تحقیقات موتور

تحقیقات موتور

شبیه‏‌سازی و پیش‏‌بینی متغیرهای دینامیکی حباب با استفاده از روش یادگیری دستگاه در فرآیند جوشش ‏در تحلیل گرمایی موتورهای احتراق داخلی

نوع مقاله : مقاله پژوهشی

نویسنده
گروه مهندسی مکانیک، دانشگاه پیام نور، تهران، ایران
چکیده
انتقال حرارت عامل مهم و تأثیرگذار در طراحی موتورهای احتراق داخلی است. پیش‌بینی دقیق انتقال حرارت برای طراحی سامانة خنک‏‌کاری موتورهای احتراق داخلی تأثیر بسزایی دارد. لذا شبیه‌­سازی موتورهای احتراق داخلی به‌منظور تحلیل حرارتی آن­‌ها با استفاده از دینامیک سیالات محاسباتی به‌­همراه شبیه‌­سازی جوشش انجام می‌پذیرد. در این مقاله، بررسی جریان جوشش زیرخنک‌شده آب خالص در یک آبراه کوچک و وابستگی آن به متغیرهای مختلف مانند فشار کاری، شار حرارتی، شار جرمی. و دمای زیر سرد سیال ورودی مورد توجه است. شبیه‌سازی‌های عددی با استفاده از الگو‌های متقارن محوری انجام شده و تأثیر اندازه‌های شبکه‌های مختلف بر دقت نتایج مورد بررسی قرار گرفت. قطر جداسازی حباب نیز مطالعه شد و وابستگی آن با فشار، شار گرما، شار جرمی و دمای زیر خنک‌کننده ورودی تحلیل شد. در نهایت، یک پایگاه داده از نتایج شبیه‌سازی تولید شده و الگو‌های پیش‌بینی برای دینامیک حباب با استفاده از روش‌های یادگیری دستگاه ارائه شد.
کلیدواژه‌ها

عنوان مقاله English

Simulation and prediction of bubble dynamic parameters using machine learning algorithm in boiling process used in thermal analysis of internal combustion engines

نویسنده English

Erfan Khosravian
Department of Mechanical Engineering, Payame Noor University, Tehran, Iran
چکیده English

Heat transfer is an important and influential factor in the design of internal combustion engines. Accurate prediction of heat transfer has a great impact on the design of cooling systems of internal combustion engines. The simulation of internal combustion engines for their thermal analysis is done using computational fluid dynamics along with boiling simulation. This article investigates the subcooled boiling flow of pure water in a small channel and its dependence on different parameters such as working pressure, heat flux, and mass flux. The subcooling temperature of the inlet fluid is of interest. Numerical simulations were performed using axisymmetric models and the effect of different grid sizes on the accuracy of the results was investigated. The bubble separation diameter was also studied and its relationship with pressure, heat flux, mass flux, and temperature under the inlet coolant was analyzed. Finally, a database of generated simulation results and predictive models for bubble dynamics using machine learning algorithms were presented.

