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مدلسازی با استفاده از شبکه عصبی مصنوعی جهت پیش بینی هدایت حرارتی نانوسیال نانولوله کربنی چند جداره عامل دار – آب و ارائه رابطه تجربی جدید | ||
| مدل سازی در مهندسی | ||
| مقاله 6، دوره 16، شماره 53، تیر 1397، صفحه 67-73 اصل مقاله (717.74 K) | ||
| نوع مقاله: مقاله مکانیک | ||
| شناسه دیجیتال (DOI): 10.22075/jme.2017.5708. | ||
| نویسندگان | ||
| مسعود افرند1؛ محمد همت اسفه* 2 | ||
| 1استادیار، گروه مهندسی مکانیک، واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران | ||
| 2دانشگاه آزاد اسلامی، واحد خمینی شهر، باشگاه پژوهشگران جوان و نخبگان، اصفهان، ایران | ||
| تاریخ دریافت: 21 آبان 1394، تاریخ بازنگری: 27 بهمن 1396، تاریخ پذیرش: 17 خرداد 1396 | ||
| چکیده | ||
| در این مقاله ، بر اساس نتایج آزمایشگاهی، و با استفاده از روش برازش منحنی و شبکه عصبی مصنوعی اثر دما و کسر حجمی نانولوله‌ها بر ضریب هدایت حرارتی نانوسیال نانولوله کربنی چند جداره عامل دار-آب بررسی شد. یک رابطه دقیق به صورت تابعی از کسر حجمی و دما برای پیش بینی ضریب هدایت حرارتی نانوسیال ارائه شد. همچنین شبکه های عصبی مختلفی به منظور مدلسازی ضریب هدایت حرارتی نانوسیال طراحی شد. در این شبکه‌ها دما و کسر حجمی به عنوان متغیرهای ورودی و ضریب هدایت حرارتی به عنوان متغیر خروجی در نظر گرفته شد. شبکه عصبی بهینه با در نظر گرفتن حداقل خطا در پیش بینی ضریب هدایت حرارتی نانوسیال به دست آمد. مقایسه‌ها نشان داد که شبکه عصبی مصنوعی می‌تواند پیش بینی دقیق‌تری نسبت به روش برازش منحنی در تخمین ضریب هدایت حرارتی این نانوسیال ارائه کند. همچنین نتایج نشان داد که رابطه تجربی ارائه شده به وسیله روش برازش منحنی دارای دقت قابل قبولی است. | ||
| کلیدواژه‌ها | ||
| مدلسازی؛ شبکه عصبی؛ هدایت حرارتی؛ رابطه تجربی؛ نانوسیال؛ نالوله کربنی چند جداره | ||
| عنوان مقاله [English] | ||
| Artificial neural network modeling for prediction of thermal conductivity of functionalized MWCNTs/water nanofluids and a new empirical correlation | ||
| نویسندگان [English] | ||
| Masoud Afrand1؛ Mohammad Hemmat Esfe2 | ||
| 1iran | ||
| 2iran | ||
| چکیده [English] | ||
| In this paper, based on experimental data, by employing regression method and artificial neural network, the effects of temperature and nanotubes concentration on thermal conductivity of COOH functionalized Multi Walled Carbon Nano Tubes / water was investigated . A very accurate correlation for thermal conductivity ratio was suggested as a function of temperature and solid volume fraction . Artificial neural network modeling was performed. Temperature and solid volume fraction were employed as input variables and thermal conductivity ratio was used as outputs variable. Optimized ANN by considering minimum prediction error was obtained. Comparisons showed that the ANN can more precisely predict the thermal conductivity ratio of COOH functionalized Multi Walled Carbon Nano Tubes/water nanofluids. The results also revealed that the empirical correlation has an acceptable accuracy.Experimental results showed that the thermal conductivity has a direct and reverse relationshipThe existing correlations in literature were unable to predict viscosity data, hence, a new correlation has been proposed. | ||
| کلیدواژه‌ها [English] | ||
| modeling, Neural Network, Thermal conductivity, Empirical correlation, Nanofluid, MWCNT | ||
| مراجع | ||
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[1] Choi, S.U.S. (1995). “Enhancing thermal conductivity of fluids with nanoparticles”. ASME-Publications-Fed, Vol. 231, pp. 99-106. [2] Chandrasekar, M., Suresh, S., Chandra Bose, A. (2010). “Experimental investigations and theoretical determination of thermal conductivity and viscosity of Al2O3/water nanofluid”. Experimental Thermal and fluid Science, Vol. 34, pp. 210–216. [3] Liu, M.S., Lin, M.C.C., Wang, C.C. (2011). “Enhancements of thermal conductivities with Cu, CuO, and carbon nanotube nanofluids and application of MWNT/water nanofluid on a water chiller system”. Nanoscale Research Letter, Vol. 6, pp. 297. [4] Harish, S., Ishikawa, K., Einarsson, E., Aikawa, S., Chiashi, S., Shiomi, J., Maruyama, S. (2012). “Enhanced thermal conductivity of ethylene glycol with single-walled carbon nanotube inclusions”. International Journal of Heat and Mass Transfer, Vol. 55, pp. 3885–3890. [5] Reddy, M.C.S., Vasudeva, Rao, V. (2013) “Experimental studies on thermal conductivity of blends of ethylene glycol-water-based TiO2nanofluids”. International Communications in Heat and Mass Transfer, Vol. 46, pp.31–36. [6] Sundar, L.S., Singh, M.K., Sousa, A.C.M. (2013) “Investigation of thermal conductivity and viscosity of Fe3O4nanofluid for heat transfer applications”. International Communications in Heat and Mass Transfer, Vol. 44, pp. 