Open Access
Issue |
Int. J. Simul. Multidisci. Des. Optim.
Volume 8, 2017
|
|
---|---|---|
Article Number | A3 | |
Number of page(s) | 8 | |
DOI | https://doi.org/10.1051/smdo/2016011 | |
Published online | 23 January 2017 |
- Barron R. 1985. Cryogenic Systems. Oxford University Press. [Google Scholar]
- Atrey MD. 1998. Thermodynamic analysis of Collins helium liquefaction cycle. Cryogenics, 38, 1199–1206. [CrossRef] [Google Scholar]
- Moon WJ, Lee PY, Jin YW, Hong ES, Chang MH. 2007. Cryogenic Refrigeration Cycle for Re-Liquefaction of LNG Boil-Off Gas, in Cryocoolers 14, International Cryocooler Conference Proceedings, Boulder. [Google Scholar]
- McMahon H, Bowen R, Bleyle GJr. 1950. A perforated plate heat exchanger. Trans ASME, 72, 623–632. [Google Scholar]
- Webb RL. 1987. Enhancement of Single-phase Heat Transfer. Kakac S, Shah RK, Bergles AE, Editors. Wiley: New York. [Google Scholar]
- Xie G, Wang Q, Sunden B. 2008. Application of a genetic algorithm for thermal design of fin-and-tube heat exchangers. Heat Transfer Engineering, 29(7), 597–607. [CrossRef] [Google Scholar]
- Ozkol I, Komurgoz G. 2005. Determination of the optimum geometry of the heat exchanger body via a genetic algorithm. Numerical Heat Transfer, Part A, 48, 283–296. [CrossRef] [Google Scholar]
- Amon CH, Mikic BB. 1991. Spectral element simulations of unsteady forced convective heat transfer: application to compact heat exchanger geometries. Numerical Heat Transfer, 19(1), 1–19. [CrossRef] [Google Scholar]
- Gut JAW, Pinto JM. 2004. Optimal configuration design for plate heat exchangers. International Journal of Heat and Mass Transfer, 47, 4833–4848. [CrossRef] [Google Scholar]
- Vargas JVC, Bejan A. 2001. Thermodynamic optimization of finned crossflow heat exchangers for aircraft environmental control systems. International Journal of Heat and Fluid Flow, 22, 657–665. [CrossRef] [Google Scholar]
- Bejan A. 2001. Thermodynamic optimization of geometry in engineering flow systems. Exergy An International Journal, 4, 269–277. [CrossRef] [Google Scholar]
- Sen M, Yang KT. 2000. Applications of artificial neural networks and genetic algorithms in thermal engineering, in The CRC Handbook of Thermal Engineering. Kreith F, Editor. CRC Press: Boca Raton, FL. p. 620–661. [Google Scholar]
- Hilbert R, Janiga G, Baron R, Thévenin D. 2006. Multi-objective shape optimization of a heat exchanger using parallel genetic algorithms. International Journal of Heat and Mass Transfer, 49, 2567–2577. [CrossRef] [Google Scholar]
- Pacheco-Vega A, Sen M, Yang KT, McClain RL. 1998. Genetic Algorithms-based Predictions of Fin-Tube Heat Exchanger Performance. Proceedings of 11th International Heat Transfer Conference, August 23–28, Kyongju, Korea, Vol. 6, pp. 137–142. [Google Scholar]
- Xie GN, Wang QW. 2006. Geometrical optimization of plate-fin heat exchanger using genetic algorithms. Proceedings of the Chinese Society for Electrical Engineering, 26(7), 53–57 (in Chinese). [Google Scholar]
- Mishra M, Das PK, Sarangi S. 2004. Optimum design of crossflow plate-fin heat exchangers through genetic algorithm. International Journal of Heat Exchangers, 5(2), 379–401. [Google Scholar]
- Liang HX, Xie GN, Zeng M, Wang QW, Feng ZP. 2005. Application Genetic Algorithm to Optimization Recuperator in Micro-Turbine The 2nd International Symposium on Thermal Science and Engineering, October 23–25, Beijing, China. [Google Scholar]
- Wang QW, Liang HX, Xie GN, Zeng M, Luo LQ, Feng ZP. 2007. Genetic algorithm optimization for primary surfaces recuperator of microturbine. ASME Journal of Engineering for Gas Turbines and Power, 129, 436–442. [CrossRef] [Google Scholar]
- Pacheco-Vega A, Sen M, Yang KT, McClain RL. 2001. Correlations of fin-tube heat exchanger performance data using genetic algorithms simulated annealing and interval methods. Proceedings of ASME the Heat Transfer Division, November 11–16, New York, USA, vol. 369–5, p. 143–151. [Google Scholar]
- Cavazzuti M, Corticelli MA. 2008. Optimization of heat exchanger enhanced surfaces through multiobjective genetic algorithms. Numerical Heat Transfer, Part A, 54, 603–624. [CrossRef] [Google Scholar]
- Foli K, Okabe T, Olhofer M, Jin Y, Sendhoff B. 2006. Optimization of micro heat exchanger: CFD, analytical approach and multi-objective evolutionary algorithms. International Journal of Heat and Mass Transfer, 49, 1090–1099. [CrossRef] [Google Scholar]
- Nobile E, Pinto F, Rizzetto G. 2006. Geometric parameterization and multi-objective shape optimization of convective periodic channels. Numerical Heat Transfer B, 50, 425–453. [CrossRef] [Google Scholar]
- Manzan M, Nobile E, Pieri S, Pinto F. 2008. Multi-objective optimization for problems involving convective heat transfer, in Optimization and Computational Fluid Dynamics, Chap. 8. Thévenin D, Janiga G, Editor. Springer-Verlag: Berlin. [Google Scholar]
- Gosselin L, Tye-Gingras M, Mathieu-Potvin F. 2009. Review of utilization of genetic algorithms in heat transfer problems. International Journal of Heat and Mass Transfer, 52, 2169–2188. [CrossRef] [Google Scholar]
- Goldberg DE. 1989. Genetic algorithms in search, optimization, and machine learning. Addison-Wesley Longman Publishing Co.: Boston, USA. [Google Scholar]
- Krishnakumar K, Venkatarathnam G. 2007. On the use of time at maximum slope in determining the heat transfer coefficients in complex surfaces using the single blow transient test method. International Journal of Heat Exchangers, VII, 31–44. [Google Scholar]
- Rechenberg I. 1965. Evolutionsstrategie: Optimierung Technischer Systeme nach Prinzipien der Biologischen Evolution. Frommann−Holzboog: Stuttgart. [Google Scholar]
- Holland JH. 1975. Adaptation in Natural and Artificial Systems, 2nd edition. University of Michigan Press. [Google Scholar]
- Koza JR. 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press. [Google Scholar]
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