پیوندهای مفید
آمار
| تعداد نشریات | 21 |
| تعداد شماره‌ها | 573 |
| تعداد مقالات | 8,591 |
| تعداد مشاهده مقاله | 66,294,047 |
| تعداد دریافت فایل اصل مقاله | 6,827,805 |
Multi Objective Optimization of Shell & Tube Heat Exchanger by Genetic, Particle Swarm and Jaya Optimization algorithms; Assessment of Nanofluids as the Coolant | ||
| Journal of Heat and Mass Transfer Research | ||
| دوره 9، شماره 1 - شماره پیاپی 17، مرداد 2022، صفحه 1-16 اصل مقاله (1.16 M) | ||
| نوع مقاله: Full Length Research Article | ||
| شناسه دیجیتال (DOI): 10.22075/jhmtr.2022.23959.1350 | ||
| نویسندگان | ||
| Zahra Baniamerian* 1؛ Ali Karimi2 | ||
| 1Department of Mechanical Engineering, Tafresh University, Tafresh, Iran | ||
| 2Mechanical Energy and Engineering Department, Shahid beheshti university, Tehran, Iran | ||
| تاریخ دریافت: 22 تیر 1400، تاریخ بازنگری: 12 دی 1400، تاریخ پذیرش: 15 دی 1400 | ||
| چکیده | ||
| In this study, the design of a nanofluid driven shell and tube heat exchanger is optimized, for the first time, by use of three multi objective algorithms. Two different operating conditions are investigated to compare the performance of the algorithms based on an economic model (cost function). Based on the obtained results, the Genetic, Particle Swarm and Jaya optimization algorithms can all improve the design. The amount of design improvement by each method is 9.66%, 10.63% and 10.9% respectively. Also from the view point of optimization time, Jaya optimization algorithm has relatively less CPU time than the other two algorithms, which in fact, reduces computational costs in complicated computations. Finally, due to the good performance of Jaya optimization algorithm in comparison with other considered algorithms, the performance of the heat exchangers is evaluated for using Ag, TiO2 and Al2O3 nanofluids of 0.5% to 5 vol.% by this algorithm. A performance evaluation factor (PE) is introduced as the criterion for simultaneous investigation of thermal and hydraulic performance of nanofluids. The results show that silver nanofluid, among other ones has better performance. | ||
| کلیدواژه‌ها | ||
| Heat exchanger؛ Genetic algorithm؛ Particle swarm؛ Jaya algorithm, Nanofluid؛ Multi objective optimization | ||
| عنوان مقاله [English] | ||
| بهینه سازی مبدل حرارتی پوسته لوله از طریق سه الگوریتم مختلف بهینه سازی با در نظر گرفتن نانوسیال به عنوان سیال عامل | ||
| چکیده [English] | ||
| در این مطالعه، طراحی مبدل حرارتی پوسته و لوله مبتنی بر نانوسیال برای اولین بار با استفاده از سه الگوریتم چند هدفه بهینه سازی شده است. دو شرایط عملیاتی مختلف برای مقایسه عملکرد الگوریتم‌ها بر اساس یک مدل اقتصادی (تابع هزینه) بررسی می‌شود. بر اساس نتایج به‌دست‌آمده، الگوریتم‌های بهینه‌سازی ژنتیک، ازدحام ذرات و جایا همگی می‌توانند طراحی را بهبود بخشند. میزان بهبود طراحی با روش های بهینه‌سازی ژنتیک، ازدحام ذرات و جایا به ترتیب 9.66%، 10.63% و 10.9% است. همچنین از نظر زمان بهینه سازی، الگوریتم بهینه سازی جایا نسبت به دو الگوریتم دیگر زمان پردازش نسبتاً کمتری دارد که در واقع باعث کاهش هزینه های محاسباتی در محاسبات پیچیده می شود. در نهایت با توجه به عملکرد خوب الگوریتم بهینه سازی جایا در مقایسه با سایر الگوریتم های در نظر گرفته شده، عملکرد مبدل های حرارتی برای استفاده از نانوسیالات Ag، TiO2 و Al2O3 از 0.5% تا5%غلظت حجمی توسط این الگوریتم ارزیابی می شود. یک عامل ارزیابی عملکرد (PEC) به عنوان معیاری برای بررسی همزمان عملکرد حرارتی و هیدرولیکی نانوسیال‌ها معرفی شده است. نتایج نشان می‌دهد که نانوسیال نقره در میان سایر نانوسیال‌ها عملکرد بهتری دارد. | ||
| کلیدواژه‌ها [English] | ||
| بهینه سازی چند منظورهٖ, مبدل حرارتی, الگوریتم ژنتیک, نانوسیال | ||
| مراجع | ||
|
[1] Aly, W. I. A., 2014. Numerical study on turbulent heat transfer and pressure drop of nanofluid in coiled tube-in-tube heat exchangers. Energy Conversion and Managemen, Elsevier Ltd, 79, pp. 304–316. doi: 10.1016/j.enconman.2013.12.031.
