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.

The expected growth of civil aviation over the next twenty years places significant emphasis on revolutionary technology development aimed at mitigating the environmental impact of commercial aircraft. As the number of technology alternatives grows along with model complexity, current methods for Pareto finding and multiobjective optimization quickly become computationally infeasible. Coupled with the large uncertainty in the early stages of design, optimal designs are sought while avoiding the computational burden of excessive function calls when a single design change or technology assumption could alter the results. This motivates the need for a robust and efficient evaluation methodology for quantitative assessment of competing concepts. This research presents a novel approach that combines Bayesian adaptive sampling with surrogate-based optimization to efficiently place designs near Pareto frontier intersections of competing concepts. Efficiency is increased over sequential multiobjective optimization by focusing computational resources specifically on the location in the design space where optimality shifts between concepts. At the intersection of Pareto frontiers, the selection decisions are most sensitive to preferences place on the objectives, and small perturbations can lead to vastly different final designs. These concepts are incorporated into an evaluation methodology that ultimately reduces the number of failed cases, infeasible designs, and Pareto dominated solutions across all concepts. A set of algebraic samples along with a truss design problem are presented as canonical examples for the proposed approach. The methodology is applied to the design of ultra-high bypass ratio turbofans to guide NASA's technology development efforts for future aircraft. Geared-drive and variable geometry bypass nozzle concepts are explored as enablers for increased bypass ratio and potential alternatives over traditional configurations. The method is shown to improve sampling efficiency and provide clusters of feasible designs that motivate a shift towards revolutionary technologies that reduce fuel burn, emissions, and noise on future aircraft.