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Multi-objective optimization in a finite time thermodynamic method for dish-Stirling by branch and bound method and MOPSO algorithm |
Mohammad Reza NAZEMZADEGAN1, Alibakhsh KASAEIAN1, Somayeh TOGHYANI1, Mohammad Hossein AHMADI2(), R. SAIDUR3, Tingzhen MING4 |
1. Department of Renewable Energies, Faculty of New Science and Technologies, University of Tehran, Tehran, 1417466191, Iran 2. Faculty of Mechanical Engineering and Mechatronic, Shahrood University of Technology, Shahrood 3619995161, Iran 3. Faculty of Science and Technology, Sunway University, No. 5, Jalan Universiti, Bandar Sunway, 47500 Petaling Jaya, Malaysia; Department of Engineering, Lancaster University, Lancaster, LA1 4YW, UK 4. School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, China |
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Abstract There are various analyses for a solar system with the dish-Stirling technology. One of those analyses is the finite time thermodynamic analysis by which the total power of the system can be obtained by calculating the process time. In this study, the convection and radiation heat transfer losses from collector surface, the conduction heat transfer between hot and cold cylinders, and cold side heat exchanger have been considered. During this investigation, four objective functions have been optimized simultaneously, including power, efficiency, entropy, and economic factors. In addition to the four-objective optimization, three-objective, two-objective, and single-objective optimizations have been done on the dish-Stirling model. The algorithm of multi-objective particle swarm optimization (MOPSO) with post-expression of preferences is used for multi-objective optimizations while the branch and bound algorithm with pre-expression of preferences is used for single-objective and multi-objective optimizations. In the case of multi-objective optimizations with post-expression of preferences, Pareto optimal front are obtained, afterward by implementing the fuzzy, LINMAP, and TOPSIS decision making algorithms, the single optimum results can be achieved. The comparison of the results shows the benefits of MOPSO in optimizing dish Stirling finite time thermodynamic equations.
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Keywords
dish-Stirling
finite time model
branch and bound algorithm
multi-objective particle swarm optimization (MOPSO)
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Corresponding Author(s):
Mohammad Hossein AHMADI
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Just Accepted Date: 12 February 2018
Online First Date: 03 April 2018
Issue Date: 14 September 2020
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|
1 |
EIA. International Energy outlook.2011–09,
|
2 |
F L Curzon, B Ahlborn. Efficiency of a Carnot engine at maximum power output. American Journal of Physics, 1975, 43(1): 22–24
https://doi.org/10.1119/1.10023
|
3 |
M J Moran. On second law analysis and the failed promises of finite time thermodynamics. Energy, 1998, 23(6): 517–519
https://doi.org/10.1016/S0360-5442(98)00007-3
|
4 |
E P Gyftopoulos. Fundamentals of analysis of processes. Energy Conversion and Management, 1997, 38(15–17): 1525–1533
https://doi.org/10.1016/S0196-8904(96)00214-2
|
5 |
I Tlili, Y Timoumi, S B Nasrallah. Thermodynamic analysis of the Stirling heat engine with regenerative losses and internal irreversibilities. International Journal of Engine Research, 2008, 9(1): 45–56
https://doi.org/10.1243/14680874JER01707
|
6 |
S C Kaushik, S Kumar. Finite time thermodynamic evaluation of irreversible Ericsson and Stirling heat engines. Energy Conversion and Management, 2001, 42(3): 295–312
https://doi.org/10.1016/S0196-8904(00)00063-7
|
7 |
