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Postprocessing tool hbrs-theta_utils to CFD solvers TAU and THETA for data-driven modal decompositions

hbrs-theta_utils (github.com, H-BRS GitLab) is a tool for coherent structure analysis in fluid dynamics. It helps scientists and engineers with characterizing energetic structures in CFD data and with making fluids more tractable to analysis and engineering design. Its development started in 2015 as a research project at Bonn-Rhein-Sieg University of Applied Sciences, from 2016-2019 it was funded by BMBF project AErOmAt.

hbrs-theta_utils is a postprocessing tool to CFD solvers TAU and THETA for data-driven modal decompositions. TAU and THETA do numerical flow simulations, e.g. predicting flows around airfoils of wind turbines or aircrafts. hbrs-theta_utils allows us to decompose these (in)compressible flows using various methods, e.g. PCA/POD or DMD, to extract relevant features. It also allows to export these flow fields into a VTK files for visualization, e.g. with ParaView. It is designed operate in parallel on distributed large-scale datasets at HPC clusters like the Platform for Scientific Computing at BRSU.

⚠️ DEPRECATION NOTICE: Apparently, this code meets the inevitable fate of many state-funded research projects. It has not been actively worked on since 2020. Software consists of teams of people. If you want people to continue developing a project after it ceases to be their personal interest, fund them for it. ⚠️

Under the hood

hbrs-theta_utils is a CLI application written in C++17 and using MPI for distributed computations at HPC clusters.

Its pca command first reads time series of 3d velocity fields from distributed netCDF files, which were e.g. created during flow simulations in TAU or THETA. These snapshots of x-, y- and z-velocities are then decomposed using Principal Component Analysis (PCA) / Proper Orthogonal Decomposition (POD) from hbrs-mpl, a generic C++ library for math and statistics. PCA is a dimensionality reduction technique that transforms data in high-dimensional space to a space of fewer dimensions. With command line argument --pcs the user selects which principal components to keep and drop. The reassembled and possibly reduced dataset is then written to disk using the same distributed file format as the input.

The visualize command reads an unstructured 3d grid and a time series of 3d velocity fields, both from distributed netCDF files. The grid contains all geometries (tetraeders, prisms, surfacetriangles, ...) that are used within flow simulations, stored in a proprietary TAU format. Before simulation, this grid is split and distributed across MPI processes, using TAU's preprocessing tool. For visualization, each process has to exchange x-, y- and z-velocities at its local grid boundaries with neighbours. Then, for each simulated time step, the 3d velocity field and the grid are written as a set of *.pvtu files to disk. For that, the Visualization Toolkit (VTK) is used to export the 3d geometry objects, their surfaces colored with 3d velocities, to parallel unstructured grid (*.pvtu) files. These *.pvtu files can then be opened and viewed in ParaView or using pvserver for distributed visualization on a cluster.

All functionality is heavily being tested using automated and extensive unit tests. It has been applied to a real-world dataset, the airflows around a side mirror of a car, utilizing 19 Mio. grid points, 1.000 time steps, 330GB simulation files, 600 MPI processes and 1.6TB of RAM. Its decompositions have been verified by scientists from DLR.

hbrs-theta_utils builds heavily upon C++ libraries hbrs-mpl and Elemental which provide HPC-ready data structures and algorithms for linear algebra and dimension reduction.

The full tech stack consists of:

About TAU & THETA

TAU is a

software system for the prediction of viscous and inviscid flows about complex geometries from the low subsonic to the hypersonic flow regime, employing hybrid unstructured grids.

It is developed and distributed by German Aerospace Center aka Deutsches Zentrum für Luft- und Raumfahrt (DLR).

TAU
The DLR-TAU-Code (TAU=Triangular Adaptive Upwind) is a software for the numerical flow simulation based on the (U)RANS or the hybrid RANS/LES approach using a finite-volume discretization for adaptable unstructured grids. TAU allows for flow predictions around complex moving geometries over a wide range of Mach numbers and has been established as a production code in the European aircraft industry, as well as a research tool for new aerospace technologies.

