Configure the Micro Manager

The Micro Manager is configured with a JSON file. An example configuration file is

{
    "micro_file_name": "micro_solver",
    "coupling_params": {
        "config_file_name": "precice-config.xml",
        "macro_mesh_name": "macro-mesh",
        "read_data_names": {"temperature": "scalar", "heat-flux": "vector"},
        "write_data_names": {"porosity": "scalar", "conductivity": "vector"}
    },
    "simulation_params": {
        "macro_domain_bounds": [0.0, 1.0, 0.0, 1.0, 0.0, 1.0],
        "micro_dt": 1.0
    },
    "diagnostics": {
      "output_micro_sim_solve_time": "True"
    }
}

This example configuration file is in examples/micro-manager-config.json.

The path to the file containing the Python importable micro simulation class is specified in the micro_file_name parameter. If the file is not in the working directory, give the relative path.

There are three main sections in the configuration file, the coupling_params, the simulation_params and the optional diagnostics.

Coupling Parameters

Simulation Parameters

Diagnostics

Adding diagnostics in the preCICE XML configuration

If the parameter data_from_micro_sims is set, the data to be output needs to be written to preCICE, and an export tag needs to be added for the participant Micro-Manager. For example, let us consider the case that the data porosity, which is a scalar, needs to be exported. Unless already defined, define the data, and then write it to preCICE. Also, add an export tag. The resulting entries in the XML configuration file look like:

<data:scalar name="porosity"/>

<participant name="Micro-Manager">
  ...
  <write-data name="porosity" mesh="macro-mesh"/>
  <export:vtu directory="Micro-Manager-output" every-n-time-windows="5"/>
</participant>

If output_micro_sim_solve_time is set, add similar entries for the data micro_sim_time in the following way:

<data:scalar name="micro_sim_time"/>

<participant name="Micro-Manager">
  ...
  <write-data name="micro_sim_time" mesh="macro-mesh"/>
  <export:vtu directory="Micro-Manager-output" every-n-time-windows="5"/>
</participant>

Domain decomposition

The Micro Manager can be run in parallel. For a parallel run, set the desired partitions in each axis by setting the decomposition parameter. For example, if the domain is 3D and the decomposition needs to be two partitions in x, one partition in y, and sixteen partitions in for z, the setting is

"simulation_params": {
    "macro_domain_bounds": [0, 1, 0, 1, 0, 1],
    "decomposition": [2, 1, 16]
}

For a 2D domain, only two values need to be set for decomposition. The total number of partitions provided in the decomposition should be the same as the number of processors provided in the mpirun/mpiexec command.

Adaptivity

The Micro Manager can adaptively control micro simulations. The adaptivity strategy is taken from

1. Redeker, Magnus & Eck, Christof. (2013). A fast and accurate adaptive solution strategy for two-scale models with continuous inter-scale dependencies. Journal of Computational Physics. 240. 268-283. 10.1016/j.jcp.2012.12.025.

2. Bastidas, Manuela & Bringedal, Carina & Pop, Iuliu Sorin. (2021). A two-scale iterative scheme for a phase-field model for precipitation and dissolution in porous media. Applied Mathematics and Computation. 396. 125933. 10.1016/j.amc.2020.125933.

All the adaptivity parameters are chosen from the second publication.

To turn on adaptivity, set "adaptivity": True in simulation_params. Then under adaptivity_settings set the following variables:

The primary tuning parameters for adaptivity are the history parameter \(\Lambda\), the coarsening constant \(C_c\), and the refining constant \(C_r\). Their effects can be interpreted as:

• Higher values of the history parameter \(\Lambda\) imply lower significance of the similarity measures in the previous timestep on the similarity measure and thus adaptivity state in the current timestep.
• Higher values of the coarsening constant \(C_c\) imply that more active simulations from the previous timestep will remain active in the current timestep.
• Higher values of the refining constant \(C_r\) imply that less inactive points from the previous timestep will become active in the current timestep.

Example of adaptivity configuration is

"simulation_params": {
    "macro_domain_bounds": [0, 1, 0, 1, 0, 1],
    "adaptivity": "True",
    "adaptivity_settings" {
        "type": "local",
        "data": ["temperature", "porosity"],
        "history_param": 0.5,
        "coarsening_constant": 0.3,
        "refining_constant": 0.4,
        "every_implicit_iteration": "True"
    }
}

Adding adaptivity in the preCICE XML configuration

If adaptivity is used, the Micro Manager will attempt to write two scalar data per micro simulation to preCICE, called active_state and active_steps.

The Micro Manager uses the output functionality of preCICE, hence these data sets need to be manually added to the preCICE configuration file. In the mesh and the participant Micro-Manager add the following lines:

<data:scalar name="active_state"/>
<data:scalar name="active_steps"/>

<mesh name="macro-mesh">
    <use-data name="active_state"/>
    <use-data name="active_steps"/>
</mesh>

<participant name="Micro-Manager">
    <write-data name="active_state" mesh="macro-mesh"/>
    <write-data name="active_steps" mesh="macro-mesh"/>
</participant>

Interpolate a crashed micro simulation

If the optional dependency sklearn is installed, the Micro Manager will derive the output of a crashed micro simulation by interpolating outputs from similar simulations. To enable this, set "interpolate_crash": "True" in the simulation_params section of the configuration file.

For more details on the interpolation see the crash handling documentation.

Next step

After creating a configuration file you are ready to run the Micro Manager.