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Journal of Vibration Testing and System Dynamics 9(1) (2025) 77--88 | DOI:10.5890/JVTSD.2025.03.005

P. Priyadharshini, M. Vanitha Archana

Department of Mathematics, PSG College of Arts & Science, Coimbatore, Tamil Nadu, India

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Abstract

Recent progress in industries have led to nanofluids with superior thermal attributes to clear fluids. These improvements act as motivation for the present work, which emphasizes the heat and mass transfer properties of magnetohydrodynamic incompressible nanofluid flow towards a stretching sheet with the addition of thermal radiation. The mathematical model is framed in the knowledge of fundamental conversation laws and followed by transmuted into a dimensionless form employing similarity variables. The set of these converted equations are numerically treated through Wolfram language. An influence of germane parameters on boundary layer are scrutinized and graphically visualized to deliver the applicability of the present model. Furthermore, the machine learning technique for predicting the physical nature of flow is innovated as a novelty. In place of the classical simulation method, this work uncover the new technique to anticipate the physical quantities more accurately. The findings demonstrate that the thermal profile is an escalating term of the nonlinear radiation parameter. The outcomes are authenticated by comparing the current exploration with some earlier studies in certain instances and the reliability is highlighted with a aid of table format. The multiple linear regression accurately predicted the measurement of engineering concerns with the minimal error $10^{-3}$. The present optimization technique delivers a robust and intellectual perspective on industrial processes, such as solar technologies, polymer extrusion, metal processing, rubber sheet production, etc.

References

1. [1]	Ghasemi, S.E., Hatami, M., and Ganji, D.D. (2013), Analytical thermal analysis of air-heating solar collectors, Journal of Mechanical Science and Technology, 27(11), 3525–3530.

2. [2]	Amanulla, C.H., Wakif, A., and Saleem, S. (2020), Numerical study of a Williamson fluid past a semi-infinite vertical plate with convective heating and radiation effects, Diffusion Foundations, 28, 1–15.

3. [3]	Ghasemi, S.E. and Hatami, M. (2021), Solar radiation effects on MHD stagnation point flow and heat transfer of a nanofluid over a stretching sheet, Case Studies in Thermal Engineering, 25, p.100898.

4. [4]	Ghasemi, S.E., Mohsenian, S., Gouran, S., and Zolfagharian, A. (2022), A novel spectral relaxation approach for nanofluid flow past a stretching surface in presence of magnetic field and nonlinear radiation, Results in Physics, 32, p.105141.

5. [5]	Goedbloed, J.P. (Hans). (2004), Stepfaan Poedls: Principles of Magnetohydrodynamics, the Press Syndicate of the University of Combridge.

6. [6]	Rashidi, M.M., Kavyani, N., Abelman, S., and Uddm, M.J. (2014), Freidoonimehr, Double diffusive magnetohydrodynamic (MHD) mixed convective slip flow along a radiating moving vertical flat plate with convective boundary condition, Plos One, 9, 109-404.

7. [7]	Thiagarajan, M. and Dinesh Kumar, M. (2020), Viscous and ohmic heating effects on MHD flow of nanofluid past a porous stretching sheet with thermal radiation and heat generation/absorption: Copper-Alumina water, Journal of Vibration Testing and System Dynamics, 4(1), 65-78.

8. [8]	Prasannakumara, B.C., Gireesha, B.J., Krishnamurthy, M.R., and Kumar, K.G. (2017), MHD flow and nonlinear radiative heat transfer of Sisko nanofluid over a nonlinear stretching sheet, Informatics in Medicine Unlocked, 9, 123-132.

9. [9]	Irfan, M. and Farooq, M.A. (2020), Thermophoretic MHD free stream flow with variable internal heat generation/absorption and variable liquid characteristics in a permeable medium over a radiative exponentially stretching sheet, Journal of Materials Research and Technology, 9, 4855-4866.

10. [10]	Das, S.K., Choi, S.U., Yu, W., and Pradeep, T. (2007), Nanofluids: Science and Technology, Willey.

11. [11]	Choi, S.U.S. and Eastman, J.A. (1995), Enhancing thermal conductivity of fluids with nanoparticles, United States, 99-105.

12. [12]	Choi, S.U.S., Zhang, Z.G., Yu, W., Lockwood, F.E., and Grulke, E.A. (2001), Anomalously thermal conductivity enhancement in nanotube suspensions, Applied Physics Letters, 79, 2252-2254.

13. [13]	Amanulla, C.H., Nagendra, N., and Reddy, M.S. (2018), Numerical simulations on magnetohydrodynamic non-newtonian nanofluid flow over a semi-infinite vertical surface with slipeffects, Journal of Nanofluids, 7(4), 718-730.

14. [14]	Rahimah, J., Roslinda, N., and Pop, I. (2017), Flow and heat transfer of magnetohydrodynamic three-dimensional Maxwell nanofluid over a permeable stretching/shrinking surface with convective boundary conditions, International Journal of Mechanical Sciences, 124, 166-173.

15. [15]	Patil, P.M., Shashikant, A., and Momoniant, E. (2020), Transport phenomena in MHD mixed convective nanofluid flow, International Journal of Numerical Methods for Heat $\&$ Fluid Flow, 30, 769-791.

16. [16]	Nadeem, S., Haq, R.U., and Khan, Z.H. (2013), Numerical solution of non- Newtonian nanofluid flow over a stretching sheet, Applied Nanoscience, 4, 625-31.

