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Python Automation for LS-DYNA DOE and Injury Data Reporting

30 Chapters
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01
Course overview
scope, concepts, and technical context.
technical
02
LS-DYNA input deck structure
keyword files, include hierarchy, and parameterization points for automation.
LS-DYNAstructure
03
Python environment setup for LS-DYNA automation
required libraries, directory conventions, and execution wrappers.
environmentLS-DYNA
04
Parsing and modifying LS-DYNA keyword files with Python
reading *INCLUDE_TRANSFORM, *PARAMETER, and *DEFINE cards.
modifyingLS-DYNA
05
Design of Experiments (DOE) fundamentals for crashworthiness and injury biomechanics
factors, levels, responses, and sampling strategies.
ExperimentsDOE
06
Generating DOE matrices in Python
full factorial, Latin Hypercube, and optimal designs using pyDOE and SciPy.
GeneratingDOE
07
Template-based LS-DYNA model generation
parameter substitution, include file management, and case directory creation.
Template-basedLS-DYNA
08
Automating LS-DYNA execution
job submission, solver invocation, and monitoring with Python subprocess and queue systems.
AutomatingLS-DYNA
09
Handling LS-DYNA output files
parsing d3plot, binout, and ASCII results with Python libraries and custom readers.
HandlingLS-DYNA
10
Extracting injury metrics from LS-DYNA simulations
HIC, NIC, chest deflection, and other biomechanical criteria.
ExtractingLS-DYNA
11
Post-processing time-history data
filtering, peak detection, and integration for injury criteria calculation.
Post-processingtime-history
12
Building a Python pipeline for batch DOE runs
orchestration, error handling, and restart strategies.
Buildingpipeline
13
Data management for DOE studies
structured storage, metadata tracking, and reproducibility using SQLite or pandas.
managementDOE
14
Statistical analysis of DOE results
ANOVA, regression models, and sensitivity analysis with Python.
Statisticalanalysis
15
Surrogate modeling and optimization
response surface methodology and Gaussian processes for injury prediction.
Surrogatemodeling
16
Automated report generation
templating with Jinja2 and document creation using Python-docx or ReportLab.
Automatedgeneration
17
Visualizing injury data
matplotlib and Plotly for contour plots, trend charts, and comparative dashboards.
Visualizingmatplotlib
18
Case study
frontal impact DOE for occupant injury assessment—from parameterization to report.
DOEoccupant
19
Case study
side impact DOE with barrier variation and injury risk curves.
DOEvariation
20
Case study
pedestrian protection DOE using legform and headform impactors.
pedestrianprotection
21
Integrating LS-DYNA DOE with optimization loops
Python-driven design updates and convergence checks.
IntegratingLS-DYNA
22
Parallel and distributed computing for LS-DYNA DOE
multiprocessing, cluster integration, and cloud deployment.
Paralleldistributed
23
Quality assurance for automated LS-DYNA workflows
validation checks, unit tests, and regression testing.
assuranceautomated
24
Handling large datasets from DOE
efficient I/O, chunking, and parallel post-processing.
Handlingdatasets
25
Custom injury risk functions
developing and calibrating probabilistic injury models from DOE outputs.
functionsdeveloping
26
Automating LS-DYNA model verification and validation (V&V) tasks with Python.
Automating LS-DYNA model verification and validation (V&V) tasks with Python.
AutomatingLS-DYNA
27
Workflow integration
coupling Python automation with LS-DYNA's LS-OPT and LS-PrePost scripting.
Workflowintegration
28
Real-time monitoring and logging of LS-DYNA jobs
dashboards and alerting with Python.
Real-timemonitoring
29
Best practices for maintainable automation code
modular design, configuration files, and documentation.
practicesmaintainable
30
Capstone project
end-to-end Python automation for a DOE study and injury report generation.
Capstoneend-to-end