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Design optimization often relies on computational models, which are subjected to a validation process to ensure their accuracy. Because validation of computer models in the entire design space can be costly, we have previously proposed an approach where design optimization and model validation, are concurrently performed using a sequential approach with variable-size local domains. We used test data and statistical bootstrap methods to size each local domain where the prediction model is considered validated and where design optimization is performed. The method proceeds iteratively until the optimum design is obtained. This method however, requires test data to be available in each local domain along the optimization path. In this paper, we refine our methodology by using polynomial regression to predict the size and shape of a local domain at some steps along the optimization process without using test data.

Improved propulsion system cooling remains an important challenge in the transportation industry as heat generating components, embedded in ground vehicles, trend toward higher heat fluxes and power requirements. The further minimization of the thermal management system power consumption necessitates the integration of parallel heat rejection strategies to maintain prescribed temperature limits. When properly designed, the cooling solution will offer lower noise, weight, and total volume while improving system durability, reliability, and power efficiency. This study investigates the integration of high thermal conductivity (HTC) materials, carbon fibers, and heat pipes with conventional liquid cooling to create a hybrid “thermal bus” to move the thermal energy from the heat source(s) to the ambient surroundings. The innovative design can transfer heat between the separated heat source(s) and heat sink(s) without sensitivity to gravity.

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.

Designing an efficient cooling system with low power consumption is of high interest in the automotive engineering community. Heat generated due to the propulsion system and the on-board electronics in ground vehicles must be dissipated to avoid exceeding component temperature limits. In addition, proper thermal management will offer improved system durability and efficiency while providing a flexible, modular, and reduced weight structure. Traditional cooling systems are effective but they typically require high energy consumption which provides motivation for a paradigm shift. This study will examine the integration of passive heat rejection pathways in ground vehicle cooling systems using a “thermal bus”. Potential solutions include heat pipes and composite fibers with high thermal properties and light weight properties to move heat from the source to ambient surroundings.

Enhanced electric motor performance in transportation vehicles can improve system reliability and durability over rigorous operating cycles. The design of innovative heat rejection strategies in electric motors can minimize cooling power consumption and associated noise generation while offering configuration flexibility. This study investigates an innovative electric motor cooling strategy through bench top thermal testing on an emulated electric motor. The system design includes passive (e.g., heat pipes) cooling as the primary heat rejection pathway with supplemental conventional cooling using a variable speed coolant pump and radiator fan(s). The integrated thermal structure, “cradle”, transfers heat from the motor shell towards an end plate for heat dissipation to the ambient surroundings or transmission to an external thermal bus to remote heat exchanger.

Hybrid electric vehicles offer the advantages of reduced emissions and greater travel range in comparison to conventional and electric ground vehicles. Regardless of propulsion strategy, efficient cooling of electric motors remains an open challenge due to the operating cycles and ambient conditions. The onboard thermal management system must remove the generated heat so that the motors and other vehicle components operate within their designed temperature ranges. In this article, an integrated thermal structure, or cradle, is designed to efficiently transfer heat within the motor housing to the end plates for transmission to an external heat exchanger. A radial array of heat pipes function as an efficient thermal connector between the motor and heat connector, or thermal bus, depending on the configuration. Cooling performance has been evaluated for various driving cycles.

It is of considerable interest to developers of military vehicles, in early phases of the concept design process as well as in Analysis of Alternatives (AoA) phase, to quickly predict occupant injury risk due to under-body blast loading. The most common occupant injuries in these extremely short duration events arise out of the very high vertical acceleration of vehicle due to its close proximity to hot high pressure gases from the blast. In a prior study [16], an extensive parametric study was conducted in a systematic manner so as to create look-up tables or automated software tools that decision-makers can use to quickly estimate the different injury responses for both stroking and non-stroking seat systems in terms of a suitable blast load parameter. The primary objective of this paper is to quantitatively evaluate the accuracy of using such a tool in lieu of building a detailed model for simulation and occupant injury assessment.

The NATO Reference Mobility Model (NRMM)[1] is the primary mobility software used by the US Department of Defense, its contractors and NATO countries to evaluate various metrics of proposed vehicle systems for acquisition. The NRMM is a vehicle mobility performance model developed in the 1970's[2] that combines mobility related technologies into one comprehensive software package designed to predict the physically constrained vehicle and terrain interaction while operating in both on and off road environments. The empirically based relationships within NRMM are measurements taken from actual vehicles run over a variety of terrains and are geared towards vehicles weighing more than 1500 pounds. As the Army focuses on a lighter, faster and more mobile fighting force, standard military vehicles are decreasing in size with many newultra lightweight autonomous systems being designed.

Military vehicles are typically armored, hence the open surface area for heat rejection is limited. Hence, the cooling parasitic load for a given heat rejection can be considerably higher and important to consider upfront in the system design. Since PEMFCs operate at low temp, the required cooling flow is larger to account for the smaller delta temperature to the air. This research aims to address the combined problem of optimal sizing of the lithium ion battery and PEM Fuel Cell stack along with development of the scalable power split strategy for small a PackBot robot. We will apply scalable physics-based models of the fuel cell stack and balance of plant that includes a realistic and scalable parasitic load from cooling integrated with existing scalable models of the lithium ion battery. This model allows the combined optimization that captures the dominant trends relevant to component sizing and system performance.

