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Automated MBSE Model Generation of Space Systems

Description:

OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): Trusted AI and Autonomy; Advanced Computing and Software

 

The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws.

 

OBJECTIVE: Design, develop, and demonstrate SysML model generation techniques to automate the creation of new models of systems using static text-based design documentation.

 

DESCRIPTION: The Department of Defense vision for digital engineering is to modernize how the Department designs, develops, delivers, operates, and sustains systems. The United States Space Force (USSF) uses Model-Based Systems Engineering (MBSE) approaches to acquire new systems, including the delivery of System Modeling Language (SysML) format of architectures and designs. The USSF is also building digital ecosystems and Live, Virtual, Constructive (LVC) capabilities to simulate the employment of space systems through the use of digital models. The USSF needs new model-generation solutions to automate and standardize the creation of new digital models from existing static-based documentation of legacy systems that are not already represented in model-based formats. This will enable the integration of diverse Space Platforms in a common digital environment for warfighting simulation to predict and evaluate the Space Order of Battle.

 

PHASE I: As this is a Direct-to-Phase-II (D2P2) topic, no Phase I awards will be made as a result of this topic. To qualify for this D2P2 topic, the Government expects the Offeror to demonstrate feasibility by means of a prior “Phase I-type” effort that does not constitute work undertaken as part of a prior SBIR/STTR funding agreement.As part of the "Phase I-type" feasibility demonstration, Offerors shall provide evidence of their firms' experience developing SysML models and AI/ML applications that can perform similar tasks. Phase I-type efforts include: developing SysML models of DoD space systems, simulating the employment of DoD systems within an integrated simulation framework, modeling new systems using a Government Reference Model (GRM) for a DoD system, and employing Artificial Intelligence techniques to automate the generation of information from existing documentation.

 

PHASE II: Develop and deliver an AI capability that will be hosted on a government system and used to automate the generation of SysML models from existing system documentation. Demonstrate the ability to use digital threads to integrate newly generated models with an integrated simulation framework. Develop techniques to include a Government Reference Model as a reference for the newly generated models. Demonstrate the ability to generate SysML models of a space system from text-based documentation. GFE is not anticipated.

 

PHASE III DUAL USE APPLICATIONS: Develop a strategy to transition prototype capabilities for digital transformation across USSF commands and organizations. Develop and support a strategy to adapt model generation from a government-provided GRM to align with evolving MBSE standards across the USSF. Generate the necessary documentation to train engineers to effectively use the AI application to generate new models for various purposes. Support activities to ensure the training of users and sustainment of the application on government information systems. Assist the government in quantifying the operational impact of model-based design, development, operations, and sustainment.

 

REFERENCES:

  1. Department of Defense Digital Engineering Strategy, Office of the Deputy Assistant Secretary of Defense for Systems Engineering, Washington, D.C., 2018. Accessed: July 5, 2023, [Online]. Available: https://ac.cto.mil/wp-content/uploads/2019/06/2018-Digital-Engineering-Strategy_Approved_PrintVersion.pdf.;

 

KEYWORDS: Artificial Intelligence; Digital Engineering; Digital Thread; MBSE; Warfighting Simulation

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