NATO Reference Mobility Model Research Articles

A new contact and road model for multi-body dynamic simulation of wheeled vehicles on soft-soil terrain

AbstractIn this paper, a new high-performance and memory-efficient contact and road model is developed. Specifically, the road is modeled as a rectangular structured grid of deformable springs in the vertical direction, thus enabling fast execution. The new road model stands out due to its ability to handle large road scenarios by allocating computer memory dynamically for each spring, resulting in efficient memory utilization. Furthermore, each spring represents a small road patch that entails various information, such as the soil elevation, the soil properties, and the soil compaction, allowing for complicated simulations incorporating spatially varying soil properties and phenomena related to the multi-pass effect. In addition, using the new contact model, complex terrain geometries are handled in a computationally efficient way by approximating locally the irregular road profile with a suitable equivalent plane. For this, two different strategies are proposed, namely the radial basis function (RBF) interpolation method and the 3D enveloping contact model. Finally, the proposed techniques are implemented in Altair MotionSolve, a comprehensive multi-body simulation software for complex mechanical systems. In particular, a single-wheel test bed is initially examined followed by a four-wheeled rover model and the next-generation NATO reference mobility model (NG-NRMM). In all cases, the proposed model is validated by using available experimental data. Lastly, a case involving both wheeled and tracked vehicles is also examined by using a shared road model.

Estimation of plowing forces on vehicles driving through deep snow

Determining trafficability of winter surfaces is a critical capability for Army operations in cold regions. Over the years, a substantial amount of research has been conducted to characterize different winter surfaces (and different vehicles, tires, and tracks on these surfaces) for this purpose. Trafficability model for snow and ice have been implemented in numerous tools currently in use by the Army including the NATO Reference Mobility Model (NRMM), GeoWATCH, and other stand-alone implementations. However, these models are generally based on empirical testing of vehicles driving through snow of shallow to moderate depth and do not adequately capture additional compressive and inertial plowing forces as well as friction drag forces resulting from deep snow being pushed forward by the vehicle bumper, nose, compression caused by the undercarriage, or undercarriage components catching the snow (e.g. suspension arms).In this paper we propose a generalizable snow plowing model for estimating the force required for a vehicle to travel through deep snow. The results of the model compare well to empirical measurements of the force required for a mid-weight all-terrain tactical vehicle to move through different depths of deep snow (over the bumper) and at different ride height settings.

Next-generation NATO reference mobility model complex terramechanics – Part 1: Definition and literature review

The US army along with NATO member and partner nations’ militaries need an accurate software tool for predicting ground vehicle mobility (such as speed-made-good and fuel-consumption) on world-wide terrains where military vehicles may be required to operate. Currently, the NATO Reference Mobility Model (NRMM) is the only NATO recognized tool for assessing ground vehicle mobility. NRMM was developed from the 1960s to the 1980s and relies on steady-state empirical formulas which may not be accurate for new military ground vehicles. A NATO research task group (RTG-248) was established from 2016 to 2018 to develop the NG-NRMM (next-generation NRMM) software tool requirements and an NG-NRMM prototype which uses high-fidelity “simple” or “complex” terramechanics models for the terrain/soil along with modern 3D multibody dynamics software tools for modeling the vehicle. NG-NRMM Complex Terramechanics (CT) models are those that utilize full 3D soil models capable of predicting the 3D soil reaction forces on the vehicle surfaces (including tires, tracks, legs, and under body) and the 3D flow and deformation of the soil including both elastic and plastic deformation under any 3D loading condition. In Part 1 of this paper, an overview of the full spectrum of terramechanics models from the highest fidelity to the lowest fidelity is presented along with a literature review of CT ground vehicle mobility models.

Next-generation NATO reference mobility model complex terramechanics – Part 2: Requirements and prototype

In part 2 of this paper, the Complex Terramechanics (CT) software tools requirements recommended by the NATO research task group RTG-248 are presented along with example simulations from a CT prototype software tool which attempts to satisfy the requirements.

