Empowering More Authoritative Decisioning for Research/Testing/Analysis of Energetic Fills

DTRA’s mission requires finding the match between the research and definition of explosive’s formulation and warhead performance properties and the defining of the expected explosive effects to defeat target structures (e.g., produce fragmentation, perform some chemical, biological defeats) which are different target effects than “simply blowing things up” (e.g., fragmentation).  Even in 2020, warhead properties scaling and the determining desired target effects from explosives formulations is still more of an art than a science with a heavy reliance on explosives subject matter experts (SMEs or greybeards) knowledge. Despite significant advances in Digital Engineering (DE) and Machine Learning (ML) technologies, the adoption of such is still slow across the DoD for both support of existing and development of new weapon systems.  These advancements in “Big Data” DE capabilities and ML algorithms are making for highly favorable cost benefit tradeoffs for investing in these modern DE-based tools in the form of a DTRA wide, interoperable Explosives Operations System (EOS).  A DE enabled EOS provides a collaborative knowledge capture across current siloed research data, development of common core database(s) and analysis tools, tools interoperability and integration, explosive formulation process capability, etc.  For DTRA to continue to be on the forefront of explosives research and to provide near real-time support to the warfighter, the DE transformation must take place to integrate greybeard/SME knowledge, legacy and new test data, with both existing and new analysis tools into a collaborative DE and ML empowered EOS.  DE with ML empowerment is a key enabling technology that can provide Digital Thread/Twin (DT/DTw) capabilities offering speed and agility driven authoritative decisioning which can significantly reduce both the risks and time associated with weapon systems research and development across the DoD. RJ Lee Group, Inc. (RJLG) will develop/integrate/implement a data-agnostic prototype DE smart discovery and decisioning toolset (the EOS) to improve data mining, complex data transformation and conflict identification capabilities.  This capability will give DTRA explosives research engineers and scientists the tools required to accelerate advances in explosives research to better enable the DoD to rapidly respond to future threats.   RJLG will use its data ingestion/indexing/metadata-extraction/semantic-linking/data-curation capability, commercially referred to as SEAMS as the basis to build the envisioned EOS toolset.  The ultimate architecture will augment the existing SEAMS platform with more complex ML algorithms.  The tool-suite will have capabilities to drive more authoritative decisioning, pattern/trend analysis and conflict analysis with the goal to drive more authoritative decisioning and speed/agility for DTRA. The technology can be applied cross-industry in organizations where timely, data-driven decision making is critical.