Modern naval missions rely increasingly on a complex array of electronic systems for a variety of functions including communications, sensing, weaponry, and countermeasures, greatly increasing the power requirements for future ships. To meet these needs, ships are transitioning to more electric architectures and require the infusion of new technologies. Since technology development resources are often limited, it is vital to intelligently choose the most promising designs to invest in. However, this type of decision-making is not necessarily straightforward. Firstly, the ship itself is a highly intricate system, with thousands of components and interactions that can be modeled at a variety of fidelity levels and time scales. Secondly, the final decision depends on the various objectives, sometimes conflicting, of each decision-maker involved in the process, making traceability and justification for the final selection a challenge. Therefore, there is a need for a design decision-support environment that brings together all the technical data about the performance aspects of various ship designs and technologies in a way that allows stakeholders to interact with the information in a more dynamic, traceable manner, in order to make more informed decisions.
This paper is introducing an environment that is being created as part of a study with the Electric Ship Research and Development Consortium (ESRDC) on High-Temperature Superconducting (HTS) technologies sponsored by the U.S. Office of Naval Research (ONR) and presents some potential examples of how stakeholders could use it in the decision-making context. This environment is built upon the foundation of the Technology Identification, Evaluation, and Selection (TIES) methodology, originally formulated for aircraft design, now adapted for electric ships. TIES was developed to address the need for a design methodology that accounts for the uncertain nature of new technologies, integrating probabilistic analysis and other advanced design methods, as well as the multi-objective nature of modern design decision-making. The environment walks a user through the steps of the TIES methodology, starting from defining key objectives and metrics of interest and then moving to generation of a baseline and ship architecture alternatives via morphological analysis. Semi-automated modeling and simulation capabilities were then developed to enable the creation of a set of potential designs, allowing for a broader design space exploration than would be possible with traditional point-design-based processes, allowing the user to more clearly see the impact of lower-level ship parameters on system-level metrics of interest. Constraint and sensitivity analyses are then performed to gain a further understanding of the relationships and tradeoffs among the ship design variables, what regions of the design space are most promising, and what requirements most strongly impact the space of feasible designs. The environment then leverages the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to allow users to dial in relative importance weightings of the system-level metrics of interest to determine which ship designs best meet their needs. Once several promising ship designs have been selected based on all the information shown in the environment, these options can be analyzed in more detail.
