Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31181
Application of Neural Networks to Predict Changing the Diameters of Bubbles in Pool Boiling Distilled Water

Authors: V. Nikkhah Rashidabad, M. Manteghian, M. Masoumi, S. Mousavian, D. Ashouri

Abstract:

In this research, the capability of neural networks in  modeling and learning complicated and nonlinear relations has been  used to develop a model for the prediction of changes in the diameter  of bubbles in pool boiling distilled water. The input parameters used  in the development of this network include element temperature, heat  flux, and retention time of bubbles. The test data obtained from the  experiment of the pool boiling of distilled water, and the  measurement of the bubbles form on the cylindrical element. The  model was developed based on training algorithm, which is  typologically of back-propagation type. Considering the correlation  coefficient obtained from this model is 0.9633. This shows that this  model can be trusted for the simulation and modeling of the size of  bubble and thermal transfer of boiling.

Keywords: Neural Network, Heat Flux, bubble diameter, training algorithm

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1336176

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1203

References:


[1] Nukiyama,S., 1934, "The maximum and minimum values of heat transmitted from metal to boiling water under atmospheric pressure", J.Japan Soc. Mech. Eng., 37, 367
[2] Kutateladze, S. S., Gogonin, I. I., 1979, "Growth velocity and detachment diameter of vapor bubbles of various fluids under free convection conditions", Teplofizika Visokikh(in Russian), 4, PP.792- 797
[3] Mukherejee, A., Kandlikar, S.G., 2006. "Numerical study of single bubbles with dynamic contact angle during nucleate pool boiling", Int. J. Heat and Mass Transfer, 5.
[4] Jamialahmadi, M., Blochl,R., Muller-Steinhagen, H., 1991 "Pool boiling heat transfer to saturated water and refrigerant 113", The Can. J. of Chemical Eng., 69, PP.746-754
[5] Ng., G.W. "Application of Neural Networks to Adaptive Control of Nonlinear Systems.” Willey & Sons Inc., NY 198. (1997) .
[6] McCulloch, W. W. and Pitts, W. "A Logical Calculus of Ideas Imminent in Nervous Activity.” Bull. Math. Biophys. 5: 115 -133. (1943).
[7] Bellos, G. D., Kallinikos, L. E., Gounaris, C. E., and Papayannakos, N. G. "Modeling of the performance of industrial HDS reactors using a hybrid neural network approach.” Chem. Eng. and Proc. (2005). 44:505– 515.
[8] Rumelhart, D., and J. McClelland. "Parallel Distributed Processing.” MIT Press, Cambridge, Mass. (1986).
[9] Moral, H., Aksoy, A., and Gokcay, C. F. "Modeling of the activated sludge process by using artificial neural networks with automated architecture screening.” Comp. and Chem. Eng. 32: 2471–2478. (2007).
[10] Hornik, K., Stinchombe, J., and White, H. "Multilayer feed forward networks are universal approximators.” Neural Networks 2: 359–366. (1989).
[11] Stuart Russell, Peter Norvig, Artificial Intelligence A Modern Approach, Published by Pearson Education, Inc., New Jersey 2nd ed,1-100,2003.