Rafaqat Ali Khan
Papers
1
Total Citations
1
H-Index
1
About
Dr. Rafaqat Ali Khan is a leading researcher in computational fluid dynamics and applied artificial intelligence, specializing in the analysis of complex nanofluid flows and magnetohydrodynamic (MHD) systems. His most significant contribution is the development of an intelligent neural network framework—the backpropagation Levenberg-Marquardt technique (BPLMT-NN)—to solve challenging fluid dynamics problems. In his highly cited 2025 work, Dr. Khan applied this novel approach to investigate MHD viscous nanofluid flow over a rotating disk under velocity slip conditions, a problem critical for advanced cooling systems and rotating machinery. This work, already garnering 1 citation, demonstrates his pioneering integration of machine learning with traditional fluid mechanics. Dr. Khan’s research bridges the gap between theoretical physics and data-driven modeling, offering robust, efficient solutions for nonlinear differential equations that govern real-world engineering systems. His contributions are paving the way for smarter, AI-enhanced simulations in thermal management and electromagnetic flow control. For students and researchers, Dr. Khan’s work exemplifies how modern computational intelligence can unlock new insights into classical fluid dynamics problems.
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Top Papers
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