Description:
OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): Space Technology
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: Develop modeling and simulation capabilities which highlight the interaction between space systems and aggregations of terrestrial (air, land, sea) systems coordinating to deliver effects in multi-domain operations scenarios.
DESCRIPTION: The USSF Commander's Campaign Support Plan (CSP) outlines the United States Space Force (USSF) will support Geographic Combatant Commands (GCCs) by organizing, training, equipping, and presenting a ready Space Force with an eye towards collaborative partnerships that yield decisive operational capabilities. Modern force design is moving towards concepts of “Mosaic Warfare” and distributed and connected groups of force components collaborating in new ways. As these force designs evolve terrestrially, so too will the way in which they are supported by space services. Enabling modeling and simulation engines to explore the multi-dimensional graphs structures which arise when analyzing connected and collaborating groups of agents (aka digital twins) while evaluating the utility of the delivered effects is an area of ongoing and rapidly evolving research. This topic explores the integration of model-based systems engineering (MBSE) system representation in systems modeling language (SysML) with agent-based modeling and simulation frameworks to enable high fidelity analytical representations of the connections between system design decisions, and the impact of the increase in space service performance on the utility of the service-dependent platforms in the fight.
PHASE I: Define and develop a concept for a workable prototype or design to address at a minimum the basic capabilities of the stated objective. Define uses cases and specific application for a new capability. Demonstrate technical feasibility to meet the capabilities of the stated objective for one use case.
PHASE II: The solution for this topic will employ multiple modeling and simulation frameworks for the representation of systems in their respective domains and integrate methods for the representation of these systems in SysML such that responsive models may be generated by the system representations as input. Further, multi-dimensional graph visualization and processing tools will be integrated to enable means to explain the complex relationships between interacting and collaborating agents in multi-domain scenarios. Evaluation of the kill-webs will be carried out to a point where, via the use of concrete performance metrics or measures of expected capability equivalent across domains, space-enabled kill-webs can be meaningfully compared to kill-webs that are not space-enabled.
PHASE III DUAL USE APPLICATIONS: Develop a strategy to transition prototype residual capabilities and incremental proliferation based on USAF/USSF requirements.
REFERENCES:
1. Bernal, Iric. "Optimizing Engagement Simulations Through the Advanced Framework for Simulation, Integration, and Modeling (AFSIM) Software." PhD diss., The Ohio State University, 2020;
2. Tobin, Josh, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, and Pieter Abbeel. "Domain randomization for transferring deep neural networks from simulation to the real world." In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 23-30. IEEE, 2017;
3. Fernandez, Nicolas F., Gregory W. Gundersen, Adeeb Rahman, Mark L. Grimes, Klarisa Rikova, Peter Hornbeck, and Avi Ma’ayan. "Clustergrammer, a web-based heatmap visualization and analysis tool for high-dimensional biological data." Scientific data 4 (2017)
KEYWORDS: model-based systems engineering (MBSE); agent-based modeling and simulation; digital twin engineering; Advanced Framework for Simulation
