Adaptive neuro-fuzzy inference system prediction of thermal transport in Casson ternary hybrid nanofluid thin film flow for biomedical applications

Maddina Dinesh Kumar; S. Mamatha Upadhya; Nehad Ali Shah; C. S. K. Raju; Se-Jin Yook; Maddina Dinesh Kumar; S. Mamatha Upadhya; Nehad Ali Shah; C. S. K. Raju; Se-Jin Yook

doi:10.3934/math.20251204

AIMS Mathematics

2025, Volume 10, Issue 11: 27381-27411. doi: 10.3934/math.20251204

Previous Article Next Article

Research article Special Issues

Adaptive neuro-fuzzy inference system prediction of thermal transport in Casson ternary hybrid nanofluid thin film flow for biomedical applications

1.
School of Mechanical Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea
2.
Faculty of Mathematics, Institute of Management, Kristu Jayanti University, Kothanur, Bengaluru, Karnataka 560077, India
3.
Department of Mechanical Engineering, Sejong University, Seoul 05006, South Korea
4.
Department of Mathematics, School of Computer Science and Artificial Intelligence, SR University, Warangal-506371, Telangana, India
^†These authors contributed equally to this work and are co-first authors.

Received: 17 September 2025 Revised: 01 November 2025 Accepted: 10 November 2025 Published: 25 November 2025
MSC : 76A02, 76A05, 76A20, 76W05

In this work, the adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) were used to anticipate the behavior of heat transfer in ternary hybrid nanofluid flow through thin films. For intricate heat transfer processes in nanofluid applications, the combined ANFIS-PSO model improved forecast accuracy. The simulated PDEs were converted into ODEs by varying similarity factors. A ternary hybrid nanofluid across the surface, radiation, heat source/sink, and non-uniform magnetic field were used to theoretically examine the unique properties of unstable thin film flow; nanoparticles, namely $ MWCNT, SWCNT $, and $ Ti{O_2} $, were used in both the Casson and non-Casson scenarios, using the base fluid, blood. The ANFIS-PSO models were trained using the numerical results from MATLAB's built-in BVP4C function to control the complexity and predict the results. Plotting and analysis were done to see how various flow parameters affect temperature, velocity, and heat transfer. A ternary hybrid nanofluid without a Casson scenario had a higher Nusselt number, per the study's conclusions, than a Casson ternary nanofluid with blood as the base fluid with nanoparticles $ SWCNT + Ti{O_2} + MWCNT. $ It was discovered that the truth values are accurately predicted by ANFIS-PSO models, and in most of the runs in Table 3, in Case-2, the rate of heat transmission is 1% higher than in Case-1.
- heat source/sink,
- radiation,
- magnetohydrodynamics (MHD),
- thin film,
- optimization of particle swarms with an adaptive neuro-fuzzy inference system
Citation: Maddina Dinesh Kumar, S. Mamatha Upadhya, Nehad Ali Shah, C. S. K. Raju, Se-Jin Yook. Adaptive neuro-fuzzy inference system prediction of thermal transport in Casson ternary hybrid nanofluid thin film flow for biomedical applications[J]. AIMS Mathematics, 2025, 10(11): 27381-27411. doi: 10.3934/math.20251204

Related Papers:

Abstract

In this work, the adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) were used to anticipate the behavior of heat transfer in ternary hybrid nanofluid flow through thin films. For intricate heat transfer processes in nanofluid applications, the combined ANFIS-PSO model improved forecast accuracy. The simulated PDEs were converted into ODEs by varying similarity factors. A ternary hybrid nanofluid across the surface, radiation, heat source/sink, and non-uniform magnetic field were used to theoretically examine the unique properties of unstable thin film flow; nanoparticles, namely $ MWCNT, SWCNT $, and $ Ti{O_2} $, were used in both the Casson and non-Casson scenarios, using the base fluid, blood. The ANFIS-PSO models were trained using the numerical results from MATLAB's built-in BVP4C function to control the complexity and predict the results. Plotting and analysis were done to see how various flow parameters affect temperature, velocity, and heat transfer. A ternary hybrid nanofluid without a Casson scenario had a higher Nusselt number, per the study's conclusions, than a Casson ternary nanofluid with blood as the base fluid with nanoparticles $ SWCNT + Ti{O_2} + MWCNT. $ It was discovered that the truth values are accurately predicted by ANFIS-PSO models, and in most of the runs in Table 3, in Case-2, the rate of heat transmission is 1% higher than in Case-1.

