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Fourier Accelerated Nodal Solvers (FANS)

Fourier Accelerated Nodal Solvers (FANS) is an FFT-based homogenization solver designed to handle microscale multiphysics problems. This repository contains a C++ implementation of FANS, built using CMake and MPI for parallel computations.

Example Image

Table of contents

Dependencies

FANS has the following dependencies:

  • A C++ compiler with OpenMP support (e.g. GCC, or Clang with OpenMP libraries installed)
  • CMake (version 3.0 or higher) (+ Unix file utility for creating .deb packages)
  • Git (for cloning this repo)
  • MPI (mpicc and mpic++)
  • HDF5 with parallel support
  • Eigen3
  • FFTW3 with MPI support

Installing dependencies

We recommend installing the dependencies using a package manager. For example, using apt, the following commands are run:

apt-get install \
    libhdf5-dev \
    libopenmpi-dev \
    libeigen3-dev \
    libfftw3-dev \
    libfftw3-mpi-dev

Also, we recommend to set up a Python virtual environment for the FANS_Dashboard:

apt-get install \
    time \
    htop \
    python3 \
    python3-pip \
    python3-venv \
    python-is-python3 \
    python3-dev

python -m venv ~/venvs/FANS
source ~/venvs/FANS/bin/activate
python -m pip install h5py lxml

We also provide a set of Docker images to work with FANS within an isolated environment.

Installing dependencies using Spack

Spack is a package manager designed for high-performance computing environments. It simplifies the installation of complex software stacks, making it ideal for setting up FANS on remote systems.

  1. Install Spack: If Spack is not installed, set it up with the following commands:

    git clone https://github.com/spack/spack.git
cd spack/bin
source ./spack

  2. Install Dependencies: Once Spack is set up, you can install the required dependencies:

    spack install cmake
spack install mpi
spack install hdf5 +cxx +mpi
spack install eigen
spack install fftw +mpi

    Alternatively, optimized FFTW implementations can be used depending on your system's architecture, for example amdfftw (For AMD systems) or cray-fftw (For Cray systems) or fujitsu-fftw (For Fujitsu systems).

  3. Load Dependencies Once dependencies are installed, you can load them before building:

    spack load cmake mpi hdf5 eigen fftw

Building

  1. Clone the repository:

    git clone https://github.com/DataAnalyticsEngineering/FANS.git
cd FANS

  2. Configure the build using CMake:

    mkdir build
cd build
cmake ..

  3. Compile:

    cmake --build . -j

The compilation symlinks the generated FANS binary into the test/ directory for convenience.

Configuring a build

The following CMake configuration options exist:

  • CMAKE_BUILD_TYPE: Sets the build type. Common values are Debug, Release, RelWithDebInfo, and MinSizeRel.

  • FANS_BUILD_STATIC: Build static library instead of shared library.

    • Default: OFF
    • Usage: -DFANS_BUILD_STATIC=ON

  • CMAKE_INTERPROCEDURAL_OPTIMIZATION: Enable inter-procedural optimization (IPO) for all targets.

    • Default: ON (if supported)
    • Usage: -DCMAKE_INTERPROCEDURAL_OPTIMIZATION=OFF
    • Note: When you run the configure step for the first time, IPO support is automatically checked and enabled if available. A status message will indicate whether IPO is activated or not supported.

Installing

Install FANS (system-wide) using the following options:

  1. Using CMake (sudo required if --prefix is omitted):

    cmake --install . [--prefix <install-dir>]

  2. Using .deb packages (only debian based distros; sudo required):

    cpack -G "DEB"
apt install packages/fans_<version>_<architecture>.deb
apt install packages/fans-dev_<version>_<architecture>.deb

Input File Format

FANS requires a JSON input file specifying the problem parameters. Example input files can be found in the test/input_files directory. It is recommended to use these files as a reference to create your own input file.

Microstructure Definition

"ms_filename": "microstructures/sphere32.h5",
"ms_datasetname": "/sphere/32x32x32/ms",
"ms_L": [1.0, 1.0, 1.0]
  • ms_filename: This specifies the path to the HDF5 file that contains the microstructure data.
  • ms_datasetname: This is the path within the HDF5 file to the specific dataset that represents the microstructure.
  • ms_L: Microstructure length defines the physical dimensions of the microstructure in the x, y, and z directions.

