Comparison of Neural Networks and Fuzzy System for Estimation of Heat Transfer Between Contacting Surfaces
محورهای موضوعی : Mechanical EngineeringShayan Fathi 1 , Mohammad Eftekhari 2 , Arman Adamian 3
1 - Department of Mechanical Engineering,
Central Tehran Branch, Islamic Azad University,
Tehran, Iran
2 - Department of Mechanical Engineering,
Central Tehran Branch, Islamic Azad University,
Tehran, Iran
3 - Department of Mechanical Engineering,
Central Tehran Branch, Islamic Azad University,
Tehran, Iran
کلید واژه: Thermal Contact Conductance (TCC), Electronic Chipset, ANFIS, Neural Networks,
چکیده مقاله :
Neural networks can be used in various subjects, such as the discovery of relationships, identification, system modelling, optimization and nonlinear pattern recognition. One of the interesting applications of this algorithm is heat transfer estimation between contacting surfaces. In the current investigation, a comparison study is done for temperature transfer function estimation between contacting surfaces using Group Method of Data Handling (GMDH) neural networks and ANFIS (Adaptive Neuro Fuzzy Inference System) algorithm. Different algorithms are trained and tested by means of input–output data set taken from the experimental study and the inverse solution using the Conjugate Gradient Method (CGM) with the adjoint problem. Eventually, the optimal model has been chosen based on the common error criteria of root mean square error. According to the obtained results among different models, ANFIS with gaussmf membership function has the best algorithm for identification of TCC between two contacting surfaces with 0.1283 error. Also, the inverse method has the lowest error for thermal contact conductance estimation between fixed contacting surfaces with root mean square error of 0.211.
[1] Minges, M. L., Thermal Contact Resistance, Wright-Patterson Air Force Base, A Review of the Literature, Technical Report, 1, AFML-TR-65-375, Dayton, Ohio, USA, 1966.
[2] Moore, C. J., Heat Transfer Across Surfaces, Ph.D. Dissertation, Southern Methodist University, Dallas, Texas, USA, 1967.
[3] Moore, C. J., Atkins, H., and Blum, H. A., Subject Classification Bibliography for Thermal Contact Resistance Studies, ASME, Vol. 3, No. 68, 1968, pp. 1-5.
[4] Zhu, Z., Zhang, L., Zhang, C., Li, R., and Gu, S., Experimental Investigation of Transient Contact Heat Transfer Between 300M and 5CrNiMo, International Journal of Heat Mass Transfer, Vol. 5, No. 96, 2016, pp. 451–457.
[5] Tariq, A., Asif, M., Experimental Investigation of Thermal Contact Conductance for Nominally Flat Metallic Contact, Heat Mass Transfer, Vol. 2, No. 52, 2016, pp. 291–307.
[6] Ding, C., Wang, R., Thermal Contact Conductance of Stainless Steel-GFRP Interface Under Vacuum Environment, Experimental Thermal Fluid Science, Vol. 42, No. 31, 2012, pp. 1–5.
[7] Sponagle, B., Groulx, D., Measurement of Thermal Interface Conductance at Variable Clamping Pressures Using a Steady State Method, Applied Thermal Engineering, Vol. 96, No. 31, 2016, pp. 671–681.
[8] Rosochowska, M., Measurements of Thermal Contact Conductance, Journal of Material Processing Technology, Vol. 2, No. 135, 2002, pp. 204-210.
[9] Misra, P., Nagaraju, J., An Experimental Study to Show the Effect of Thermal Stress on Thermal Contact Conductance at Sub-Megapascal Contact Pressures, Journal of Heat Transfer, Vol. 132, No. 9, 2010, pp. 94-101.
[10] Zhu, Z., Zhang, W. L., Wu, Q. K., and Gu, S. D., An Experimental Investigation of Thermal Contact Conductance of Hastelloy C-276 Based on Steady-State Heat Flux Method, International Communication of Heat Mass Transfer, Vol. 41, No. 3, 2013, pp. 63–67.
[11] Dongmei, B., Huanxin, C., and Ye. T., Influences of Temperature and Contact Pressure on Thermal Contact Resistance at Interfaces at Cryogenic Temperatures, Cryogenics, Vol. 52, No. 9, 2012, pp. 403-409.
