand J. Kulkarni
International Journal of Modelling, Identification and Control (IJMIC), Vol. 40, No. 4, 2022

Abstract: Aim of this research is to investigate the performance characteristics of thermoacoustic engine using multi stopping criterion multi feature-based multi-objective cohort intelligence (MOCI) algorithm. MOCI and the state-of-the-art algorithms are applied to study performance characteristics of a thermoacoustic engine (TAE). Exploratory and statistical analyses revealed better performance of the MOCI algorithm on qualitative and quantitative performance metrics. Post optimality analysis showed a better region of interest for an analyst and the desirable working ranges for each variable of TAE design. Pressure-frequency relationship showed high correlation and it is useful for future study and detailed investigation of thermoacoustic phenomenon. MOCI established competitive results which are useful in benchmarking TAE performances in future researches. The effective design of TAE using MOCI algorithm aids in the sustainable development of society in terms of affordable and clean energy, clean climate and responsible consumption and production.

Online publication date: Wed, 14-Sep-2022

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