References

[1]	C. Y. Wang, Liquid film on an unsteady stretching surface, Quart. Appl. Math., 48 (1990), 601–610. https://doi.org/10.1090/qam/1079908 doi: 10.1090/qam/1079908
[2]	H. I. Andersson, J. B. Aarseth, B. S. Dandapat, Heat transfer in a liquid film on an unsteady stretching surface, Int. J. Heat Mass Tran., 43 (2000), 69–74. https://doi.org/10.1016/S0017-9310(99)00123-4 doi: 10.1016/S0017-9310(99)00123-4
[3]	M. S. Abel, J. Tawade, M. M. Nandeppanavar, Effect of non-uniform heat source on MHD heat transfer in a liquid film over an unsteady stretching sheet, Int. J. Nonlin. Mech., 44 (2009), 990–998. https://doi.org/10.1016/j.ijnonlinmec.2009.07.004 doi: 10.1016/j.ijnonlinmec.2009.07.004
[4]	K. A. Kumar, N. Sandeep, V. Sugunamma, I. L. Animasaun, Effect of irregular heat source/sink on the radiative thin film flow of MHD hybrid ferrofluid, J. Therm. Anal. Calorim., 139 (2020), 2145–2153. https://doi.org/10.1007/s10973-019-08628-4 doi: 10.1007/s10973-019-08628-4
[5]	M. K. Prasad, S. H. Naveenkumar, C. S. K. Raju, S. M. Upadhya, Nonlinear thermal convection on unsteady thin film flow with variable properties, J. Phys.: Conf. Ser., 1427 (2020), 012016. 0
[6]	G. Gomathy, B. R. Kumar, Impacts of nanoparticle shapes on Ag-water nanofluid thin film flow through a porous medium with thermal radiation and ohmic heating, J. Therm. Anal. Calorim., 149 (2024), 6877–6895. https://doi.org/10.1007/s10973-023-12609-z doi: 10.1007/s10973-023-12609-z
[7]	A. Bhandari, Effect of thin film thickness of ferrofluid on inclined bioconvective flow, Chinese J. Phys., 92 (2024), 959–974. https://doi.org/10.1016/j.cjph.2024.10.019 doi: 10.1016/j.cjph.2024.10.019
[8]	F. Z. Wang, A. M. Saeed, V. Puneeth, N. A. Shah, M. S. Anwar, K. Geudri, et al., Heat and mass transfer of Ag-H₂O nano-thin film flowing over a porous medium: A modified Buongiorno's model, Chinese J. Phys., 84 (2023), 330–342. https://doi.org/10.1016/j.cjph.2023.01.001 doi: 10.1016/j.cjph.2023.01.001
[9]	M. Qayyum, A. Tahir, A. Bariq, A. Akgül, S. T. Saeed, Modelling and analysis of thin film flow of Fuzzified Johnson Segelman nanofluid using a fuzzy extension of the He-Laplace scheme, Math. Comp. Model. Dyn., 29 (2023), 286–314. https://doi.org/10.1080/13873954.2023.2276440 doi: 10.1080/13873954.2023.2276440
[10]	D. Pal, D. Chatterjee, Magneto-thermos heat transfer of a chemically reactive and viscous dissipative Casson nanofluid thin film over an unsteady stretching surface with variable thermal conductivity, J. Nanofluids, 12 (2023), 1973–1986. https://doi.org/10.1166/jon.2023.2055 doi: 10.1166/jon.2023.2055
[11]	P. Bathmanaban, E. P. Siva, S. S. Santra, S. S. Askar, A. Foul, S. Nandi, Heat and mass transfer in double-diffusive mixed convection of Casson fluid: biomedical applications, Colloid Polym. Sci., 302 (2024), 1635–1669. https://doi.org/10.1007/s00396-024-05286-3 doi: 10.1007/s00396-024-05286-3
[12]	R. K. Dash, K. N. Mehta, G. Jayaraman, Casson fluid flow in a pipe filled with a homogeneous porous medium, Int. J. Eng. Sci., 34 (1996), 1145–1156. https://doi.org/10.1016/0020-7225(96)00012-2 doi: 10.1016/0020-7225(96)00012-2
