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Abstrakty
Thermo-electric Coolers (TECs) nowadays are applied in a wide range of thermal energy systems. This is due to their superior features where no refrigerant and dynamic parts are needed. TECs generate no electrical or acoustical noise and are environmentally friendly. Over the past decades, many researches were employed to improve the efficiency of TECs by enhancing the material parameters and design parameters. The material parameters are restricted by currently available materials and module fabricating technologies. Therefore, the main objective of TECs design is to determine a set of design parameters such as leg area, leg length and the number of legs. Two elements that play an important role when considering the suitability of TECs in applications are rated of refrigeration (ROR) and coefficient of performance (COP). In this paper, the review of some previous researches will be conducted to see the diversity of optimization in the design of TECs in enhancing the performance and efficiency. After that, single-objective optimization problems (SOP) will be tested first by using Genetic Algorithm (GA) and Simulated Annealing (SA) to optimize geometry properties so that TECs will operate at near optimal conditions. Equality constraint and inequality constraint were taken into consideration.
Czasopismo
Rocznik
Tom
Strony
155--176
Opis fizyczny
Bibliogr. 36 poz., rys., tab.
Twórcy
autor
- Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, Malaysia
autor
- Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, Malaysia
autor
- Department of Electrical & Electronic Engineering, Universiti Teknologi PETRONAS, Malaysia
autor
- Department of Power Systems, HCMC University of Technology, Vietnam
Bibliografia
- [1] D. Rowe and G. Min: Design theory of thermoelectric modules for electrical power generation. IEE Proc. Science, Measurement and Technology, 143 (1963), 351-356.
- [2] Y.-H. Cheng and W.-K. Lin: Geometric optimization of thermoelectric coolers in a confined volume using genetic algorithms. Applied Thermal Engineering, 25 (2005), 2983-2997.
- [3] Y.-H. Cheng and C. Shih: Maximizing the cooling capacity and COP of twostage thermoelectric coolers through genetic algorithm. Applied Thermal Engineering, 26 (2006), 937-947.
- [4] C. Yi-Hsiang and S. Chunkuan: A novel application of Genetic Algorithms to optimizing two-stage thermoelectric coolers. Proc. of the 6th World Congress on Intelligent Control and Automation, Dalian, China, (2006), 3704-3708.
- [5] H. J. Goldsmid: The Thermoelectric and Related Effects. In Introduction to Thermoelectricity, Springer, 2009, 1-6.
- [6] P. K. S. Nain, S. Sharma and J. M. Giri: Non-dimensional multi-objective performance optimization of single stage thermoelectric cooler. In Lecture Notes in Computer Science 6457, Simulated Evolution and Learning, Proc. 8th Int. Conf., Kanpur, India, (2010), 404-413.
- [7] P. K. S. Nain, J. M. Giri, S. Sharma and K. Deb: Multi-objective performance optimization of thermo-electric coolers using dimensional structural parameters. Proc. First Int. Conf. on Swarm, Evolutionary, and Memetic Computing, Chennai, India, (2010), 607-614.
- [8] Y.-X. Huang, X.-D. Wang, C.-H. Cheng and D. T.-W. Lin: Geometry optimization of thermoelectric coolers using simplified conjugate-gradient method. Energy, 59 (2013), 689-697.
- [9] X. Xu and S. Wang: Optimal simplified thermal models of building envelope based on frequency domain regression using genetic algorithm. Energy and Buildings, 39 (2007), 525-536.
- [10] R. Venkata Rao and V. Patel: Multi-objective optimization of two stage thermoelectric cooler using a modified teaching-learning-based optimization algorithm. Engineering Applications of Artificial Intelligence, 26 (2013), 430-445.
- [11] H.-S. Kou, J.-J. Lee and C.-W. Chen: Optimum thermal performance of microchannel heat sink by adjusting channel width and height. Int. Communications in Heat and Mass Transfer, 35 (2008), 577-582.
- [12] J. Eynard, S. Grieu and M. Polit: Wavelet-based multi-resolution analysis and artificial neural networks for forecasting temperature and thermal power consumption. Engineering Applications of Artificial Intelligence, 24 (2011), 501-516.
- [13] H. Gozde and M. C. TaplamacioglU: Automatic generation control application with craziness based particle swarm optimization in a thermal power system, Int. J. of Electrical Power & Energy Systems, 33 (2011), 8-16.
