Computational Flow Physics Group

University of California San Diego

Last seen: 18 giorni fa | Attivo dal 2019

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.

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Triadic Orthogonal Decomposition
MATLAB implementation of Triadic Orthogonal Decomposition (TOD)

circa un mese fa | 8 download |

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SLICK: Data-driven prediction of turbulent flows
Stochastic Low-Dimensional Inflated Convolutional Koopman model (Chu & Schmidt, PRSA 2025). Code developed by Tianyi Chu.

5 mesi fa | 2 download |

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Spectral proper orthogonal decomposition (SPOD)
Implements the frequency domain form of proper orthogonal decomposition (POD)

9 mesi fa | 29 download |

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LinStab2D
Stability and resolvent analysis of compressible viscous flows in MATLAB

oltre un anno fa | 5 download |

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bmd

quasi 3 anni fa | 7 download |

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Spectral EOF (SEOF) of weather and climate data
2-D and 3-D spectral empirical orthogonal function analysis (SEOF) of ECMWF reanalysis data

quasi 7 anni fa | 4 download |

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Streaming Spectral Proper Orthogonal Decomposition
A low-memory streaming algorithm for spectral proper orthogonal decomposition (SPOD) of stationary random data

oltre 7 anni fa | 3 download |

5.0 / 5