Best practices#

Session management#

Reuse MAPDL instances

Keep the same MAPDL instance open for multiple operations to improve performance. Only restart when necessary, such as when clearing the database.

Clean shutdown

Always disconnect properly from MAPDL instances to free resources.

Error handling

Check tool results for errors and handle them gracefully in your workflow.

Command execution#

Batch commands

Use run_multiple_commands instead of individual commands for better performance.

Verify state

Use check_mapdl_status periodically to verify the session state.

Use comments

Add comments in the MAPDL session to document your workflow for clarity.

Data handling#

Extract efficiently

Extract only the data you need rather than loading entire result sets.

Cache results

Store extracted data in Python variables to avoid repeated extraction.

Validate data

Check that extracted data (such as positive stresses and reasonable displacements) make sense.

Visualization#

Screenshots after key steps

Take screenshots after geometry definition, meshing, and solving to verify progress.

Custom plots for analysis

Use custom Matplotlib plots for detailed analysis beyond MAPDL’s built-in capabilities.

Export for documentation

Save high-quality plots for reports and documentation.

Workflow design#

Modular workflows

Break complex analyses into smaller, independent steps.

Error recovery

Design workflows that can recover from errors without complete restart.

Progress feedback

Include status updates and progress indicators in long-running workflows.

Parameter validation

Validate all input parameters before sending to MAPDL.

Performance#

Minimize restarts

Avoid restarting MAPDL unless absolutely necessary.

Efficient meshing

Use adaptive meshing and mesh refinement selectively.

Result processing

Process results in Python rather than repeatedly querying MAPDL.

Parallel operations

Consider launching multiple MAPDL instances for independent analyses.

Common patterns#

Parameter sweep#

  1. Define parameter ranges.

  2. Clear results between runs.

  3. Update parameters.

  4. Run analysis.

  5. Extract results.

  6. Aggregate results across runs.

Convergence study#

  1. Run analysis with coarse mesh.

  2. Refine mesh selectively.

  3. Re-run analysis.

  4. Compare results.

  5. Repeat until converged.

Sensitivity analysis#

  1. Identify key parameters.

  2. Vary parameters one at a time.

  3. Record output for each variation.

  4. Analyze parameter importance.

  5. Focus detailed studies on important parameters.