Publication

The Department of Defense’s Community of Interest for Engineered Resilient Systems (ERS), led by the US Army Engineer Research and Development Center (ERDC), calls for systems that are effective over their life cycle, even when the mission context changes beyond its initial intention. Towards this end, tradespace analysis is of great importance, which enables adaptable designs using diverse systems models that can easily be modified and reused, and the ability to iterate those designs quickly with a clear linkage to evolving mission needs. The Georgia Tech Research Institute (GTRI) is co-developing a web-based, collaborative tradespace environment along with ERDC for the ERS Community of Interest. This leverages GTRI’s expertise in collaborative, executable Model-Based Systems Engineering (MBSE), and ERDC’s leadership of the DoD High Performance Computer (HPC) infrastructure and operations research expertise.

Tradespace analysis facilitates and enables the vision and goals of the ERS program. It is a proven operations research method used to assess design trades. Coupled with data generated from engineering models, operational simulations, and other authoritative sources, tradespace analysis can be used throughout a system’s lifecycle, particularly within the requirements generation phase, to expand the number of feasible alternatives analyzed. To facilitate this tradespace analysis, a collaborative environment is required to allow researchers to input multiple variables with linear and non-linear relationships to investigate viable trades and view second, third, and higher order effects of changing parameters. ERS therefore aims to create a comprehensive tradespace analytics capability that supports complex DoD systems under a wide range of operation scenarios. This effort produced a collaborative, modular open architecture software framework, which allows users to conduct trade studies leveraging executable MBSE integrated with HPC assets. This enables communication of complex results to stakeholders in order to support effective decision processes.

This Final Report will walk through the systems engineering processes applied, as well as demonstrate a notional “design-execute-explore” workflow. During the “design” step, a user would identify capability needs and/or gaps, and derive requirements; a decision maker may apply utility functions to those needs to support analyzing alternatives during the “explore” step. This “define” step includes the definition of the system itself, to include a physical decomposition (which increases in detail with further iteration). Finally, the design step includes the characterization of any modeling and/or analysis required to assess the system(s) of interest. This report will demonstrate how the Systems Modeling Language was used to achieve these goals via a collaborative webauthoring tool. The “execute” step includes the process of selecting the attributes to be varied, and their bounds. This includes the ability to identify sampling methods, to include various forms of Monte Carlo simulation and Designs of Experiments. Further, the user has the ability whether to select to locally or remotely hosted modeling and simulation assets, such as those run via HPC. Finally, the “explore” step enables a user to develop a custom dashboard by selecting visualizations of interest, to include classic descriptive analytics such as interactive histograms and 4-D bubble plots, as well as more advanced techniques to enable rapid Analyses of Alternatives, based on Multi-Objective Decision Analysis leveraging utility functions identified in an earlier step.