7–14. [7] Jeong, J., Li, C., Kwon, Y., Lee, J., Hyung Kim, S., Yun, R. (2013). “Particle shape effect on the viscosity and thermal conductivity of ZnO nanofluids”. International journal of Refrigeration, Vol. 36, pp. 2233-2224. [8] Hachey, M.A., Nguyen, C.T., Galanis, N., Pop, C.V. (2014). “Experimental investigation of Al2O3 nanofluids thermal properties and rheology – Effects of transient and steady-state heat exposure”. International Journal of Thermal Sciences, Vol. 76, pp. 155-167. [9] Pang, C., Lee, J.W., Kang, Y.T. (2015). “Review on combined heat and mass transfer characteristics in nanofluids”. International Journal of Thermal Sciences, Vol. 87, pp. 49-67. [10] Hemmat Esfe, M., Afrand, M., Karimipour, A., Yan, W.-M., Sina, N. (2015). “An experimental study on thermal conductivity of MgO nanoparticles suspended in a binary mixture of water and ethylene glycol”. International Communications in Heat and Mass Transfer, Vol. 67, pp. 173-175. [11] Chon, C.H., Kihm, K.D., Lee, S.P., Choi, S.U.S. (2005). “Empirical correlation finding the role of temperature and particle size for nanofluid (Al2O3) thermal conductivity enhancement”. Applied Physics Letter, Vol. 87, pp. 153107. [12] Li, C.H., Peterson, G.P. (2006). “Experimental investigation of temperature and volume fraction variations on the effective thermal conductivity of nanoparticle suspensions (nanofluids)”. Journal of Applied Physics, Vol. 99, pp. 084314-1-084314-8. [13] Vajjha, R.S., Das, D.K. (2009). “Measurement of thermal conductivity of three nanofluids and development of new correlations”. International Journal of Heat and Mass Transfer, Vol. 52, pp. 4675–4682. [14] Duangthongsuk, W., Wongwises, S. (2009). “Measurement of temperature-dependent thermal conductivity and viscosity of TiO2-water nanofluids”. Experimental Thermal and Fluid Science, Vol. 33, pp. 706-714. [15] Teng, Tun-Ping, Hung, Yi-Hsuan, Teng, Tun-Chien, Mo, Huai-En, Hsu, How-Gao. (2010). “The effect of alumina/water nanofluid particle size on thermal conductivity”. Applied Thermal Engineering, Vol. 30, pp. 2213-2218. [16] Ghanbarpour, M., Bitaraf Haghigi, E., Khodabandeh, R. (2014). “Thermal properties and rheological behavior of water based Al2O3 nanofluid as a heat transfer fluid”. Experimental Thermal and Fluid Science, Vol. 53, pp. 227–235. [17] Toghraie, D., Chaharsoghi, V.A., Afrand, M. (2016). “Measurement of thermal conductivity of ZnO–TiO2/EG hybrid nanofluid”. Journal of Thermal Analysis and Calorimetry, Vol. 125, pp. 527-535. [18] Soltanimehr, M., Afrand, M. (2016). “Thermal conductivity enhancement of COOH-functionalized MWCNTs/ethylene glycol–water nanofluid for application in heating and cooling systems”. Applied Thermal Engineering, Vol. 105, pp. 716-723. [19] Sarbolookzadeh Harandi, S., Karimipour, A., Afrand, M., Akbari, M., D'Orazio, A. (2016). “An experimental study on thermal conductivity of F-MWCNTs–Fe3O4/EG hybrid nanofluid: Effects of temperature and concentration”. International Communications in Heat and Mass Transfer, Vol. 76, pp. 171-177. [20] Hemmat Esfe, M., Saedodin, S., Mahian, O., Wongwises, S. (2014). “Thermophysical properties, heat transfer and pressure drop of COOH-functionalized multi walled carbon nanotubes/water nanofluids”. International Communications in Heat and Mass Transfer, Vol. 58, pp. 176–183. [21] Papari, M.M., Yousefi, F., Moghadasi, J., Karimi, H., Campo, A. (2011). “Modeling thermal conductivity augmentation of nanofluids using diffusion neural networks”. International Journal of Thermal Sciences, Vol. 50, pp. 44–52. [22] Hojjat, M., Etemad, S. Gh., Bagheri, R., Thibault, J. (2011). “Thermal conductivity of non-Newtonian nanofluids: experimental data and modeling using neural network”. International Journal of Heat and Mass Transfer, Vol. 54, pp. 1017–1023. [23] Longo, G. A., Zilio, C., Ceseracciu, E., Reggiani, M. (2012) “ Application of Artificial Neural Network (ANN) for the prediction of thermal conductivity of oxide-water nanofluids”. Nano Energy, Vol. 1, pp. 290–296. [24] Hemmat Esfe, M., Saedodin, S., Bahiraei, M., Toghraie, D., Mahian, O., Wongwises, S. (2014). “Thermal conductivity modeling of MgO/EG nanofluids using experimental data and artificial neural network”. Journal of Thermal Analysis and Calorimetry, Vol. 118, pp. 287–294. [25] Afrand, M., Toghraie, D., Sina, N. (2016). “Experimental study on thermal conductivity of water-based Fe3O4 nanofluid: Development of a new correlation and modeled by artificial neural network”. International Communications in Heat and Mass Transfer, Vol. 75, pp. 262-269. | ||
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