Evaluation of Corpuscular Particle Method (CPM) in LS-DYNA for Airbag Modeling

2020-01-0978

04/14/2020

Event
WCX SAE World Congress Experience
Authors Abstract
Content
This paper presents a systematic study to assess maturity of Corpuscular Particle Method (CPM) to accurately predict airbag deployment kinematics and its overall responses. The study was performed in three phases: (1) a correlation assessment of CPM predicted inflator characteristics to closed tank tests; (2) a correlation assessment of CPM predicted airbag deployment kinematics, airbag pressure, reaction force from a static deployment of a Driver Airbag (DAB) and (3) a correlation prediction of the impactor force by CPM versus impactor force from physical drop tower tests. These studies were repeated using the Uniform Pressure Method (UPM), to compare the numerical methods for their accuracy in predicting the physical test, computational cost, and applicability.
Results from the study suggest that CPM satisfies the fundamental energy laws, and accurately captures the realistic airbag deployment kinematics, especially during the early deployment stage, unlike UPM. CPM accurately predicts airbag pressure, reaction force, and the impactor force for a fully deployed airbag. CPM reasonably estimates these parameters in the highly dynamic stages of the unfolding airbag, unlike UPM. Additionally, the computational cost of CPM is on par with UPM, in spite of the discretized fluid model and the need for a more detailed airbag FEA model.
The results suggest that CPM is suitable for modeling occupant interaction with a fully deployed airbag. It is also suitable for modeling interaction of an Out-of-Position (OOP) occupant to a deploying airbag. CPM is also suitable for estimating airbag breakout forces from roof-rail trims and seat tear seams.
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DOI
https://doi.org/10.4271/2020-01-0978
Pages
11
Citation
Anantharaju, N., Uduma, K., and Shi, Y., "Evaluation of Corpuscular Particle Method (CPM) in LS-DYNA for Airbag Modeling," SAE Technical Paper 2020-01-0978, 2020, https://doi.org/10.4271/2020-01-0978.
Additional Details
Publisher
Published
Apr 14, 2020
Product Code
2020-01-0978
Content Type
Technical Paper
Language
English