[12] Sunil Kumar, S., Abilash, P. M., and Ramamurthi, K., Thermal Contact Conductance for Cylindrical and Spherical Contacts, Heat and Mass Transfer, Vol. 40, No. 9, 2004, pp. 679-688, DOI: 10.1007/s00231-003-0433-0.
[13] Surya K., Tariq, A., Steady State Experimental Investigation of Thermal Contact Conductance Between Curvilinear Contacts Using Liquid Crystal Thermography, International Journal of Thermal Sciences, Vol. 6, No. 118, 2017, pp. 53-68.
[14] McGee, G. R., Schankula, M. H., and Yovanovich, M. M., Thermal Resistance of Cylinder-Flat Contacts: Theoretical Analysis and Experimental Verification of a Line-Contact Model, Nuclear Engineering and Design, Vol. 86, No. 3, 1985, pp. 369-381.
[15] Asif, M., Tariq, A., Correlations of Thermal Contact Conductance for Nominally Flat Metallic Contact in Vacuum, Experimental Heat Transfer, A Journal of Thermal Energy Generation, Transport, Storage, and Conversion, Vol. 29, No. 4, 2016.
[16] Bahrami, M., Modeling of Thermal Joint Resistance for Sphere-Flat Contacts in a Vacuum, A Thesis Presented to the University of Waterloo in Fulfillment of the Thesis Requirement for the Degree of Doctor of Philosophy, 2004.
[17] Astrom, K. J., Eykhoff, P., System Identification, A Survey, Automatica, Vol. 7, No. 3, 1971, pp. 123-126.
[18] Shojaeefard, M. H., Ghaffarpour, M., and Noorpoor, A. R., Thermal Contact Analysis Using Identification Method, Heat Transfer Engineering, Vol. 29, No. 1, 2008, pp. 85–96.
[19] Goudarzi, K., Moosaei, A., and Gharaati, M., Applying Artificial Neural Networks (ANN) to the Estimation of Thermal Contact Conductance in the Exhaust Valve of Internal Combustion Engine, Applied Thermal Engineering, Vol. 3, No. 87, 2015, pp. 688-697.
[20] Motahari-Nezhad, M., Mazidi, S. M., An Adaptive Neuro-Fuzzy Inference System (ANFIS) Model for Prediction of Thermal Contact Conductance Between Exhaust Valve and Its Seat, Applied Thermal Engineering, V. 105, No. 25, 2016, pp. 613-621.
[21] Koza, J., Genetic Programming, On the Programming of Computers by means of Natural Selection, MIT Press, 1992.
[22] Iba, H., Kuita, T., Degaris, H., and Sator, T., System Identification Using Structured Genetic Algorithms, Proceeding of 5th International Conference on Genetic Algorithms, ICGA’93, USA, 1993.
[23] Porter, B., Nariman-Zadeh, N., Genetic Design of Computed-Torque Controllers for Robotic Manipulators, IASTED International Conference on Systems and Control., Switzerland, 1994.
[24] Ozisik, M. N., Orlande, H. R. B., Inverse Heat Transfer: Fundamentals and Applications, First ed, New York, 352 pages, 2000.
[25] Alifanov, O. M., Inverse Heat Transfer Problems, Springer-Verlag, Berlin, 1994, pp. 348.
[26] Beck, J.V., Blackwell, B., Clair, and C. R. St, Inverse Heat Conduction: Ill Posed Problems, Wiley, New York, 1985, pp. 308.
[27] Shojaefard, M. H., Ghaffarpour, M., and Noorpoor, A. R., Thermal Contact Analysis Using Identification Method, Heat Transfer Engineering, Vol. 29, No. 1, 2011, pp. 85-96.
[28] Kartalopoulos, S. V., Understanding Neural Networks and Fuzzy Logic-Basic Concepts and Applications, IEEE Neural Networks Council, Prentice Hall, New-Delhi, 2000.
[29] Kondo, T., Ueno, J., Multi-Layered GMDH-Type Neural Network Self-Selecting Optimum Neural Network Architecture and Its Application to 3-Dimensional Medical Image Recognition of Blood Vessels, International Journal of Innovative Computing, Information and Control, Vol. 4, No.1, 2008, pp.175-187.