Problem Type and Material Model

"matmodel": "LinearElasticIsotropic",
"material_properties": {
    "bulk_modulus": [62.5000, 222.222],
    "shear_modulus": [28.8462, 166.6667]
}
  • problem_type: This defines the type of physical problem you are solving. Common options include "thermal" problems and "mechanical" problems.
  • matmodel: This specifies the material model to be used in the simulation. Examples include LinearThermalIsotropic for isotropic linear thermal problems, LinearElasticIsotropic for isotropic linear elastic mechanical problems, PseudoPlasticLinearHardening/PseudoPlasticNonLinearHardening for plasticity mimicking model with linear/nonlinear hardening, and J2ViscoPlastic_LinearIsotropicHardening/J2ViscoPlastic_NonLinearIsotropicHardening for rate dependent J2 plasticity model with linear/nonlinear isotropic hardening.
  • material_properties: This provides the necessary material parameters for the chosen material model. For thermal problems, you might specify conductivity, while mechanical problems might require bulk_modulus, shear_modulus, and more properties for advanced material models. These properties can be defined as arrays to represent multiple phases within the microstructure.

Solver Settings

"method": "cg",
"error_parameters":{
    "measure": "Linfinity",
    "type": "absolute",
    "tolerance": 1e-10
},
"n_it": 100,
  • method: This indicates the numerical method to be used for solving the system of equations. cg stands for the Conjugate Gradient method, and fp stands for the Fixed Point method.
  • error_parameters: This section defines the error parameters for the solver. Error control is applied on the finite element nodal residual of the problem.
    • measure: Specifies the norm used to measure the error. Options include Linfinity, L1, or L2.
    • type: Defines the type of error measurement. Options are absolute or relative.
    • tolerance: Sets the tolerance level for the solver, defining the convergence criterion based on the chosen error measure. The solver iterates until the solution meets this tolerance.
  • n_it: Specifies the maximum number of iterations allowed for the FANS solver.

Macroscale Loading Conditions

"macroscale_loading":   [
                            [
                                [0.004, -0.002, -0.002, 0, 0, 0],
                                [0.008, -0.004, -0.004, 0, 0, 0],
                                [0.012, -0.006, -0.006, 0, 0, 0],
                                [0.016, -0.008, -0.008, 0, 0, 0],
                            ],
                            [
                                [0, 0, 0, 0.002, 0, 0],
                                [0, 0, 0, 0.004, 0, 0],
                                [0, 0, 0, 0.006, 0, 0],
                                [0, 0, 0, 0.008, 0, 0],
                            ]
                        ],
  • macroscale_loading: This defines the external loading applied to the microstructure. It is an array of arrays, where each sub-array represents a loading condition applied to the system. The format of the loading array depends on the problem type:
  • For thermal problems, the array typically has 3 components, representing the temperature gradients in the x, y, and z directions.
  • For mechanical problems, the array must have 6 components, corresponding to the components of the strain tensor in Mandel notation (e.g., [[ε_11, ε_22, ε_33, √2 ε_12, √2 ε_13, √2 ε_23]]).

In the case of path/time-dependent loading as shown, for example as in plasticity problems, the macroscale_loading array can include multiple steps with corresponding loading conditions.

Results Specification

"results": ["stress_average", "strain_average", "absolute_error", "phase_stress_average", "phase_strain_average",
            "microstructure", "displacement", "stress", "strain"]

  • results: This array lists the quantities that should be stored into the results HDF5 file during the simulation. Each string in the array corresponds to a specific result:

    • stress_average and strain_average: Volume averaged- homogenized stress and strain over the entire microstructure.
    • absolute_error: The L-infinity error of finite element nodal residual at each iteration.
    • phase_stress_average and phase_strain_average: Volume averaged- homogenized stress and strain for each phase within the microstructure.
    • microstructure: The original microstructure data.
    • displacement: The displacement fluctuation field (for mechanical problems) and temperature fluctuation field (for thermal problems).
    • stress and strain: The stress and strain fields at each voxel in the microstructure.

  • Additional material model specific results can be included depending on the problem type and material model.

Examples

Execute the run_tests.sh file to run tests, which are also examples. For example, to run a linear elastic mechanical homogenization problem for a 6 othonormal load cases on a microstructure image of size 32 x 32 x 32 with a single spherical inclusion,

mpiexec -n 2 ./FANS input_files/test_LinearElastic.json test_results.h5

Acknowledgements

Funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy - EXC 2075 – 390740016. Contributions by Felix Fritzen are funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) within the Heisenberg program - DFG-FR2702/8 - 406068690; DFG-FR2702/10 - 517847245 and through NFDI-MatWerk - NFDI 38/1 - 460247524. We acknowledge the support by the Stuttgart Center for Simulation Science (SimTech).

Contributors