SBIR/STTR Research Award

Implementation of Task Variance Model in Security Assessment Models

Award identifier ab729738e28933a471b1e9348eae57cabe04a801471e12ee5a5e6561e5b4077fAwarding agency Department of Energy

Award amount

$1,088,293

Program and phase

SBIR · Phase II

Award year

2022

Explore This Research Award in CaptureIQ

Research the technology, evaluate the recipient and agency, identify related opportunities, and explore potential follow-on work with CaptureIQ.

Award details

Awarding agency
Department of Energy
Branch
Not reported
Recipient company
RHINOCORPS LTD. CO.
Proposal award date
2022-08-22
Contract end date
2024-08-21
UEI
QH3DZGNHN7K3
Solicitation number
Not reported
Topic code
C52-39r

Research abstract

The costs to operate a nuclear power plant have increased while market forces have continued to drive down the cost of electricity. Securing these plants is an on-going expense that typically accounts for 7-12% of the total cost of power generation. Furthermore, research suggests that 20% of the entire workforce is comprised of physical security related personnel. To ensure that future nuclear energy production from advanced reactors is affordable, safe, and secure, new methods should be examined to optimize security using a realistic representation of the adversary’s ability to perform tasks in a combat environment. During SBIR Phase I, research was completed into integration methods for the Risk Informed Timeline (RIT) software and the data it produces. In support of this research, limited scope performance testing was conducted at an NRC-licensee to collect the necessary experimental data and develop distributions of probability of success for a specific breaching technique employed at exercises frequently across multiple NRC-licensee sites. These experimental random distributions were used to generate data files using the RIT software that were ingested into a prototype plugin module for use in a commercial-off-the-shelf (COTS) combat simulation. To illustrate the value of this data and prototype, the modeling and simulation tool was used to generate security metrics and a comparison was made for several attack scenarios from one of the NRC-licensees. To support this preliminary study, simulation results were compared for a set of attack scenarios where in one case the conservative assumption that all breaches succeeded 100% of the time and in the other the risk informed method applied random distributions to determine the likelihood of success for each of the relevant breaches. Additionally, a variant of each of the related scenarios was created that included reduced protective force posts. The results indicate there was a positive impact to the security system effectiveness and a reduction in the depth of penetration into the security layers that was measurable even for defensive strategies with fewer officers and thus lower operational costs. Phase II will focus on creating a production-quality and commercialized plugin module that works with the existing COTS modeling and simulation tool suite. Additionally, the RIT approach will be expanded from one task type in the prototype to include several more types of player tasks and actions in the COTS tool suite. This module will allow for industry specific performance data to be incorporated as a library of task times and probabilities of success. A commercialization of this tool, in combination with the COTS combat simulation, will allow existing and advanced reactor designers to develop a security strategy that is based upon realistic performance data for complex tasks that will likely result in lower operating costs.

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Research funding history

4 SBIR/STTR records share this UEI (2006–2024). Reported award amounts total $1,564,723. Reported amounts are not federal obligations.

Phases: Phase I, Phase II

Agencies: Department of Energy, Department of Homeland Security

Agency intelligence

Department of Energy

Back to SBIR/STTR Research Awards

Don't take our word for it. Ask AI about RFPCapture.

See what leading AI assistants have to say about RFPCapture and CaptureIQ.

Gemini opens without a prefilled question. The question is copied to your clipboard when the button works. You can also copy it here:

What is RFPCapture.com and its CaptureIQ platform from Smart Logic AI? How does it help federal contractors discover opportunities, evaluate bid/no-bid decisions, research government contracts, and prepare proposals? How does it compare with other federal capture platforms? Use independent sources where available.