[2] Rao, R. V., Rai, D. P. and Balic, J., 2017. A multi-objective algorithm for optimization of modern machining processes. Engineering Applications of Artificial Intelligence. Elsevier Ltd, 61(March), pp. 103–125. doi: 10.1016/j.engappai.2017.03.001.
[3] Arsana, I. M. and Rahardjo, M. A. H., 2020. Simulation study on efficiency of woven matrix wire and tube heat exchanger. International Journal of Engineering Transactions C: Aspects, 33(12), pp. 2572–2577. doi: 10.5829/ije.2020.33.12c.19.
[4] Atapattu, S., Tellambura, C. and Jiang, H., 2014, Performance measurements. SpringerBriefs in Computer Science, 0(9781493904938), pp. 41–62. doi: 10.1007/978-1-4939-0494-5_4.
[5] Balamurugan, S. and Samsoloman, D. P., 2014. Experiment. 00(2013), pp. 59–66.
[6] Baniamerian, Z., Mehdipour, R. and Murshed, S. M. S., 2019. An experimental investigation of heat of vaporization of nanofluids. Journal of Thermal Analysis and Calorimetry, doi: 10.1007/s10973-019-08202-y.
[7] Bock Choon Pak, Y. I. C., 2013. Hydrodynamic and Heat Transfer Study of Dispersed Fluids With Submicron Metallic Oxide. Experimental Heat Transfer : A Journal of Thermal Energy Transport, Storage, and Conversion, January 2013, pp. 37–41.
[8] Borzuei, M. and Baniamerian, Z., 2020. Role of nanoparticles on critical heat flux in convective boiling of nanofluids: Nanoparticle sedimentation and Brownian motion. International Journal of Heat and Mass Transfer, Elsevier Ltd, 150, p. 119299. doi:10.1016/j.ijheatmasstransfer.2019.119299.
[9] Dalkılıç, A. S. et al., 2020. Optimization of the finned double-pipe heat exchanger using nanofluids as working fluids. Journal of Thermal Analysis and Calorimetry, Springer International Publishing, 5(0123456789). doi: 10.1007/s10973-020-09290-x.
[10] Ghorbani, M. and Ranjbar, S. F., 2019. Optimization of compressed heat exchanger efficiency by using genetic algorithm. International Journal of Applied Mechanics and Engineering, 24(2), pp. 461–472. doi: 10.2478/ijame-2019-0029.
[11] GUTHRIE KM., 1969. Capital Cost Estimating. Chemical Engineer, doi: 10.1021/ie50502a032.
[12] Hajabdollahi, Z., Hajabdollahi, H. and Kim, K. C., 2020. Heat transfer enhancement and optimization of a tube fitted with twisted tape in a fin-and-tube heat exchanger. Journal of Thermal Analysis and Calorimetry, Springer International Publishing, 140(3), pp. 1015–1027. doi: 10.1007/s10973-019-08668-w.
[13] Hanson, F. V., 1989. Heat exchangers selection, design and construction. Fuel Processing Technology, pp. 87–88. doi: 10.1016/0378-3820(89)90046-5.
[14] Hoghoj, L. C. et al., 2020. Topology optimization of two fluid heat exchangers. International Journal of Heat and Mass Transfer, 163(July). doi: 10.1016/j.ijheatmasstransfer.2020.120543.
[15] Hojjati, A. et al., 2018. Application and comparison of NSGA-II and MOPSO in multi-objective optimization of water resources systems. Journal of Hydrology and Hydromechanics, 66(3), pp. 323–329. doi: 10.2478/johh-2018-0006.
[16] Jeklin, A., 2016. 済無 No Title, vol, 1, pp.1-23.
[17] John, A. K. and Krishnakumar, K., 2017. Performing multiobjective optimization on perforated plate matrix heat exchanger surfaces using genetic algorithm. International Journal for Simulation and Multidisciplinary Design Optimization, 8, pp. 0–7. doi: 10.1051/smdo/2016011.
[18] Khanmohammadi, Shoaib et al., 2020. Triple-objective optimization of a double-tube heat exchanger with elliptic cross section in the presence TiO2 nanofluid. Journal of Thermal Analysis and Calorimetry, Springer International Publishing, 140(1), pp. 477–488. doi: 10.1007/s10973-019-08744-1.
[19] Khoshvaght-Aliabadi, M., 2014. Influence of different design parameters and Al2O3-water nanofluid flow on heat transfer and flow characteristics of sinusoidal-corrugated channels. Energy Conversion and Management, Elsevier Ltd, 88, pp. 96–105. doi: 10.1016/j.enconman.2014.08.042.
[20] Maïga, S. E. B. et al., 2006. Heat transfer enhancement in turbulent tube flow using Al2O 3 nanoparticle suspension. International Journal of Numerical Methods for Heat and Fluid Flow, 16(3), pp. 275–292. doi: 10.1108/09615530610649717.