S C Kaushik, S Kumar. Finite time thermodynamic analysis of endoreversible Stirling heat engine with regenerative losses. Energy, 2000, 25(10): 989–1003
https://doi.org/10.1016/S0360-5442(00)00023-2
|
8 |
S C Kaushik, S K Tyagi, S K Bose, M K Singhal. Performance evaluation of irreversible Stirling and Ericsson heat pump cycles. International Journal of Thermal Sciences, 2002, 41(2): 193–200
https://doi.org/10.1016/S1290-0729(01)01297-2
|
9 |
M Costea, S Petrescu, C Harman. The effect of irreversibilities on solar Stirling engine cycle performance. Energy Conversion and Management, 1999, 40(15–16): 1723–1731
https://doi.org/10.1016/S0196-8904(99)00065-5
|
10 |
I Urieli, M Kushnir. The ideal adiabatic cycle-a rational basis for Stirling engine analysis. In: Proceeding of 17th IECEC, Los Angeles, CA, USA, 1982
|
11 |
F Wu, L Chen, C Wu, F Sun. Optimum performance of irreversible Stirling engine with imperfect regeneration. Energy Conversion and Management, 1998, 39(8): 727–732
https://doi.org/10.1016/S0196-8904(97)10036-X
|
12 |
S Petrescu, M Costea, C Harman, T Florea. Application of the direct method to irreversible Stirling cycles with finite speed. International Journal of Energy Research, 2002, 26(7): 589–609
https://doi.org/10.1002/er.806
|
13 |
Y Timoumi, I Tlili, S Ben Nasrallah. Design and performance optimization of GPU-3 Stirling engines. Energy, 2008, 33(7): 1100–1114
https://doi.org/10.1016/j.energy.2008.02.005
|
14 |
C H Cheng, Y J Yu. Numerical model for predicting thermodynamic cycle and thermal efficiency of a beta-type Stirling engine with rhombic-drive mechanism. Renewable Energy, 2010, 35(11): 2590–2601
https://doi.org/10.1016/j.renene.2010.04.002
|
15 |
O E Ataer. Numerical analysis of regenerators of piston type Stirling engines using Lagrangian formulation. International Journal of Refrigeration, 2002, 25(5): 640–652
https://doi.org/10.1016/S0140-7007(01)00051-2
|
16 |
I Tlili. Finite time thermodynamic evaluation of endoreversible Stirling heat engine at maximum power conditions. Renewable & Sustainable Energy Reviews, 2012, 16(4): 2234–2241
https://doi.org/10.1016/j.rser.2012.01.022
|
17 |
F Formosa, G Despesse. Analytical model for Stirling cycle machine design. Energy Conversion and Management, 2010, 51(10): 1855–1863
https://doi.org/10.1016/j.enconman.2010.02.010
|
18 |
F Formosa. Coupled thermodynamic-dynamic semi-analytical model of free piston Stirling engines. Energy Conversion and Management, 2011, 52(5): 2098–2109
https://doi.org/10.1016/j.enconman.2010.12.014
|
19 |
I Iwamoto, K Toda, K Hirata, M Takeuchi, T Yamamoto. Comparison of low and high temperature differential Stirling engines. In: Proceedings of the 8th International Stirling engine conference, Anacona, Italy, 1997
|
20 |
M H Ahmadi, H Hosseinzade. Investigation of solar collector design parameters effect onto solar Stirling engine efficiency. Journal of Applied Mechanical Engineering, 2012, 1(01): 1–4
https://doi.org/10.4172/2168-9873.1000102
|
21 |
L B Erbay, H Yavuz. Analysis of Stirling heat engine at maximum power conditions. Energy, 1997, 22(7): 645–650
https://doi.org/10.1016/S0360-5442(96)00159-4
|
22 |
Y Li , Y L He, W W Wang . Optimization of solar-powered Stirling heat engine with finite-time thermodynamics. Renewable Energy, 2011, 36(1): 421–427
https://doi.org/10.1016/j.renene.2010.06.037
|
23 |
A Sharma, S K Shukla, A K Rai. Finite time thermodynamic analysis and optimization of Solar-Dish Stirling heat engine with regenerative losses. Thermal Science, 2011, 15(4): 995–1009
https://doi.org/10.2298/TSCI110418101S
|
24 |
H Sayyaadi. Multi-objective approach in thermoenvironomic optimization of a benchmark cogeneration system. Applied Energy, 2009, 86(6): 867–879
https://doi.org/10.1016/j.apenergy.2008.08.017
|
25 |
