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We have recently obtained experimental data and used them to develop computational models to quantify occupant impact responses and injury risks for military vehicles during frontal crashes. The number of experimental tests and model runs are however, relatively small due to their high cost. While this is true across the auto industry, it is particularly critical for the Army and other government agencies operating under tight budget constraints. In this study we investigate through statistical simulations how the injury risk varies if a large number of experimental tests were conducted. We show that the injury risk distribution is skewed to the right implying that, although most physical tests result in a small injury risk, there are occasional physical tests for which the injury risk is extremely large. We compute the probabilities of such events and use them to identify optimum design conditions to minimize such probabilities.

The aim of this analysis was to model the effect of adding stiffening ribs in structural aluminum components by friction stir processing (FSP) Nano material into the aluminum matrix. These stiffening ribs could dampen, redirect, or otherwise alter the transmission of energy waves created from automotive, ballistic, or blast shocks to improve noise, vibration, and harshness (NVH) and structural integrity (reduced joint stress) response. Since the ribs are not created by geometry changes they can be space efficient and deflect blast / ballistic energy better than geometry ribbing, resulting in a lighter weight solution. The blast and ballistic performance of different FSP rib patterns in AL 5182 and AL 7075 were simulated and compared to the performance of an equivalent weight of RHA plate FSP helps to increase localized strength and stiffness of the base metal, while achieving light weighting of the base metal.

This contribution demonstrates the use of high performance computing, specifically Graphics Processing Unit (GPU) based computing, for the simulation of tracked ground vehicles. The work closes a gap in physics based simulation related to the inability to accurately characterize the 3D mobility of tracked vehicles on granular terrains (sand and/or gravel). The problem of tracked vehicle mobility on granular material is approached using a discrete element method that accounts for the interaction between the track and each discrete particle in the terrain. This continuum approach captures the dynamics of systems with more than 1,000,000 bodies interacting simultaneously. Two factors render the approach feasible. First, the frictional contact problem between the terrain and the vehicle draws on a convex optimization methodology in which the solution becomes the first order optimality condition of a cone complementarity problem.

In this paper, the capability of three methods of modelling detonation of high explosives (HE) buried in soil viz., (1) coupled discrete element & particle gas methods (DEM-PGM) (2) Structured - Arbitrary Lagrangian-Eulerian (S-ALE), and (3) Arbitrary Lagrangian-Eulerian (ALE), are investigated. The ALE method of modeling the effects of buried charges in soil is well known and widely used in blast simulations today [1]. Due to high computational costs, inconsistent robustness and long run times, alternate modeling methods such as Smoothed Particle Hydrodynamics (SPH) [2, 9] and DEM are gaining more traction. In all these methods, accuracy of the analysis relies not only on the fidelity of the soil and high explosive models but also on the robustness of fluid-structure interaction. These high-fidelity models are also useful in generating fast running models (FRM) useful for rapid generation of blast simulation results of acceptable accuracy.

Fe-Mn-Al-C steel alloys have been previously studied for their potential as an alternative steel alloy for Rolled Homogeneous Armor (RHA). Prior examination of the material system has shown promise in this capacity due to the high strength and reduced density of Mn steels as compared to RHA. The prior tested materials were both wrought and cast versions but were all less than an inch in thickness. The alloy is once again being examined, but this time in thicker wrought plate. The aim of the current body of work is to develop a Military Specification (MIL-SPEC) for this new class of ballistically capable material. For industry and communities interested in such material development, the purpose of this paper, then, is to provide a summary of the processing parameters, the prior ballistic and dynamic material testing, cutting and welding approaches, and the extent of progress on industrial sized thick plate development.

A Computational Fluid Dynamics (CFD) study was conducted on four-vehicle platoons, and the aerodynamic data is then coupled with a high-fidelity truck simulation software (TruckSim) to determine fuel efficiency. Previous studies typically have focused on identical two vehicle platoons, whereas this study accounted for more complex platoon configurations. Heavy duty vehicles (HDVs), both military and commercial, make up a significant percentage of fuel consumption. This study aimed to quantify fuel savings of a platoon consisting of dissimilar trucks and trailers, thus reducing vehicle operational cost. The vehicle platoon featured two M915 trucks and two Peterbilt 579 trucks with dissimilar trailer configurations. An unloaded flatbed trailer, a centered 20 ft shipping container, two 20 ft shipping containers, and a 53 ft box trailer configurations were utilized.

