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Python Automation for PAM-CRASH Simulation Workflows

30 Chapters
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01
PAM-CRASH simulation workflow overview
model setup, solver execution, post-processing, and automation opportunities.
PAM-CRASHsimulation
02
Python environment for CAE automation
virtual environments, package management, and integration with PAM-CRASH installation directories.
environmentCAE
03
PAM-CRASH input deck structure
keyword blocks, include files, and parameterization points for automated generation.
PAM-CRASHstructure
04
Python file I/O for simulation data
reading and writing PAM-CRASH input decks, result files, and metadata with pathlib and csv.
I/Osimulation
05
Automating model parameter sweeps
generating variant input decks from templates using string substitution and Jinja2.
Automatingparameter
06
Batch job submission for PAM-CRASH
scripting solver runs with subprocess, job queues, and resource monitoring.
submissionPAM-CRASH
07
Parsing PAM-CRASH output files
extracting time-history data, energy summaries, and error messages with regular expressions.
PAM-CRASHextracting
08
Post-processing automation with Python
reading PAM-CRASH results into NumPy arrays and generating plots with Matplotlib.
Post-processingautomation
09
Automating model checks and validation
scripting pre-run sanity checks for material definitions, contacts, and boundary conditions.
Automatingvalidation
10
Python-driven design of experiments for crash simulation
generating DOE matrices and mapping to PAM-CRASH parameters.
Python-drivenexperiments
11
Workflow orchestration for PAM-CRASH
chaining pre-processing, solving, and post-processing steps with Python scripts.
Workfloworchestration
12
Automating mesh and geometry updates
Python scripts for morphing, remeshing, and updating PAM-CRASH models.
Automatinggeometry
13
Extracting and reporting key crash metrics
scripting occupant injury criteria, intrusion, and energy absorption calculations.
Extractingreporting
14
Python interfaces to PAM-CRASH
using command-line options, REST APIs, and file-based communication for automation.
interfacesPAM-CRASH
15
Error handling and logging in simulation automation
robust scripts for long-running PAM-CRASH jobs.
handlingsimulation
16
Automating material model calibration
Python loops for fitting PAM-CRASH material cards to test data.
Automatingmaterial
17
Parallel and distributed execution
Python multiprocessing and job schedulers for large PAM-CRASH DOE studies.
Paralleldistributed
18
Data management for simulation campaigns
organizing input decks, results, and metadata with Python and SQLite.
managementsimulation
19
Automating post-processing report generation
Python scripts to create PDF or HTML summaries from PAM-CRASH results.
Automatingpost-processing
20
Integration with CAE ecosystems
Python automation for PAM-CRASH within ANSA, Meta, and other pre/post processors.
IntegrationCAE
21
Version control and reproducibility
Git and Python for tracking PAM-CRASH model changes and automation scripts.
reproducibilitytracking
22
Automating optimization loops
coupling Python optimizers with PAM-CRASH for crashworthiness design.
Automatingoptimization
23
Real-time monitoring and dashboards
Python tools for tracking PAM-CRASH solver progress and intermediate results.
Real-timemonitoring
24
Automating model conversion and translation
Python scripts for importing/exporting PAM-CRASH data to other formats.
Automatingconversion
25
Python-based sensitivity analysis and uncertainty quantification for PAM-CRASH models.
Python-based sensitivity analysis and uncertainty quantification for PAM-CRASH models.
Python-basedsensitivity
26
Automating regression testing of PAM-CRASH models
Python test suites for model consistency and performance.
Automatingregression
27
Cloud and HPC automation for PAM-CRASH
Python scripts for remote job submission and data transfer.
HPCautomation
28
Machine learning integration
Python pipelines for surrogate modeling and anomaly detection in PAM-CRASH results.
integrationpipelines
29
Case study
end-to-end automation of a full vehicle crash simulation workflow with Python.
end-to-endautomation
30
Best practices and maintainability
structuring Python automation projects for PAM-CRASH in production environments.
practicesmaintainability