Computational Flow Physics Group
University of California San Diego
Followers: 5 Following: 0
We develop advanced numerical simulation and data-driven analysis tools to understand, model, and predict turbulent and multiphysics flows in engineering and nature. Our work combines high-fidelity computation with modal decomposition and feature extraction to reveal coherent flow structures and translate them into predictive reduced-order models for forecasting and optimization. We focus on aerospace problems, including jet noise control, unsteady aerodynamics and aeroacoustics, transition, dynamic stall, aero-optics, and hypersonics. While our work is grounded in physics-based modeling, we also curiously explore where modern machine learning can complement first-principles simulation, statistical methods, and classical closure techniques. Beyond aerospace, our methods extend to complex geophysical flows, with applications in atmospheric science and physical oceanography.
Feeds
제출됨
Triadic Orthogonal Decomposition
MATLAB implementation of Triadic Orthogonal Decomposition (TOD)
1일 전 | 다운로드 수: 1 |
제출됨
SLICK: Data-driven prediction of turbulent flows
Stochastic Low-Dimensional Inflated Convolutional Koopman model (Chu & Schmidt, PRSA 2025). Code developed by Tianyi Chu.
4개월 전 | 다운로드 수: 2 |
제출됨
Spectral proper orthogonal decomposition (SPOD)
Implements the frequency domain form of proper orthogonal decomposition (POD)
8개월 전 | 다운로드 수: 16 |
제출됨
LinStab2D
Stability and resolvent analysis of compressible viscous flows in MATLAB
1년 초과 전 | 다운로드 수: 13 |
제출됨
Spectral EOF (SEOF) of weather and climate data
2-D and 3-D spectral empirical orthogonal function analysis (SEOF) of ECMWF reanalysis data
6년 초과 전 | 다운로드 수: 1 |
제출됨
Streaming Spectral Proper Orthogonal Decomposition
A low-memory streaming algorithm for spectral proper orthogonal decomposition (SPOD) of stationary random data
7년 초과 전 | 다운로드 수: 2 |
