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Python LS-DYNA Binout and Time History Data Analysis

30 Chapters
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01
Course overview
scope, concepts, and technical context of LS-DYNA binout and time history data analysis with Python.
technicalLS-DYNA
02
LS-DYNA output architecture
binout file structure, time history databases, and relationship to d3plot and ASCII outputs.
LS-DYNAarchitecture
03
Python environment setup
installing and configuring lasso-python, numpy, pandas, matplotlib, and related libraries for LS-DYNA data workflows.
environmentinstalling
04
Reading binout files with lasso-python
opening files, listing available branches, and inspecting metadata.
lasso-pythonavailable
05
Navigating binout hierarchy
understanding root, state, and time history branches for nodes, elements, and materials.
Navigatinghierarchy
06
Extracting nodal time history data
displacement, velocity, acceleration, and coordinate transformations.
Extractingdisplacement
07
Extracting element time history data
stress, strain, resultant forces, and section forces for shells, solids, and beams.
Extractingresultant
08
Extracting rigid body and joint data
center of mass motion, interface forces, and joint kinematics from binout.
Extractinginterface
09
Extracting contact and interface data
contact forces, penetration, and friction energy from binout time histories.
Extractinginterface
10
Extracting energy data
kinetic, internal, hourglass, sliding, and total energy time histories for model validation.
Extractinginternal
11
Working with multiple binout files
batch reading, parameter sweeps, and comparative analysis across simulations.
multipleparameter
12
Data conversion to pandas DataFrames
structuring time history arrays for efficient manipulation and analysis.
conversionDataFrames
13
Time series resampling and synchronization
aligning data from different output intervals and simulation runs.
resamplingsynchronization
14
Filtering and smoothing
applying Butterworth, SAE, and moving average filters to LS-DYNA time history signals.
Filteringsmoothing
15
Signal processing for crash and impact
peak detection, pulse duration, HIC, and NIC calculations from acceleration data.
processingdetection
16
Frequency domain analysis
FFT, PSD, and spectrogram computation for vibration and NVH post-processing.
Frequencyanalysis
17
Statistical analysis of time histories
mean, standard deviation, percentiles, and correlation across channels.
Statisticalanalysis
18
Visualization with matplotlib
single and multi-axis plots, subplots, and annotated time history charts.
Visualizationmatplotlib
19
Interactive visualization with plotly
zoomable, hover-enabled dashboards for exploring binout data.
Interactivevisualization
20
Automated report generation
exporting plots and tables to PDF, Excel, and HTML using Python.
Automatedgeneration
21
Handling large binout files
memory-efficient reading, chunked processing, and parallelization strategies.
Handlingmemory-efficient
22
Error handling and data validation
detecting missing branches, corrupted files, and inconsistent time bases.
handlingvalidation
23
Case study
occupant safety analysis—extracting and processing dummy acceleration and force data from binout.
occupantanalysis
24
Case study
crashworthiness assessment—computing intrusion, velocity, and energy metrics from full vehicle simulations.
crashworthinessassessment
25
Case study
drop test and impact analysis—processing force-displacement and energy absorption curves.
analysisprocessing
26
Case study
NVH and modal analysis—extracting frequency response and mode shapes from time history data.
NVHanalysis
27
Case study
blast and explosion simulation—analyzing pressure, impulse, and structural response time histories.
explosionsimulation
28
Integrating Python scripts into LS-DYNA workflows
automation with LS-OPT, LS-PrePost, and batch queuing systems.
IntegratingLS-DYNA
29
Custom binout parsers
reading binary format directly with Python when lasso-python is unavailable or insufficient.
directlylasso-python
30
Best practices and reproducibility
version control, unit testing, documentation, and sharing analysis pipelines for LS-DYNA binout data.
practicesreproducibility