Taira Lab - Computational and Data-Driven Fluid Dynamics Group

Our group studies a variety of fluid mechanics problems with research interests in the areas of computational fluid dynamics, flow control, data science, network theory, and unsteady aerodynamics. Our studies leverage numerical simulations performed on high-performance computers.

Point of Contact: Kunihiko Taira
Department of Mechanical and Aerospace Engineering, UCLA

Codes on this webpage are provided for educational and research purposes only (no commercial uses permitted). The codes are written for readability and are not optimized for computation speed. While efforts were made to post bug-free codes, users are strongly advised to verify the codes for their use. We deliver the code as is and do not assume any responsibility.

Codes:

3D Printing of Fluid Flow Structures

Print your flow!

Ref: Taira, Sun, & Canuto, arXiv 2017

Matlab code (m file, arXiv (note), released Jan 2017)

Spectral sparsification

Sparsifies graph/network with preserved graph spectra

Ref: Spielman & Srivastava, SIAM J Sci 2011; Nair & Taira, JFM 2015

Matlab code, example, user guide (zip, released Dec 2016)

Bi-periodic spectral incompressible flow solver

DNS/Fourier spectral/4th-order Runge-Kutta method

Ref: Taira, Nair, & Brunton, JFM 2016

Matlab code (m file); Ref: link, arXiv; released July 2017.

Machine learning based super resolution

Reconstructs flow field from coarse flow data

Ref: Fukami, Fukagata, & Taira, JFM 2019

Python sample code (py, released April 2019)

Randomized resolvent analysis

Performs resolvent analysis using sketching with random test matrix

Ref: Ribeiro, Yeh, & Taira, PR Fluids 2020

Matlab code (m file); Ref: link, arXiv; released Aug 2020.