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ADRPM (Acoustic Detection Range Prediction Model) is a software program that models the propagation of acoustic energy through the atmosphere and evaluates detectable distance. ADRPM predicts the distance of detection for a noise source based on the acoustic signature of the source. The acoustic signature of a vehicle is computed by combining BEA and EBEA computations with nearfield measurements. The computed signature is utilized as the input to ADRPM. Once the initial detection range is predicted the main contributors to the acoustic detection are identified by ADRPM and their location on the vehicle is modified in order to assess the corresponding effect to the detectable distance of the vehicle.

With the emergence of intelligent ground vehicles, an objective evaluation of vehicle mobility has become an even more challenging task. Vehicle mobility refers to the ability of a ground vehicle to traverse from one point to another, preferably in an optimal way. Numerous techniques exist for evaluating the mobility of vehicles on paved roads, both quantitatively and qualitatively, however, capabilities to evaluate their off-road performance remains limited. Whereas a vehicle’s off-road mobility may be significantly enhanced with intelligence, it also introduces many new variables into the decision making process that must be considered. In this paper, we present a decision analytic framework to accomplish this task. In our approach, a vehicle’s mobility is modeled using an operator’s preferences over multiple mobility attributes of concern. We also provide a method to analyze various operating scenarios including the ability to mitigate uncertainty in the vehicles inputs.

In-wheel electric motors open up new prospects to radically enhance the mobility of autonomous electric vehicles with four or more driving wheels. The flexibility and agility of delivering torque individually to each wheel can allow significant mobility improvements, agile maneuvers, maintaining stability, and increased energy efficiency. However, the fact that individual wheels are not connected mechanically by a driveline system does not mean their drives do not impact each other. With individual torques, the wheels will have different longitudinal forces and tire slippages. Thus, the absence of driveline systems physically connecting the wheels requires new approaches to coordinate torque distribution. This paper solves two technical problems. First, a virtual driveline system (VDS) is proposed to emulate a mechanical driveline system virtually connecting the e-motor driveshafts, providing coordinated driving wheel torque management.

This paper addresses some aspects of an on-going multiyear research project for US Army TARDEC. The focus of the research project has been the enhancement of the overall vehicle reliability prediction process. This paper describes briefly few selected aspects of the new integrated reliability prediction approach. The integrated approach uses both computational mechanics predictions and experimental test databases for assessing vehicle system 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.

Integrated health monitoring technologies are being developed for military ground vehicles to enable condition based maintenance in the short term and prognostic health management in the long term. Technical issues related to health monitoring of a military HMMWV are examined using a dynamic simulation model. Both free and forced vibration response analyses are conducted to examine the effects of damage and operational conditions on the vehicle response. The higher frequency modal properties are found to be sensitive to frame and cross member damage whereas the lower frequency sprung modal properties are not. Changes due to adding up armor are found to be much larger than those due to damage. In addition, cross member damage affects the higher frequency modes whereas damage to the left or right frames causes changes to the modal behavior across the entire frequency range making this type of damage most detectable.

The complete lifecycle of complex systems, such as ground vehicles, consists of multiple phases including design, manufacturing, operation and sustainment (O&S) and finally disposal. For many systems, the majority of the lifecycle costs are incurred during the operation and sustainment phase, specifically in the form of uncertain maintenance costs. Testing and analysis during the design phase, including reliability and supportability analysis, can have a major influence on costs during the O&S phase. However, the cost of the analysis itself must be reconciled with the expected benefits of the reduction in uncertainty. In this paper, we quantify the value of performing the tests and analyses in the design phase by treating it as imperfect information obtained to better estimate uncertain maintenance costs.

Integrated modeling of vehicle, tire and terrain is a fundamental challenge to be addressed for off-road autonomous navigation. The complexities arise due to lack of tools and techniques to predict the continuously varying terrain and environmental conditions and the resultant non-linearities. The solution to this challenge can now be found in the plethora of data driven modeling and control techniques that have gained traction in the last decade. Data driven modeling and control techniques rely on the system’s repeated interaction with the environment to generate a lot of data and then use a function approximator to fit a model for the physical system with the data. Getting good quality and quantity of data may involve extensive experimentation with the physical system impacting developer’s resource. The process is computationally expensive, and the overhead time required is high.

