George L. Mason

Development of a Multi-Year Database to Assess Off-Road Mobility Algorithms in Fine-Grained Soils

A gap in off-road mobility is the availability of the test data necessary for the development and validation of tyre, track and soil models. This paper presents the results from a multi-year program to create the database records for off-road vehicle environments (DROVE), a consolidated database of laboratory and field test results. A fine-grained soil component was added to DROVE by compiling 2657 test results. Performance parameters including drawbar pull, motion resistance, torque, sinkage, and slip were included in the database. The database was used to evaluate the accuracy of existing algorithms used in the vehicle-terrain interface (VTI) model. The results indicated that the VTI algorithms can provide reasonable predictions for a few parameters but for others, such as motion resistance, further improvements are warranted. The DROVE dataset can be used for several applications including evaluation of existing mobility models, development of improved algorithms, and validation of numerical simulations.

New algorithms for predicting longitudinal motion resistance of wheels on dry sand

Predicting the resisting forces against a vehicle’s wheel during movement in loose sand is critical for optimizing tractive capabilities in desert regions, sand dunes, and beaches. We review existing braked, powered, and towed motion resistance equations and present improved algorithms based on field and laboratory measurements when the cone index is used to define soil strength. The algorithm predictions are compared against measured values available through Database Records for Off-road Vehicle Environments (DROVE), a database of tests conducted with wheeled vehicles. The examination of braked, towed, and powered motion resistance algorithms is considered for loads varying from 0.187 to 4.49 kN, tire diameters from 0.377 to 1.05 m, and soil strengths from 50 to 800 kPa. A simplified motion resistance algorithm was developed for each operation type utilizing a bootstrap technique. Simple relationships using wheel slip and the ratio of the contact pressure to the cone index are shown to provide predictions of motion resistance with accuracy comparable to more complex empirical models.

A general model for inferring terrain surface roughness as a root–mean–square to predict vehicle off–road ride quality

Vehicle maximum speed for off-road operations is limited by the absorbed power via vertical acceleration to the driver for a given terrain Root-Mean-Square surface roughness (RMS). RMS calculation requires centimetre-scale terrain elevation data; however, previous work by the authors has shown that RMS can be modelled using a 5-metre terrain profile’s Fractal Dimension (FD) and Power Spectral Density (PSD) DC offset. Presented is a study of the effects of surface elevation data resolution on the model. Forty-nine ride courses were down-sampled from 30 centimetre to 0.91, 1.83, 2.74, 3.66, 4.57, 5.49, 6.40, 7.32, and 8.23 metre spacings, and an RMS model at each spacing was generated using linear regression techniques. The effects of data resolution on the RMS model were studied, and a continuous model for RMS as a function of FD and DC offset across elevation data resolutions for up to 7 metre sample spacing was developed. Results of the model’s use in predicting off road military vehicle mobility are presented.