A Case Study in Scalable, Low-Cost Deployment of a Digital Ecosystem for Engineering Complex Systems

The transition to more highly integrated naval vessels with significant, high power electric power systems for advanced sensors, weapons, and propulsion boost introduces significant complexity in managing power distribution, thermal control, and cross-domain system interactions-necessitating a cohesive digital engineering approach. This paper presents a lowcost, rapidly deployable Digital Ecosystem architecture tailored to support model-based systems engineering (MBSE) and digital twin development in electric ship programs. Aligned with DoDI 5000.97, the study implements a “three-legged” integration strategy using CSV files, Python scripts, and MATLAB routines to connect a SysML system model in Cameo with external analytical tools. This architecture enables seamless validation of system behavior and performance against requirements, including integration with CAD-derived geometries and physics-based simulations such as antenna pattern and structural analysis. Generative AI (ChatGPT) was used to accelerate model development by transforming legacy documents into structured, import-ready datasets. The result is a scalable, interoperable framework for digital thread and digital twin workflows that supports the agility, traceability, and cross-discipline coordination demanded by modern electric ship design and sustainment.