Progressive Model Generation for Adaptive Resilient System SoftwareDigital Thread Modeling Environment (DTME)

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8650-19-C-2934
Agency Tracking Number: F2-11189
Amount: $749,989.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: N13A-T014
Solicitation Number: 2013.1
Solicitation Year: 2013
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-08-05
Award End Date (Contract End Date): 2021-08-05
Small Business Information
1050 W NASA Blvd, Melbourne, FL, 32901
DUNS: 038379579
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Dr Kate Ryan
 (321) 409-5252
Business Contact
Phone: (919) 244-3946
Research Institution
 Vanderbilt University
 Andrew Budell
 2301 Vanderbilt Place
Nashville, TN, 37240
 (615) 322-2631
 Nonprofit college or university
The ability to align Digital Threads (DT) to Digital Twins (DTw) is a critical component required to achieve reduction in total ownership cost of current and future Air Force (AF) programs. The connecting of the DT to the DTw is a key requirement to achieve model-based system engineering. One can view the connection between DT and DTw as the ability to develop robust models of the real-world system (represented by the vast amount of data collected) that represent the interactions and dependencies across complex systems. Digital Thread Modeling Environment (DTME) uses a model-based approach to identifying dependencies and vulnerabilities among system components. This capability is vital for DT/DTw to identify starting points for analysis when performing root cause analysis on component anomalies as well as when there are deviations in the digital twin with respect to the real-world, data driven digital thread. This innovative approach is applied to the creation of digital threads that will interact with Digital twins to seamlessly exchange information across complex Air Force systems. This will facilitate systems engineering what-if scenarios in the digital twin to fully understand the operational digital thread data and perform analysis of alternatives. The ability to quickly understand which digital twin

* Information listed above is at the time of submission. *

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