[13]	S. M. Alghamdi, A. Hanif, R. S. Almufarij, E. A. Shokralla, M. D. Alshahrani, I. Ragab, et al., Tuning the microstructure and thermoelectric response of β-FeSi₂ thin films via cobalt doping, J. Mater. Sci.: Mater. Electron., 36 (2025), 1807. https://doi.org/10.1007/s10854-025-15897-3 doi: 10.1007/s10854-025-15897-3
[14]	F. Z. Duraihem, R. L. V. R. Devi, P. Prakash, T. K. Sreelakshmi, S. Saleem, P. Durgaprasad, et al., Enhanced heat and mass transfer characteristics of multiple slips on hydro-magnetic dissipative Casson fluid over a curved stretching surface, Int. J. Mod. Phys. B, 37 (2023), 2350229. https://doi.org/10.1142/S0217979223502296 doi: 10.1142/S0217979223502296
[15]	M. A. Fahmy, A computerised DRBEM model for generalised magneto-thermo-visco-elastic stress waves in functionally graded anisotropic thin film/substrate structures, Lat. Am. J. Solids Stru., 11 (2014), 386–409.
[16]	D. Thenmozhi, M. E. Rao, C. Nagalakshmi, R. R. Devi, P. D. Selvi, Lie similarity analysis of MHD Casson fluid flow with heat source and variable viscosity over a porous stretching sheet, International Journal of Thermofluids, 23 (2024), 100804. https://doi.org/10.1016/j.ijft.2024.100804 doi: 10.1016/j.ijft.2024.100804
[17]	S. U. S. Choi, J. A. Eastman, Enhancing thermal conductivity of fluids with nanoparticles, ASME International Mechanical Engineering Congress & Exposition, San Francisco, California, USA, 1995, 99–105. https://doi.org/10.1115/IMECE1995-0926
[18]	J. Hakami, A. Ashfaq, A. R. Abd-Elwahed, M. D. Alshahrani, E. A. Shokralla, I. Ragab, et al., High-performance Bi-doped SnS thin films: A route to enhanced thermoelectric power for miniaturised devices, Int. Commun. Heat Mass, 169 (2025), 109759. https://doi.org/10.1016/j.icheatmasstransfer.2025.109759 doi: 10.1016/j.icheatmasstransfer.2025.109759
[19]	S. M. Upadhya, S. Suresh, M. D. Kumar, P. D. Selvi, S. S. K. Raju, C. S. K. Raju, et al., Comparison of SWCNT plus MWCNT and SWCNT+MWCNT+Fe₃O₄ nanofluid across a spinning disk with suspended joule heating and non-linear thermal radiation: Multi-linear optimisation, Numerical Heat Transfer Part B-Fundamentals, 86 (2025), 1111–1137. https://doi.org/10.1080/10407790.2024.2302858 doi: 10.1080/10407790.2024.2302858
[20]	S. G. Krishna, M. Shanmugapriya, R. Sundareswaran, P. S. Kumar, MANFIS approach for predicting heat and mass transport of bio-magnetic ternary hybrid nanofluid using Cu/Al₂O₃/MWCNT nanoadditives, Biomass Conv. Bioref., 14 (2024), 11175–11190. https://doi.org/10.1007/s13399-022-02989-x doi: 10.1007/s13399-022-02989-x
[21]	A. Kumar, M. A. Hassan, Heat transfer in flat tube car radiator with CuO-MgO-TiO₂ ternary hybrid nanofluid, Powder Technol., 434 (2024), 119275. https://doi.org/10.1016/j.powtec.2023.119275 doi: 10.1016/j.powtec.2023.119275
[22]	A. Mishra, G. Pathak, A comparative analysis of MoS₂-SiO₂/H₂O hybrid nanofluid and MoS₂-SiO₂-GO/H₂O ternary hybrid nanofluid over an inclined cylinder with heat generation/absorption, Numer. Heat Tr. A-Appl., 85 (2024), 2724–2753. https://doi.org/10.1080/10407782.2023.2228483 doi: 10.1080/10407782.2023.2228483
[23]	C. M. Mohana, B. R. Kumar, Numerical and semi-analytical approaches for heat transfer analysis of ternary hybrid nanofluid flow: A comparative study, Math. Comput. Simulat., 226 (2024), 66–90. https://doi.org/10.1016/j.matcom.2024.06.019 doi: 10.1016/j.matcom.2024.06.019
