CAES is pleased to announce the recipients of CAES Collaboration Program Development Funds for 2022. This marks the fifth year in which the collaboration funds have been awarded to projects led by INL researchers in partnership with faculty members/researchers from the CAES universities. The goal is to establish and foster relationships between the CAES entities in research, education and innovation. The 2022 program began in February with a call for proposals. After reviewing a record number of submissions (23), CAES leadership selected 13 proposals best-suited to enhance collaborative relationships among the CAES entities in at least one of the focus areas outlined in the CAES Strategy. Here is a rundown:
Net Zero: Utilization of Waste Products from Agricultural and Biomass Industries to Reduce Concrete Emissions
This proposal aims to develop a roadmap for utilizing local and regional waste products such as Carbonation Lime Residue (CLR) and Biomass ash to replace a considerable portion of cement in concrete. The proposal supports new research programs at INL and the CAES universities and can significantly benefit INL's Net-Zero initiative by reducing the carbon footprint of concrete.
INL Principal Investigator (PI): Kunal Mondal; Idaho State University PI: Mustafa Mashal
Nuclear Energy focus area
Hydrogen Production Technology
This project calls for developing a proposal related to hydrogen production technology to respond to a call for proposals from DOE's Office of Energy Efficiency and Renewable Energy.
INL PI: Hanping Ding; University of Idaho PI: Haiyan Zhao
Innovative Energy Systems focus area
Mobile Robot for Security Applications in Remotely Operated Advanced Reactors
The project aims to develop a technical approach for conducting experimental research in the use of robots for security and oversight applications such as intrusion detection, threat assessment, inspection and perimeter patrolling of current and future nuclear power plants. This proposed effort will extensively explore various technical and regulatory aspects of using four-legged robots for security by leveraging INL's expertise in physical security combined with state-of-the-art and unique experimental facilities at ISU's Disaster Response Complex (DRC). The proposed work will result in a detailed roadmap identifying and describing near-term and long-term research and development activities, funding opportunities and outcomes to enable deployment of mobile robots for security at current nuclear facilities as well as for advanced reactors.
INL PI: Vaibhav Yadav; Idaho State University PI: Mustafa Mashal
Nuclear Energy focus area
Using Artificial Intelligence to Guide the Run-In of a Pebble Bed Reactor
The run-in process for a High-Temperature Gas-Cooled Pebble Bed Reactor (PBR) requires a multi-month, multi-phase transition from fresh lower enriched fuel mixed with graphite pebbles to an equilibrium core with higher enriched fuel which is recycled for multiple passes through the core. This process involves a significant number of design variables (i.e. reactor power, pebble enrichment) and objectives (i.e. fuel utilization, time to full power), which can leverage the decision making prowess of artificial intelligence (AI) to guide the transition. The goal of this project is to determine the approach, methodology and tools required for implementing an AI capable of solving a temporal-bifurcating problem such as the run-in process of a PBR and documenting this in a white paper for use in future proposal calls.
INL PI: Ryan Stewart; ISU PI: Leslie Kerby
Nuclear Energy and Computing, Data and Visualization focus areas
Fundamentals of Computational Analysis of Thermal Systems: Curriculum Development
This work calls for the development of a graduate-level curriculum in computational thermal hydraulics that is designed to prepare students to immediately contribute to research, development and analysis efforts currently underway in the areas of nuclear energy, innovative energy systems and nuclear-powered space exploration. This work will engage in workforce development and education, minimizing the on-the-job training required to equip students to meaningfully contribute to those work scopes. Beyond training students, this effort will also reinforce wconnections between technical staff at INL and their collaborators at the CAES universities.
The outcome will be a detailed lesson plan, lecture series, and enrichment activities based on extensive experience from INL's Nuclear Science and Technology directorate, and the curriculum development experience at the designated university partners. This will include a combination of lectures, hands-on enrichment exercises and analysis projects integrating the various skill sets necessary to execute system-level thermal hydraulics research, development and design.
INL PI: Joshua Fishler; ISU/Boise State University PI: Amir Ali & Lan Li
Nuclear Energy; Innovative Energy Systems; and Computing, Data and Visualization focus areas
Investigation on Designing a Framework of Utilizing Sensor Data in Virtual Training for Disaster Response Preparedness and Response
Disaster response training can benefit from sensor data and mixed reality (MR). Previous work has been done in the area of applying sensor data in emergency management. Implementing sensor data and sensor networks can improve logistics management in incident sites regarding situational awareness, resource tracking and team communication. MR offers an opportunity to generate various immersive scenarios efficiently, at low-cost, while being less risky than real-life drills for first responders. However, a lack of studies bringing these two components together for disaster response training exists. This project calls for investigating research on sensor data usage and its application, and designing a framework that brings sensor data into training for disaster response.
