Partners And Projects

Adaptive and Agile Resilience Control Architectures

The basis of resilient threat-resilient information networks requires consideration of all threats and measures by which we determine proper operation. These measures, which can be categorized as cyber and physical security, process efficiency and stability, and process compliance, provided the operating requirements that are monitored for situational awareness and definition of the power grid or process state. Traditional concepts of redundancy, diversity, and defense in depth that were once only considered for reliability can be broadened for application to all measures. New concepts that research the human system responses, both benevolent operator and malicious actor interactions, as well as the complex interdependencies of distributed information networks require consideration. The move from reactive to proactive control of plants and mechanisms by which the evaluation and verification of designs is considered all the way from design through implementation stages of resilient information networks is enabled by this paradigm shift.

Decision Support for Recycling of e-Waste

Received funding from the Remade Institute, to develop a decision support framework (DSF) for e-waste recycling and refurbishment. The DSF provides insights on questions important to the industry and will improve industry performance by providing a system-level understanding of e-waste recycling supply chains from local to continental scales.

Defense Nuclear Nonproliferation (DNN) Program

The development of new advanced reactors (Gen IV) increases the importance of new methods to understand diversion and misuse scenarios, and determine mitigation pathways. INL is developing a complete digital twin framework for safeguards by design. This provides the opportunity for comprehensive understanding of nuclear fuel cycle facility operations to significantly strengthen nuclear safeguards and nonproliferation regime.

Infrastructure Trustworthiness Assessment & Proactive Control

Untrustworthy data can lead to poor automated system control, difficulty in decision making, and frustrating end-user experiences as a minimum, but with nation state attacks, cascading outages and wide scale loss of infrastructures. This data is very often very large in scale and comes from heterogeneous sources. Moreover, information technology (IT) and operation technology (OT) operators who now find themselves responsible for cybersecurity come from a variety of backgrounds, differing decision support requirements, and knowledge capabilities. To effectively abstract the complexity of cybersecurity and simultaneously address the variety of roles, knowledge, and need, a design is needed that performs much of the required analysis for the user and presents only relevant information in a consistent way.

Intelligent Cyber Detection & Feedback Mechanisms

Traditional protections offered by intrusion detection devices are dependent on known signatures. Any “lockdown” of these devices can lead to performance effects on the automation system, as well as the production of many false positives for network security engineers to evaluate. The cyber challenge is to provide a resilient cyber feedback system that can be quickly implemented and provide diverse techniques for cyber recognition that can be analyzed based on individual networks. This system must be lightweight in its performance requirements and proactive in design, using analytics to advance modification of the automation network protections faster than the attacker can respond

Land Suitability for Production of Dedicated Bioenergy Crops

A geospatial multi-criteria site suitability framework was developed to support decision making for incorporating bioenergy crop production within agricultural landscapes.

Light Water Reactor Sustainability Program

The U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program has been a pioneer in introducing AI methods to automate human activities for the nuclear energy sector, as the program has recognized the potential of AI for enabling great cost savings for nuclear plants. The program works directly with the nuclear power industry.

National Reactor Innovation Center (NRIC) Program

The National Reactor Innovation Center (NRIC) has led new advanced demonstration projects using a model-based systems engineering approach. Project requirements are traced to their satisfying elements in SysML and LML-based models used to guide design decisions. Activity models are integrated with Discrete Event and Monte Carlo simulation to check for correctness, integrate cost and schedule, and monitor expected performance. The program is working to develop integrations between MBSE, engineering, operations, and traditional CAD software to enable a cloud-based, digital thread in design.

Nuclear Energy Advanced Modeling and Simulation (NEAMS) Program

The Nuclear Energy Advanced Modeling and Simulation (NEAMS) program is a U.S. Department of Energy-Office of Nuclear Energy (DOE-NE) program developing advanced modeling and simulation tools and capabilities to accelerate the deployment of advanced nuclear energy technologies, including light-water reactors (LWRs), non-light-water reactors (non-LWRs), and advanced fuels.

Role-based, Cyber-Physical State and Context Awareness

Dynamic visualizations change how the data is represented in real-time which allows users to quickly understand where changes in the system are happening, the priorities of response, the root cyber or physical cause and potential for larger, potentially cascading failures. Dynamic data visualization allows for visual representations of data that can be altered as the represented data changes. This can allow for quick and intuitive understanding of changes in the system as size, color, length, etc., is altered devices can be connected to and communicate with each other over a network. This can allow for an efficient peer-to-peer resource sharing system to be implemented across the system. Resource sharing adds an additional layer to visualization by allowing local visual implementations to change dynamically based on global data as well.

Transformational Challenge Reactor (TCR) Program

INL is assisting in the development of the requirements and definition of design-agnostic digital platform that will support the TCR core manufacturing and overall program goals. The digital platform consists of design data, modeling data, in-situ data, ex-situ data, and integral test data as well as established links between these five categories.