The use of multiobjective optimization in identifying systems that account for changes in customer needs, operating environments, system design concepts, and analysis models over time is generally not explored. Providing solutions that anticipate, account for, and allow for these changes over time is a significant challenge to manufacturers and design engineers. Products that adapt to these changes through the addition and/or subtraction of modules can reduce production costs through product commonality, and cater to customization and adaptation. In terms of identifying sets of non-dominated designs, these changes result in the concept of dynamic Pareto frontiers, or dynamic s-Pareto frontiers when sets of system concepts are simultaneously evaluated over time. In this dissertation, a five-step optimization-based design method identifying a set of optimal adaptive product designs that satisfy the predicted changes by moving from one location on the dynamic s-Pareto frontier to another through the addition of a module and/or through reconfiguration is developed. Development of this five-step method was separated into four phases. The first two phases of developments respectively focus on Pareto and s-Pareto cases, where changes in concepts, models, and environments that would effect the Pareto/s-Pareto frontier are ignored due to limitations in traditional optimization problem formulations. To overcome these limitations, and allow for these changes, the third phase of developments presents a generic optimization formulation capable of identifying a dynamic s-Pareto frontier, while the fourth phase adapts the phase three method to incorporate this new dynamic optimization formulation. Example implementations of the four phases of developments were respectively provided through the design of a modular UAV, a hurricane and flood resistant modular residential structure, a simple aircraft design example inspired by the Lockheed C-130 Hercules, and a modular truss system. Noting that modular products only represent one approach for dealing with changes in preferences, environments, models, and concepts, the final research contribution connects the presented method with parallel research developments in collaborative product design and design principles identification, followed by two case study implementations of this unifying design approach in the development of a modular irrigation pump and a modular plywood cart for developing countries.

This thesis addresses the challenges associated with thermal management systems (TMS) evaluation and selection in the conceptual design of hypersonic, air-breathing vehicles with sustained cruise. The proposed methodology identifies analysis tools and techniques which allow the proper investigation of the design space for various thermal management technologies. The design space exploration environment and alternative multi-objective decision making technique defined as Pareto-based Joint Probability Decision Making (PJPDM) is based on the approximation of 3-D Pareto frontiers and probabilistic technology effectiveness maps. These are generated through the evaluation of a Pareto Fitness function and Monte Carlo analysis. In contrast to Joint Probability Decision Making (JPDM), the proposed PJPDM technique does not require preemptive knowledge of weighting factors for competing objectives or goal constraints which can introduce bias into the final solution. Preemptive bias in a complex problem can degrade the overall capabilities of the final design. The implementation of PJPDM in this thesis eliminates the need for the numerical optimizer which is required with JPDM in order to improve upon a solution. In addition, a physics-based formulation is presented for the quantification of TMS safety effectiveness corresponding to debris impact/damage and how it can be applied towards risk mitigation. Lastly, a formulation loosely based on non-preemptive Goal Programming with equal weighted deviations is provided for the resolution of the inverse design space. This key step helps link vehicle capabilities to TMS technology subsystems in a top-down design approach. The methodology provides the designer more knowledge up front to help make proper engineering decisions and assumptions in the conceptual design phase regarding which technologies show greatest promise, and how to guide future technology research.

The past decades have seen the state of the art in aerospace system design progress from a scope of simple optimization to one including robustness, with the objective of permitting a single system to perform well even in off-nominal future environments. Integrating flexibility, or the capability to easily modify a system after it has been fielded in response to changing environments, into system design represents a further step forward. One challenge in accomplishing this rests in that the decision-maker must consider not only the present system design decision, but also sequential future design and operation decisions. Despite extensive interest in the topic, the state of the art in designing flexibility into aerospace systems, and particularly space systems, tends to be limited to analyses that are qualitative, deterministic, single-objective, and/or limited to consider a single future time period. To address these gaps, this thesis develops a stochastic, multi-objective, and multi-period framework for integrating flexibility into space system design decisions. Central to the framework are five steps. First, system configuration options are identified and costs of switching from one configuration to another are compiled into a cost transition matrix. Second, probabilities that demand on the system will transition from one mission to another are compiled into a mission demand Markov chain. Third, one performance matrix for each design objective is populated to describe how well the identified system configurations perform in each of the identified mission demand environments. The fourth step employs multi-period decision analysis techniques, including Markov decision processes (MDPs) from the field of operations research, to find efficient paths and policies a decision-maker may follow. The final step examines the implications of these paths and policies for the primary goal of informing initial system selection. Overall, this thesis unifies state-centric concepts of flexibility from economics and engineering literature with sequential decision-making techniques from operations research. The end objective of this thesis' framework and its supporting analytic and computational tools is to enable selection of the next-generation space systems today, tailored to decision-maker budget and performance preferences, that will be best able to adapt and perform in a future of changing environments and requirements. Following extensive theoretical development, the framework and its steps are applied to space system planning problems of (1) DARPA-motivated multiple- or distributed-payload satellite selection and (2) NASA human space exploration architecture selection.