THETA
The DLR-THETA-Code (THETA=Turbulent Heat Release Extension of the TAU-Code) was developed for the simulation of incompressible combustion chamber flows. Further areas of application include two-phase flow using the Volume-of-Fluid method, which is used to simulate fuel sloshing in tanks of upper stages of rockets, as well as the high-fidelity simulation of flows around wind turbines and in the surrounding terrain taking important atmospheric parameters into account.

For details about TAU and THETA navigate to:

How to build, install and run code using Docker or Podman

For a quick and easy start into developing with C++, a set of ready-to-use Docker/Podman images jm1337/debian-dev-hbrs and jm1337/debian-dev-full (supports more languages) has been created. They contain a full development system including all tools and libraries necessary to hack on distributed decomposition algorithms and more (Docker Hub, source files for Docker images).

Install Docker or Podman

  • On Debian 10 (Buster) or Debian 11 (Bullseye) just run sudo apt install docker.io or follow the official install guide for Docker Engine on Debian
  • On Ubuntu 18.04 LTS (Bionic Beaver) and Ubuntu 20.04 LTS (Focal Fossa) just run sudo apt install docker.io (from bionic/universe and focal/universe repositories) or follow the official install guide for Docker Engine on Ubuntu
  • On Windows 10 follow the official install guide for Docker Desktop on Windows
  • On Mac follow the official install guide for Docker Desktop on Mac
  • On Fedora, Red Hat Enterprise Linux (RHEL) and CentOS follow the official install guide for Podman

Setup and run container

# docker version 18.06.0-ce or later is recommended
docker --version

# fetch docker image
docker pull jm1337/debian-dev-hbrs:bullseye

# log into docker container
docker run -ti jm1337/debian-dev-hbrs:bullseye
# or using a persistent home directory, e.g.
docker run -ti -v /HOST_DIR:/home/devil/ jm1337/debian-dev-hbrs:bullseye
# or using a persistent home directory on Windows hosts, e.g.
docker run -ti -v C:\YOUR_DIR:/home/devil/ jm1337/debian-dev-hbrs:bullseye

Podman strives for complete CLI compatibility with Docker, hence you may use the alias command to create a docker alias for Podman:

alias docker=podman

Build and run code inside container

Execute the following commands within the Docker/Podman container:

# choose a compiler
export CC=clang-10
export CXX=clang++-10
# or
export CC=gcc-10
export CXX=g++-10

# fetch, compile and install prerequisites
git clone --depth 1 https://github.com/JM1/hbrs-cmake.git
cd hbrs-cmake
mkdir build && cd build/
# install to non-system directory because sudo is not allowed in this docker container
cmake \
    -DCMAKE_INSTALL_PREFIX=$HOME/.local \
    ..
make -j$(nproc)
make install
cd ../../

git clone --depth 1 https://github.com/JM1/hbrs-mpl.git
cd hbrs-mpl
mkdir build && cd build/
cmake \
    -DCMAKE_INSTALL_PREFIX=$HOME/.local \
    -DHBRS_MPL_ENABLE_ELEMENTAL=ON \
    -DHBRS_MPL_ENABLE_MATLAB=OFF \
    -DHBRS_MPL_ENABLE_TESTS=OFF \
    -DHBRS_MPL_ENABLE_BENCHMARKS=OFF \
    ..
make -j$(nproc)
make install
cd ../../

# fetch, compile and install hbrs-theta_utils
git clone --depth 1 https://github.com/JM1/hbrs-theta_utils.git
cd hbrs-theta_utils
mkdir build && cd build/
cmake \
    -DCMAKE_INSTALL_PREFIX=$HOME/.local \
    -DHBRS_THETA_UTILS_ENABLE_TESTS=ON \
    ..
make -j$(nproc)
ctest --verbose --output-on-failure
make install

For more examples on how to build and test this code see .gitlab-ci.yml.

License

GNU General Public License v3.0 or later

See LICENSE.md to see the full text.

Author

Jakob Meng @jm1 (github.com, Web)