Multi-objective optimization for GPU3 Stirling engine by combining multi-objective algorithms |
| |
| Affiliation: | 1. Centre for Energy Sciences, Department of Mechanical Engineering, Faculty of Engineering, 50603 Kuala Lumpur University of Malaya, Malaysia;2. Mechanical Engineering Department, Collage of Engineering, King Saud University, 11421 Riyadh, Saudi Arabia;3. Dept. of Mechanical Engineering, Dhaka University of Engineering and Technology, Gazipur, 1700, Bangladesh;1. École de Technologie Supérieure, 1100 Notre-Dame Ouest, Montréal, Québec, Canada;2. Arista Renewable Energies, 2648 Av. Desjardins, Montréal, Québec, Canada;1. South China University of Technology, Wushan Road 381#, Tianhe District, Guangzhou 510640, China;2. Jiangsu University, Jiangsu, Xuefu Road 301#, Jingkou District, Zhenjiang 212013, China;1. Huadian Electric Power Research Institute, Hangzhou, Zhejiang 310030, China;2. Department of Energy Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China;3. Key Laboratory of Thermo-Fluid Science and Engineering, Ministry of Education, School of Energy and Power Engineering, Xi''an Jiaotong University, Xi''an 710049, China;1. Ocean Engineering and Technology Research Center, Iranian National Institute for Oceanography and Atmospheric Science, Tehran, Iran;2. Griffith School of Engineering, Gold Coast Campus, Griffith University, QLD, 4222, Australia |
| |
| Abstract: | Stirling engine has become preferable for high attention towards the use of alternate renewable energy resources like biomass and solar energy. Stirling engine is the main component of dish Stirling system in thermal power generation sector. Stirling engine is an externally heating engine, which theoretical efficiency is as high as Carnot cycle's, but actual ones are always far below compared with the Carnot efficiency. A number of studies have been done on multi-objective optimization to improve the design of Stirling engine. In the current study, a multi-objective optimization method, which is a combination of multiple optimization algorithms including differential evolution, genetic algorithm and adaptive simulated annealing, was proposed. This method is an attempt to generalize and improve the robustness and diversity with above three kinds of population based meta-heuristic optimization techniques. The analogous interpreter was linked and interchanged to find the best global optimal solution for Stirling engine performance optimization. It decreases the chance of convergence at a local minimum by powering from the fact that these three algorithms run parallel and members from each population and technique are swapped. The optimization considers five decision variables, including engine frequency, mean effective pressure, temperature of heating source, number of wires in regenerator matrix, and the wire diameter of regenerator, as multiple objectives. The Pareto optimal frontier was obtained and a final optimal solution was also selected by using various multi-criteria decision making methods including techniques for Order of Preference by Similarity to Ideal Solution and Simple Additive Weighting. The multi-objective optimization indicated a way for GPU-3 Stirling engine to obtain an output power of more than 3 kW and an increase by 5% in thermal efficiency with significant decrease in power loss due to flow resistance. |
| |
| Keywords: | Stirling engine Optimization Differential evolution Genetic algorithm Adaptive simulated annealing |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