Efficient generation of accurate mobility maps using machine learning algorithms

U.S Army’s mission is to develop, integrate, and sustain the right technology solutions for all manned and unmanned ground vehicles, and mobility is a key requirement for all ground vehicles. Mobility focuses on ground vehicles’ capabilities that enable them to be deployable worldwide, operationally mobile in all environments, and protected from symmetrical and asymmetrical threats. In order for military ground vehicles to operate in any combat zone, the planners require a mobility map that gives the maximum predicted speeds on these off-road terrains. In the past, empirical and semi-empirical techniques (Ahlvin and Haley, 1992; Haley et al., 1979) were used to predict vehicle mobility on off-road terrains such as the NATO Reference Mobility Model (NRMM). Because of its empirical nature, the NRMM method cannot be extrapolated to new vehicle designs containing advanced technologies, nor can it be applied to lightweight robotic vehicles.The mobility map is a function of different parameters such as terrain topology and profile, soil type (mud, snow, sand, etc.), vegetation, obstacles, weather conditions, and vehicle type and characteristics.A physics-based method such as the discrete element method (DEM) (Dasch et al., 2016) was identified by the NATO Next Generation NRMM Team as a potential high fidelity method to model the soil. This method allows the capture of the soil deformation as well as its non-linear behavior. Hence it allows the simulation of the vehicle on any off-road terrain and have an accurate mobility map generated. The drawback of the DEM method is the required simulation time. It takes several weeks to generate the mobility map because of the large number of soil particles (millions) even while utilizing high performance computing.One approach to reduce the computational time is to use machine learning algorithms to predict the mobility map. Machine learning (Boutell et al., 2004; Burges, 1998; Barber et al., 1997) can lead to very accurate mobility predictions over a wide range of terrains. Machine learning is divided into two categories: the supervised and the unsupervised learning. Supervised learning requires the training data to be labeled into predetermined classes, while the unsupervised learning does not require the training data to be labeled. Machine learning can help generate mobility maps using trained models created from a minimum number of simulation runs. In this study different supervised machine learning algorithms such as the support vector machine (SVM), the nearest neighbor classifier (k-NN), decision trees, and boosting methods were used to create trained models labeled as 2 classes for the ‘go/no-go’ map, 5 classes for the 5-speed map, and 7 classes for the 7-speed map. The trained models were created from the physics-based simulation runs of a nominal wheeled vehicle traversing on a cohesive soil.

Predicting terrain parameters for physics-based vehicle mobility models from cone index data

To provide terrain data for the development of physics-based vehicle mobility models, such as the Next Generation NATO Reference Mobility Model, there is a desire to make use of the vast amount of cone index (CI) data available. The challenge is whether the terrain parameters for physics-based vehicle mobility models can be predicted from CI data. An improved model for cone-terrain interaction has been developed that takes into account both normal pressure and shear stress distributions on the cone-terrain interface. A methodology based on Derivative-Free Optimization Algorithms (DFOA) has been developed in combination with the improved model to make use of continuously measured CI vs. sinkage data for predicting the three Bekker pressure-sinkage parameters, kc, kϕ and n, and two cone-terrain shear strength parameters, cc and ϕc. The methodology has been demonstrated on two types of soil, LETE sand and Keweenaw Research Center (KRC) soils, where continuous CI vs. sinkage measurements and continuous plate pressure vs. sinkage measurements are available. The correlations between the predicted pressure-sinkage relationships based on the parameters derived from continuous CI vs. sinkage measurements using the DFOA-based methodology and that measured were generally encouraging.

Testing Design Optimization for Uncertainty Reduction in Generating Off-Road Mobility Map Using a Bayesian Approach

Abstract The Next Generation NATO Reference Mobility Model (NG-NRMM) plays a vital role in vehicle mobility prediction and mission planning. The complicated vehicle–terrain interactions and the presence of heterogeneous uncertainty sources in the modeling and simulation (M&S) result in epistemic uncertainty/errors in the vehicle mobility prediction for given terrain and soil conditions. In this paper, the uncertainty sources that cause the uncertainty in mobility prediction are first partitioned into two levels, namely uncertainty in the M&S and uncertainty in terrain and soil maps. With a focus on the epistemic uncertainty in the M&S, this paper presents a testing design optimization framework to effectively reduce the uncertainty in the M&S and thus increase the confidence in generating off-road mobility maps. A Bayesian updating approach is developed to reduce the epistemic uncertainty/errors in the M&S using mobility testing data collected under controllable terrain and soil conditions. The updated models are then employed to generate the off-road mobility maps for any given terrain and soil maps. Two types of design strategies, namely testing design for model selection and testing design for uncertainty reduction, are investigated in the testing design framework to maximize the information gain subject to limited resources. Results of a numerical example demonstrate the effectiveness of the proposed mobility testing design optimization framework.

