AI powered thermal scrutiny of magnetized inclined ternary nanofluid through convergent-divergent ducts
This study gives the investigation of ternary nanofluid flow through convergent–divergent geometries under an inclined magnetized environment using a hybrid computational framework that integrates the traditional bvp4c numerical solver with an artificial neural network (ANN) model employing three hi...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
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| Series: | Case Studies in Thermal Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214157X25009591 |
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| Summary: | This study gives the investigation of ternary nanofluid flow through convergent–divergent geometries under an inclined magnetized environment using a hybrid computational framework that integrates the traditional bvp4c numerical solver with an artificial neural network (ANN) model employing three hidden layers. The magnetic field introduces an additional degree of control overflow behavior and thermal boundary development. The bvp4c scheme is first utilized to generate highly accurate numerical solutions for velocity, temperature, and skin friction under various magnetic inclinations and nanoparticle volume fractions. These solutions are then used as training data for the three-hidden-layer ANN. The magnetic field generates Lorentz forces that reduce fluid velocity leading to increased viscous heating and enhanced thermal energy dissipation within the system. |
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| ISSN: | 2214-157X |