Key research themes
1. How can computational and experimental methods improve drag coefficient prediction for aerospace and automotive configurations?
This research theme focuses on leveraging Computational Fluid Dynamics (CFD), wind tunnel testing, and numerical simulations to calculate and validate drag coefficients of complex geometries in aerospace and automotive engineering. Accurate prediction of drag coefficients through validated models facilitates design optimization, improves fuel efficiency, and supports reliable performance assessments.
2. What are the influences of fluid rheology and flow conditions on drag coefficients for particles and flow past surfaces?
This theme investigates how non-Newtonian fluid properties like viscoelasticity, microstructure, and porous media interactions impact drag coefficients of solid particles and surfaces immersed in these fluids. Understanding such effects is crucial for modeling particle-laden flows in industrial processes, environmental contexts, and biological systems, informing drag models that consider elasticity, suspension concentration, and fluid microstructure.
3. How can machine learning and optimization algorithms enhance drag coefficient predictions and flow control for improved design and energy efficiency?
This theme examines the use of advanced data-driven techniques, including deep learning, genetic algorithms, and metaheuristic optimization, to model complex relationships in drag coefficient estimation. Such approaches enable efficient, automated calibration of drag parameters in engineering systems, support design optimization in aerospace and marine contexts, and facilitate active flow control strategies to reduce drag and energy consumption.