This paper introduces a framework for analyzing distributed ship systems. The increase in interconnected and interdependent systems aboard modern naval vessels has significantly increased their complexity, making them more vulnerable to cascading failures and emergent behavior that arise only once the system is complete and in operation. There is a need for a systematic approach to describe and analyze distributed systems at the conceptual stage for naval vessels. Understanding the relationships between various aspects of these distributed systems is crucial for uninterrupted naval operations and vessel survivability. The framework introduced in this paper decomposes information about an individual system into three views: the physical, logical, and operational architectural representations. These representations describe the spatial and functional relationships of the system, together with their temporal behavior characteristics. This paper defines how these primary architectural representations are used to describe a system, the interrelations between the architectural blocks, and how those blocks fit together. A list of defined terms is presented, and a preliminary set of requirements for specific design tools to model these architectures is discussed. A practical application is introduced to illustrate how the framework can be used to describe the delivery of power to a high energy weapon.

This paper proposes a method to elucidate families of ship designs generated by the TU Delft packing approach using data clustering algorithms. The authors explore whether commonly used data science techniques can extract new information from the existing data. To test this hypothesis this paper applies data clustering algorithms to a test case of layouts of a Mine Counter Measures Vessel (MCMV) generated by the packing approach. Results look to improve the understanding of the multidimensional structure of the data, as well as to improve the comprehension and visualization of the complex interactions between the design and performance space.