Journal of Applied and Computational Mechanics

A Deep Learning Approach to Predict the Flow Field and Thermal ‎Patterns of Nonencapsulated Phase Change Materials ‎Suspensions in an Enclosure‎

Document Type : Research Paper

Authors

1 Department of Electrical Engineering, Arak Branch, Islamic Azad University‎, Arak, Iran

2 Faculty of Electrical Engineering, Arak University of Technology, Arak, Iran‎

Abstract

The flow and heat transfer of a novel type of functional phase change nanofluids, nano-‎encapsulated phase change suspensions, is investigated in the present study using a deep neural ‎networks framework. A deep neural network was used to learn the natural convection flow and ‎heat transfer of the phase change nanofluid in an enclosure. A dataset of flow and heat transfer ‎samples containing 3290 samples of the flow field and temperature distributions was used to ‎train the deep neural network. The design variables were fusion temperature of nanoparticles, ‎Stefan number, and Rayleigh number. The results showed that the proposed combination of a ‎feed-forward neural network and a convolutional neural network as a deep neural network could ‎robustly learn the complex physics of flow and heat transfer of phase change nanofluids. The ‎trained neural network could estimate the flow and heat transfer without iterative and costly ‎numerical computations. The present neural network framework is a promising tool for the design ‎and prediction of complex physical systems‎.

Keywords

Main Subjects

Publisher’s Note Shahid Chamran University of Ahvaz remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

