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This paper presents a computational framework for the physics-based simulation of light vehicles operating on discrete terrain. The focus is on characterizing through simulation the mobility of vehicles that weigh 1000 pounds or less, such as a reconnaissance robot. The terrain is considered to be deformable and is represented as a collection of bodies of spherical shape. The modeling stage relies on a novel formulation of the frictional contact problem that requires at each time step of the numerical simulation the solution of an optimization problem. The proposed computational framework, when run on ubiquitous Graphics Processing Unit (GPU) cards, allows the simulation of systems in which the terrain is represented by more than 0.5 million bodies leading to problems with more than one million degrees of freedom.

Since transient vehicle HVAC computational fluids (CFD) simulations take too long to solve in a production environment, the goal of this project is to automatically create a lumped-parameter flow network from a steady-state CFD that solves nearly instantaneously. The data mining algorithm k-means is implemented to automatically discover flow features and form the network (a reduced order model). The lumped-parameter network is implemented in the commercial thermal solver MuSES to then run as a fully transient simulation. Using this network a “localized heat transfer coefficient” is shown to be an improvement over existing techniques. Also, it was found that the use of the clustering created a new flow visualization technique. Finally, fixing clusters near equipment newly demonstrates a capability to track localized temperatures near specific objects (such as equipment in vehicles).

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.

Mercury is a high-fidelity, physics-based object-oriented software for conducting simulations of vehicle performance evaluations for requirements and engineering metrics. Integrating cutting-edge, massively parallel modeling techniques for soft, cohesive and dry granular soil that will integrate state-of-the-art soil simulation with high-fidelity multi-body dynamics and powertrain modeling to provide a comprehensive mobility simulator for ground vehicles. The Mercury implements the Chrono::Vehicle dynamics library for vehicle dynamics, which provides multi-body dynamic simulation of wheeled and tracked vehicles. The powertrain is modeled using the Powertrain Analysis Computational Environment (PACE), a behavior-based powertrain analysis based on the U.S. Department of Energy’s Autonomie software. Vehicle -terrain interaction (VTI) is simulated with the Ground Contact Element (GCE), which provides forces to the Chrono-vehicle solver.

Optimizing the trade-off between reliability and cost of operating a microgrid, including vehicles as both loads and sources, can be a challenge. Optimal energy management is crucial to develop strategies to improve the efficiency and reliability of microgrids, as well as new communication networks to support optimal and reliable operation. Prior approaches modeled the grid using MATLAB, but did not include the detailed physics of loads and sources, and therefore missed the transient effects that are present in real-time operation of a microgrid. This article discusses the implementation of a physics-based detailed microgrid model including a diesel generator, wind turbine, photovoltaic array, and utility. All elements are modeled as sources in Simulink. Various loads are also implemented including an asynchronous motor. We show how a central control algorithm optimizes the microgrid by trying to maximize reliability while reducing operational cost.

The US Army Tank Automotive Research, Development and Engineering Center (TARDEC) has developed a unique physics based modeling & simulation (M&S) capability using Computational Fluid Dynamics (CFD) techniques to optimize automatic fire extinguishing system (AFES) designs and complement vehicle testing for both occupied and unoccupied spaces of military ground vehicles. The modeling techniques developed are based on reduced global kinetics for computational efficiency and are applicable to fire suppressants that are being used in Army vehicles namely, bromotrifluoromethane (Halon 1301), heptafluoropropane (HFC-227ea, trade name FM200), sodium-bicarbonate (SBC) powder, water + potassium acetate mixture, and pentafluoroethane (HFC-125, trade name, FE-25). These CFD simulations are performed using High Performance Computers (HPC) that enable the Army to assess AFES designs in a virtual world at far less cost than physical-fire tests.

Understanding reliability is critical in design, maintenance and durability analysis of engineering systems. A reliability simulation methodology is presented in this paper for vehicle fleets using limited data. The method can be used to estimate the reliability of non-repairable as well as repairable systems. It can optimally allocate, based on a target system reliability, individual component reliabilities using a multi-objective optimization algorithm. The algorithm establishes a Pareto front that can be used for optimal tradeoff between reliability and the associated cost. The method uses Monte Carlo simulation to estimate the system failure rate and reliability as a function of time. The probability density functions (PDF) of the time between failures for all components of the system are estimated using either limited data or a user-supplied MTBF (mean time between failures) and its coefficient of variation.

