Artificial Intelligence, Advanced Materials, and Thermofluid Modeling for Energy and Aerodynamic Systems

Dear Colleagues,

The rapid evolution of advanced materials, thermofluid sciences, computational modeling, and artificial intelligence is transforming the design and operation of modern engineering systems. Applications ranging from aerospace and transportation to renewable energy, manufacturing, and thermal management increasingly rely on accurate predictive models capable of describing complex interactions among materials, heat transfer, fluid dynamics, and aerodynamic phenomena.

Traditional approaches based on Computational Fluid Dynamics (CFD), Finite Element Analysis (FEA), heat transfer theory, and multiphysics simulations continue to provide the scientific foundation for understanding these systems. However, recent advances in machine learning, deep learning, surrogate modeling, digital twins, and physics-informed artificial intelligence have created new opportunities to accelerate simulations, optimize designs, improve predictive accuracy, and enable real-time decision-making.

This Special Issue aims to bring together researchers working at the intersection of advanced materials, thermofluids, aerodynamics, numerical modeling, and artificial intelligence. Contributions addressing theoretical developments, computational methods, experimental validations, and industrial applications are particularly welcome.

This Special Issue aligns with the scope of engineering, applied sciences, materials science, and computational mechanics by emphasizing mathematical modeling, numerical simulation, optimization, and AI-assisted approaches for complex physical systems.

Topics of Interest Include, but Are Not Limited To:

• Advanced materials for thermal and aerodynamic applications;
• Computational Fluid Dynamics (CFD);
• Heat and mass transfer modeling;
• Aerodynamics and fluid mechanics;
• Turbulence modeling and flow optimization;
• Thermal management systems;
• Smart and multifunctional materials;
• Energy conversion and renewable energy systems;
• Machine learning for thermofluid analysis;
• Deep learning-assisted CFD;
• Physics-informed neural networks (PINNs);
• Surrogate and reduced-order models;
• Digital twins for energy and aerospace systems;
• AI-assisted materials design and characterization;
• Optimization using genetic algorithms, PSO, and metaheuristics;
• Multiphysics modeling and simulation;
• Fluid–structure interaction;
• Predictive maintenance and fault diagnosis;
• Data-driven engineering systems.

Prof. Dr. Juvenal Rodriguez-Resendiz
Dr. Luis Angel Iturralde Carrera
Guest Editors

Manuscript Submission Information

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