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Python Crash Simulation Data Extraction and Analysis

30 Chapters
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01
Crash simulation data landscape
sources, formats, and extraction challenges in LS-DYNA, PAM-CRASH, and Radioss output decks.
simulationlandscape
02
Python environment setup for CAE data workflows
managing dependencies, virtual environments, and Jupyter for reproducible analysis.
environmentCAE
03
Parsing keyword-based simulation input decks
reading *NODE, *ELEMENT, and *PART cards with Python text processing.
keyword-basedsimulation
04
Extracting time-history data from d3plot and binout files
binary format structure and Python reader libraries.
Extractingtime-history
05
Handling ASCII result files
efficient parsing of nodout, elout, and secforc using NumPy and pandas.
HandlingASCII
06
Building a unified data extraction pipeline
from raw solver output to structured DataFrames.
Buildingextraction
07
Time-series alignment and synchronization
merging channels from multiple files with different time steps.
Time-seriesalignment
08
Signal processing for crash pulses
filtering, baseline correction, and SAE J211 channel frequency class implementation.
processingfiltering
09
Extracting occupant safety metrics
HIC, chest deflection, and femur load from hybrid III dummy output.
Extractingoccupant
10
Vehicle kinematics extraction
acceleration, velocity, and displacement from nodal time history data.
kinematicsextraction
11
Energy absorption analysis
internal energy, kinetic energy, and hourglass energy from glstat and matsum.
absorptionanalysis
12
Section force and moment extraction
processing cross-section data for structural integrity assessment.
extractionprocessing
13
Intrusion and deformation measurement
computing relative displacements from nodal coordinates.
Intrusiondeformation
14
Barrier and impactor data extraction
processing rigid wall force and prescribed motion files.
impactorextraction
15
Airbag and seatbelt data extraction
handling gas flow, pressure, and restraint force outputs.
seatbeltextraction
16
Batch processing of multiple simulation runs
automating extraction across design of experiments.
processingmultiple
17
Data quality checks and validation
detecting missing channels, spikes, and inconsistent sampling.
validationdetecting
18
Efficient storage and retrieval
using HDF5 and Parquet for large crash simulation datasets.
Efficientretrieval
19
Interactive visualization of crash data
matplotlib and plotly for time-history and contour plots.
Interactivevisualization
20
Comparative analysis of simulation runs
overlaying curves and computing correlation metrics.
Comparativeanalysis
21
Statistical analysis of crash simulation results
distributions, regression, and sensitivity studies.
Statisticalanalysis
22
Frequency-domain analysis
FFT and power spectral density for noise and vibration assessment.
Frequency-domainanalysis
23
Occupant injury risk curves
logistic regression and injury probability from extracted metrics.
Occupantlogistic
24
Structural performance metrics
calculating crush force efficiency and specific energy absorption.
Structuralperformance
25
Automated report generation
templating with Jinja2 and exporting to PDF and HTML.
Automatedgeneration
26
Integration with optimization workflows
feeding extracted data into modeFRONTIER or Optuna.
Integrationoptimization
27
Real-time data extraction from running simulations
monitoring and in-situ analysis.
Real-timeextraction
28
Machine learning for crash data
feature extraction and surrogate model development.
extractionsurrogate
29
Cloud and HPC data handling
parallel extraction and distributed storage for large datasets.
HPChandling
30
Case study
end-to-end extraction and analysis of a full frontal crash simulation.
end-to-endextraction