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Artificial Intelligence based modeling for unsteady cross nanofluid flow over a stretching sheet
Shuangcen Li1, Muhammad Asif Zahoor Raja2, Zahoor Shah3
1School of Intelligent Manufacturing, Sichuan University Jinjiang College, Meishan, Sichuan 620860, China.
Computational Biology and Chemistry
|July 30, 2026
Summary
This study applies the Levenberg-Marquardt Algorithm with Non-linear Auto Regressive Exogenous (NA REX) to model nanofluid flow. The AI approach demonstrated high accuracy and dependability in predicting fluid dynamics under magnetic and thermophoretic effects.
Area of Science:
- Fluid Dynamics
- Artificial Intelligence
- Nanotechnology
Background:
- Nanofluid flow dynamics are crucial in various engineering applications.
- Understanding heat and mass transfer in nanofluids requires advanced modeling techniques.
- Traditional methods may struggle with complex, coupled phenomena like magnetic fields and particle motion.
Purpose of the Study:
- To investigate the efficacy of the Levenberg-Marquardt Algorithm with Non-linear Auto Regressive Exogenous (NA REX) for simulating nanofluid flow.
- To analyze the influence of magnetic fields, Brownian motion, and thermophoresis on nanofluid behavior.
- To validate the AI model's accuracy against established numerical methods.
Main Methods:
- Modified ordinary differential equations were solved to generate a dataset.
- The Levenberg-Marquardt Algorithm within a NA REX framework was employed for training, validation, and testing.
- Dataset partitioning (80% training, 10% validation, 10% testing) was utilized.
- Model performance was evaluated by comparing results with the Adams method.
Main Results:
- The Levenberg-Marquardt Algorithm with NA REX achieved high accuracy, with mean square deviation levels as low as 1.2855E-13.
- Stability and accuracy were confirmed through mean square deviation, regression, histogram error, and absolute error analyses.
- Velocity showed inverse proportionality to unsteadiness and magnetic coefficients, and direct proportionality to viscosity ratio.
- Temperature was inversely proportional to Biot and thermophoretic coefficients; concentration increased with temperature.
Conclusions:
- The Levenberg-Marquardt Algorithm with NA REX is a highly accurate and dependable technique for modeling complex nanofluid dynamics.
- The AI approach effectively captures the intricate interplay of physical parameters like magnetic fields and thermophoresis.
- This method offers efficient training and reliable prediction for nanofluid transport phenomena.
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