Off-road vehicle operation requires constant decision-making under great uncertainty. Such decisions are multi-faceted and range from acquisition decisions to operational decisions. A major input to these decisions is terrain information in the form of soil properties. This information needs to be propagated to path planning algorithms that augment them with other inputs such as visual terrain assessment and other sensors. In this sequence of steps, many resources are needed, and it is not often clear how best to utilize them. We present an integrated approach where a mission’s overall performance is measured using a multiattribute utility function. This framework allows us to evaluate the value of acquiring terrain information and then its use in path planning. The computational effort of optimizing the vehicle path is also considered and optimized. We present our approach using the data acquired from the Keweenaw Research Center terrains and present some results.

Tradespace analysis is used to define the characteristics of the solution space for a vehicle design problem enabling decision-makers (DMs) to evaluate the risk-benefit posture of a vehicle design program. The tradespace itself is defined by a set of functional objectives defined by vehicle simulations and evaluating the performance of individual design solutions that are modeled by a set of input variables. Of special interest are efficient design solutions because their perfomance is Pareto meaning that none of their functional objective values can be improved without decaying the value of another objective. The functional objectives are derived from a combination of simulations to determine vehicle performance metrics and direct calculations using vehicle characteristics. The vehicle characteristics represent vendor specifications of vehicle subsystems representing various technologies.

Determining the validity of the design space early in the conceptualization of a project can make the difference between project success and failure. Early assessment of technical feasibility, project risk, technical readiness and realistic performance expectations based on models with different levels of fidelity, uncertainty, and technical robustness is a challenging mission critical task for large procurement projects. Tradespace exploration uses model-based engineering analysis, design exploration methods, and multi-objective optimization techniques to enable project stakeholders to make informed decisions and tradeoffs concerning the scope, schedule, budget, performance and risk profile of a project. As the intersection with a number of project stakeholders, tradespace studies can provide a significant impact upon the direction and decision-making in a project.

Accurate terrain models provide the chassis designer with a powerful tool to make informed design decisions early in the design process. It is beneficial to characterize the terrain as a stochastic process, allowing limitless amounts of synthetic terrain to be created from a small number of parameters. A continuous-state Markov chain is proposed as an alternative to the traditional discrete-state chain currently used in terrain modeling practice. For discrete-state chains, the profile transitions are quantized then characterized by a transition matrix (with many values). In contrast, the transition function of a continuous-state chain represents the probability density of transitioning between any two states in the continuum of terrain heights. The transition function developed in this work uses a location-scale distribution with polynomials modeling the parameters as functions of the current state.

Tradespace exploration (TSE) describes the activity occurring early in the design process through which stakeholders explore a broad solution space in search of more-optimal alternatives. In doing so, these stakeholders attempt to maximize the utility inherent in the chosen solution while understanding the tradeoffs and compromises that may be required to find an acceptable solution. In the field of vehicle design, tradespaces are often comprised of vast amounts of alternatives which increases the complexity of the decision-making process. Additionally, the number of stakeholders has grown, as decision-makers seek to include more variety in both perspectives and expertise. As such, decision-making stakeholders can often find themselves working at odds and attempting to maximize vastly different objectives in the process. One way to rectify these contrasting viewpoints can be to intentionally introduce a group framing prior to the start of decision making.

Strategies for evaluating the impact of mission requirements on the design of mission-specific vehicles are needed to enable project managers to assess potential benefits and associated costs of changes in requirements. Top-level requirements that cause significant cascaded difficulties on lower-level requirements should be identified and presented to decision-makers. This paper aims to introduce formal methods and computational tools to enable the analysis and allocation of mission requirements.

Tradespace exploration (TSE) is an important aspect of the early stages of the design process, in which stakeholders search for the most optimal solutions within a design variable-bounded solution space. This decision-making process requires stakeholders to understand the trade-offs and compromises that may be required to choose a solution. In order for stakeholders to make these decisions appropriately, information must be presented in an efficient manner and should ensure that the trade-offs between solutions are clearly visible. Existing visualizations often struggle to elucidate these trade-offs, and can rapidly become difficult to understand as the dimensionality of the tradespace increases. In this paper, the benefits and drawbacks to these existing methods will be discussed. In addition, this paper will explore potential methods to improve information presentation for TSE, including framing, visual steering, and visualization options.

