Physics-informed neural networks and wavelet technique for heat transfer analysis in radial porous fins with magnetic and internal heat effects
Bibliographic record
Abstract
No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.
Source: Not provided · Quality: missing
Research context
OpenAlex topics: Heat Transfer and Optimization · Nanofluid Flow and Heat Transfer · Model Reduction and Neural Networks
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (45)
Show source-provided references
1. Arslanturk, C.: Correlation equations for optimum design of annular fins with temperature dependent thermal conductivity. Heat Mass Transf. 45, 519–525 (2009)10.1007/s00231-008-0446-9 ↗
2. Atouei, S.A., Hosseinzadeh, K., Hatami, M., Ghasemi, S.E., Sahebi, S.A.R., Ganji, D.D.: Heat transfer study on convective–radiative semi-spherical fins with temperature-dependent properties and heat generation using efficient computational methods. Appl. Therm. Eng. 89, 299–305 (2015)10.1016/j.applthermaleng.2015.05.084 ↗
3. Aziz, A. (2006). Advanced heat conduction theory with a symbolic algebra package. Computational methods. Springer, 829–48.
4. Aziz, A., Fang, T.: Thermal analysis of an annular fin with (a) simultaneously imposed base temperature and base heat flux and (b) fixed base and tip temperatures. Energy Convers. Manag. 52(7), 2467–2478 (2011)10.1016/j.enconman.2011.02.004 ↗
5. Aziz, A., Torabi, M., Zhang, K.: Convective–radiative radial fins with convective base heating and convective–radiative tip cooling: homogeneous and functionally graded materials. Energy Convers. Manag. 74, 366–376 (2013)10.1016/j.enconman.2013.05.034 ↗
6. Bai, J., Lin, Z., Wang, Y., Wen, J., Liu, Y., Rabczuk, T., Feng, X.Q.: Energy-based physics-informed neural network for frictionless contact problems under large deformation. Comput. Methods Appl. Mech. Eng. 437, 117787 (2025)10.1016/j.cma.2025.117787 ↗
7. Bararnia, H., Esmaeilpour, M.: On the application of physics informed neural networks (PINN) to solve boundary layer thermal-fluid problems. Int. Commun. Heat Mass Transf. 132, 105890 (2022)10.1016/j.icheatmasstransfer.2022.105890 ↗
8. Baslem, A., Sowmya, G., Gireesha, B.J., Prasannakumara, B.C., Rahimi-Gorji, M., Hoang, N.M.: Analysis of thermal behavior of a porous fin fully wetted with nanofluids: convection and radiation. J. Mol. Liq. 307, 112920 (2020)10.1016/j.molliq.2020.112920 ↗
9. Berkhahn, S., Ehrhardt, M.: A physics-informed neural network to model COVID-19 infection and hospitalization scenarios. Adv. Contin. Discrete Models 2022(1), 61 (2022)10.1186/s13662-022-03733-5 ↗
10. Chiu, C.H., Chen, C.K.: Application of the decomposition method to thermal stresses in isotropic circular fins with temperature-dependent thermal conductivity. Acta Mech. 157(1), 147–158 (2002)10.1007/BF01182160 ↗
11. Chiu, P.H., Wong, J.C., Ooi, C., Dao, M.H., Ong, Y.S.: CAN-PINN: a fast physics-informed neural network based on coupled-automatic–numerical differentiation method. Comput. Methods Appl. Mech. Eng. 395, 114909 (2022)10.1016/j.cma.2022.114909 ↗
12. Chu, H.P., Chen, C.L.: Hybrid differential transform and finite difference method to solve the nonlinear heat conduction problem. Commun. Nonlinear Sci. Numer. Simul. 13(8), 1605–1614 (2008)10.1016/j.cnsns.2007.03.002 ↗
13. Culham, J.R., Teertstra, P., Yovanovich, M.M.: Natural convection modeling of heat sinks using web-based tools. Electron. Cool. 6, 44–51 (2000)