INL PI: Xingyue Yang, Rajiv Khadka, & John Koudelka; ISU PI: Mustafa Mashal
Computing, Data and Visualization focus area
Leveraging Cyber Informed Engineering and Cyber for All Methodologies to Secure Energy Infrastructure and Drive Workforce Development Efforts
Recently, DOE and INL have teamed together to create a national Cyber-Informed Engineering (CIE) initiative, focused on the energy sector as part of the Securing Energy Infrastructure Task Force. Boise State University's Sin Ming Loo recently contributed to the development of the CIE strategy, which aims to engineer out cyber risk across energy systems and develop secure by design cyber-physical infrastructure. The first pillars of this strategy include Awareness and Education which are the areas this project will address. Given the participants' expertise and leadership in this area, they propose leveraging BSU's on-line asynchronous platform to develop an initial offering and make it available to the CAES entities for inclusion in their coursework and curriculum while extending the reach of the Cyber for All training to high school teachers and offering it as dual credit to students to help build a stronger cyber pipeline throughout Idaho, generating interest and awareness while creating future CIE students across Idaho intuitions.
INL PI: Eleanor Taylor; BSU PI: Sing Ming Loo
Cybersecurity and Innovative Energy Systems focus areas
Improving the electron shuttling efficiency of activated carbon in relation to biological nitrogen removal during water treatment
This project seeks important factors associated with using carbonaceous materials as regenerable electron shuttle for biological nitrogen removal from water. Upon achieving the stated milestones of the project, the findings can be useful for scale-up projects for improving water quality of aquifers like the Snake River Plain aquifer. Simultaneously, the material aspect of the project could lead to the design of carbonaceous materials to store an increasing amount of electron as a source of energy.
INL PI: Asef Redwan; ISU PI: Anirban Chakraborty
Energy-Water Nexus focus area
Spent Fuel Management: Innovative Cementitious Composites for Extending the Service-life of Nuclear Wastes Dry Storage Systems
The need for safe and reliable ways to store nuclear waste has attracted the attention of researchers. Interim storage, such as wet or dry storage, is used successfully for both research reactor fuel and commercial spent nuclear fuel (SNF). These systems are usually designed as sealed, welded canisters placed inside concrete storage module. Other methods include SNF dry storage in steel casks. The main concern for concrete systems is that concrete materials degrade over time and lose strength and durability due to chemical attack or freeze-thaw cycles. This could make them more prone to failure during extended periods of dry storage, especially when stressed during an event such as an earthquake. With the latest evolution of material science, new concrete composites with ultra-high strength and outstanding thermal and mechanical properties need to be implemented in such structures. This proposal will present and summarize the latest technology in concrete composites and the applicability and implementation roadmap of such material in SNF structures. It will identify candidates to increase the service-life of SNF dry storage systems, lengthen the inspection intervals, and potentially, save millions of dollars spent on maintenance and rehabilitation of those structures.
INL PI: Elmar Eidelpes & Gabriel Ilevbare; UI PI: Ahmed Ibrahim
Nuclear Energy focus area
Developing Machine Learning Based Force Field for Predicting Radiation Resistance of High Entropy Alloys
Recently, a new class of metal alloys, of single-phase multicomponent composition namely high entropy alloys (HEAs), have been shown to exhibit promising mechanical, magnetic, and corrosion resistance properties particularly at high temperatures. These features make them potential candidates for components of next-generation nuclear reactors and other high-radiation environments that involve high temperatures combined with corrosive environments and extreme radiation exposure. However, their microstructure effect on radiation tolerance remains unexplored. This project intends to improve the understanding of the radiation-damage tolerance would be to ensure the proper design of the nuclear reactor components.
INL PI: Md Riaz Kayser & Ahmed Hamed; ISU PI: Mostafa Fouda
Nuclear Energy focus area
Spark Plasma Sintering of Diamond Coated Silicon Carbide
The objective of this project is to develop high-performance silicon carbon and diamond composites through surface decoration of silicon carbide with diamond and spark plasma sintering. The sintered composites will possess a range of important attributes and they are promising for accident-tolerant fuel and other applications in existing and advanced nuclear reactors.
INL PI: Junhua Jiang; BSU PI: Brian Jaques
Nuclear Energy and Advanced Manufacturing focus areas
Nanostructured Ferritic Alloys
Nanostructured ferritic alloys (NFAs) are candidates to be used as high temperature structural and fuel cladding materials in advanced nuclear reactors. These alloys have excellent high-temperature strength and radiation damage tolerance. This project will focus on developing a proposal for neutron irradiation studies of NFAs and the corresponding evolution of microstructure and mechanical properties. The developed proposal will be submitted to Nuclear Science User Facilities (NSUF) or Nuclear Energy Enabling Technologies (NEET).
INL PI: Cheng Sun; UI PI: Indrajit Charit
Advanced Manufacturing focus area
Machine Learning Atom Probe Data Set
Accurate evaluation and analysis of microstructure, microchemistry and performance of materials are challenging. particularly for materials used in nuclear reactors, which are exposed to harsh environments (e.g., high temperature, irradiation, stress, and corrosion). Characterization techniques such as atom probe tomography (APT) have the ability to provide accurate three-dimensional chemistry at the nanoscale; however, conventional manual data processing and correlation analysis are time-consuming and error-prone and lack reproducibility. This project aims to develop machine-learning tools for automatically processing datasets generated from APT, and to establish the correlation between material's microstructures and chemistry along with external stimuli (e.g., radiation conditions).
INL PI: Mukesh Bachhav; UI PI: Min Xian
Advanced Manufacturing focus area