1. Izadi S., Armaghani T., Ghasemiasl R., Chamkha A.J., Molana M. A comprehensive review on mixed convection of nanofluids in various shapes of enclosures, Powder Technology, 2019, 343, 880-907.
2. Sajid M.U., Ali H.M. Recent advances in application of nanofluids in heat transfer devices: a critical review, Renewable and Sustainable Energy Reviews, 2019, 103, 556-92.
3. Sadeghi H.M., Babayan M., Chamkha A. Investigation of using multi-layer PCMs in the tubular heat exchanger with periodic heat transfer boundary condition, International Journal of Heat and Mass Transfer, 2020, 147, 118970.
4. Tayebi T., Chamkha A.J. Magnetohydrodynamic natural convection heat transfer of hybrid nanofluid in a square enclosure in the presence of a wavy circular conductive cylinder, Journal of Thermal Science and Engineering Applications, 2020, 12(3), 031009.
5. Selimefendigil F., Öztop H.F., Chamkha A.J. Role of magnetic field on forced convection of nanofluid in a branching channel, International Journal of Numerical Methods for Heat & Fluid Flow, 2020, 30(4), 1755-1772.
6. Sheremet M.A., Öztop H.F., Abu-Hamdeh N. Thermogravitational convection of Al2O3-H2O nanoliquid in a square chamber with intermittent blocks, International Journal of Numerical Methods for Heat & Fluid Flow, 2020, 30(3), 1365-1378.
7. Bondarenko D.S., Sheremet M.A., Oztop H.F., Ali M.E. Natural convection of Al2O3/H2O nanofluid in a cavity with a heat-generating element, Heatline visualization, International Journal of Heat and Mass Transfer, 2019, 130, 564-74.
8. Alsabery A.I., Ismael M.A., Chamkha A.J., Hashim I. Impact of finite wavy wall thickness on entropy generation and natural convection of nanofluid in cavity partially filled with non-Darcy porous layer, Neural Computing and Applications, 2020, 1-21.
9. Dogonchi A., Sheremet M.A., Ganji D., Pop I. Free convection of copper–water nanofluid in a porous gap between hot rectangular cylinder and cold circular cylinder under the effect of inclined magnetic field, Journal of Thermal Analysis and Calorimetry, 2019, 135(2), 1171-84.
10. Selimefendigil F., Chamkha A.J. MHD mixed convection of nanofluid in a three-dimensional vented cavity with surface corrugation and inner rotating cylinder, International Journal of Numerical Methods for Heat & Fluid Flow, 2020, 30(4), 1637-1660.
11. Selimefendigil F., Oztop H.F., Sheremet M.A., Abu-Hamdeh N. Forced convection of Fe3O4-water nanofluid in a bifurcating channel under the effect of variable magnetic field, Energies, 2019, 12(4), 666.
12. Molana M., Dogonchi A., Armaghani T., Chamkha A.J., Ganji D., Tlili I. Investigation of hydrothermal behavior of Fe3O4-H2O nanofluid natural convection in a novel shape of porous cavity subjected to magnetic field dependent (MFD) viscosity, Journal of Energy Storage, 2020, 30, 101395.
13. Selimefendigil F., Chamkha A.J. MHD mixed convection of Ag–MgO/water nanofluid in a triangular shape partitioned lid-driven square cavity involving a porous compound, Journal of Thermal Analysis and Calorimetry, 2020, 1-18.
14. Ghalambaz M., Chamkha A.J., Wen D. Natural convective flow and heat transfer of nano-encapsulated phase change materials (NEPCMs) in a cavity, International Journal of Heat and Mass Transfer, 2019, 138, 738-49.
15. Håstad J., Goldmann M. On the power of small-depth threshold circuits, Computational Complexity, 1991, 1(2), 113-29.
16. Farimani A.B., Gomes J., Pande V.S. Deep learning the physics of transport phenomena, arXiv preprint, arXiv: 170902432, 2017.
17. Seyfi B., Rassoli A., Imeni M., Fatouraee N. Characterization of the Nonlinear Biaxial Mechanical Behavior of Human Ureter Using Constitutive Modeling and Artificial Neural Networks, Journal of Applied and Computational Mechanics, 2020, doi: 10.22055/JACM.2020.33703.2272.
18. Alzghoul A., Jarndal A., Alsyouf I., Bingamil A.A., Ali M.A., Albaiti S. On the Usefulness of Pre-processing Methods in Rotating‎ Machines Faults Classification using Artificial Neural Network, Journal of Applied and Computational Mechanics, 2021, 7(1), 254-261.
19. Taheri M.H., Askari N., Mahdavi M.H. Prediction of entrance length for magnetohydrodynamics channels flow using numerical simulation and artificial neural network, Journal of Applied and Computational Mechanics, 2020, 6(3), 582-92.
20. Ermis K., Erek A., Dincer I. Heat transfer analysis of phase change process in a finned-tube thermal energy storage system using artificial neural network, International Journal of Heat and Mass Transfer, 2007, 50(15), 3163-75.
21. Azwadi C.S.N., Zeinali M., Safdari A., Kazemi A. Adaptive-Network-Based Fuzzy Inference System Analysis to Predict the Temperature and Flow Fields in a Lid-Driven Cavity, Numerical Heat Transfer, Part A: Applications, 2013, 63(12), 906-20.
22. Selimefendigil F., Akbulut Y., Sengur A., Oztop H.F. MHD conjugate natural convection in a porous cavity involving a curved conductive partition and estimations by using Long Short-Term Memory Networks, Journal of Thermal Analysis and Calorimetry, 2020, 140(3), 1457-68.
23. Zhou S., Liu X., Du G., Liu C., Zhou Y. Comparison study of CFD and artificial neural networks in predicting temperature fields induced by natural convention in a square enclosure, Thermal Science, 2019, 23, 3481-92.
24. Akbari E., Karami A., Nazari S., Ashjaee M. An intelligent integrated approach of Jaya optimization algorithm and neuro-fuzzy network to model the natural convection in an open round cavity, International Journal of Modelling and Simulation, 2020, 40(2), 87-103.
25. Agarap A.F. Deep learning using rectified linear units (relu), arXiv preprint, arXiv: 180308375, 2018.
26. Krizhevsky A., Sutskever I., Hinton G.E., editors. Imagenet classification with deep convolutional neural networks, Advances in neural information processing systems, 2012.
27. Ioffe S., Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift, arXiv preprint, arXiv: 150203167, 2015.
28. Oliphant T.E. A guide to NumPy, Trelgol Publishing, USA, 2006.
29. Van Der Walt S., Schönberger J., Nunez-Iglesias J., Boulogne F., Warner J., Yager N., et al. scikit-image: image processing in Python, PeerJ, 2014, 2, 453.
30. Hunter J.D. Matplotlib: A 2D graphics environment, Computing in Science & Engineering, 2007, 9(3), 90.
31. Chollet F., Others. Keras. https://keras.io; 2015.
32. Martín A., Ashish A., Barham P., Brevdo E, Chen Z., Citro C., et al. TensorFlow : Large-Scale Machine Learning on Heterogeneous Systems, 2015.
33. Kingma D.P., Ba J. Adam: A method for stochastic optimization, arXiv preprint, arXiv: 14126980, 2014.
Journal of Applied and Computational Mechanics
Volume 8, Issue 4
October 2022
Pages 1270-1278