This research paper addresses the ground vehicle reliability prediction process based on a new integrated reliability prediction framework. The integrated stochastic framework combines the computational physics-based predictions with experimental testing information for assessing vehicle reliability. The integrated reliability prediction approach incorporates the following computational steps: i) simulation of stochastic operational environment, ii) vehicle multi-body dynamics analysis, iii) stress prediction in subsystems and components, iv) stochastic progressive damage analysis, and v) component life prediction, including the effects of maintenance and, finally, iv) reliability prediction at component and system level. To solve efficiently and accurately the challenges coming from large-size computational mechanics models and high-dimensional stochastic spaces, a HPC simulation-based approach to the reliability problem was implemented.

A dynamometer test methodology was developed for evaluation of HMMWV axle efficiency with hypoid gearsets, comparing those having various degrees of superfinish versus new production axles as well as used axles removed at depot maintenance. To ensure real-world applicability, a HMMWV variant vehicle model was created and simulated over a peacetime vehicle duty cycle, which was developed to represent a mission scenario. In addition, tractive effort calculations were then used to determine the maximum input torques. The drive cycle developed above was modified into two different profiles having varying degrees of torque variability to determine if the degree of variability would have a significant influence on efficiency in the transient dynamometer tests. Additionally, steady state efficiency performance is measured at four input pinion speeds from 700-2500 rpm, five input torques from 50 - 400 N⋅m, and two sump temperatures, 80°C and 110°C.

For existing fleets such as the U.S. military ground vehicle fleet, there are few ways to reduce vehicle fuel consumption that don’t involve expensive retrofitting. Replacing standard lubricants with those that can achieve higher vehicle efficiencies is one practical and inexpensive way to improve fleet fuel efficiency. In an effort to identify axle gear lubricants that can reduce the fuel consumption of its fleet, the U.S. Army is developing a stationary axle efficiency test stand and procedure. In order to develop this capability, on-track vehicle fuel consumption testing was completed using light, medium, and heavy tactical wheeled vehicles following a modified SAE J1321 type test procedure. Tested lubricants included a baseline SAE 80W-90, a fuel efficient SAE 75W-90, and a fuel efficient SAE 75W-140. Vehicle testing resulted in reductions in fuel consumption of up to 2%.

This paper presents a computationally-efficient model of heat convection due to air circulation produced by rotor motion in the air gap of an electric machine. The model calculates heat flux at the boundaries of the rotor and stator as a function of the rotor and stator temperatures and rotor speed. It is shown that, under certain assumptions, this mapping has the homogeneity property. This property, among others, is used to pose a structure for the proposed model. The coefficients of the model are then determined by fitting the model to the results of a commercial Computational Fluid Dynamics (CFD) simulation program. The accuracy of the new model is compared to the CFD results, shown an error of less than 0.3% over the studied operating range.

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.

The recently published Lightweight Combat Vehicle Science and Technology Campaign [1] recommended the Army develop a quantitative understanding of the operational impact that weight reduction has to the Army and create appropriate metrics that would better reflect the performance trade with regards to weight. That paper raised the question of what a ton of weight is worth in operational effectiveness and cost. This paper is an attempt to clarify this complex topic. The impact of select programmatic considerations, operational considerations, and financial considerations are discussed. Throughout, the paper provides example analyses based on vehicle weight, performance, and cost data. The paper closes with a discussion of the issues presented, research recommendations, and closing comments.

Reliability and resiliency (R&R) definitions differ depending on the system under consideration. Generally, each engineering sector defines relevant R&R metrics pertinent to their system. While this can impede cross-disciplinary engineering projects as well as research, it is a necessary strategy to capture all the relevant system characteristics. This paper highlights the difficulties associated with defining performance of such systems while using smart microgrids as an example. Further, it develops metrics and definitions that are useful in assessing their performance, based on utility theory. A microgrid must not only anticipate load conditions but also tolerate partial failures and remain optimally operating. Many of these failures happen infrequently but unexpectedly and therefore are hard to plan for. We discuss real life failure scenarios and show how the proposed definitions and metrics are beneficial.

