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Modelling cross-diffusion in MHD Williamson nanofluid flow over a nonlinear stretching surface via Morlet wavelet
Khalid Arif1, Syed Tauseef Saeed2, Muhammad Naeem Aslam3
1Department of Mathematics and Statistics, The University of Lahore, Lahore, 54770, Pakistan.
Scientific Reports
|July 27, 2025
Summary
This study introduces a hybrid computational method (MWNNs-PSO-NNA) to analyze magnetohydrodynamic (MHD) Williamson nanofluid flow. The novel technique accurately models complex fluid dynamics, showing significant potential for engineering applications.
Area of Science:
- Fluid Dynamics
- Nanotechnology
- Computational Science
Background:
- Magnetohydrodynamics (MHD) describes fluid motion in magnetic fields.
- Nanofluids offer enhanced thermal properties compared to conventional fluids.
- Williamson nanofluid models non-Newtonian fluid behavior.
Purpose of the Study:
- To develop and validate a novel hybrid computational method for analyzing MHD flow of Williamson nanofluid.
- To investigate the influence of Soret and Dufour effects in a porous medium.
- To assess the impact of various parameters on velocity, temperature, and concentration profiles.
Main Methods:
- Similarity transformation to convert partial differential equations to ordinary differential equations.
- Hybrid computational approach using Morlet Wavelet Neural Networks (MWNNs) and Particle Swarm Optimization (PSO) with a Neural Network (NNA).
- Validation through 100 independent runs and statistical metrics (MSE, TIC).
Main Results:
- The MWNNs-PSO-NNA model demonstrated high accuracy with low MSE and TIC values.
- Increased Williamson number, magnetic field, porosity, and stretching index reduced velocity.
- Brownian motion and Williamson number enhanced temperature; Soret and Brownian motion increased concentration.
Conclusions:
- The proposed hybrid model (MWNNs-PSO-NNA) is computationally robust and effective for complex fluid flow problems.
- The study provides insights into the behavior of Williamson nanofluid under various physical influences.
- Findings are applicable to engineering and applied sciences involving nanofluid dynamics.
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