Faster Method of Simulating Military Vehicles Exposed to Fragmenting Underbody IED Threats

2017-01-0264

03/28/2017

Features
Event
WCX™ 17: SAE World Congress Experience
Authors Abstract
Content
In this paper, the capability of three methods of modelling detonation of high explosives (HE) buried in soil viz., (1) coupled discrete element & particle gas methods (DEM-PGM) (2) Structured - Arbitrary Lagrangian-Eulerian (S-ALE), and (3) Arbitrary Lagrangian-Eulerian (ALE), are investigated. The ALE method of modeling the effects of buried charges in soil is well known and widely used in blast simulations today [1]. Due to high computational costs, inconsistent robustness and long run times, alternate modeling methods such as Smoothed Particle Hydrodynamics (SPH) [2, 9] and DEM are gaining more traction. In all these methods, accuracy of the analysis relies not only on the fidelity of the soil and high explosive models but also on the robustness of fluid-structure interaction. These high-fidelity models are also useful in generating fast running models (FRM) useful for rapid generation of blast simulation results of acceptable accuracy. The main focus of this study is to understand the limitations & strengths of DEM-PGM and S-ALE methods compared to the widely used traditional ALE method. S-ALE method reduces the computational time 45% compared to ALE and DEM-PGM method also reduces computational time 40% compared to ALE. DEM_PGM method has the ability to capture fragmentation and its secondary effects on soldiers and other interior structures. With the development of the DEM-PGM and S-ALE methods, predicting the effect of secondary impact of fragmenting objects in addition to the existing capabilities will be a significant value added to the military ground vehicle programs.
Meta TagsDetails
DOI
https://doi.org/10.4271/2017-01-0264
Pages
6
Citation
Babu, V., Thyagarajan, R., and Ramalingam, J., "Faster Method of Simulating Military Vehicles Exposed to Fragmenting Underbody IED Threats," SAE Technical Paper 2017-01-0264, 2017, https://doi.org/10.4271/2017-01-0264.
Additional Details
Publisher
Published
Mar 28, 2017
Product Code
2017-01-0264
Content Type
Technical Paper
Language
English