Among all the vehicle rollover test procedures, the SAE J2114 dolly rollover test is the most widely used. However, it requires the test vehicle to be seated on a dolly with a 23° initial angle, which makes it difficult to test a vehicle over 5,000 kg without a dolly design change, and repeatability is often a concern. In the current study, we developed and implemented a new dynamic rollover test methodology that can be used for evaluating crashworthiness and occupant protection without requiring an initial vehicle angle. To do that, a custom cart was designed to carry the test vehicle laterally down a track. The cart incorporates two ramps under the testing vehicle’s trailing-side tires. In a test, the cart with the vehicle travels at the desired test speed and is stopped by a track-mounted curb.

Real-time driving simulations are an important tool for verifying vehicle and vehicle component designs with a driver in the loop. They not only provide a cost effective solution but also an ability to verify designs in a safe and controlled operating environment. A real-time driving experiment has been developed for Stryker to compare the ride and handling performance of a baseline passive suspension to that of a Magneto-Rheological (MR) semi-active damper suspension. The Tank Automotive Research Development and Engineering Center (TARDEC) has integrated this new suspension into a real time vehicle dynamics model of the Stryker using the MR suspension model developed by the Original Equipment Manufacturer (OEM). Using this real-time model and the TARDEC Ride Motion Simulator (RMS), TARDEC associates, along with associates from the Stryker Program Management office and the suspension OEM were able to drive and compare the passive and MR Stryker in a virtual environment.

The objective of this study was to optimize the occupant restraint systems for a light tactical vehicle in frontal crashes. A combination of sled testing and computational modeling were performed to find the optimal seatbelt and airbag designs for protecting occupants represented by three size of ATDs and two military gear configurations. This study started with 20 sled frontal crash tests to setup the baseline performance of existing seatbelts, which have been presented previously; followed by parametric computational simulations to find the best combinations of seatbelt and airbag designs for different sizes of ATDs and military gear configurations involving both driver and passengers. Then 12 sled tests were conducted with the simulation-recommended restraint designs. The test results were further used to validate the models. Another series of computational simulations and 4 sled tests were performed to fine-tune the optimal restraint design solutions.

A full-system, end-to-end blast modeling and simulation of vehicle underbody buried blast events typically includes detailed modeling of soil, high explosive (HE) charge and air. The complex computations involved in these simulations take days to just capture the initial 50-millisecond blast-off phase, and in some cases, even weeks. The single most intricate step in the buried blast event simulation is in the modeling of the explosive loading on the underbody structure from the blast products; it is also one of the most computationally expensive steps of the simulation. Therefore, there is significant interest in the modeling and simulation community to develop various methodologies for fast running tools to run full simulation events in quicker turnarounds of time.

In this study, we extend and improve on the methods introduced by Brudnak et al. [1] by adding a second objective to the reduction of test time. This second objective under consideration is to diminish or reduce the number of cuts or deletions to the time histories during an editing process. As discussed in [1], segment-based methods consider each segment for retention or deletion based on its own localized severity, not considering the segments around it. As a result, retained segments can be widely scattered in the time domain depending on signal characteristics and therefore a large number of cuts can be induced unintentionally. Regardless of the joining method, such cuts and joins require artificial signal processing and should therefore be minimized. In this paper we present techniques to minimize these cuts while at the same time maintaining our original goals of time reduction and severity retention.

In this paper a Multi-Level System (MLS) optimization algorithm is presented and utilized for the multi-discipline design of a ground vehicle track. The MLS can guide the decision making process for designing a complex system where many alternatives and many mutually competing objectives and disciplines need to be considered and evaluated. Mathematical relationships between the design variables and the multiple discipline performance objectives are developed adaptively as the various design considerations are evaluated and as the design is being evolved. These relationships are employed for rewarding performance improvement during the decision making process by allocating more resources to the disciplines which exhibit the higher level of improvement. The track analysis demonstrates how a multi-discipline design approach can be pursued in ground vehicle applications.