14. Eshaghi, M.S., Anitescu, C., Thombre, M., Wang, Y., Zhuang, X., Rabczuk, T.: Variational physics-informed neural operator (VINO) for solving partial differential equations. Comput. Methods Appl. Mech. Eng. 437, 117785 (2025)10.1016/j.cma.2025.117785 ↗
15. Faheem, M., Raza, A., Khan, A.: Collocation methods based on Gegenbauer and Bernoulli wavelets for solving neutral delay differential equations. Math. Comput. Simul 180, 72–92 (2021)10.1016/j.matcom.2020.08.018 ↗
16. Gireesha, B.J., Gowtham, K.J.: Efficient hypergeometric wavelet approach for solving lane-emden equations. J. Comput. Sci. 82, 102392 (2024)10.1016/j.jocs.2024.102392 ↗
17. Gireesha, B.J., Sowmya, G., Srikantha, N.: Heat transfer in a radial porous fin in the presence of magnetic field: a numerical study. Int. J. Ambient Energy 43(1), 3402–3409 (2022)10.1080/01430750.2020.1831599 ↗
18. Gorla, R.S.R., Bakier, A.Y.: Thermal analysis of natural convection and radiation in porous fins. Int. Commun. Heat Mass Transf. 38(5), 638–645 (2011)10.1016/j.icheatmasstransfer.2010.12.024 ↗
19. Gupta, S., Ranta, S.: Legendre wavelet based numerical approach for solving a fractional eigenvalue problem. Chaos Solitons Fractals 155, 111647 (2022)10.1016/j.chaos.2021.111647 ↗
20. Hatami, M., Ganji, D.D.: Investigation of refrigeration efficiency for fully wet circular porous fins with variable sections by combined heat and mass transfer analysis. Int. J. Refrig. 40, 140–151 (2014)10.1016/j.ijrefrig.2013.11.002 ↗
21. Hoshyar, H.A., Ganji, D.D., Majidian, A.R.: Least square method for porous fin in the presence of uniform magnetic field. J. Appl. Fluid Mech. 9(2), 661–668 (2016)10.18869/acadpub.jafm.68.225.24245 ↗
22. Ji, W., Qiu, W., Shi, Z., Pan, S., Deng, S.: Stiff-pinn: Physics-informed neural network for stiff chemical kinetics. J. Phys. Chem. A 125(36), 8098–8106 (2021)10.1021/acs.jpca.1c05102 ↗
23. Keshavarz, E., Ordokhani, Y., Razzaghi, M.: The Taylor wavelets method for solving the initial and boundary value problems of Bratu-type equations. Appl. Numer. Math. 128, 205–216 (2018)10.1016/j.apnum.2018.02.001 ↗
24. Khani, F., Darvishi, M.T., Gorla, R.R., Gireesha, B.J.: Thermal analysis of a fully wet porous radial fin with natural convection and radiation using the spectral collocation method. Int. J. Appl. Mech. Eng. 21(2), 377–392 (2016)10.1515/ijame-2016-0023 ↗
25. Kiwan, S.: Thermal analysis of natural convection porous fins. Transp. Porous Media 67, 17–29 (2007)10.1007/s11242-006-0010-3 ↗
26. Kraus, A.D., Aziz, A., Welty, J., Sekulic, D.P.: Extended surface heat transfer. Appl. Mech. Rev. 54(5), B92–B92 (2001)10.1115/1.1399680 ↗
27. Kundu, B.: Analysis of thermal performance and optimization of concentric circular fins under dehumidifying conditions. Int. J. Heat Mass Transf. 52(11–12), 2646–2659 (2009)10.1016/j.ijheatmasstransfer.2008.12.017 ↗
28. Kundu, B., Barman, D.: Analytical study on design analysis of annular fins under dehumidifying conditions with a polynomial relationship between humidity ratio and saturation temperature. Int. J. Heat Fluid Flow 31(4), 722–733 (2010)10.1016/j.ijheatfluidflow.2010.01.010 ↗
29. Kundu, B., Das, P.K.: Performance analysis and optimization of annular fin with a step change in thickness. J. Heat Transfer 123(3), 601–604 (2001)10.1115/1.1351165 ↗
30. Patel, T., Meher, R.: Thermal analysis of porous fin with uniform magnetic field using Adomian decomposition Sumudu transform method. Nonlinear Eng. 6(3), 191–200 (2017)10.1515/nleng-2017-0021 ↗