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

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.

Comparison of simulation models NRMM and NTVPM for assessing military tracked vehicle cross-country performance

Mobility of ground vehicles is an important issue for defence operations. In the United States and some other NATO countries, the NATO Reference Mobility Model (NRMM) is currently used to evaluate military ground vehicle mobility. The cross-country performance prediction module of NRMM is based on empirical relations established with test data primarily collected decades ago. It has inherent limitations, such as the uncertainty whether the empirical relations can be extrapolated beyond the test conditions upon which they were derived. This paper describes a comparison of the empirically-based NRMM with the physics-based Nepean Tracked Vehicle Performance Model (NTVPM) for assessing the cross-country performance of military tracked vehicles. It examines the soundness of the methodologies of NRMM and NTVPM, the adequacy of the vehicle design parameters and terrain characteristics that they take into account, the user friendliness of their operations, and the correlations between the measured cross-country performance of a notional tracked vehicle (an armoured personnel carrier) and that predicted by the two simulation models on sandy terrain, muskeg and snow-covered terrain. Based on the results of the evaluation, it appears that the physics-based NTVPM has potential as the basis for the development of the next generation simulation model for assessing the cross-country performance of military tracked vehicles.

Full-featured ground vehicle mobility analysis using different soil moisture sources

We investigated the influence of using three different soil moisture sources WindSat (a satellite), land information system (LIS) (a computer model), and in situ ground sensors in a full-featured ground vehicle mobility analysis model. Soil moisture, soil type, land use, and slope were used to estimate the mobility in dry and wet scenarios for six vehicles using the NATO Reference Mobility Model (NRMM). The dry scenario showed nearly the same results for all three soil moisture sources. In the wet scenario, modelled travel times based on LIS were about 26% faster compared to those from the in situ moisture. Modelled times based on WindSat averaged 11% slower than those from the in situ moisture. For the test case we found agreement between the results of the current full-feature model against a previously-developed model using only soil moisture time series. This demonstrates that soil moisture can be important even as one terrain factor among many.

Using a modified version of the Magic Formula to describe the traction/slip relationships of tyres in soft cohesive soils

The tractive force/slip relationships of pneumatic tyres are required as inputs to vehicle performance prediction models such as the NATO Reference Mobility Model. They can also be used to calculate the tractive efficiency and work output of vehicles such as farm tractors, especially important when the vehicles are performing high drawbar pull operations; the effects of altering tyre size, tyre pressure and ballast can be predicted. The so-called Magic Formula is widely used for describing the force/slip relationships of pneumatic tyres on hard road surfaces. The coefficients in the Magic Formula are derived from experimental measurements. Relationships are then developed to describe the coefficients as functions of normal load on the tyre. The paper describes how the Magic Formula can be adapted to describe the tractive force/slip relationships of tyres in soft cohesive soils. The coefficients are made functions of Mobility Number instead of normal load. Mobility Number is an empirical system for estimating the tractive performance of tyres in soft soils at a single value of slip. The method could be extended to cover lateral tyre forces or other soil types if suitable experimental data is available.

Comparing the NRMM (VCI), MMP and VLCI traction models

A military vehicle’s ability to traverse soft soil is a significant aspect of its performance. It is therefore important to be able to accurately predict and compare the soft soil performance of wheeled and tracked vehicles. The paper compares the vehicle cone index (VCI) model used in the NATO Reference Mobility Model (NRMM) with the mean maximum pressure (MMP) and vehicle limiting cone index (VLCI) models. The wheeled vehicle models compare reasonably well but the VLCI model for tracked vehicles indicates markedly higher limiting go/no-go soil strengths compared to the VCI and MMP models. Some of the relationships used in the VCI model appear rather irrational and could be significantly simplified. There is a considerable lack of reliable experimental data for tracked vehicles especially in the low traction region.

