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research-article

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

[+] Author and Article Information
Kyung Choi

The University of Iowa., Iowa City, IA
kyung-choi@uiowa.edu

Nicholas Gaul

RAMDO Solutions, Iowa City, IA
nicholas-gaul@ramdosolutions.com

Paramsothy Jayakumar

U.S. Army TARDEC, Warren, MI
paramsothy.jayakumar.civ@mail.mil

Tamer Wasfy

Advanced Science and Automation Corp., Indianapolis, IN
tamerwasfy@ascience.com

Matt Funk

Esri, Inc., Redlands, CA
mfunk@esri.com

1Corresponding author.

ASME doi:10.1115/1.4041350 History: Received May 23, 2018; Revised August 21, 2018

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/NOGO 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 input parameters, a Dynamic Kriging (DKG) surrogate model is obtained for the maximum speed of the NATC Wheeled Vehicle Platform complex terramechanics model. 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/NOGO decisions. It is found that the deterministic map of the region of interest has probability of only 25% to achieve the indicated speed.

Section 4: U.S. Gov Employees + Reg Authors
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