Provide a JSON file to configure the Micro Manager.

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

{
    "micro_file_name": "micro_solver",
    "coupling_params": {
        "precice_config_file_name": "precice-config.xml",
        "macro_mesh_name": "macro-mesh",
        "read_data_names": ["temperature", "heat-flux"],
        "write_data_names": ["porosity", "conductivity"]
    },
    "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.

Micro Manager Configuration

Parameter Description
micro_file_name Path to the file containing the Python importable micro simulation class. If the file is not in the working directory, give the relative path from the directory where the Micro Manager is executed.
output_directory Path to output directory for logging and performance metrics. Directory is created if not existing already.
memory_usage_output_type Set to either local, global, or all. local outputs rank-wise peak memory usage. global outputs global averaged peak memory usage. all outputs both local and global levels. All output is to a CSV file with the peak memory usage (RSS) in every time window, in MBs.
memory_usage_output_n Frequency of output of memory usage (integer which is number of time windows).

Apart from the base settings, there are three main sections in the configuration file, coupling parameters, simulation parameters, and diagnostics.

Coupling Parameters

Parameter Description
precice_config_file_name Path to the preCICE XML configuration file from the current working directory.
macro_mesh_name Name of the macro mesh as stated in the preCICE configuration.
read_data_names A list with the names of the data to be read from preCICE.
write_data_names A list with the names of the data to be written to preCICE.

Simulation Parameters

Parameter Description
macro_domain_bounds Minimum and maximum bounds of the macro-domain, having the format [xmin, xmax, ymin, ymax, zmin, zmax] in 3D and [xmin, xmax, ymin, ymax] in 2D.
Domain decomposition parameters See section on domain decomposition. But default, the Micro Manager assumes that it will be run in serial.
Adaptivity parameters See section on adaptivity. By default, adaptivity is disabled.
micro_dt Initial time window size (dt) of the micro simulation.

Diagnostics

Parameter Description
— —H
data_from_micro_sims A Python dictionary with the names of the data from the micro simulation to be written to VTK files as keys and "scalar" or "vector" as values.
output_micro_sim_solve_time If true, the Micro Manager writes the wall clock time of the solve() function of each micro simulation.
micro_output_n Frequency of calling the optional output functionality of the micro simulation in terms of number of time steps. If not given, micro_sim.output() is called every time step.

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 solve_cpu_time in the following way:

<data:scalar name="solve_cpu_time"/>

<participant name="Micro-Manager">
  ...
  <write-data name="solve_cpu_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:

Parameter Description
type Set to either local or global. The type of adaptivity matters when the Micro Manager is run in parallel. local means comparing micro simulations within a local partitioned domain for similarity. global means comparing micro simulations from all partitions, so over the entire domain.
data List of names of data which are to be used to calculate if micro-simulations are similar or not. For example ["temperature", "porosity"].
adaptivity_every_n_time_windows Frequency of adaptivity computation (integer which is number of time windows).
output_type Set to either local, global, or all. local outputs rank-wise adaptivity metrics. global outputs global averaged metrics. all outputs both local and global metrics.
output_n Frequency of output of adaptivity metrics (integer which is number of time windows).
history_param History parameter \(\Lambda\), set as \(\Lambda >= 0\).
coarsening_constant Coarsening constant \(C_c\), set as \(0 =< C_c < 1\).
refining_constant Refining constant \(C_r\), set as \(0 =< C_r < 1\).
every_implicit_iteration If true, adaptivity is calculated in every implicit iteration.
If False, adaptivity is calculated once at the start of the time window and then reused in every implicit time iteration. Default false.
similarity_measure Similarity measure to be used for adaptivity. Can be either L1, L2, L1rel or L2rel. By default, L1 is used. The rel variants calculate the respective relative norms. This parameter is optional. Default L1.
lazy_initialization Set to true to lazily create and initialize micro simulations. If selected, micro simulation objects are created only when the micro simulation is activated for the first time. Default: false.

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,
        "lazy_initialization": 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.

Parameter Description
active_state 1 if the micro simulation is active in the time window, and 0 if inactive.
active_steps Summation of active_state up to the current time window.

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.