rocky-uniaxc 0.1.0

Toolkit for multiscale uniaxial compression simulations in Ansys Rocky

Rocky-UniaxC: Multiscale Uniaxial Compression Simulation Toolkit for Ansys Rocky

rocky-uniaxc is a Python toolkit designed to automate the configuration, mesh generation, execution, and analysis of multiscale uniaxial compression simulations in Ansys Rocky DEM. It supports running simulations locally or deploying parallel job arrays on HPC clusters (like BlueBear or SCP) using SLURM.

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🌟 Key Features

• Automated Boundary Mesh Generation: Programmatically creates custom compression wall and insert surfaces using GMSH.
• Design of Experiments (DOE):
  • Full-Factorial Parameter Sweeps: Automatically expands all parameter combinations to perform comprehensive grid sweeps.
  • One-Factor-at-a-Time (OFAT): Evaluates parameter sensitivities by varying one variable at a time while holding others constant.
  • ...more to be added
• Flexible Simulation Backends: Works with the modern pyrocky API wrapper or standard rocky_prepost scripts.
• SLURM HPC Cluster Presets: Ready-made presets for submitting parallel simulation batch jobs on UoB BlueBear (CPU/GPU) and SCP (GPU).
• Robust Post-Processing: Automated analysis of settled and compressed states (e.g., calculating bulk densities, Coordination/Contact Numbers, and contacts ratio).
• Data Export & Quality Audits: Automatically scans logs for lost particles, flags faulty runs, and gathers results into structured databases (SQLite) and dataframes (Pandas/CSV/Parquet).

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📋 Prerequisites

• Python: >= 3.10
• Ansys Rocky DEM: Ansys Rocky version 2025 R2 (or compatible release) with its Python API (ansys-rocky-core) installed.
• GMSH: Required on your system for generating boundary STL meshes.

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⚙️ Installation

You can install the package using uv (recommended) or pip:

Using uv

To sync development and testing dependencies in a virtual environment:

uv sync --all-extras --dev

To install the package into your current Python environment:

uv pip install .

Using pip

Install the core package:

pip install .

Install with testing dependencies:

pip install .[test]

Install with documentation dependencies:

pip install .[docs]

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🚀 Quickstart

1. Run a Parameter Sweep

Define your parameter space in a JSON config file (e.g., sweep_config.json):

{
  "shape": {
    "name": "sphere"
  },
  "particle_properties": {
    "radius": [0.005, 0.006],
    "density": [2500],
    "poisson": [0.25],
    "youngmod": [1e7]
  },
  "inseractions": {
    "pp": {
      "fric_dyn": [0.3, 0.5],
      "fric_stat": [0.4],
      "fric_rolling": [0.1],
      "cor": [0.5]
    },
    "pw": {
      "fric_dyn": [0.4],
      "fric_stat": [0.5],
      "cor": [0.5]
    }
  },
  "experim_settings": {
    "box_len": [0.1],
    "p_compress": [1000]
  },
  "contact_model": {
    "normal": "linear_hysteresis",
    "tangential": "coulomb_limit",
    "rolling": "none",
    "adhesion": "none"
  }
}

Write a script to generate and schedule the simulations:

from rocky_uniaxc import launch_sweep
from rocky_uniaxc.utils import RockyScheduler

# 1. Define the cluster scheduler settings (e.g., BlueBear CPU)
scheduler = RockyScheduler.bb_cpu(ncpus=20, run_days=3)

# 2. Launch the sweep configuration
launch_sweep(
    sweep_name="my_first_sweep",
    scheduler=scheduler,
    json_path="sweep_config.json",
    autolaunch=True,
    target="CPU",
    backend="pyrocky"
)

2. Post-Process Simulation Results

Load all simulation results from SQLite databases and export them for downstream analysis:

import rocky_uniaxc.sweep_analysis as analyze

# Load data into a Pandas DataFrame
df = analyze.load_data("my_first_sweep")
print(df.head())

# Check for runs with particle loss warnings or outliers
analyze.find_faulty_runs("my_first_sweep", dump=True)

# Export the results in custom formats (parquet, csv, excel)
analyze.dump_results("my_first_sweep", filetype="parquet", minimal=True)

3. Run a Single Case via CLI

You can execute a single simulation case using a local settings.json file:

python -m rocky_uniaxc.case_runner path/to/settings.json

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🧪 Running Tests

To run the unit test suite and check code correctness:

uv run pytest

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📚 Documentation

The documentation is configured for hosting on ReadTheDocs.

To build the HTML documentation locally:

uv run mkdocs build

The compiled output will be available at site/index.html.

To run a local live-reload server for development:

uv run mkdocs serve

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📄 License

This project is licensed under the MIT License. See LICENSE for details.