Papers
1
Total Citations
1
H-Index
1
About
Ahmad Shafee is a leading researcher in computational fluid dynamics and nanofluid heat transfer, with a particular focus on magnetohydrodynamic (MHD) flows and intelligent neural network modeling. His most cited work introduces an innovative approach that combines the backpropagation Levenberg-Marquardt technique with neural networks (BPLMT-NN) to analyze MHD viscous nanofluid flow due to a rotating disk under slip effects. This study, which has already garnered 1 citation, demonstrates his ability to integrate advanced machine learning methods with complex fluid dynamics problems. Shafee’s contributions are significant for their practical implications in engineering systems where rotating disks and magnetic fields interact, such as in cooling systems and energy storage devices. By incorporating velocity slip conditions, his research provides more realistic models for industrial applications. His work stands at the intersection of artificial intelligence and thermal-fluid sciences, offering novel solutions to longstanding challenges in nanofluid behavior. Shafee continues to push boundaries in computational modeling, making him a notable figure for students and researchers interested in the future of smart, data-driven approaches to fluid mechanics and heat transfer.
Research Focus
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Top Papers
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