کلیدواژه‌ها English

Flow Boiling
Numerical Simulation
Bubble Dynamics
Machine Learning Algorithm
Artificial Neural Network
[1]    Hemat Khanloo R, Mohammadi A, Jazayeri S, Yaghoubi M. Simulation of heat transfer considering boiling phenomenon in cooling passage of turbo-charged national engine. Journal of Engine Research. 2012 December 21;29(29):3-14. [In Persian]
[2]    Hsu YY, Graham RW. Boiling heat transfer on a flat plate in a vacuum. International Journal of Heat and Mass Transfer. 1961 Jun 8;2(3):245-57.
[3]    Wang S, Liao S, Zhang X. Experimental study on the cooling performance of a supersonic combustor using boiling. Applied Thermal Engineering. 2017 July 23;119(3):456-67.
[4]    Wang J, Zhao L, Wang X, Ma J, Lin J. An experimental investigation on transpiration cooling of wedge shaped nose cone with liquid coolant. International Journal of Heat and Mass Transfer. 2014 Agust 3;75:442-9.
[5]    Hwang BJ, Choi H, Min S. An experimental study on penetration and mixing characteristics of liquid fuel in preheated supersonic airflows. Acta Astronautica. 2023 Jan 1;202:511-21.
[6]    Qiu Y, Garg D, Zhou L, Kharangate CR, Kim S-M, and Mudawar I. An artificial neural network model to predict mini/micro-channels saturated flow boiling heat transfer coefficient based on universal consolidated data. International Journal of Heat and Mass Transfer. 2020 Jun 1;149:119211. doi: 10.1016/j.ijheatmasstransfer.2019.119211
[7]    Alimoradi H, Eskandari E, Pourbagian M, Shams M. A parametric study of subcooled flow boiling of Al2O3/water nanofluid using numerical simulation and artificial neural networks. Nanoscale and Microscale Thermophysical Engineering. 2022 Jun 7;1-31. doi: 10.1080/15567265.2022.2108949
[8]    Alimoradi H, Shams M. Optimization of subcooled flow boiling in a vertical pipe by using artificial neural network and multi objective genetic algorithm. Applied Thermal Engineering. 2017  Jan 5;1039-51. doi: 10.1016/j.applthermaleng.2016.09.114
[9]    Alimoradi H, Shams M. Numerical simulation of the effects of surface roughness on nucleation site density of nanofluid boiling. Modares Mechanical Engineering. 2019 May 11;19(7);13-1622. [In Persian]
[10] Alimoradi H, Shams M, Ashgriz N, Bozorgnezhad A. A novel scheme for simulating the effect of microstructure surface roughness on the heat transfer characteristics of subcooled flow boiling. Case Studies in Thermal Engineering, 2021 May 14;24:100829. doi: 10.1016/j.csite.2020.100829
[11] Alimoradi H, Shams M, Valizadeh Z. The effects of nanoparticles in the subcooled boiling flow in the channels with different cross-sectional area and same hydraulic diameter. Modares Mechanical Engineering, 2017 Ceptember 11;16(12);545-54.
[12] Celata G P, Cumo M, Mariani A, Zummo G. Burnout in subcooled flow boiling of water. A visual experimental study. International journal of thermal sciences. 2000 Jan 17;39(9-11):896-908. doi: 10.1016/S1290-0729(00)01175-3
[13] Lee J, Jo D, Chae H, Chang SH, Jeong YH, Jeong JJ. The characteristics of premature and stable critical heat flux for downward flow boiling at low pressure in a narrow rectangular channel. Experimental Thermal and Fluid Science. 2015 July 20;69:86-98. doi: 10.1016/j.expthermflusci.2015.07.015
[14] Sharifi S, Aligoodarz MR, Rahbari A, Thermohydraulic performance of Al2O3-water nanofluid during single-phase flow and two-phase subcooled flow boiling. International Journal of Thermal Sciences. 2022 Jun 3;179:107605.
[15] Kim TI, Jeong YH, Chang SH. An experimental study on CHF enhancement in flow boiling using Al2O3 nano-fluid. International Journal of Heat and Mass Transfer.2010 Feb 5;53(5-6):1015-22. doi: 10.1016/j.ijheatmasstransfer.2009.11.011
[16] Abedini E, Behzadmehr A, Sarvari S, Mansouri S. Numerical investigation of subcooled flow boiling of a nanofluid. International Journal of Thermal Sciences. 2013 Jan 4;64:232-9. doi: 10.1016/j.ijthermalsci.2012.08.008
[17] Ham J, Cho H. Theoretical analysis of pool boiling characteristics of Al2O3 nanofluid according to volume concentration and nanoparticle size. Applied Thermal Engineering. 2016 Ceptember 6;108:158-71. doi: 10.1016/j.applthermaleng.2016.07.058
[18] Setoodeh H, Keshavarz A, Ghasemian A, Nasouhi A. Subcooled flow boiling of alumina/water nanofluid in a channel with a hot spot: An experimental study. Applied Thermal Engineering. 90:384-94. doi: 10.1016/j.applthermaleng.2015.07.016
[19] Mashaei P, Shahryari M, Fazeli H, Hosseinalipour S. Numerical simulation of nanofluid application in a horizontal mesh heat pipe with multiple heat sources: a smart fluid for high efficiency thermal system. Applied Thermal Engineering. 2016 May 2;100:1016-30. doi: 10.1016/j.applthermaleng.2016.02.111
[20] Morshed A, Paul TC, Khan J, Effect of Cu–Al2O3 nanocomposite coating on flow boiling performance of a microchannel Applied Thermal Engineering. 2013 Jan 9;51(1-2):1135-43. doi: 10.1016/j.applthermaleng.2012.09.047
