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Multi-objective optimization of a Stirling heat engine using TS-TLBO (tutorial training and self learning inspired teaching-learning based optimization) algorithm

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  • Patel, Vivek
  • Savsani, Vimal

Abstract

In the present work, TS-TLBO (tutorial training and self learning inspired teaching-learning-based optimization) algorithm is proposed and investigated for the multi-objective optimization of a Stirling heat engine. The exploration and exploitation capacity of the basic MO-TLBO (multi objective teaching-learning-based optimization) is enhance by introducing the concept of tutorial training and self motivated learning. The multi-objective TS-TLBO algorithm uses a grid-based approach to adaptively assess the non-dominated solutions maintained in an external archive. Optimization of a Stirling heat engine is carried out by considering two and three objective functions simultaneously for the maximization of thermal efficiency, output power and minimization of total pressure drop of the engine. Application examples are presented to demonstrate the effectiveness and accuracy of the proposed algorithm.

Suggested Citation

  • Patel, Vivek & Savsani, Vimal, 2016. "Multi-objective optimization of a Stirling heat engine using TS-TLBO (tutorial training and self learning inspired teaching-learning based optimization) algorithm," Energy, Elsevier, vol. 95(C), pages 528-541.
    • Handle: RePEc:eee:energy:v:95:y:2016:i:c:p:528-541
    DOI: 10.1016/j.energy.2015.12.030
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    1. Patel, Vivek & Savsani, Vimal & Mudgal, Anurag, 2017. "Many-objective thermodynamic optimization of Stirling heat engine," Energy, Elsevier, vol. 125(C), pages 629-642.
    2. Chin-Hsiang Cheng & Duc-Thuan Phung, 2021. "Numerical Optimization of the β-Type Stirling Engine Performance Using the Variable-Step Simplified Conjugate Gradient Method," Energies, MDPI, vol. 14(23), pages 1-14, November.
    3. Ahmadi, Mohammad H. & Ahmadi, Mohammad-Ali & Pourfayaz, Fathollah, 2017. "Thermal models for analysis of performance of Stirling engine: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 168-184.

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