31. Peng, H.S., Chen, C.L.: Hybrid differential transformation and finite difference method to annular fin with temperature-dependent thermal conductivity. Int. J. Heat Mass Transf. 54(11–12), 2427–2433 (2011)10.1016/j.ijheatmasstransfer.2011.02.019 ↗
32. Raza, J., Raza, M., Mustaq, T., Qureshi, M.I.: Supervised machine learning techniques for optimization of heat transfer rate of Cu-H2O nanofluid flow over a radial porous fin. Multidiscip. Model. Mater. Struct. 19(4), 680–706 (2023)10.1108/MMMS-08-2022-0153 ↗
33. Rostami, Y.: An effective computational approach based on Hermite wavelet Galerkin for solving parabolic Volterra partial integro differential equations and its convergence analysis. Math. Model. Anal. 28(1), 163–179 (2023)10.3846/mma.2023.15690 ↗
34. Samaniego, E., Anitescu, C., Goswami, S., Nguyen-Thanh, V.M., Guo, H., Hamdia, K., Rabczuk, T.: An energy approach to the solution of partial differential equations in computational mechanics via machine learning: concepts, implementation and applications. Comput. Methods Appl. Mech. Eng. 362, 112790 (2020)10.1016/j.cma.2019.112790 ↗
35. Schnurr, N.M., Cothran, C.A.: Radiation from an array of gray circular fins of trapezoidal profile. AIAA J. 12(11), 1476–1480 (1974)10.2514/3.49532 ↗
36. Shiralashetti, S.C., Angadi, L.M., Deshi, A.B., Kantli, M.H.: Haar wavelet method for the numerical solution of Klein–Gordan equations. Asian-Eur. J. Math. 9(01), 1650012 (2016)10.1142/S1793557116500121 ↗
37. Sowmya, G., Gireesha, B.J., Prasannakumara, B.C.: Scrutinization of different shaped nanoparticle of molybdenum disulfide suspended nanofluid flow over a radial porous fin. Int. J. Numer. Methods Heat Fluid Flow 30(7), 3685–3699 (2019)10.1108/HFF-08-2019-0622 ↗
38. Sowmya, G., Gireesha, B.J., Madhu, M.: Analysis of a fully wetted moving fin with temperature-dependent internal heat generation using the finite element method. Heat Transf. 49(4), 1939–1954 (2020)10.1002/htj.21701 ↗
39. Srivastava, H.M., Shah, F.A., Nayied, N.A.: Fibonacci wavelet method for the solution of the non-linear Hunter-Saxton equation. Appl. Sci. 12(15), 7738 (2022)10.3390/app12157738 ↗
40. Sun, J., Liu, Y., Wang, Y., Yao, Z., Zheng, X.: BINN: a deep learning approach for computational mechanics problems based on boundary integral equations. Comput. Methods Appl. Mech. Eng. 410, 116012 (2023)10.1016/j.cma.2023.116012 ↗
41. Wang, Y., Sun, J., Rabczuk, T., Liu, Y.: DCEM: a deep complementary energy method for linear elasticity. Int. J. Numer. Methods Eng. 125(24), e7585 (2024)10.1002/nme.7585 ↗
42. Wang, Y., Bai, J., Eshaghi, M.S., Anitescu, C., Zhuang, X., Rabczuk, T., Liu, Y.: Transfer learning in physics‐informed neurals networks: full fine‐tuning, lightweight fine‐tuning, and low‐rank adaptation. Int. J. Mech. Syst. Dyn. (2025). https://doi.org/10.1002/msd2.7003010.1002/msd2.70030 ↗
43. Yadav, S.S., Kumar, A.: Numerical solution of convection-diffusion equation using Haar wavelet collocation method with Neumann’s boundary conditions. Elem. Educ. Online 20(6), 2505–2513 (2021)
44. Yan, J., Zhang, H., Qian, X., Chen, X., Song, S.: A novel regularized model for the logarithmic Klein-Gordon equation. Appl. Numer. Math. 176, 19–37 (2022)10.1016/j.apnum.2022.02.007 ↗
45. Zarling, J. P., & Haynes, F. D. (1991, May). Thermosiphon-based designs and applications for foundations built on permafrost. In SPE International Arctic Technology Conference (pp. SPE-22114). SPE.10.2118/22114-MS ↗