[24]	D. Mohanty, G. Mahanta, S. Shaw, Irreversibility and thermal performance of nonlinear radiative cross-ternary hybrid nanofluid flow about a stretching cylinder with industrial applications, Powder Technol., 433 (2024), 119255. https://doi.org/10.1016/j.powtec.2023.119255 doi: 10.1016/j.powtec.2023.119255
[25]	P. Rana, S. Gupta, G. Gupta, Optimization of heat transfer by nonlinear thermal convection flow past a solid sphere with Stefan blowing and thermal slip using Taguchi method, Int. Commun. Heat Mass, 141 (2023), 106580. https://doi.org/10.1016/j.icheatmasstransfer.2022.106580 doi: 10.1016/j.icheatmasstransfer.2022.106580
[26]	P. Rana, X. Y. Zhu, I. Pop, Multiplicity and stability of solutions in transport phenomena of non-Newtonian Carreau nanofluid with nonlinear Rosseland thermal approximations and neural prediction, Case Stud. Therm. Eng., 68 (2025), 105821. https://doi.org/10.1016/j.csite.2025.105821 doi: 10.1016/j.csite.2025.105821
[27]	G. K. Ramesh, R. Saadeh, J. K. Madhukesh, A. Qazza, U. Khan, A. Zaib, et al., Neural network algorithms of a curved Riga sensor in a ternary hybrid nanofluid with chemical reaction and Arrhenius kinetics, J. Radiat. Res. Appl. Sc., 17 (2024), 101078. https://doi.org/10.1016/j.jrras.2024.101078 doi: 10.1016/j.jrras.2024.101078
[28]	P. Rana, J. P. Ma, Y. R. Zhang, J. Q. Chen, G. Gupta, Finite element and neural computations for energy system containing conductive solid body and bottom circular heaters utilizing Ag-MgO (50: 50)/water hybrid nanofluid, J. Magn. Magn. Mater., 577 (2023), 170775. https://doi.org/10.1016/j.jmmm.2023.170775 doi: 10.1016/j.jmmm.2023.170775
[29]	T. Gul, M. Bilal, M. Shuaib, S. Mukhtar, P. Thounthong, Thin film flow of the water‐based carbon nanotubes hybrid nanofluid under the magnetic effects, Heat Transf., 49 (2020), 3211–3227. https://doi.org/10.1002/htj.21770 doi: 10.1002/htj.21770
[30]	R. Ali, A. Shahzad, K. us Saher, Z. Elahi, T. Abbas, The thin film flow of Al₂O₃ nanofluid particle over an unsteady stretching surface, Case Stud. Therm. Eng., 29 (2022), 101695. https://doi.org/10.1016/j.csite.2021.101695 doi: 10.1016/j.csite.2021.101695
[31]	A. Divya, T. Jithendra, M. Z. Khan, A. Noorwali, K. M. Othman, A novel prediction model for the analysis of Ree-Eyring fluid with hall current in Darcy-Forchheimer porous media based on machine learning technique, Propuls. Power Res., 14 (2025), 527–551. https://doi.org/10.1016/j.jppr.2025.09.003 doi: 10.1016/j.jppr.2025.09.003
[32]	H. Waqas, S. A. Khan, T. Muhammad, Thermal analysis of magnetized flow of AA7072-AA7075/blood-based hybrid nanofluids in a rotating channel, Alex. Eng. J., 61 (2022), 3059–3068. https://doi.org/10.1016/j.aej.2021.08.033 doi: 10.1016/j.aej.2021.08.033
[33]	M. Ibrahim, M. I. Khan, Mathematical modelling and analysis of SWCNT-Water and MWCNT-Water flow over a stretchable sheet, Comput. Meth. Prog. Bio., 187 (2020), 105222. https://doi.org/10.1016/j.cmpb.2019.105222 doi: 10.1016/j.cmpb.2019.105222
[34]	H. Xu, I. Pop, X. C. You, Flow and heat transfer in a nano-liquid film over an unsteady stretching surface, Int. J. Heat Mass Tran., 60 (2013), 646–652. https://doi.org/10.1016/j.ijheatmasstransfer.2013.01.046 doi: 10.1016/j.ijheatmasstransfer.2013.01.046

Reader Comments

Your name:*

Email:*
© 2025 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)