Finite Element Modeling of Tires on Snow

Abstract Vehicle movement on unpaved surfaces is important to military, agriculture, forestry, mining, construction, and recreation industries. Because of the complicated nature of vehicle-terrain interaction, comprehensive modeling of off-road mobility is often done using empirical algorithms. The desire to incorporate more physics into performance models has generated great interest in applying numerical modeling techniques in a full three-dimensional analysis, accounting for the deformation of both the tire and the terrain. In this study, a three-dimensional finite element model was constructed to simulate a tire rolling over snow. The snow was modeled as an inelastic material using critical-state constitutive modeling and plasticity theory. The snow material model was generated from experiments on the mechanical deformation of snow and was validated using a plate sinkage test. The tire models represent a range of sizes accommodating light-truck and off-road military vehicles and were rolled on snow of various depths. The combined tire-terrain models were validated using force measurements collected with instrumented vehicles and with measured snow deformation. The model results were also compared to vehicle mobility predictions made using the winter algorithms of the NATO Reference Mobility Model. These comparisons illustrate the agreement between the finite element models and field measurements of motion resistance forces and snow deformation under the tire.

A methodology for quantitatively assessing vehicular rutting on terrains

This paper presents a quantitative method for assessing the environmental impact of terrain/vehicle interactions during tactical missions. Area wide mobility analyses were conducted using three standard US military tracked and wheeled vehicles over terrain regions representing both fine-grained and course-grained soils. The NATO reference mobility model, Version 2, was used to perform the on- and off-road mobility analysis. Vehicle and terrain characterizations along with different climate scenarios were used as input parameters to predict vehicle rut depth performance for the different vehicles and terrain conditions. The vehicles’ performance was statistically mapped over these terrain regions for percent area traveled and the resulting rut depth created by each vehicle. A selection of tactical scenarios for each vehicle was used to determine rut depth for a range of vehicle missions. A vehicle mission severity rating method, developed at the US Army Engineer Research and Development Center, was used to rate the selected missions and resulting rut depths.

Maneuver analysis methodology to predict vehicle impacts on training lands

Tactical mobility analysis techniques were merged with land management strategies to assess potential impacts of vehicle operations on training areas for rangeland planning and management. A vehicle mobility analysis was performed for a suite of vehicle types using the NATO Reference Mobility Model (NRMM II). Input parameters include terrain information (soil type, slope, vegetation, surface roughness, soil strength), terrain surface condition based on climate (terrain strength, freeze–thaw, moisture content, snow cover), and vehicle specifications (tire, power train, weight on each axle, ground clearance, dimensions, ride). The vehicle performance was spatially mapped over the terrain for different seasons of the year and used to calculate the maneuverable acreage, which was compared to acreage needed for training requirements. This can be related to land capability based on expected training impact (Maneuver Impact Miles, MIM) and Land Condition Curves which link training density to land condition. This methodology can be used to determine the suitability of training lands and the degree of land management or rehabilitation expected. The methodology was applied to the transformation of the Alaska training lands to support a new brigade unit called the Stryker Brigade Combat Team (SBCT3), but is equally useful for other training areas and military units. For summer use, Alaska training lands are capable of supporting four times the projected training requirements. For winter, when the ground is frozen, more than 10 times the area needed was available.

Overview of cold regions mobility modeling at CRREL

Over the last several decades, the Cold Regions Research and Engineering Laboratory (CRREL) has extensively tested and analyzed issues related to vehicle performance in winter. Using this knowledge and the experimental database, models were developed to capture the important elements for cold regions mobility performance. These models span a range of resolutions and fidelities and include three-dimensional finite element models of tire–terrain interaction, vehicle dynamics models of vehicles on winter surfaces, semi-empirical cold regions algorithms for winter performance within the NATO reference mobility model (NRMM), all-season vehicle performance in force-on-force war-gaming simulations, and vehicle–surface interaction for real-time vehicle simulators. Each of these types of models is presented along with examples of their application.

Stochastic vehicle mobility forecasts using the nato reference mobility model

The NATO Reference Mobility Model (NRMM) is a comprehensive means of predicting the speeds of military vehicles in on-road, off road, and gap-crossing contexts. The model has been in service for many years and helps user communities concerned with vehicle design, wargaming, and strategic planning. Recent developments in computer hardware and software are creating an opportunity for NRMM to serve a tactical role on the battlefield. Adaptation of NRMM to this role requires that its users come to grips with the collection of digital data to describe vehicle, terrain, and scenario data in a real-time environment. This paper discusses the performance of NRMM when selected inputs and algorithms contain random components. A developmental pathway is outlined that leads from current deterministic mobility forecasts to stochastic forecasts capable of suggesting the risks taken when speed predictions must be made in the presence of data and algorithm errors. Concepts that express measures of confidence for wide-area mobility forecasts when errors are known with small-area detail are described. Several numerical examples are given.