[21] Memari, A., Rahim, A. R. A. and Ahmad, R. B., 2015, An integrated production-distribution planning in green supply chain: A multi-objective evolutionary approach. Procedia CIRP, Elsevier B.V., 26(September 2014), pp. 700–705. doi:10.1016/j.procir.2015.03.006.
[22] McDonald, A. G. and Magande, H. L., 2012. Fundamentals of Heat Exchanger Design. Introduction to Thermo-Fluids Systems Design, pp. 127–211. doi: 10.1002/9781118403198.ch4.
[23] Miansari, M. et al., 2020. Energy and exergy analysis and optimization of helically grooved shell and tube heat exchangers by using Taguchi experimental design. Journal of Thermal Analysis and Calorimetry, Springer International Publishing, 139(5), pp. 3151–3164. doi: 10.1007/s10973-019-08653-3.
[24] Najafi, H. and Najafi, B., 2010. Multi-objective optimization of a plate and frame heat exchanger via genetic algorithm. Heat and Mass Transfer/Waerme- und Stoffuebertragung, 46(6), pp. 639–647. doi: 10.1007/s00231-010-0612-8.
[25] Rao, R. V., Rai, D. P. and Balic, J., 2017. A multi-objective algorithm for optimization of modern machining processes. Engineering Applications of Artificial Intelligence, Elsevier Ltd, 61(March), pp. 103–125. doi: 10.1016/j.engappai.2017.03.001.
[26] Rohsenow Editor, W. M. et al., 1998. HANDBOOK OF HEAT TRANSFER MCGRAW-HILL Chapter 1. Basic Concepts of Heat Transfer 1.1 The Equation of Motion (Momentum Equation) / Chapter 2. Thermophysical Properties 2.1’, Handbook of Heat Transfer.
[27] Sadeghzadeh, H., Aliehyaei, M. and Rosen, M. A., 2015, Optimization of a finned shell and tube heat exchanger using a multi-objective optimization genetic algorithm. Sustainability (Switzerland), 7(9), pp. 11679–11695. doi: 10.3390/su70911679.
[28] Sadeghzadeh, H., Ehyaei, M. A. and Rosen, M. A., 2015. Techno-economic optimization of a shell and tube heat exchanger by genetic and particle swarm algorithms. Energy Conversion and Management. Elsevier Ltd, 93, pp. 84–91. doi: 10.1016/j.enconman.2015.01.007.
[29] Shoheib, M. M., Hamzehei, M. and Shahrooi, S., 2019. Investigating tubes material selection on thermal stress in shell side inlet zone of a vertical shell and tube heat exchanger. Journal of Heat and Mass Transfer Research, 6(2), pp. 21–30. doi: 10.22075/jhmtr.2018.14041.1203.
[30] Sieder, E. N. and Tate, G. E., 1936. Heat Transfer and Pressure Drop of Liquids in Tubes. Industrial and Engineering Chemistry, 28(12), pp. 1429–1435. doi: 10.1021/ie50324a027.
[31] Taal, M. et al., 2003. Cost estimation and energy price forecasts for economic evaluation of retrofit projects. Applied Thermal Engineering, 23(14), pp. 1819–1835. doi: 10.1016/S1359-4311(03)00136-4.
[32] Taghilou, M., Ghadimi, B. and Seyyedvalilu, M. H., 2014. Optimization of Double Pipe Fin-pin Heat Exchanger using Entropy Generation Minimization. International Journal of Engineering, 27(9 (C)), pp. 1431–1438. doi: 10.5829/idosi.ije.2014.27.09c.13.
[33] Turgut, O. E., 2017. Multi-objective thermal design optimization of plate frame heat exchangers through Global Best Algorithm. Bitlis Eren University, Journal of Science and Technology, 7(1), pp. 33–33. doi: 10.17678/beuscitech.322141.
[34] Uddin, M. J., Rahman, M. M. and Alam, S., 2016. Fundamentals of Nanofluids : Evolution , Applications and New Theory. International Journal of Biomathematics and Systems Biology, 2(1), pp. 1–31.
[35] Valipour, M. S., Biglari, M. and Assareh, E., 2016. Thermal-Economic Optimization of Shell and Tube Heat Exchanger by using a new Multi-Objective optimization method. Journal of Heat and Mass Transfer Research (JHMTR), 3(1), pp. 67–76.
[36] Zhao, L., Huo, Z. and Yin, H., 2013. Heat exchanger network optimization considering pressure drop constraints based on minimum operating cost. International Journal of Online Engineering, 9 (SPECIALISSUE.6), pp. 33–37. doi: 10.3991/ijoe.v9iS6.2798. | ||
|
آمار تعداد مشاهده مقاله: 512 تعداد دریافت فایل اصل مقاله: 388 |
||