17. [17]	Li, Y.X., Alshbool, M.H., Lv, Y.P., Khan, I., Khan, M.R., and Issakhov, A. (2021), Heat and mass transfer in MHD Williamson nanofluid flow over an exponentially porous stretching surface, Case Studies in Thermal Engineering, 26, p.100975.

18. [18]	Makinde, O.D. and Aziz, A. (2011), Boundary laye flow of a nanofluid past a stretching sheet with a convective boundary condition, International Journal of Thermal Sciences, 50, 1326-1332.

19. [19]	Fang, T. (2008), Flow and heat transfer characteristics of boundary layers over a stretching surface with a uniform-shear free stream, International Journal of Heat and Mass Transfer, 51, 2199-2213.

20. [20]	Zhang, Y., Chan, W., and Jaitly, N. (2017), Very deep convolutional networks for end-to-end speech recognition, IEEE International Conference on Acoustics, Speech and Signal Processing(ICASSP), IEEE Press, 4845-4849.

21. [21]	Wen, X., Liu, Y.Z., and Li, Z.Y. (2018), Data mining of a clean signal from highly noisy data based on compressed data fusion: a fast-responding pressure sensitive paint application, Physics of Fluids, 30, Article number 097103.

22. [22]	Hu, Q., Luo, Z.Q., Xiao, W.S., and Li, C.J. (2023), Performance comparison of cooperative spectrum sensing models based on machine learning, Journal of Vibration Testing and System Dynamics, 7(2), 153-167.

23. [23]	Brenner, M.P., Eldredge, J.D., and Freund, J.B. (2019), Perspective on machine learning for advancing fluid mechanics, Physical Review Fluids, 4(10), p.100501.

24. [24]	Rosenblatt, F. (1958), The perceptron: A probabilistic model for information storage and organization in the brain, Psychological Review, 6, 386-408.

25. [25]	Rushdi, M.A., Yoshida, S., Watanabe, K., and Ohya, Y. (2021), Machine learning approaches for thermal updraft prediction in wind solar tower systems, Renewable Energy, 177, 1001-1013.

26. [26]	Kumar, P.M. and Kavitha, R. (2020), Regression analysis and behavioral study of predictor factors on thermal conductivity of nanofluids using soft computing tool, Materials Today: Proceedings, 21, 438-444.

27. [27]	Elayarani, M., Shanmugapriya, M., and Senthil Kumar, P. (2021), Intensification of heat and mass transfer process in MHD carreau nanofluid flow containing gyrotactic microorganisms, Chemical Engineering and Processing - Process Intensification, 160, 108299.

28. [28]	Elayarani, M., Shanmugapriya, M., and Senthil Kumar, P. (2019), Estimation of magnetohydrodynamic radiative nanofluid flow over a porous non-linear stretching surface: application in biomedical research, IET Nanobiotechnol, 13(9), 911-922.

29. [29]	Hossein, A., Jafari, S.S., and Friedoonimehr, N. (2016), Analytical approximation of MHD nanofluid flow induced by a stretching permeable surface using Buongiorno's model, Ain Shams Engineering Journal, 9(4), 525-536.

30. [30]	Priyadharshini, P. and Archana, M.V. (2023), Augmentation of magnetohydrodynamic nanofluid flow through a permeable stretching sheet employing Machine learning algorithm, Examples and Counterexamples, 3, p.100093.

31. [31]	Raptis, A. (1998), Radiation and free convection flow through a porous medium, International Communications in Heat and Mass Transfer, 25, 289–295.

32. [32]	Sparrow, E.M. and Cess, R.D. (1978), Radiation Heat Transfer, Hemisphere, Washington.

33. [33]	Ghasemi, S.E. and Hatami, M. (2021), Solar radiation effects on MHD stagnation point flow and heat transfer of a nanofluid over a stretching sheet, Case Studies in Thermal Engineering, 25, 100898.

34. [34]	Wang, C.Y. (1989), Free convection on a vertical stretching surface, ZAMM- I Applied Mathematica and Mechanics/Zeitschrift for Angewandte Mathematik and Mechanik, 69, 418-420.

35. [35]	Reddy, G.R. and Sidawi, L. (1994), Free convection on a vertical stretching surface with suction and blowing, Applied Scientific Research, 52, 247-257.

36. [36]	Hayat, T., Naseema Aslam, Alsaedi, A., and Rafiq, M. (2017), Numerical study for MHD peristaltic transport of Sisko nanofluid in a curved channel, International Journal of Heat and Mass Transfer, 109, 1281-1288.

37. [37]	Aziz, A., Alsaedi, A., Muhammad, T., and Hayat, T. (2018), Numerical study for heat generation/absorption in flow of nanofluid by a rotating disk, Results in Physics, 8, 785–792.

38. [38]	Ullah, I., Alghamdi, M., Xia, W., Shah, S.I., and Khan, H. (2021), Activation energy effect on the magnetized-nanofluid flow in a rotating system considering the exponential heat source, International Communications in Heat and Mass Transfer, 128, p.105578.

39. [39]	Priyadharshini, P., Archana, M.V., Ahammad, N.A., Raju, C.S., Yook, S.J., and Shah, N.A. (2022), Gradient descent machine learning regression for MHD flow: Metallurgy process, International Communications in Heat and Mass Transfer, 138, 106307.