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Implications of decision analysis (DA) on engineering design are important and well-documented. However, widespread adoption has not occurred. To that end, the authors recently proposed decision topologies (DT) as a visual method for representing decision situations and proved that they are entirely consistent with normative decision analysis. This paper addresses the practical issue of assessing the DTs of a designer using their responses. As in classical DA, this step is critical to encoding the DA's preferences so that further analysis and mathematical optimization can be performed on the correct set of preferences. We show how multi-attribute DTs can be directly assessed from DM responses. Furthermore, we show that preferences under uncertainty can be trivially incorporated and that topologies can be constructed using single attribute topologies similarly to multi-linear functions in utility analysis. This incremental construction simplifies the process of topology construction.

Many efficient modeling methods have been developed for analyzing the effects of component-level variations and uncertainties on the system-level response of complex structures. However, relatively little work has addressed the efficient or agile modeling of variations in the connections between components. Such a capability would be useful for simulation (e.g., performing reliability analysis accounting for spot welding variations) and design (e.g., determining fastener locations for up-armor kits) of commercial and military ground vehicle structures. In this work, a component mode synthesis approach to structural modeling is enhanced by also modeling variations in the connections between components. With this framework, changes in the joining or fastening of the components can be considered in a structural analysis or design process. The components are condensed statically or dynamically with all the candidate joining nodes being retained as active degrees of freedom.

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.

When performing vibration analysis of complex vehicle structures, it is often important to be able to evaluate the effects of design changes in one or more substructures (e.g., for design optimization). It may also be convenient to allow different components to be modeled independently by different groups or organizations. For both cases, it is inevitable that some substructures will have non-matching finite element meshes at the interface where they are physically connected. Thus, a key challenge is to be able to handle the dynamic assembly of components with non-matching meshes and the subsequent global vibration analysis in a systematic and efficient manner. To tackle this problem, the enhancement of component mode synthesis methods for handling finite element models partitioned into non-matching substructures is considered in this paper. Some existing methods are reviewed, and new methods are developed.

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.

Modeling and simulation of ground vehicles can be a computationally expensive problem due to the complexity of high-fidelity vehicle models. Often to determine mobility metrics, multiple stochastic simulations need to be evaluated. Surrogate models, or models of models, offer a means to reduce the computational cost of these simulation efforts. Since various types of surrogate models are available to the user, choosing the best surrogate model for a simulation is mostly the challenging process. In this paper, the process of selecting surrogate models and its uses based on model metafeatures is presented. The approach formulates this decision as a trade-off among three main drivers, required dataset size (how much information is necessary to compute the surrogate model), surrogate model accuracy (how accurate the surrogate model must be) and total computational time (how much time is required for the surrogate modeling process).