Robust autonomous navigation in unstructured environments is an unsolved problem and critical to the operation of autonomous military and rescue ground vehicles. Two-dimensional path planners operating on occupancy grids or costs maps can produce infeasible paths when the operational area includes complex terrain. Recently, sample-based path planners that plan on LiDAR-acquired point-cloud maps have been proposed. These approaches require no discretization of the operational area and provide direct pose estimation by modeling vehicle and terrain interaction. In this paper, we show that direct sample-based path planning on point clouds is effective and robust in unstructured environments. Robustness is demonstrated by completing a system parameter sensitivity analysis of the system in an Unreal simulation environment and partnered with field validation.

This paper describes a joint SAE automotive and aerospace Recommended Practice SAE J3168 now in development to standardize a process for Reliability Physics Analysis. This is a science-based approach to implement Physics-of-Failure research in conducting durability simulations in a Computer Aided Engineering Environment. It is used to calculate failure mechanism susceptibilities and estimate the likelihood of failure and the expected durability life of Electrical, Electronic and Electromechanical components and equipment, due to stresses such as mechanical shock, vibration, temperature cycling, etc. Reliability Physics Analysis is based on the material science principle of stress driven damage accumulation in materials. The process enables the identification of potential failure risks early in the design phase so that such risks can be designed out in order to efficiently design high reliable and robustness into electronic products.

Variant modeling techniques have been developed to allow systems engineers to model multiple similar variants in a product line as a single variant model. In this paper, we expand on this past work to explore the extent to which variant modeling in SysML can be applied to a broad range of dissimilar systems, covering the entire domain of ground vehicles, in single reference architecture model. Traditionally, a system’s structure is decomposed into subsystems and components. However, this method is found to be ineffective when modeling variants that are functionally similar but structurally different. We propose to address this challenge by first decomposing the system not only by subsystem but also by high-level function. This pattern is particularly useful for situations where two variants perform the same function, but one variant performs the function using one subsystem, whereas the other variant performs the same function using one or more different subsystems.

The integration of electric motors into ground vehicle propulsion systems requires the effective removal of heat from the motor shell. As the torque demand varies based on operating cycles, the generated heat from the motor windings and stator slots must be rejected to the surroundings to ensure electric machine reliability. In this paper, an electric motor cooling system design will be optimized for a light duty autonomous vehicle. The design variables include the motor cradle volume, the number of heat pipes, the coolant reservoir dimensions, and the heat exchanger size while the cost function represents the system weight, overall size, and performance. The imposed requirements include the required heat transfer per operating cycle (6, 9, 12kW) and vehicle size, component durability requirement, and material selection. The application of a nonlinear optimization package enabled the cooling system design to be optimized.

A significant challenge for wheel- and propeller-driven amphibious vehicles during swimming operations involves the egress from bodies of water. The vehicle needs to be able to swim to the bank, and then propel itself up the bank using water propellers and wheels simultaneously. To accurately predict the ability of the vehicle to climb the bank, it is important to accurately model: (1) the interaction of the flow through the propellers, around the vehicle hull, and over the bank; (2) the wheel / bank interaction; (3) the suspension system spring, damping, and motion-limiting forces, tire deformation and loading characteristics, and wheel and hull motions (both translation and rotation); and (4) the drivetrain power distribution to the wheels. Detailed modeling and simulation of these physics and processes - such as the wheel, hull, and suspension system motions and force interactions, propeller rotation and resulting flow, etc. - would be highly computationally expensive.

Vehicular control systems are characterized with numerous complex interactions with a steady rise of autonomous functions, which makes it more challenging for designers and safety engineers to identify unexpected failures. These systems tend to be highly integrated and exhibit features like concurrency for which traditional verification and validation techniques (i.e. testing and simulation) are insufficient to provide rigorous and complete assessment. Model Checking, a well-known formal verification technique, can be used to rigorously prove the correctness of such systems according to design Requirements. In particular, Model Checking is a method for formally verifying finite-state concurrent systems. Specifications about the system are expressed as temporal logic formulas, and efficient symbolic algorithms are used to traverse the model defined by the system and check if the specification holds or not.

Off road navigation demands ground robots to traverse complex and often changing terrain. Classification and assessment of terrain can improve path planning strategies by reducing travel time and energy consumption. In this paper we introduce a terrain classification and assessment framework that relies on both exteroceptive and proprioceptive sensor modalities. The robot captures an image of the terrain it is about to traverse and records corresponding vibration data during traversal. These images are manually labelled and used to train a support vector machine (SVM) in an offline training phase. Images have been captured under different lighting conditions and across multiple locations to achieve diversity and robustness to the model. Acceleration data is used to calculate statistical features that capture the roughness of the terrain whereas angular velocities are used to calculate roll and pitch angles experienced by the robot.