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 fourobjective 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.
Similar content being viewed by others
References
EIA. International Energy outlook. 2011–09, https://www.eia.gov/pressroom/presentations/howard_09192011.pdf
Curzon F L, Ahlborn B. Efficiency of a Carnot engine at maximum power output. American Journal of Physics, 1975, 43(1): 22–24
Moran M J. On second law analysis and the failed promises of finite time thermodynamics. Energy, 1998, 23(6): 517–519
Gyftopoulos E P. Fundamentals of analysis of processes. Energy Conversion and Management, 1997, 38(15–17): 1525–1533
Tlili I, Timoumi Y, Nasrallah S B. Thermodynamic analysis of the Stirling heat engine with regenerative losses and internal irreversibilities. International Journal of Engine Research, 2008, 9(1): 45–56
Kaushik S C, Kumar S. Finite time thermodynamic evaluation of irreversible Ericsson and Stirling heat engines. Energy Conversion and Management, 2001, 42(3): 295–312
Kaushik S C, Kumar S. Finite time thermodynamic analysis of endoreversible Stirling heat engine with regenerative losses. Energy, 2000, 25(10): 989–1003
Kaushik S C, Tyagi S K, Bose S K, Singhal M K. Performance evaluation of irreversible Stirling and Ericsson heat pump cycles. International Journal of Thermal Sciences, 2002, 41(2): 193–200
Costea M, Petrescu S, Harman C. The effect of irreversibilities on solar Stirling engine cycle performance. Energy Conversion and Management, 1999, 40(15–16): 1723–1731
Urieli I, Kushnir M. The ideal adiabatic cycle-a rational basis for Stirling engine analysis. In: Proceeding of 17th IECEC, Los Angeles, CA, USA, 1982
Wu F, Chen L, Wu C, Sun F. Optimum performance of irreversible Stirling engine with imperfect regeneration. Energy Conversion and Management, 1998, 39(8): 727–732
Petrescu S, Costea M, Harman C, Florea T. Application of the direct method to irreversible Stirling cycles with finite speed. International Journal of Energy Research, 2002, 26(7): 589–609
Timoumi Y, Tlili I, Ben Nasrallah S. Design and performance optimization of GPU-3 Stirling engines. Energy, 2008, 33(7): 1100–1114
Cheng C H, Yu Y J. 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
Ataer O E. Numerical analysis of regenerators of piston type Stirling engines using Lagrangian formulation. International Journal of Refrigeration, 2002, 25(5): 640–652
Tlili I. Finite time thermodynamic evaluation of endoreversible Stirling heat engine at maximum power conditions. Renewable & Sustainable Energy Reviews, 2012, 16(4): 2234–2241
Formosa F, Despesse G. Analytical model for Stirling cycle machine design. Energy Conversion and Management, 2010, 51(10): 1855–1863
Formosa F. Coupled thermodynamic-dynamic semi-analytical model of free piston Stirling engines. Energy Conversion and Management, 2011, 52(5): 2098–2109
Iwamoto I, Toda K, Hirata K, Takeuchi M, Yamamoto T. Comparison of low and high temperature differential Stirling engines. In: Proceedings of the 8th International Stirling engine conference, Anacona, Italy, 1997
AhmadiMH, Hosseinzade H. Investigation of solar collector design parameters effect onto solar Stirling engine efficiency. Journal of Applied Mechanical Engineering, 2012, 1(01): 1–4
Erbay L B, Yavuz H. Analysis of Stirling heat engine at maximum power conditions. Energy, 1997, 22(7): 645–650
Li Y, He Y L, Wang W W. Optimization of solar-powered Stirling heat engine with finite-time thermodynamics. Renewable Energy, 2011, 36(1): 421–427
Sharma A, Shukla S K, Rai A K. Finite time thermodynamic analysis and optimization of Solar-Dish Stirling heat engine with regenerative losses. Thermal Science, 2011, 15(4): 995–1009
Sayyaadi H. Multi-objective approach in thermoenvironomic optimization of a benchmark cogeneration system. Applied Energy, 2009, 86(6): 867–879
AhmadiMH, Sayyaadi H, Dehghani S, Hosseinzade H. Designing a solar powered Stirling heat engine based on multiple criteria: maximized thermal efficiency and power. Energy Conversion and Management, 2013, 75: 282–291
Chen C, Ho C, Yau H. Performance analysis and optimization of a solar powered Stirling engine with heat transfer considerations. Energies, 2012, 5(12): 3573–3585
Jafari S, Mohammadi B, Boroujerdi A A. Multi-objective optimization of a Stirling-type pulse tube refrigerator. Cryogenics, 2013, 55–56: 53–62
Ahmadi M H, Mohammadi A H, Dehghani S, Barranco-Jiménez M. 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
Ahmadi M H, Hosseinzade H, Sayyaadi H, Mohammadi A H, Kimiaghalam F. 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
Ahmadi M H, Sayyaadi H, Mohammadi A H, Barranco-Jiménez M. Thermo-economic multi-objective optimization of solar dish- Stirling engine by implementing evolutionary algorithm. Energy Conversion and Management, 2013, 73: 370–380
Lazzaretto A, Toffolo A. Energy, economy and environment as objectives in multi-criterion optimization of thermal systems design. Energy, 2004, 29(8): 1139–1157
AhmadiMH, AhmadiMA, Mohammadi A H, Feidt M, Pourkiaei S M. Multi-objective optimization of an irreversible Stirling cryogenic refrigerator cycle. Energy Conversion and Management, 2014, 82: 351–360
Chaitou H, Nika P. Exergetic optimization of a thermoacoustic engine using the particle swarm optimization method. Energy Conversion and Management, 2012, 55: 71–80
Duan C, Wang X, Shu S, Jing C, Chang H. Thermodynamic design of Stirling engine using multi-objective particle swarm optimization algorithm. Energy Conversion and Management, 2014, 84: 88–96
Toghyani S, Kasaeian A, Ahmadi M H. Multi-objective optimization of Stirling engine using non-ideal adiabatic method. Energy Conversion and Management, 2014, 80: 54–62
Ahmadi M H, Mehrpooya M, Pourfayaz F. 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
Ahmadi M H, Ahmadi M A, Mellit A, Pourfayaz F, Feidt M. 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
Ahmadi M H, Ahmadi M A, Feidt M. Thermodynamic analysis and evolutionary algorithm based on multi-objective optimization of performance for irreversible four-temperature-level refrigeration. Mechanics & Industry, 2015, 16(2): 207
Kennedy J, Eberhart R. Particle swarm optimization. In: Proceeding of International Conference on Neural Networks. Perth, Australia, 1995
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Nazemzadegan, M.R., Kasaeian, A., Toghyani, S. et al. Multi-objective optimization in a finite time thermodynamic method for dish-Stirling by branch and bound method and MOPSO algorithm. Front. Energy 14, 649–665 (2020). https://doi.org/10.1007/s11708-018-0548-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11708-018-0548-0