This paper describes the author's experiences in the design, validation and field-testing of a low cost, online oil condition monitor for diesel driven Army ground vehicles. This online oil condition monitor utilizes a multi-frequency approach to electrochemical impedance spectroscopy to interrogate and evaluate fluid health in near real time. A dual microcontroller processing architecture embedded in the sensor itself executes an oil-health evaluation algorithm and provides estimates of lubricant remaining useful life, as well as identification of the primary mode of degradation of the fluid. These data are transmitted off the sensor via J1939 compliant CAN messages. In this paper the unique application requirements, which formed the foundation of the development process, are discussed, and the technical and design challenges associated with producing a military grade smart-sensor at a sufficiently low price point for widespread adoption in the ground vehicle market are detailed.

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.

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 describes modeling and simulation technologies used to simulate the electrical systems of Army vehicles using Matlab/Simulink coupled with graphical user interface software. The models were built using Mathworks' Matlab/Simulink software in conjunction with the SimPowerSystems Toolbox, a toolkit provided by Mathworks that provides models of basic electrical components such as capacitors and inductors, in addition to more advanced components such as diodes and IGBT's. The current results of this ongoing effort are presented and discussed.

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 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.

Vehicles with power exporting capability are microgrids since they possess electrical power generation, onboard loads, energy storage, and the ability to interconnect. The unique load and silent watch requirements of some military vehicles make them particularly well-suited to augment stationary power grids to increase power resiliency and capability. Connecting multiple vehicles in a peer-to-peer arrangement or to a stationary grid requires scalable power management strategies to accommodate the possibly large numbers of assets. This paper describes a military ground vehicle power management scheme for vehicle-to-grid applications. The particular focus is overall fuel consumption reduction of the mixed asset inventory of military vehicles with diesel generators typically used in small unit outposts.

Through inverse dynamics-based modeling and computer simulations for a 6×6 Unmanned Ground Vehicle (UGV) - a 6×6 truck - in stochastic terrain conditions, this paper analytically presents a coupled impact of different driveline system configurations and a suspension design on vehicle dynamics, including vehicle mobility, and energy efficiency. A new approach in this research work involves an estimation of each axle contribution to the level of potential mobility loss/increase and/or energy consumption increase/ reduction. As it is shown, the drive axles of the vehicle interfere with the vehicle's dynamics through the distribution of the wheels' normal reactions and wheel torques. The interference causes the independent system dynamics to become operationally coupled/fused and thus diminishes vehicle mobility and energy efficiency. The analysis is done by the use of new mobility indices and energy efficiency indices which are functionally coupled/fused.

The U. S. Army Tank Automotive Research, Development and Engineering Center (TARDEC) National Automotive Center (NAC) owns a fleet of ten Hydrogen Hybrid Internal Combustion Engine (H2ICE) vehicles that have been demonstrated in various climates from 2008 through 2010. This included demonstrations in Michigan, Georgia, California and Hawaii. The fleet was consolidated into a single location between July 2009 and April 2010. Between July of 2009 and January of 2011, data collection was completed on the fleet of H2ICE vehicles deployed to Oahu, Hawaii for long-term duration testing. The operation of the H2ICE vehicles in Hawaii utilized standard operation of a non-tactical vehicle at a real-world military installation. The vehicles were fitted with data acquisition equipment to record the operation and performance of the H2ICE vehicles; maintenance and repair data was also recorded for the fleet of vehicles.

Motion cueing algorithms can improve the perceived realism of a driving simulator, however, data on the effects on driver performance and simulator sickness remain scarce. Two novel motion cueing algorithms varying in concept and complexity were developed for a limited maneuvering workspace, hexapod/Stuart type motion platform. The RideCue algorithm uses a simple swing motion concept while OverTilt Track algorithm uses optimal pre-positioning to account for maneuver characteristics for coordinating tilt adjustments. An experiment was conducted on the US Army Tank Automotive Research, Development and Engineering Center (TARDEC) Ride Motion Simulator (RMS) platform comparing the two novel motion cueing algorithms to a pre-existing algorithm and a no-motion condition.

The Powertrain Analysis and Computational Environment (PACE) is a powertrain simulation tool that provides an advanced behavioral modeling capability for the powertrain subsystems of conventional or hybrid-electric vehicles. Due to its origins in Argonne National Lab’s Autonomie, PACE benefits from the reputation of Autonomie as a validated modeling tool capable of simulating the advanced hardware and control features of modern vehicle powertrains. However, unlike Autonomie that is developed and executed in Mathwork’s MATLAB/Simulink environment, PACE is developed in C++ and is targeted for High-Performance Computing (HPC) platforms. Indeed, PACE is used as one of several actors within a comprehensive ground vehicle co-simulation system (CRES-GV MERCURY): during a single MERCURY run, thousands of concurrent PACE instances interact with other high-performance, distributed MERCURY components.