Guest Editors
Dr. Shajar Abbas
Email: shajarabbasgat@gmail.com
Affiliation: Centre for Advanced Studies in Pure and Applied Mathematics, Bahauddin Zakariya University, Multan, Pakistan
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Research Interests: analytical fluid dynamics, computational fluid dynamics, heat and mass transfer, fractional calculus, nanofluids, artificial intelligence (AI), machine learning
Dr. Mushtaq Ahmad
Email: math7690@yahoo.com
Affiliation: Department of Mathematics and Statistics, University of Southern Punjab, Multan, Pakistan
Homepage:
Research Interests: fluid dynamics, heat and mass transfer, fractional calculus, nanofluids
Summary
Industrial thermal systems are critical in advanced manufacturing, energy production, chemical processing, and thermal management applications, where efficient heat and mass transfer determine system performance and energy efficiency. High-performance thermal systems often involve complex phenomena such as non-Newtonian and fractional fluid behaviors, hybrid nanofluid transport, turbulent flows, thermal stratification, and coupled multi-physics interactions. Accurate modeling and optimization of these processes require advanced computational and AI-driven approaches capable of capturing nonlinear, memory-dependent, and multi-scale dynamics in industrial environments.
With the rapid development of fractional calculus modeling, artificial intelligence, and machine learning, researchers can now simulate, predict, and optimize nanofluid-based fractional fluid systems with unprecedented accuracy and efficiency. This Special Issue invites high-quality research and review articles that explore AI-assisted modeling, simulation, and experimental studies of fractional nanofluid dynamics in industrial thermal engineering. Contributions should aim to enhance thermal performance, reduce energy consumption, and provide novel solutions for complex industrial heat transfer systems.
Topics of interest include, but are not limited to:
· AI-enhanced modeling of fractional nanofluid flow and heat transfer
· Hybrid and multi-component nanofluids for industrial applications
· Turbulent and laminar flow optimization using data-driven and fractional models
· Multiphysics simulations integrating fluid flow, heat transfer, and structural effects
· Energy efficiency, process optimization, and thermal management in industrial systems
Submissions that integrate real-time predictive modeling, material optimization, and industrial process enhancement are especially encouraged.
Keywords
fractional fluid systems, AI-assisted nanofluid dynamics, industrial thermal engineering, heat transfer, multiphysics simulation, energy efficiency, process optimization
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