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An ANFIS Approach for Predicting MHD Radiative Hybrid Nanofluid Flow Attributes with Activation Energy Effect

  • Research Article-Chemical Engineering
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Abstract

The current research examines the potential of the Adaptive Neuro-Fuzzy Inference System (ANFIS) technique in predicting the transport properties of a hybrid water-based nanofluid made of SWCNTs (single-walled carbon nanotubes) and MWCNTs (multi-walled carbon nanotubes) past a moving wedge with activation energy, radiation, and MHD (magnetohydrodynamic) effects. The flow problem under consideration has been theoretically characterised, and computation is accomplished by adopting the shooting strategy and the Runge–Kutta–Fehlberg fourth-fifth order (RKF45) numerical scheme. By utilizing the pertinent flow variables as inputs, three ANFIS models have been proposed to anticipate the skin friction coefficient (\({\widehat{S}}_{f}\)), Nusselt number (\({\widehat{H}}_{t}\)), and Sherwood number (\({\widehat{M}}_{t}\)). The ANFIS models’ performances are measured using statistical evaluation criteria. The estimated ANFIS outcomes and the simulated numerical values are in excellent accordance with the correlation coefficients (R) of 0.96499, 0.99596, and 0.975211 for \({\widehat{S}}_{f}\), \({\widehat{H}}_{t}\), and \({\widehat{M}}_{t}\), respectively. The provided ANFIS models are robust, efficient, and reliable, and they precisely forecast the results.

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Data Availability

Data will be available by made on request.

Abbreviations

\(\widehat{u},\widehat{v}\) :

Velocity components

\(x,y\) :

Space coordinates

\({\widehat{u}}_{w}, {\widehat{u}}_{e}\) :

Velocity at the surface and far-field

\(m\) :

Wedge parameter

\(B\) :

Variable magnetic field

\({\widehat{T}}_{w}, {\widehat{C}}_{w}\) :

Surface temperature and concentration

\({\widehat{T}}_{\mathrm{fs}}, {\widehat{C}}_{\mathrm{fs}}\) :

Free stream temperature and concentration

\({D}_{B}\) :

Brownian diffusion coefficient

\({D}_{T}\) :

Thermophoretic diffusion coefficient

\({k}_{r}\) :

Chemical reaction rate

\({E}_{a}\) :

Modified arrhenius function

\({k}^{*}\) :

Mean absorption coefficient

\(f\) :

Dimensionless velocity

\(M\) :

Magnetic parameter

\(\mathrm{Pr}\) :

Prandtl number

\(R\) :

Radiation parameter

\(\mathrm{Nb}\) :

Brownian motion parameter

\(\mathrm{Nt}\) :

Thermophoresis parameter

\(\mathrm{Le}\) :

Lewis number

\(\mathrm{Rc}\) :

Dimensionless chemical reaction rate

\(E\) :

Activation energy parameter

\({\widehat{S}}_{f}\) :

Skin friction coefficient

\({\widehat{H}}_{t}\) :

Nusselt number

\({\widehat{M}}_{t}\) :

Sherwood number

\(\mathrm{Re}\) :

Reynolds number

\(\beta\) :

Hartree pressure gradient

\(\widehat{\Omega }\) :

Wedge angle

\(\sigma\) :

Electrical conductivity

\({n}_{0}\) :

Fitted rate constant

\({\rho }_{\mathrm{hnf}}\) :

Density of hybrid nanofluid

\({\mu }_{\mathrm{hnf}}\) :

Dynamic viscosity of hybrid nanofluid

\({(\rho {C}_{p})}_{\mathrm{hnf}}\) :

Heat capacity of hybrid nanofluid

\({k}_{\mathrm{hnf}}\) :

Thermal conductivity of hybrid nanofluid

\({\varphi }_{1}, {\varphi }_{2}\) :

Nanoparticle volume fraction of SWCNT and MWCNT

\(\zeta\) :

Similarity variable

\(\psi\) :

Stream function

\({\vartheta }_{f}\) :

Kinematic viscosity of fluid

\({\sigma }^{*}\) :

Stefan-Boltzmann constant

\(\theta\) :

Dimensionless temperature

\(\phi\) :

Dimensionless concentration

\(\gamma\) :

Moving wedge parameter

\(\delta\) :

Temperature difference parameter

\(f\) :

Fluid

\(\mathrm{nf}\) :

Nanofluid

\(\mathrm{hnf}\) :

Hybrid nanofluid

\(w, fs\) :

Surface and free stream

\({m}_{1},{m}_{2}\) :

SWCNT and MWCNT nanoparticle

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Funding

This research is not financially supported by any funding agencies.

Author information

Authors and Affiliations

  1. Department of Mathematics, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, Tamil Nadu, 603 110, India

    M. Shanmugapriya, R. Sundareswaran & M. Elayarani

  2. Centre for Pollution Control and Environmental Engineering, Pondicherry University, Chinna Kalapet, Puducherry, 605014, India

    P. Senthil Kumar

Authors
  1. M. Shanmugapriya
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  2. R. Sundareswaran
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  3. P. Senthil Kumar
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  4. M. Elayarani
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Contributions

MS contributed to Conceptualization, Validation and Supervision. RS contributed to Investigation, Resources and Formal analysis. PSK contributed to Conceptualization, Validation and Supervision. ME contributed to Investigation, Methodology, and Writing—Original Draft.

Corresponding authors

Correspondence to M. Shanmugapriya or P. Senthil Kumar.

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Conflict of interest

Authors declares that no competing interests for this research article.

Ethical Approval

This article does not contain any studies involving animal or human participants performed by any of the authors.

Appendix

Appendix

See Tables 2, 3 and 4.

Table 2 Skin friction coefficient (\(f^{\prime\prime}\left( 0 \right)\)) dataset
Full size table
Table 3 Nusselt number (\(\theta^{\prime}\left( 0 \right)\)) dataset
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Table 4 Sherwood number (\(\phi^{\prime}\left( 0 \right)\)) dataset
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Shanmugapriya, M., Sundareswaran, R., Kumar, P.S. et al. An ANFIS Approach for Predicting MHD Radiative Hybrid Nanofluid Flow Attributes with Activation Energy Effect. Arab J Sci Eng 48, 16373–16387 (2023). https://doi.org/10.1007/s13369-023-08260-3

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