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Research Papers

Framework of Reliability-Based Stochastic Mobility Map for Next Generation NATO Reference Mobility Model

[+] Author and Article Information
K. K. Choi

Mem. ASME,
Department of Mechanical Engineering,
The University of Iowa,
Iowa City, IA 52242
e-mail: kyung-choi@uiowa.edu

Paramsothy Jayakumar

U.S. Army TARDEC,
Warren, MI 48397
e-mail: paramsothy.jayakumar.civ@mail.mil

Matthew Funk

Esri, Inc.,
Redlands, CA 92373
e-mail: mfunk@esri.com

Nicholas Gaul

Mem. ASME
RAMDO Solutions,
Iowa City, IA 52242
e-mail: nicholas-gaul@ramdosolutions.com

Tamer M. Wasfy

Advanced Science and Automation Corporation,
Indianapolis, IN 46256
e-mail: tamerwasfy@ascience.com

Contributed by the Design Engineering Division of ASME for publication in the JOURNAL OF COMPUTATIONAL AND NONLINEAR DYNAMICS. Manuscript received May 23, 2018; final manuscript received August 21, 2018; published online January 7, 2019. Assoc. Editor: Radu Serban.This work was authored in part by a U.S. Government employee in the scope of his/her employment. ASME disclaims all interest in the U.S. Government's contributions.Distribution Statement A: Approved for public release; distribution unlimited. OPSEC#798.

J. Comput. Nonlinear Dynam 14(2), 021012 (Jan 07, 2019) (10 pages) Paper No: CND-18-1228; doi: 10.1115/1.4041350 History: Received May 23, 2018; Revised August 21, 2018

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 modeling and simulation capabilities. The framework is for carrying out uncertainty quantification (UQ) 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 nevada automotive test center (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.

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References

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Wasfy, T. M. , Jayakumar, P. , Mechergui, D. , and Sanikommu, S. , 2016, “ Prediction of Vehicle Mobility on Large-Scale Soft-Soil Terrain Maps Using Physics-Based Simulation,” NDIA Ground Vehicle Systems Engineering and Technology Symposium, Modeling and Simulation, Testing and Validation (MSTV) Technical Session, Novi, MI.
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RAMDO, 2018, RAMDO Solutions, LLC, Iowa City, IA.

Figures

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Fig. 1

Next generation NATO reference mobility model mobility map generation

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Fig. 2

Elevation variability and distribution

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Fig. 3

arcgis slope calculation toolbox

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Fig. 4

Variability of soil properties

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Fig. 5

Generation of reliability-based stochastic mobility map

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Fig. 6

Complex terramechanics model of NATC wheeled vehicle platform

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Fig. 7

Propagation of variability to generate reliability-based stochastic mobility maps

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Fig. 8

Distribution of Speed Made Good for each cell of raster

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Fig. 9

Reliability-based stochastic and deterministic Speed Made Good mobility maps

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Fig. 10

Reliability-based GO/NOGO decision maps

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