Hybrid RANS/LES, Wal-Modelled/Resolve LES and Lattice-Boltzmann Method

In-depth investigation of flow physics and noise generation mechanisms

Full space-time Galerkin discretization of the energetic weak form for the boundary integral equation

Scattering of broadband, transient and rotating noise sources

Complete vehicle acoustic simulation in seconds on GPU

Coupled CFD-CAA discrete adjoint via algorithmic differentiation 

Machine-accurate and dual-consistent adjoint gradient

Highly efficient design sensitivity evaluation and accurate enforcement of design constraints

RANS-based fast Random Particle-Mesh (fRPM) method for turublent boundary layer noise prediction

Random Vortex Particle Method (RVPM) for blade-wake/blade-vortex interaction noise prediction

Physics-informed field-inversion machine learning to optimally enhance turbulence and noise models with limited high-fidelity data

Multi-fidelity noise model development using active and transfer learning