[30] Kondo, T., Ueno, J., Logistic GMDH-Type Neural Network and Its Application to Identification of X-Ray Film Characteristic Curve, Journal of Advanced Computational Intelligence an Intelligent Informatics, Vol. 11, No. 3, 2007, pp. 312-318.
[31] Kondo, T., GMDH Neural Network Algorithm Using the Heuristic Self-Organization Method and its Application to the Pattern Identification Problem, Proc. of the 37th SICE Annual Conference, Vol. 23, No. 6, 1998, pp.1143-1148.
[32] Farlow, S. J., Self-Organizing Methods in Modeling, GMDH-Type Algorithm, New York: Marcel Dekker Inc, 1984.
[33] Ivakhnenko, A. G., Heuristic Self-Organization in Problems of Engineering Cybernetics, Automatica, Vol. 6, No.2, 1970, pp. 207-219.
[34] Kondo, T., Pandya, A. S., and Zurada, J. M., GMDH-Type Neural Networks with a Feedback Loop and Their Application to Nonlinear System Identification, Smart Engineering System: Neural Networks, Fuzzy Logic, Evolutionary Programing, Data Mining, and Rough Sets, ASME Press, Vol. 5, No. 9, 1999, pp.117-124.
[35] Dolenko, S. A., Orlov, Y. V., and Persiantsev, I. G., Practical Implementation and Use of Group Method of Data Handling (GMDH): Prospects and Problems, Proceedings of the ACEDC’96, University of Plymouth, UK, 1996.
[36] Ivakhnenko, A. G., Polynomial Theory of Complex Systems, IEEE Transaction on System, Man and Cybernetics, Vol. 1, No. 3, 1971, pp. 364–378.
[37] Nariman-Zadeh, N., Darvizeh, A., Darvizeh, M., and Gharababaei, H., Modelling of Explosive Cutting Process of Plates Using GMDH-Type Neural Network and Singular Value Decomposition, Journal of Materials Processing Technology, Vol. 5, No. 128, 2002, pp. 80–87.
[38] Ahmadi, M. H., Ahmadi, M. A., Mehrpooya, M., and Rosen, M. A., Using GMDH Neural Networks to Model the Power and Torque of a Stirling Engine, Sustainability, The 4th World Sustainability Forum, 2015, pp. 2243-2255, doi: 10.3390/su7022243.
[39] Bagheri, A., Nariman-Zadeh, N., Siavash, A. S., and Khoobkar, A. R., GMDH Type Neural Networks and Their Application to the Identification of the Inverse Kinematic Equations of Robotic Manipulators, International Journal of Engineering, Vol. 18, No. 2, 2005, pp. 135-143.
[40] Iba, H., DeGaris, H., and Sato, T., A Numerical Approach to Genetic Programming for System Identification, Evolutionary Computation, Vol. 3, No. 4, 1996, pp. 417–452.
[41] Sanchez, G., Frausto-Solis, A., Ojeda-Bustamante, J., Attribute Selection Impact on Linear and Nonlinear Regression Models for Crop Yield Prediction, The Scientific World Journal, Vol. 13, No. 6, 2014, pp. 1-10. doi:10.1155/2014/509429.
[42] Elçiçek, H., Akdoğan, E., and Karagöz, S., The Use of Artificial Neural Network for Prediction of Dissolution Kinetics, Science World Journal, Vol. 4, No. 3, 2014, pp. 1-9, doi:10.1155/2014/194874.
[43] Jang, J. S. R., Sun, C. T., Functional Equivalence Between Radial Basis Function Networks and Fuzzy Inference Systems, IEEE Transactions on Neural Networks, Vol. 4, No. 1, 1999, pp. 156–159.
[44] Brown, M., Harris, C., Neuro-Fuzzy Adaptive Modeling and Control, New York: Prentice-Hall, 1994.
[45] Mu-Yen Chen., A Hybrid ANFIS Model for Business Failure Prediction Utilizing Particle Swarm Optimization and Subtractive Clustering, Information Sciences, Vol. 22, No. 3, 2013, pp. 180–195.
