DOI: 10.5281/zenodo.13998256
title: Occupant Simulation Database and FE-Model based on Honda Accord 2024 Simplified Passenger Model and SOBOL Sampling with 8,192 samples and HIII05F, HIII50M, HIII95M

description: Database and FE-models with 24,576 Honda Accord 2014 passenger occupant simulations. 

 
description: The FE-model is based on the Honda Accord 2014 model incl. interior, published by NHTSA in 2019. The FE-models feature the passenger with the restraint system components belt, PAB, and CAB. As human surrogates, the HIII-05F, HIII50M, HIII95M were used. The original FE-model was incrementally simplified. The models from each step are documented in Plaschkies, F. (2024). FE-model Simplification Chain of Honda Accord 2014 for Occupant Safety Assessment in Front Load Cases. Zenodo. https://doi.org/10.5281/zenodo.13998073. The database was filled using the last model (990_Carpet_Rigid).

Five factors were varied using Sobol-sampling. The factors were: scaling of 1DOF pulse, angle of pulse-vector, PAB mass-flow, PAB opening time of adaptive vent, force of belt load limiter. Each sample was repeated with the HIII-05F, HIII50M, HIII95M as FE-simulation.

The sampling was generated by: g = scipy.stats.qmc.Sobol(d=5, scramble=False, seed=None, optimization=None)doe = g.random_base2(m=13)Python 3.11, SciPy 1.14.0

The file structure was documented in the content.info file. The database has 8,192 samples (equals 24,576 FE-simulations).

The models are a supplement to a dissertation by F. Plaschkies with the estimated publication in 2025.
description: |-- contents.info|       File structure and content overview||-- fe_models.zip| |     LS-Dyna FE-models with set parameters according DOE| |     Unit system of FE-models is mm-t-s, all simulations in v12, mpp| |-- Assemblies| | |      Assembling key files| | |-- sobol_sampling_m13_f5| | |-- V*| | | |-- V*.key| |-- Pool| | |     Sub-models| | |     Dummy models:| | |         HIII05 (outer layer only!) from  Guha, Sarba; Bhalsod, Dilip; Krebs, Jacob (2011): LSTC Hybrid III 5th Fast Dummy Positioning & Post-Processing. Dummy Version: LSTC.H3_5TH_FAST.111130_V2.0. LSTC Michigan.| | |         HIII50 (outer layer only!) from  Guha, Sarba; Bhalsod, Dilip; Krebs, Jacob (2011): LSTC Hybrid III 50th Fast Dummy Positioning & Post-Processing. Dummy Version: LSTC.H3_50TH_FAST.111130_V2.0. LSTC Michigan.| | |         HIII95 (outer layer only!) accordingly| | |-- *.k||-- databases.zip| |     Extracted data from FE-simulations| |-- summary_statistics| | |     Data distribution of selected injury criteria and related signals| | |     of Top10 highest values| | |-- *.pdf| |-- results_info.json| |       File overview| |-- dropped_ids.json| |       ID to be dropped due to likely numerical issues| |-- doe.parquet| |       DOE setting in parquet file format (with index if read by pandas)| |       Index is SIM_ID (id of particular FE-simulation)| |       Factors are| |           PAB_M_SCAL = Inflator Mass Flow In Scaling [1]| |           PAB_vent_T = PAB Mass Flow Out Scaling [s]| |           Pulse_X_Scale = Translational Scaling of Pulse [1]| |           Pulse_Angle = Z Rotation of Pulse (analogous buck rotation on sled track) [DEG]| |           SLL = Retractor Load Limiter B0 [kN]| |           PERC = Dummy percentile (5, 50 95)| |-- sim_id_2_id.parquet| |       Parser file in parquet file format (with index if read by pandas)| |       Index is SIM_ID (id of particular FE-simulation)| |       Columns are| |           ID = Sample ID (all factors apart from PERC are same)| |           PERC = Dummy percentile (5, 50 95)| |-- channels.parquet| |       From FE-simulations extracted channels as parquet file format (with index if read by pandas)| |       Indices are| |           TIME = Time stamp in ms, sampled at 10kHz| |           ID = Sample ID (all factors apart from PERC are same)| |           PERC = Dummy percentile (5, 50 95)| |       Columns are channels named in extended ISO MME Format| |           Units: acceleration (AC) [g], velocity (VE) [m/s],| |                  displacement (DS) [mm], force (FO) [kN],| |                  moment (MO) [Nm], volume (VO) [L],| |                  pressure (PR) [kPa], temperature (TP) [K],| |                  mass total, in, out (MT, MI, MO) [g]| |-- injury_criteria.parquet| |       From channels calculated injury criteria as parquet file format (with index if read by pandas)| |       Indices are| |           ID = Sample ID (all factors apart from PERC are same)| |           PERC = Dummy percentile (5, 50 95)| |       Columns are Injury criteria with units as defined by e.g. EuroNCAP