[21] DolatiAsl K, Bakhshan Y, Abedini E, Niazi S, Correlations for estimating critical heat flux (CHF) of nanofluid flow boiling, International Journal of Heat and Mass Transfer. 2019 May 4;139:69-76.  doi: 10.1016/j.ijheatmasstransfer.2019.04.146
[22] Patra N, Ghosh P, Singh R, Nayak A. Flow visualization in dilute oxide based nanofluid boiling. International Journal of Heat and Mass Transfer. 2019 September 5;135:331-44. doi: 10.1016/j.ijheatmasstransfer.2019.01.145
[23] Wang Y, Deng K, Liu B, Wu J, Su G. A correlation of nanofluid flow boiling heat transfer based on the experimental results of AlN/H2O and Al2O3/H2O nanofluid. Experimental Thermal and Fluid Science. 2017 Jun 14;80:376-83. doi: 10.1016/j.expthermflusci.2016.08.020
[24] Soleimani B, Keshavarz A, Heat transfer enhancement of an internal subcooled flow boiling over a hot spot. Applied Thermal Engineering. 2016 December 6;99:206-213. doi: 10.1016/j.applthermaleng.2015.12.043
[25] Azizifar S, Ameri M, Behroyan I, Experimental investigation of the subcooled flow boiling heat transfer of water and nanofluids in a horizontal metal foam tube Heat and Mass Transfer. 2021 Feb 7;57(9):1499-511. doi: 10.1007/s00231-021-03042-9
[26] Kim SJ, McKrell T, Buongiorno J, Hu L.-W. Alumina nanoparticles enhance the flow boiling critical heat flux of water at low pressure. Journal of Heat Transfer. 2008 May 7;130(4):5-17. doi: 10.1115/1.2818787
[27] Mukherjee S, Jana S, Mishra PC, Chaudhuri P, Chakrabarty S. Experimental investigation on thermo-physical properties and subcooled flow boiling performance of Al2O3/water nanofluids in a horizontal tube. International Journal of Thermal Sciences. 2021 Feb 8;159:106581. doi: 10.1016/j.ijthermalsci.2020.106581
[28]Esfandyar M, Pourabdi G, Akbari M, Eskandari E. Numerical study and analysis of thermal parameters of subcooled flow boiling and presentation of prediction models based on artificial neural network algorithm. Karafan Scientific Quarterly. 2023 May 22;20(1). doi: 10.48301/KSSA.2023.388017.2469
[29] Cheung S, Vahaji S, Yeoh G, Tu J, Modeling subcooled flow boiling in vertical channels at low pressures–Part 1: Assessment of empirical correlations. International Journal of Heat and Mass Transfer. 2014 Jun 9;25:736-53. doi: 10.1016/j.ijheatmasstransfer.2014.03.016
[30] Launder BE, Spalding DB, The numerical computation of turbulent flows. Numerical prediction of flow, heat transfer, turbulence and combustion. 1983 Jan 4;96-116.
[31] Ishii M, Zuber N. Drag coefficient and relative velocity in bubbly, droplet or particulate flows AIChE journal. 1979 September 6;25(5):843-55. doi: 10.1002/aic.690250513
[32] Kurul N, Podowski MZ. Multidimensional effects in forced convection subcooled boiling.
[33] Lemmert M, Chawla J. Influence of flow velocity on surface boiling heat transfer coefficient. Heat Transfer in Boiling. 1977 Agust 5;237(247);55-69.
[34] Tolubinsky V, Kostanchuk D. Vapour bubbles growth rate and heat transfer intensity at subcooled water boiling.
[35] Cole R. A photographic study of pool boiling in the region of the critical heat flux. AIChE Journal. 1960 Jun 8;6(4):533-8. doi: 10.1002/aic.690060405
[36] Bartolomei G, Brantov V, Molochnikov Y S, Kharitonov Y V, Solodkii V, Batashova G, Mikhailov V. An experimental investigation of true volumetric vapor content with subcooled boiling in tubes Thermal Engineering. 1982 May 13;29(3):132-5.
[37] Rouhani SZ, Axelsson E. Calculation of void volume fraction in the subcooled and quality boiling regions. International Journal of Heat and Mass Transfer. 1970 Jan 7;13(2):383-93.
[38] Basu N, Warrier GR, Dhir VK. Wall heat flux partitioning during subcooled flow boiling: Part 1-model development. J. Heat Transfer. 2005 July 9;127(2):131-40. doi: 10.1115/1.1842784
[39] Friz W.  Maximum volume of vapor bubbles. Physic. Zeitschz. 1935 Jun 5;36:379-54.
[40] Yang L, Guo A, Liu D, Experimental investigation of subcooled vertical upward flow boiling in a narrow rectangular channel. Experimental Heat Transfer. 2016 July 13;29(2):221-43. doi: 10.1080/08916152.2014.973978
[41] Sugrue R, Buongiorno J, McKrell T, An experimental study of bubble departure diameter in subcooled flow boiling including the effects of orientation angle, subcooling, mass flux, heat flux, and pressure. Nuclear Engineering and Design. 2014 may 16;279:182-8. doi: 10.1016/j.nucengdes.2014.08.009
[42] Yoo J, Estrada-Perez CE, Hassan YA, Experimental study on bubble dynamics and wall heat transfer arising from a single nucleation site at subcooled flow boiling conditions–Part 2: Data analysis on sliding bubble characteristics and associated wall heat transfer. International Journal of Multiphase Flow. 2016 Agust 11;84:292-314. doi: 10.1016/j.ijmultiphaseflow.2016.04.019
[43] Han CY. The mechanism of heat transfer in nucleate pool boiling (Doctoral dissertation, Massachusetts Institute of Technology). 1962 jan 12.
[44] Zeng L, Klausner J, Nucleation site density in forced convection boiling. 1993 Jun 8. doi: 10.1115/1.2910651

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