Guest Editor(s)
Dr Mudasar Zafar
Email: mudasar.zafar@apu.edu.my
Affiliation: School of Mathematics, Actuarial and Quantitative Studies (SOMAQS), Asia Pacific University of Technology & Innovation (APU), Bukit Jalil, 57000, Kuala Lumpur, Malaysia
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Research Interests: aerodynamics, computational fluid dynamics (CFD), magnetohydrodynamics (MHD), nanofluid dynamics, heat and mass transfer, fluid–structure interactions, sustainable and energy-efficient fluid systems, applied mathematics, AI-enabled modeling and simulation
Summary
Aerodynamics is fundamental to the performance, efficiency, and safety of modern engineering systems, including aerospace vehicles, automobiles, renewable-energy devices, and advanced industrial machinery. With recent progress in computational fluid dynamics (CFD), high-fidelity simulations, magnetohydrodynamics (MHD), and machine learning, researchers are now able to analyze, predict, and optimize aerodynamic behaviour with greater accuracy, even in complex fluid–structure interaction environments. This Special Issue aims to bring together high-quality research that introduces innovative methods, experimental investigations, modeling strategies, and practical applications in the field of aerodynamics. Topics of interest include, but are not limited to, aerodynamic optimization, turbulence modeling, nanofluid and hybrid-fluid aerodynamics, flow instability and control, unsteady flow behaviour, energy-harvesting aero-systems, and AI-driven aerodynamic prediction techniques. We also welcome studies that integrate numerical simulations with experimental validation, as well as interdisciplinary work that connects fluid mechanics with computational mathematics, materials engineering, and mechanical system design. By offering a dedicated platform for impactful contributions, this Special Issue aims to deepen the understanding of aerodynamic processes and support the development of next-generation engineering technologies with enhanced performance, durability, and energy efficiency.
Keywords
AI-driven aerodynamic optimization, deep learning for turbulence modeling, physics-informed neural networks (PINNs), surrogate models for aerodynamic prediction, fluid–structure interactions (FSI), aeroelasticity in next-generation engineering systems, high-fidelity computational fluid dynamics (CFD), unsteady flow and vortex dynamics.
Published Papers
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