Software

The Flow Modeling and Control workgroup at the Institute of Fluid Mechanics is constantly developing new tools and methods. The latest is flowTorch, which is Python library for analysis and modeling of fluid flow.

For a detailed overview on flowTorch and other software, I encourage you to check the depositories at the group Github: https://github.com/FlowModelingControl

Below, I present only two tools: xROM and SCOUT.

xROM:

xROM is a freely available tool for reduced-order modeling. One purpose of xROM is to derive dynamic POD models from snapshot data and Galerkin projection using the Navier-Stokes equations.

Moreover, xROM

• can analyze native CFD and PIV data;
• can handle a large spectrum of 2D and 3D grids (Cartesian, structured, unstructured, etc.);
• can create other modal expansions (DMD) and other modes (e.g. shift modes);
• is user-friendly through a single configuration file;
• is parallelizable.

We have used earlier versions of this tool for most of the empirical Galerkin models since 2003 and plan to continually expand xROM's capabilities (e.g.SPOD, CROM, etc.).

Reference book:

Semaan, R., Fernex, D., Weiner, A., & Noack, B. R. (2020). xROM: A Toolkit for Reduced-Order Modeling of Fluid Flows. In R. Semaan & B. R. Noack (Eds.), Machine Learning Tools in Fluid Mechanics (First Edition). https://doi.org/10.24355/dbbs.084-202007011404-0

Main developers:

• Daniel Fernex
• Andre Weiner
• Richard Semaan, and
• Bernd Noack

Download:

The xROM package can be downloaded here: https://cloudstorage.tu-braunschweig.de/getlink/fiMrBkvkBZ3e5atJzfMpApgW/xROM-1.1.zip

SCOUT:

Signal COrrection and Uncertainty quantification Toolbox, SCOUT, analyzes, characterizes, and corrects one-dimensional signals. Specifically, it allows to

1. check and correct for stationarity
2. detect spurious samples
3. check for normality
4. check for periodicity 
5. filter
6. perform spectral analysis
7. determine the integral time scale
8. perform uncertainty analysis on individual and on propagated signals through a data reduction equation. The uncertainty analysis yields uncertainties on central moments up to the fourth moment.

The novelty of SCOUT lies in combining these various methods into one compact and easy-to-use toolbox, which enables students and professionals alike to analyze, characterize, and correct for signals without expert knowledge. The program is oriented towards time traces, but an easy adaptation to spatial distributions can be performed by the user. SCOUT is available in two variants:

1. a graphical user interface (GUI) 
2. script-based version.

A key motivation of having two variants is to offer maximum flexibility to adaptively and visually adjust the analysis settings using the GUI version and to enable large batch processing capabilities and own code-integration using the script-based version. The package includes both variants as well as three example scripts with their corresponding signals.

Main developers:

• Richard Semaan
• Vikas Yadav

Download: