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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.

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.

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.

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 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.

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.