Optimal design of Stirling heat engine using an advanced optimization algorithm

The Stirling engine presents an excellent theoretical output equivalent to the output of Carnot engine and it is less pollutant and requires little maintenance. In this paper, Stirling heat engine is considered for optimization with multiple criteria. A recently developed advanced optimization algor...

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Published in:	Sadhana (Bangalore) Vol. 41; no. 11; pp. 1321 - 1331
Main Authors:	Rao, R V, More, K C, Taler, J, Ocłoń, P
Format:	Journal Article
Language:	English
Published:	New Delhi Springer India 01.11.2016 Springer Nature B.V
Subjects:	Decision making Design optimization Dimensional analysis Engineering Genetic algorithms Heat engines Linear programming Machine learning Maximization Multidimensional methods Multiple criterion Optimization algorithms Pressure loss Sorting algorithms Stirling engines Thermodynamic efficiency Stirling heat engine multi-objective optimization pressure loss teaching–learning-based optimization algorithm thermal efficiency
ISSN:	0256-2499, 0973-7677
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Abstract	The Stirling engine presents an excellent theoretical output equivalent to the output of Carnot engine and it is less pollutant and requires little maintenance. In this paper, Stirling heat engine is considered for optimization with multiple criteria. A recently developed advanced optimization algorithm namely “teaching–learning-based optimization (TLBO) algorithm” is used for maximization of output power, minimization of pressure losses and maximization of the thermal efficiency of the entire solar Stirling system. The comparisons of the proposed algorithm are made with those obtained by using the decision-making methods like linear programming technique for multi-dimensional analysis of preference (LINMAP), technique for order of preference by similarity to ideal solution (TOPSIS) and fuzzy Bellman–Zadeh method that have used the Pareto frontier gained through non-dominated sorting genetic algorithm-II (NSGA-II). The comparisons were also made with those obtained by the experimental results. It is found that the TLBO algorithm has produced comparatively better results than those given by the decision-making methods and the experimental results presented by the previous researchers.
AbstractList	The Stirling engine presents an excellent theoretical output equivalent to the output of Carnot engine and it is less pollutant and requires little maintenance. In this paper, Stirling heat engine is considered for optimization with multiple criteria. A recently developed advanced optimization algorithm namely “teaching–learning-based optimization (TLBO) algorithm” is used for maximization of output power, minimization of pressure losses and maximization of the thermal efficiency of the entire solar Stirling system. The comparisons of the proposed algorithm are made with those obtained by using the decision-making methods like linear programming technique for multi-dimensional analysis of preference (LINMAP), technique for order of preference by similarity to ideal solution (TOPSIS) and fuzzy Bellman–Zadeh method that have used the Pareto frontier gained through non-dominated sorting genetic algorithm-II (NSGA-II). The comparisons were also made with those obtained by the experimental results. It is found that the TLBO algorithm has produced comparatively better results than those given by the decision-making methods and the experimental results presented by the previous researchers.
Author	Taler, J Rao, R V More, K C Ocłoń, P
Author_xml	– sequence: 1 givenname: R V surname: Rao fullname: Rao, R V organization: Department of Mechanical Engineering, S.V. National Institute of Technology – sequence: 2 givenname: K C surname: More fullname: More, K C email: kiran.imagine67@gmail.com organization: Department of Mechanical Engineering, S.V. National Institute of Technology – sequence: 3 givenname: J surname: Taler fullname: Taler, J organization: Instytut Maszyn i Urządzeń Energetycznych, Politechnika Krakowska – sequence: 4 givenname: P surname: Ocłoń fullname: Ocłoń, P organization: Instytut Maszyn i Urządzeń Energetycznych, Politechnika Krakowska
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SubjectTerms	Decision making Design optimization Dimensional analysis Engineering Genetic algorithms Heat engines Linear programming Machine learning Maximization Multidimensional methods Multiple criterion Optimization algorithms Pressure loss Sorting algorithms Stirling engines Thermodynamic efficiency
Title	Optimal design of Stirling heat engine using an advanced optimization algorithm
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