FRAMEWORK OF RELIABILITY-BASED STOCHASTIC MOBILITY MAP FOR NEXT GENERATION NATO REFERENCE MOBILITY MODEL

2024-01-3729

11/15/2024

Event
2024 NDIA Michigan Chapter Ground Vehicle Systems Engineering and Technology Symposium
Authors Abstract
Content
ABSTRACT

A framework for generation of reliability-based stochastic off-road mobility maps is developed to support the Next Generation NATO Reference Mobility Model (NG-NRMM) using full stochastic knowledge of terrain properties and modern complex terramechanics modelling and simulation capabilities. The framework is for carrying out uncertainty quantification and reliability assessment for Speed Made Good and GO/NO-GO decisions for the ground vehicle based on the input variability models of the terrain elevation and soil property parameters. To generate the distribution of the slope at given point, realizations of the elevation raster are generated using the normal distribution. For the soil property parameters, such as cohesion, friction and bulk density, the min and max values obtained from geotechnical databases for each of the soil types are used to generate the normal distribution with a 99% confidence value range. In the framework, the ranges of terramechanics input parameters that will cover the regions of interest are first identified. Within these ranges of terramechanics input parameters, a Dynamic Kriging (DKG) surrogate model of the Speed Made Good is generated using NATC Wheeled Vehicle Platform complex terramechanics model runs at the design of experiment points. Finally, inverse reliability analysis using Monte Carlo Simulation is carried out to generate the reliability-based stochastic mobility maps for Speed Made Good and Go/NO-GO decisions. It is found that the deterministic map of the region of interest has probability of only 25% to achieve the indicated speed.

Meta TagsDetails
Pages
15
Citation
Choi, K., Gaul, N., Jayakumar, P., Wasfy, T. et al., "FRAMEWORK OF RELIABILITY-BASED STOCHASTIC MOBILITY MAP FOR NEXT GENERATION NATO REFERENCE MOBILITY MODEL," SAE Technical Paper 2024-01-3729, 2024, .
Additional Details
Publisher
Published
Nov 15
Product Code
2024-01-3729
Content Type
Technical Paper
Language
English