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Python Batch Execution and Monitoring for CAE Solvers

30 Chapters

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Course overview

scope, CAE solver batch execution workflows, monitoring requirements, and Python's role in automation.

CAE solver landscape

common solvers (Abaqus, ANSYS, Nastran, OpenFOAM, LS-DYNA), input decks, command-line interfaces, and output artifacts.

Python environment setup

virtual environments, dependency management, and packaging tools for reproducible CAE automation projects.

environmentenvironments

Subprocess fundamentals

launching CAE solvers with subprocess.Popen, run, and call, handling arguments, environment variables, and working directories.

Subprocesslaunching

Batch job definition

parameter sweeps, design of experiments, job manifests, and structured input generation for multiple solver runs.

definitionparameter

File and directory orchestration

staging input decks, managing scratch directories, and collecting output files across concurrent solver executions.

directoryorchestration

Process lifecycle management

start, poll, terminate, and kill solver processes, handling exit codes and abnormal terminations.

lifecyclemanagement

Real-time output capture

streaming stdout and stderr from CAE solvers, parsing progress messages, and detecting convergence or divergence.

Real-timestreaming

Logging and telemetry

structured logging with Python logging, log rotation, and aggregating solver status into centralized records.

telemetrystructured

Monitoring solver progress

parsing solver-specific status files, extracting residuals, time-step information, and iteration counts.

Monitoringprogress

Concurrency models

threading, multiprocessing, and asynchronous I/O for running multiple CAE solver instances efficiently.

Concurrencythreading

Job scheduling and queuing

building a simple Python-based scheduler, priority handling, and integration with HPC job schedulers (SLURM, PBS, LSF).

schedulingbuilding

Resource management

CPU affinity, memory limits, license tokens, and preventing oversubscription during batch runs.

Resourcemanagement

Error handling and retry logic

detecting solver failures, classifying errors, and implementing automated retries with backoff strategies.

handlingdetecting

Checkpointing and restart

managing solver restart files, resuming interrupted runs, and orchestrating restart workflows in Python.

Checkpointingmanaging

Data extraction and post-processing

reading solver output formats (ODB, EnSight, VTK, CSV) and automating result extraction with Python.

extractionpost-processing

Monitoring dashboards

building real-time monitoring views with matplotlib, Plotly, or web frameworks for batch job status.

Monitoringdashboards

Notification and alerting

email, Slack, and webhook integration for job completion, failure, and threshold-based alerts.

Notificationalerting

Workflow orchestration

chaining multiple solver runs, conditional branching, and dependency management using Python workflow tools.

Workfloworchestration

Containerization

Docker and Singularity for packaging CAE solvers and Python automation into portable, reproducible execution units.

ContainerizationSingularity

Cloud and remote execution

running batch jobs on cloud VMs, managing SSH connections, and synchronizing files with remote resources.

Performance profiling

measuring solver wall-clock time, I/O bottlenecks, and Python overhead in batch execution pipelines.

Performanceprofiling

Scalability patterns

horizontal scaling, distributed job execution, and handling thousands of CAE runs with Python.

Scalabilitypatterns

Security and access control

managing credentials, secure file transfer, and sandboxing solver executions in shared environments.

Securitymanaging

parameter sweep for structural analysis using Abaqus and Python batch execution with automated monitoring.

parameterstructural

CFD batch runs with OpenFOAM, real-time residual monitoring, and automated convergence detection.

LS-DYNA crash simulation batch processing with job queuing, error recovery, and result aggregation.

LS-DYNAsimulation

ANSYS Workbench automation via Python scripting, batch execution, and monitoring of multiple design points.

Testing and validation

unit testing Python automation scripts, mocking solver calls, and verifying batch execution correctness.

validationautomation

Best practices and production deployment

code organization, configuration management, CI/CD for CAE automation, and maintainability.

practicesproduction