Mathematical modeling of thermal contact resistance for different curvature contacting geometries using a robust approach

Document Type : Article


School of Mechanical Engineering, Iran University of Science and Technology, Tehran, P.O. Box 16765-163, Iran.


Nowadays having deep knowledge on thermal contact conductance (TCC) and thermal conduct resistance (TCR) existed between various type metals is interesting during heat transfer occurrence in the nuclear reactor, thermal control system of spacecraft, and heat exchangers. In this present contribution, artificial neural network (ANN) coupled with multi-layer perceptron (MLP) modeling was utilized for the prediction of transient temperature contour in various contacting surface such as flat-flat, flat-cylinder, cylinder-cylinder. In order to develop accurate transient model, position, time, and roughness parameter of metal was used as input parameter and temperature of solid bodies decided as target parameter of model. Modeling results indicates that ANN base modeling show great accuracy in comparison with other numerical methods. Also, values of average absolute relative deviation (AARD), coefficient of determination (R2) for the overall data is 0.056 and 0.996 respectively which clarifies the accuracy and robustness of the proposed model.


Main Subjects

1. Kumar, S. and Tariq, A. Steady state experimental investigation of thermal contact conductance between curvilinear contacts using liquid crystal thermography", Int. J. Therm. Sci., 118, pp. 53-68 (2017). 2. Shojaeefard, M.H. and Goudarzi, K. The numerical estimation of thermal contact resistance in contacting surfaces", Am. J. Appl. Sci., 5(11), pp. 1566-1571 (2008). 3. Wang, S., Xie, T., and Xie, H. Experimental study of the e_ects of the thermal contact resistance on the performance of thermoelectric generator", Appl. Therm. Eng., 130, pp. 847-853 (2018). 4. Seok, J., Kim, D., and Kim, S. Overall thermal conductance and thermal contact resistance in noinsulation REBCO magnet", IEEE Trans. Appl. Supercond., 28(3), pp.1-5 (2018). 5. Clausing, A.M. and Chao, B. Thermal contact resistance in a vacuum environment", J. Heat Transfer, 87(2), pp. 243-250 (1965). 6. Marotta, E.E., Fletcher, L.S., and Dietz, T.A. Thermal contact resistance modeling of non-at, roughened surfaces with non-metallic coatings", J. Heat Transfer, 123(1), pp. 11-23 (2001). 7. Mikic, B. and Rohsenow, W. Thermal contact resistance", DSR 74542-41, Mech. Eng. Department, MIT (1966). 8. Thomas, T. and Sayles, R. Random process analysis of e_ects of waviness on thermal contact resistance", ASME Conf. on Thermophys. Heat Transfer, pp. 674- 691 (1975). 9. Padilha, R.S. An analytical method to estimate spatially-varying thermal contact conductance using the reciprocity functional and the integral transform methods: Theory and experimental validation", Int. J. Heat Mass Transfer, 100, pp. 599-607 (2016). 10. Shojaeefard, M.H. and Goudarzi, K. The numerical estimation of thermal contact resistance in contacting surfaces", Am. J. Appl. Sci., 5(11), pp. 1566-1571 (2008). 11. Prasher, R. Acoustic mismatch model for thermal contact conductance of van der Waals contacts under static force", Nanoscale and Microscale Thermophys. Eng., 22(1), pp. 1-5 (2018). 12. Hemmat Esfe, M., Wongwises, S., Esfandeh, S., and Alirezaei, A. Development of a new correlation and post processing of heat transfer coe_cient and pressure drop of functionalized COOH MWCNT nanouid by arti_cial neural network", Curr. Nanosci., 14(2), pp. 104-112 (2018). 13. Hemmat Esfe, M., Ahmadi Nadooshan, A., Arshi, A., and Alirezaei, A. Convective heat transfer and pressure drop of aqua based TiO2 nanouids at different diameters of nanoparticles: Data analysis and modeling with arti_cial neural network", Physica E, 97, pp. 155-161 (2018). Shojaeefard and Tafazzoli Aghvami/Scientia Iranica, Transactions B: Mechanical Engineering 26 (2019) 2865{2871 2871 14. Abdollahi, A. and Shams, M. Arti_cial neural network modeling of a deector in a grooved channel as well as optimization of its e_ective parameters", Heat Mass Transfer, 54(1), pp. 59-68 (2018). 15. Cook, G.E. Weld modeling and control using arti_cial neural networks", IEEE. Trans. Ind. Appl., 31(6), pp. 1484-1491 (1995). 16. Hojjat, M. Modeling heat transfer of non-Newtonian nanouids by using hybrid ANN-metaheuristic optimization algorithm", J. Part. Sci. Tech., 12(3), pp. 45-54 (2018). 17. Hemmat Esfe, M., Abbasian Arani, A.A., Sha_ei Badi, R., and Rejvani, M. ANN modeling, cost performance and sensitivity analyzing of thermal conductivity of DWCNT-SiO2/EG hybrid nanouid for higher heat transfer", J. Therm. Anal. Calorim., 131(3), pp. 2381- 2393 (2018). 18. Tafarroj, M.M. Arti_cial neural network modeling of nanouid ow in a microchannel heat sink using experimental data", Int. Commun. Heat Mass Transfer, 86, pp. 25-31 (2017). 19. Ghahdarijani, A.M., Hormozi, F., and Asl, A.H. Convective heat transfer and pressure drop study on nanouids in double-walled reactor by developing an optimal multilayer perceptron arti_cial neural network", Int. Commun. Heat Mass Transfer, 84, pp. 11- 19 (2017). 20. Rumelhart, D.E., McClelland, J.L., and Group, P.R. Parallel Distributed The MIT Press, Cambridge, MA (1986). The MIT Press, Cambridge, MA (1986).