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LS-DYNA and PAM-CRASH Results Analytics with Python

30 Chapters
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01
Crash simulation results analytics landscape
LS-DYNA and PAM-CRASH output formats, solver-specific conventions, and Python's role in post-processing workflows.
simulationanalytics
02
Binary and ASCII result file structures
d3plot, binout, and PAM-CRASH .erfh/.dsy files, including state data, time history, and model metadata.
ASCIIstructures
03
Python environment setup for CAE analytics
installing and configuring numpy, scipy, pandas, matplotlib, and dedicated readers such as lasso-python and qd-engineering tools.
environmentCAE
04
Reading LS-DYNA d3plot files with Python
extracting nodal coordinates, element connectivity, and state data using lasso-python and alternative parsers.
LS-DYNAextracting
05
Reading LS-DYNA binout files
accessing time-history channels, resultant forces, energies, and section forces with pybinout and custom readers.
LS-DYNAaccessing
06
Reading PAM-CRASH result files
parsing .erfh and .dsy outputs, understanding PAM-CRASH data hierarchy, and using Python wrappers for extraction.
PAM-CRASHunderstanding
07
Time-history data extraction and management
selecting channels, resampling, synchronizing LS-DYNA and PAM-CRASH time bases, and handling missing or corrupted data.
Time-historyextraction
08
Nodal and elemental result mapping
interpolating displacement, velocity, acceleration, stress, and strain onto user-defined grids and cross-sections.
elementalinterpolating
09
Section force and moment extraction
computing and visualizing resultant forces and moments from LS-DYNA and PAM-CRASH section cards and cross-sections.
extractioncomputing
10
Energy balance analytics
extracting kinetic, internal, hourglass, and contact energies, verifying energy conservation, and flagging simulation anomalies.
analyticsextracting
11
Intrusion and deformation metrics
calculating occupant compartment intrusion, structural deformation, and displacement contours from both solvers.
Intrusiondeformation
12
Occupant safety metrics
head injury criterion (HIC), chest deflection, femur force, and other injury criteria computed from LS-DYNA and PAM-CRASH dummy outputs.
Occupantcriterion
13
Barrier and impactor analysis
processing force-displacement curves, peak forces, and energy absorption for frontal, side, and rear impact scenarios.
impactoranalysis
14
Airbag and restraint system analytics
extracting deployment timing, pressure, and force data from LS-DYNA and PAM-CRASH simulations.
restraintanalytics
15
Material model validation
comparing simulation stress-strain responses with experimental data and extracting material-specific history variables.
Materialvalidation
16
Contact and interface analytics
extracting contact forces, penetration, friction energy, and interface behavior from both solvers.
interfaceanalytics
17
Frequency and modal analysis of crash results
performing FFT, filtering, and spectral analysis on time-history data using scipy and numpy.
Frequencyanalysis
18
Statistical analysis of simulation results
computing means, variances, percentiles, and correlations across multiple runs and design variants.
Statisticalanalysis
19
Design of experiments (DOE) and parametric studies
automating LS-DYNA and PAM-CRASH runs, extracting key responses, and building response surfaces with Python.
experimentsDOE
20
Sensitivity and robustness analysis
identifying influential parameters, performing Monte Carlo simulations, and quantifying uncertainty in crash responses.
Sensitivityrobustness
21
Optimization workflows
coupling Python-driven result analytics with optimization libraries (e.g., scipy.optimize, pymoo) for crashworthiness design.
Optimizationworkflows
22
Automated report generation
creating HTML, PDF, and interactive dashboards with matplotlib, plotly, and jinja2 for LS-DYNA and PAM-CRASH results.
Automatedgeneration
23
Batch processing and pipeline automation
scripting large-scale extraction, analysis, and visualization for hundreds of simulation runs.
processingpipeline
24
Data management and database integration
storing extracted results in SQLite, PostgreSQL, or HDF5 for efficient querying and long-term archiving.
managementdatabase
25
Machine learning for crash analytics
feature engineering from LS-DYNA and PAM-CRASH outputs, surrogate modeling, and anomaly detection with scikit-learn.
analyticsLS-DYNA
26
Visualization techniques for crash results
3D contour plots, animations, section cuts, and comparative visualizations using matplotlib, VTK, and PyVista.
Visualizationtechniques
27
Validation and correlation with physical tests
aligning simulation and test data, computing correlation metrics, and generating validation reports.
Validationcorrelation
28
Handling large datasets and performance optimization
memory-efficient reading, parallel processing, and GPU acceleration for massive crash result files.
Handlingdatasets
29
Case study
full frontal impact analysis — from raw LS-DYNA d3plot and binout extraction to injury criteria and energy balance reporting.
analysisLS-DYNA
30
Case study
side impact with PAM-CRASH — extracting intrusion, dummy responses, and restraint performance, and comparing with LS-DYNA results.
PAM-CRASHextracting