Application of the computer simulation model NTVPM-86 to the development of a new version of the infantry fighting vehicle ASCOD

In the past, the task of evaluating soft-ground mobility of off-road vehicles has been carried out primarily using empirical methods (or models), such as the NATO Reference Mobility Model (NRMM) or the Rowland method based on the mean maximum pressure (MMP). The databases for these empirical methods were mostly established decades ago. Consequently, in many cases, they cannot be used in evaluating new generations of vehicles with new design features, as the mobility of these vehicles simply cannot be described within the limits of these empirical databases. Since the 1980s, a series of comprehensive and realistic simulation models for design and performance evaluation of off-road vehicles has emerged. They are based on the detailed studies of the physical nature of vehicle-terrain interaction, taking into account all major vehicle design features and pertinent terrain characteristics. This paper describes the application of one of these models, known as NTVPM-86, developed by Vehicle Systems Development Corporation, Canada, to the design and development of a new version of the ASCOD infantry fighting vehicle, produced by a joint venture formed by Empresa Nacional Santa Barbara of Spain and Steyr-Daimler-Puch of Austria. The results of field tests performed by the Military Technology Agency, Ministry of Defence, Vienna, Austria and released recently confirm that, as predicted by the NTVPM-86 model, the new version of the ASCOD has much improved performance than the original over soft terrain, including soft clay and snow-covered terrain. This is another example of the successful application of the NTVPM-86 model to the design and development of a new generation of high-speed tracked vehicles.

Expansion of the terrain input base for nepean tracked vehicle performance model, NTVPM, to accept swiss Rammsonde data from deep snow

A study of the correlation between the measured and predicted vehicle performance over undistributed and preconditioned snow using the Nepean Tracked Vehicle Performance Model, NTVPM, has been carried out. It is shown that on undisturbed snow in Fernie, British Columbia, the performance of a BV 206 predicted by NTVPM correlates very well with measured performance obtained in the field. On preconditioned snow, there is also a reasonable correlation between the measured vehicle performance and predicted one using NTVPM. It is found that predictions of vehicle performance made by NTVPM using pressure-sinkage data obtained with the Swiss Rammsonde and with the bevameter are comparable. This indicates that the pressure-sinkage data obtained using the Rammsonde can be used as input to the NTVPM for predicting tracked vehicle performance over snow. It is shown that in comparison with an earlier version, NTVPM-85, the latest version of the Nepean Tracked Vehicle Performance Model, NTVPM-86, which takes into account fully the characteristics of roadwheel suspension systems, provides improved predictions of vehicle performance over snow where track sinkage is significant. It is suggested that the computer simulation model NTVPM, using pressure-sinkage data obtained by the Rammsonde as input, could form a useful interface with cone based models, such as the NATO Reference Mobility Model, to provide them with an additional capability of predicting tracked vehicle performance over snow.

Measurement and characterization of the pressure-sinkage data for snow obtained using a Rammsonde

To examine the feasibility of using the pressure-sinkage data obtained with a Rammsonde as input to a proposed module for the NATO Reference Mobility Model for predicting tracked vehicle performance over snow, a series of measurements of penetration resistance of undisturbed and preconditioned snow was made using a Rammsonde, and compared with those obtained with a bevameter and Rammsonde cones driven by a bevameter. This paper describes the results of the field measurements made using the three types of device, as well as the approaches to the characterization of the pressure-sinkage data obtained. It is found that the data obtained using the Rammsonde, the bevameter and the Rammsonde cone driven by a bevameter are, in general, comparable, considering the possible variations of terrain conditions in the field. It is shown that the pressure-sinkage equations proposed by Bekker could form a common basis for characterizing the data obtained using the three different techniques. It is found that in soft, undisturbed snow (with density less than 0.2 g/cm 3), the small (or standard) Rammsonde cone of 4 cm in diameter cannot be used to obtain meaningful pressure-sinkage data within a certain snow depth. Under these circumstances, the use of the large Rammsonde cone of 10 cm in diameter is recommended. Over preconditioned snow, both the small and large Rammsonde cones could provide comparable data. The size effect of the sensing element (such as the diameter of the Rammsonde cone or the bevameter sinkage plate) on the pressure-sinkage relationships was examined. It is found that in many cases, the values of the pressure-sinkage parameters derived from data obtained using a single sensing element of 10 cm in diameter are comparable to those obtained using two sizes of sensing elements of 4 and 10 cm in diameter.