This laboratory conducts research in the field of Computational Fluid Dynamics (CFD) to study aerodynamics and aeroacoustics of various aerospace vehicles, including rotorcraft. We are actively involved in improving the accuracy and efficiency of numerical analysis based on Reynolds-Averaged Navier-Stokes (RANS) from grid generation methods to the development of flow solvers. Our research also focuses on enhancing the modeling of viscous flow phenomena in external flows, such as boundary layer transition. To enable rapid prediction of aerodynamics and noise, we are developing surrogate models based on CFD with machine learning techniques and applying them to research optimal aerodynamic design methods for aerospace components like rotor blades.

1. CFD Trained Invertible Neural Networks and Machine Learning for Rotor Blade Airfoil Design / National Research Foundation of Korea
2. Automated Mesh Refinement(AMR) Capability for 2-D Hamiltonian Solver / US Army Research Laboratory