M H Ahmadi, H Sayyaadi, S Dehghani, H Hosseinzade. Designing a solar powered Stirling heat engine based on multiple criteria: maximized thermal efficiency and power. Energy Conversion and Management, 2013, 75: 282–291
https://doi.org/10.1016/j.enconman.2013.06.025
|
26 |
C Chen, C Ho, H Yau. Performance analysis and optimization of a solar powered Stirling engine with heat transfer considerations. Energies, 2012, 5(12): 3573–3585
https://doi.org/10.3390/en5093573
|
27 |
S Jafari, B Mohammadi, A A Boroujerdi. Multi-objective optimization of a Stirling-type pulse tube refrigerator. Cryogenics, 2013, 55–56: 53–62
https://doi.org/10.1016/j.cryogenics.2013.02.004
|
28 |
M H Ahmadi, A H Mohammadi, S Dehghani, M Barranco-Jiménez. Multi-objective thermodynamic-based optimization of output power of solar dish-Stirling engine by implementing an evolutionary algorithm. Energy Conversion and Management, 2013, 75: 438–445
https://doi.org/10.1016/j.enconman.2013.06.030
|
29 |
M H Ahmadi, H Hosseinzade, H Sayyaadi, A H Mohammadi, F Kimiaghalam. Application of the multi-objective optimization method for designing a powered Stirling g heat engine: design with maximized power, thermal efficiency and minimized pressure loss. Renewable Energy, 2013, 60: 313–322
https://doi.org/10.1016/j.renene.2013.05.005
|
30 |
M H Ahmadi, H Sayyaadi, A H Mohammadi, M Barranco-Jiménez. Thermo-economic multi-objective optimization of solar dish-Stirling engine by implementing evolutionary algorithm. Energy Conversion and Management, 2013, 73: 370–380
https://doi.org/10.1016/j.enconman.2013.05.031
|
31 |
A, Lazzaretto A Toffolo. Energy, economy and environment as objectives in multi-criterion optimization of thermal systems design. Energy, 2004, 29(8): 1139–1157
https://doi.org/10.1016/j.energy.2004.02.022
|
32 |
M H Ahmadi, M A Ahmadi, A H Mohammadi, M Feidt, S M Pourkiaei. Multi-objective optimization of an irreversible Stirling cryogenic refrigerator cycle. Energy Conversion and Management, 2014, 82: 351–360
https://doi.org/10.1016/j.enconman.2014.03.033
|
33 |
H Chaitou, P Nika. Exergetic optimization of a thermoacoustic engine using the particle swarm optimization method. Energy Conversion and Management, 2012, 55: 71–80
https://doi.org/10.1016/j.enconman.2011.10.024
|
34 |
C Duan, X Wang, S Shu, C Jing, H Chang. Thermodynamic design of Stirling engine using multi-objective particle swarm optimization algorithm. Energy Conversion and Management, 2014, 84: 88–96
https://doi.org/10.1016/j.enconman.2014.04.003
|
35 |
S Toghyani, A Kasaeian, M H Ahmadi. Multi-objective optimization of Stirling engine using non-ideal adiabatic method. Energy Conversion and Management, 2014, 80: 54–62
https://doi.org/10.1016/j.enconman.2014.01.022
|
36 |
M H Ahmadi, M Mehrpooya, F Pourfayaz. Exergoeconomic analysis and multi objective optimization of performance of a carbon dioxide power cycle driven by geothermal energy with liquefied natural gas as its heat sink. Energy Conversion and Management, 2016, 119: 422–434
https://doi.org/10.1016/j.enconman.2016.04.062
|
37 |
M H Ahmadi, M A Ahmadi, A Mellit, F Pourfayaz, M Feidt. Thermodynamic analysis and multi objective optimization of performance of solar dish Stirling engine by the centrality of entransy and entropy generation. International Journal of Electrical Power & Energy Systems, 2016, 78: 88–95
https://doi.org/10.1016/j.ijepes.2015.11.042
|
38 |
M H Ahmadi, M A Ahmadi, M Feidt. Thermodynamic analysis and evolutionary algorithm based on multi-objective optimization of performance for irreversible four-temperature-level refrigeration. Mechanics & Industry, 2015, 16(2): 207
https://doi.org/10.1051/meca/2014080
|
39 |
J Kennedy, R Eberhart. Particle swarm optimization. In: Proceeding of International Conference on Neural Networks. Perth, Australia, 1995
|
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