Self-learning finite elements for inverse estimation of thermal constitutive models

International Journal of Heat and Mass Transfer

Volumn 49, Issue 15-16, 2006, Pages 2466-2478

  Aquino, Wilkins ^a   Brigham, John C ^a  

^a Cornell University   (United States)

Author keywords

Autoprogressive neural networks; Finite elements; Heat transfer; Inverse problems; Neural networks; Self learning finite elements

Indexed keywords

FINITE ELEMENT METHOD; INVERSE PROBLEMS; ITERATIVE METHODS; MATHEMATICAL MODELS; NEURAL NETWORKS;

AUTOPROGRESSIVE NEURAL NETWORKS; SELF-LEARNING FINITE ELEMENTS; THERMAL CONSTITUTIVE MODELS;

HEAT CONDUCTION;

FINITE ELEMENT METHOD; HEAT CONDUCTION; INVERSE PROBLEMS; ITERATIVE METHODS; MATHEMATICAL MODELS; NEURAL NETWORKS;

EID: 33747433423     PISSN: 00179310     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijheatmasstransfer.2006.01.031     Document Type: Article

References (23)

1. Scarpa F., Milano G., and Pescetti D. Thermophysical properties estimation form transient data: Kalman versus Gauss approach. Proceedings of the 1st International Conference on Inverse Problems in Engineering: Theory and Practice (1993), ASME 109-116
2. Lin J.H., Chen C.K., and Yang Y.T. Inverse method for estimating thermal conductivity in one-dimensional heat conduction problems. J. Thermophys. Heat Transfer 15 (2001) 34-41
3. Huang C.H., and Chin S.C. A two-dimensional inverse problem in imaging the thermal conductivity of a non-homogeneous medium. Int. J. Heat Mass Transfer 43 (2000) 4061-4071
4. Alifanov O.M. Inverse Heat Transfer Problems (1994), Springer-Verlag, Berlin, New York
5. Tadi M. Evaluation of a two-dimensional conductivity function based on boundary measurements. J. Heat Transfer-Trans. ASME 122 (2000) 367-371
6. Beck J.V. Inverse Heat Conduction: Ill-Posed Problems (1985), Wiley, New York
7. Ashforth-Frost S., Fontama V.N., Jambunathan K., and Hartle S.L. Role of neural networks in fluid mechanics and heat transfer. Proceedings of the 1995 IEEE Instrumentation and Measurement Technology Conference vol. 6-9 (1995), IEEE, Piscataway, NJ, USA
8. Scalabrin G., and Piazza L. Analysis of forced convection heat transfer to supercritical carbon dioxide inside tubes using neural networks. Int. J. Heat Mass Transfer 46 (2003) 1139-1154
9. Diaz G., Sen M., Yang K.T., and McClain R.L. Dynamic prediction and control of heat exchangers using artificial neural networks. Int. J. Heat Mass Transfer 44 (2001) 1671-1679
10. Jambunathan K., Hartle S.L., Ashforth-Frost S., and Fontama V.N. Evaluating convective heat transfer coefficients using neural networks. Int. J. Heat Mass Transfer 39 (1996) 2329-2332
11. Zbicinski I., Strumillo P., and Kaminski W. Hybrid neural model of thermal drying in a fluidized bed. Comput. Chem. Eng. 20 (1996) S695-S700
12. Alvarez E., Correa J.M., Riverol C., and Navaza J.M. Model based in neural networks for the prediction of the mass transfer coefficients in bubble columns. Study in Newtonian and non-Newtonian fluids. Int. Commun. Heat Mass Transfer 27 (2000) 93-98
13. Larachi F., Belfares L., and Grandjean B.P.A. Prediction of liquid-solid wetting efficiency in trickle flow reactors. Int. Commun. Heat Mass Transfer 28 (2001) 595-603
14. Divo E., Kassab A., and Rodriguez F. Characterization of space dependent thermal conductivity with a BEM-based genetic algorithm. Numer. Heat Transfer Part A-Appl. 37 (2000) 845-875
15. Ghaboussi J., Pecknold D.A., Zhang M.F., and Haj-Ali R.M. Autoprogressive training of neural network constitutive models. Int. J. Numer. Methods Eng. 42 (1998) 105-126
16. Reed R.D., and Marks R.J. Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks (1999), The MIT Press, Cambridge, MA
17. Ghaboussi J., Garrett J.H., and Wu X. Knowledge-Based Modeling of Material Behavior with Neural Networks. J. Eng. Mech.-ASCE 117 (1991) 132-153
18. Sidarta D.E., and Ghaboussi J. Constitutive modeling of geomaterials from non-uniform material tests. Comput. Geotech. 22 (1998) 53-71
19. Furukawa T., and Yagawa G. Implicit constitutive modelling for viscoplasticity using neural networks. Int. J. Numer. Methods Eng. 43 (1998) 195-219
20. Hashash Y.M.A., Jung S., and Ghaboussi J. Numerical implementation of a neural network based material model in finite element analysis. Int. J. Numer. Methods Eng. 59 (2004) 989-1005
21. Cardaliaguet P., and Euvrard G. Approximation of a function and its derivative with a neural network. Neural Networks 5 (1992) 207-220
22. Joghataie A., Ghaboussi J., and Wu X. Learning and architecture determination through automatic node generation. Proceedings of the 1995 Artificial Neural Networks in Engineering, ANNIE'95 vol. 5 (1995), ASME 45-50
23. Stelzer J.F., and Welzel R. Experiences in nonlinear-analysis of temperature-fields with finite-elements. Int. J. Numer. Methods Eng. 24 (1987) 59-73