The reliability theory of repairable systems is vastly different from that of non-repairable systems. The authors have recently proposed a ‘decision-based’ framework to design and maintain repairable systems for optimal performance and reliability using a set of metrics such as minimum failure free period, number of failures in planning horizon (lifecycle), and cost. The optimal solution includes the initial design, the system maintenance throughout the planning horizon, and the protocol to operate the system. In this work, we extend this idea by incorporating flexibility and demonstrate our approach using a smart charging electric microgrid architecture. The flexibility is realized by allowing the architecture to change with time. Our approach “learns” the working characteristics of the microgrid. We use actual load and supply data over a short time to quantify the load and supply random processes and also establish the correlation between them.

Operation & support costs for military weapon systems accounted for approximately 3/5th of the $500B Department of Defense budget in 2006. In an effort to ensure readiness and decrease these costs for ground vehicle fleets, health monitoring technologies are being developed for Condition-Based Maintenance of individual vehicles within a fleet. Dynamics-based health monitoring is used in this work because vibrations are a passive source of response data, which are global functions of the mechanical loading and properties of the vehicle. A common way of detecting faults in mechanical equipment, such as the suspension and chassis of a ground vehicle, is to compare measured operational vibrations to a reference (or healthy) signature to detect anomalies.

The U.S. Army is seeking to develop autonomous off-road mobile robots to perform tasks in the field such as supply delivery and reconnaissance in dangerous territory. A key problem to be solved with these robots is off-road mobility. We have developed a computer model of one concept robot, the “T1” omnidirectional vehicle (ODV), to study the effects of different control strategies on the robot's off-road mobility. The T1 is a lightweight robot with an innovative running-gear and control strategy to enhance mobility characteristics. We built the dynamic model of T1 in ADAMS/Car and the control system in MATLAB/Simulink. This paper presents the template-based method used to construct the ADAMS model of the T1 ODV. It also discusses effective linking of ADAMS and MATLAB for control system development. Finally, this paper includes a section describing the extension of the T1 templates to other similar ODV concepts for rapid development.

A state-of-the-art review of the technical meaning and application of the term ‘maneuver’, used by the U.S. Army and ground vehicle engineering communities, was performed with regard to various military activities, including modeling and simulation (M&S), to focus on the value and applicability of the term to military vehicle dynamics. As shown, U.S. military doctrine has built through history and experience a unique concept of maneuver-in-general and its application in U.S. Army unified land operations. Yet, the term ‘maneuver’ needs further technical categorization and characterization for the purpose of dynamics of military unmanned ground vehicles (UGVs) and vehicle design for maneuver. While the NHTSA and SAE standards and definitions provide solid foundations for M&S of cars and trucks to enhance the safety of those vehicles (manned and autonomous), occupants, and pedestrians on roads, the standards cannot address all needs of military vehicles in maneuver.

When a vehicle moves over uneven ground, motion of the sprung and unsprung masses causes dynamic shifting in the load transmitted to the ground, making the normal reaction in the tire-soil patch a continuously changing wheel parameter that may affect vehicle performance. At high loads, sinkage of the wheel can become high as the wheel digs into the soil. At low loads, the wheel can have difficulty acquiring sufficient traction. Additionally, steerability of the wheel can be diminished at very low loads. Controlling the damping forces in the suspension that is usually used to improve ride quality and stabilize motion of the sprung mass can result in an increase in the dynamic variation of the wheel normal reaction and cause vehicle performance deterioration. In this paper, a method is developed to establish boundary constraints on the dynamic normal reaction to maintain reasonable tire-terrain mobility characteristics.

To impact the decision making for military ground vehicles, we are using High Performance Computing (HPC) to speed up the time for analyzing the reliability of a design in modeling and simulation. We use parallelization to get accurate results in days rather than months. We can obtain accurate reliability prediction with modeling and simulation, using uncertainties and multiple physics-of-failure, but by utilizing parallel computing we get results in much less time than conventional analysis techniques.