PyRocky can provide a strong foundation for applying Artificial Intelligence (AI) to DEM simulation workflows.
Because simulation parameters and results can be accessed programmatically, Ansys Rocky data can be organized for AI and machine learning applications.
A possible AI-enabled workflow could include:
- Generating a structured set of Ansys Rocky simulations.
- Automatically varying selected operating and material parameters.
- Extracting relevant engineering outputs from each simulation.
- Storing the inputs and outputs in a simulation database.
- Training machine learning or surrogate models using the generated data.
- Using the trained model to rapidly estimate the performance of new configurations.
- Validating the most promising configurations with new high-fidelity Ansys Rocky simulations.
Such an approach can be useful when a complete Ansys Rocky simulation is computationally expensive, and a large design space must be evaluated.
For example, an AI model could be trained to estimate chute throughput, particle impact velocity, mixer performance, or equipment torque as a function of selected operating and design parameters, helping reduce repeated references to the same modeling use case.
The resulting model would not replace Ansys Rocky as the source of high-fidelity physics. Instead, it could act as a faster predictive layer for preliminary design exploration, optimization, or operational decision support.