- [14] P. Pezzini, O. Gomis-Bellmunt and A. Sudrià-Andreu: Optimization techniques to improve energy efficiency in power systems. Renewable and Sustainable Energy Reviews, 15 (2011), 2028-2041.
- [15] N. Sharma: A particle swarm optimization algorithm for optimization of thermal performance of a smooth flat plate solar air heater. Energy, 38 (2012), 406-413.
- [16] L. Franconi and C. Jennison: Comparison of a genetic algorithm and simulated annealing in an application to statistical image reconstruction. Statistics and Computing, 7 (1997), 193-207.
- [17] K. Miettinen: Nonlinear multiobjective optimization. 12 Springer, 1999.
- [18] K. Deb: Multi-Objective Optimization using Evolutionary Algorithms. Wiley, 2001, 13-46.
- [19] K. Deb: Optimization for Engineering Design: Algorithms and Examples. PHI Learning Pvt. Ltd., 2009.
- [20] B. Sohrabi: A comparison between Genetic Algorithm and Simulated Annealing Performance in preventive part replacement. Management Knowledge, 19(72), (2006), 102-112.
- [21] C. A. C. Coello, G. B. Lamont and D. A. Van Veldhuisen: Evolutionary Algorithms for Solving Multi-objective Problems. Springer, 2007.
- [22] K. Y. Lee and M. A. El-Sharkawi: Modern heuristic optimization techniques: theory and applications to power systems. 39 Wiley, 2008.
- [23] I. Boussaid, J. Lepagnot and P. Siarry: A Survey on Optimization Metaheuristics. Information Sciences, 2013.
- [24] X. C. Xuan, K. C. Ng, C. Yap and H. T. Chua: Optimization of two-stage thermoelectric coolers with two design configurations. Energy Conversion and Management, 43 2041-2052.
- [25] G. Li, G. Song, Y. Wu, J. Zhang and Q. Wang: A multi-objective hybrid genetic based optimization for external beam radiation. 27th Annual Int. Conf. of the Engineering in Medicine and Biology Society, (2005), 7734-7737.
- [26] M. Lei, Q. Yong, H. Di, D. Yue-Hua and S. Yi: A multi-objective hybrid genetic algorithm for energy saving task scheduling in CMP system. IEEE Int. Conf. on Systems, Man and Cybernetics, Singapore, (2008), 197-201.
- [27] S. Hui: Multi-objective optimization for hydraulic hybrid vehicle based on adaptive simulated annealing genetic algorithm. Engineering Applications of Artificial Intelligence, 23 (2010), 27-33.
- [28] P. Vasant and N. Barsoum: Hybrid Simulated Annealing and Genetic Algorithms for industrial production management problems. Proc. 2nd Global Conf. on Power Control and Optimization, Bali; Indonesia, 1159 (2009), 254-261.
- [29] H. Geng, H. Zhu, R. Xing and T. Wu: A novel hybrid evolutionary algorithm for solving multi-objective optimization problems. Intelligent Computing Technology, 7389 (2012), 128-136.
- [30] N. Shahsavari-Pour and B. Ghasemishabankareh: A novel hybrid metaheuristic algorithm for solving multi objective flexible job shop scheduling. J. of Manufacturing Systems, 32 (2013), 771-780.
- [31] O. Yamashita and S. Sugihara: High-performance bismuth-telluride compounds with highly stable thermoelectric figure of merit. J. of Materials Science, 40 (2005), 6439-6444.
- [32] C. Blum, A. Roli and M. Sampels: Hybrid metaheuristics: An emerging approach to optimization. 114 Springer, 2008.
- [33] P. Vasant: Hybrid simulated annealing and genetic algorithms for industrial production management problems. Int. J. of Computational Methods, 7 (2010), 279-297.
- [34] K. Sebaa and M. Boudour: Combination and genetic algorithms for the locations and tuning of robust power system stabilizers. Archives of Control Sciences, 17 (2007), 145-162.
- [35] P. Vasant: Hybrid mesh adaptive direct search and genetic algorithms techniques for industrial production systems. Archives of Control Sciences, 21 (2011), 299-312.
- [36] L. Franconi and C. Jennison: Comparison of a genetic algorithm and simulated annealing in an application to statistical image reconstruction. J. Statistics and Computing, 7(3), (1997), 193-207.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-e0338296-f4ca-4772-ba95-2cccc3642a62