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DOE OSTI2026

Development and Implementation of a New AI-Based Tool to Support Fast Reactor Software Model Generation and Validation

This report summarizes FY26 work to develop Maggie, an artificial intelligence-based assistant designed to support software model generation and validation activities for fast reactor analysis codes. The project established a modular, code-agnostic software architecture that separates reusable agent capabilities from code-specific knowledge and tools, with initial implementation focused on the FRP-supported fast reactor safety analysis code SAS4A/SASSYS1 (SAS). A curated SAS-specific knowledge base was assembled from the code manual, training materials, historical analysis reports, and representative input files, and was integrated through retrieval-augmented generation to ground Maggie’s responses in authoritative sources. Maggie was deployed on the internal Argonne network, where it demonstrated practical user-facing capability as a chatbot for answering natural language questions about SAS and retrieving relevant technical information. Demonstration cases also showed that Maggie can generate useful snippets of SAS input for selected modeling tasks, while highlighting current limitations in reliability and consistency for more complex input generation tasks. Overall, the FY26 effort established the technical foundation for an AI-assisted capability intended to improve the efficiency, consistency, and accessibility of fast reactor software model development at Argonne and, with further improvements, to support eventual use by the broader fast reactor community, including industry users of FRP-supported analysis tools.

Thomas, Rachel [Argonne National Laboratory (ANL),↗
DOE OSTI2022

Design and Optimization of Structured Multi-Functional Trapping Catalysts for Conversion of Hydrocarbons and NOx from Diesel and Advanced Combustion Engines

Oxides of nitrogen in the form of nitric oxide (NO) and nitrogen dioxide (NO 2 ) commonly referred to as NOx, is one of the two chemical precursors that lead to ground-level ozone, a ubiquitous air pollutant in urban areas. A major source of NOx is generated by equipment and vehicles powered by diesel engines, which have a combustion exhaust that contains NOx in the presence of excess O 2 . Vehicular emission control catalysts are ineffective in eliminating CO, hydrocarbons, and NOx during engine cold-start when exhaust temperatures are below 200°C. The objective of the project was to develop and demonstrate a multi-functional, catalyzed trap that enables vehicles with advanced combustion strategies to meet Tier 3 emissions standards while achieving the 150 °C challenge for sustained co-oxidation of HCs and CO and ≥90% NO trapping and release during warmup. Specifically, the multi-functional Lean HC+NOx (LHCNT) was developed for application in the exhaust aftertreatment of conventional diesel engines and engines having low temperature combustion (LTC) regimes. Activities included the design and synthesis of adsorbents and catalysts, screening and evaluation. Passive NOx absorbers (PNA), hydrocarbon (HC) traps, and oxidation catalysts (OC) were evaluated for use in series or as integrated devices. Predictive tools were developed utilizing the characterization and analysis of these materials, and an emission system was designed and optimized utilizing the catalyst systems. Microkinetic models were developed for the PNA for the simple NO-only feed and complex feed containing CO, H 2 , and model hydrocarbons (ethylene and dodecane). A first-principles, mechanistic-based model of the PNA was developed which utilized molecular-scale estimates (density functional theory) of energy barriers, mechanistic-based kinetics and realistic treatments of the flow and transport processes. Two new oxidation catalysts were developed (PdCu alloy, mixed copper-ceria-cobalt oxide), both of which significantly lessened the detrimental inhibition by CO on hydrocarbon and NO oxidation. A method for lessening the detrimental impact of CO on PNA activity was developed that involves use of an oxidation catalyst upstream of the PNA. The SwRI Ectolab TM burner system was applied to evaluate the baseline PNA material and confirmed performance comparable to the benchflow PNA studies using simulated exhaust. Spatially-resolved mass spectrometry (SpaciMS) was used to measure the transient spatial profiles of reacting species spanning the length of a three-function LHCNT containing PNA, HCT, and OC. The findings from this study provide diesel vehicle and catalyst companies valuable information to develop more cost effective emission control catalysts which helps to expand the use of more fuel efficient diesel power. The fundamental modeling and experimental tools and findings from this project can be applied to catalyst technologies used in the energy and chemical industries. The project led to 14 publications in the peer-reviewed literature with 2 additional currently under review. Finally, the project also led to training of several doctoral students who were placed in research jobs in industry and academia. Specifically, Mugdha Ambast (UH) has joined Cummins, Kevin Gu (UVa) has joined GM, and Abhay Gupta (UH) is to join Caterpillar.

02 PETROLEUM↗
DOE OSTI2022

Design and Optimization of Structured Multi-Functional Trapping Catalysts for Conversion of Hydrocarbons and NOx from Diesel and Advanced Combustion Engines

Oxides of nitrogen in the form of nitric oxide (NO) and nitrogen dioxide (NO 2 ) commonly referred to as NOx, is one of the two chemical precursors that lead to ground-level ozone, a ubiquitous air pollutant in urban areas. A major source of NOx is generated by equipment and vehicles powered by diesel engines, which have a combustion exhaust that contains NOx in the presence of excess O 2 . Vehicular emission control catalysts are ineffective in eliminating CO, hydrocarbons, and NO x during engine cold-start when exhaust temperatures are below 200°C. The objective of the project was to develop and demonstrate a multi-functional, catalyzed trap that enables vehicles with advanced combustion strategies to meet Tier 3 emissions standards while achieving the 150 °C challenge for sustained co-oxidation of HCs and CO and ≥90% NO trapping and release during warmup. Specifically, the multi-functional Lean HC+NOx (LHCNT) was developed for application in the exhaust aftertreatment of conventional diesel engines and engines having low temperature combustion (LTC) regimes. Activities included the design and synthesis of adsorbents and catalysts, screening and evaluation. Passive NOx absorbers (PNA), hydrocarbon (HC) traps, and oxidation catalysts (OC) were evaluated for use in series or as integrated devices. Predictive tools were developed utilizing the characterization and analysis of these materials, and an emission system was designed and optimized utilizing the catalyst systems. Microkinetic models were developed for the PNA for the simple NO-only feed and complex feed containing CO, H 2 , and model hydrocarbons (ethylene and dodecane). A first-principles, mechanistic-based model of the PNA was developed which utilized molecular-scale estimates (density functional theory) of energy barriers, mechanistic-based kinetics and realistic treatments of the flow and transport processes. Two new oxidation catalysts were developed (PdCu alloy, mixed copper-ceria-cobalt oxide), both of which significantly lessened the detrimental inhibition by CO on hydrocarbon and NO oxidation. A method for lessening the detrimental impact of CO on PNA activity was developed that involves use of an oxidation catalyst upstream of the PNA. The SwRI Ectolab TM burner system was applied to evaluate the baseline PNA material and confirmed performance comparable to the benchflow PNA studies using simulated exhaust. Spatially-resolved mass spectrometry (SpaciMS) was used to measure the transient spatial profiles of reacting species spanning the length of a three-function LHCNT containing PNA, HCT, and OC. The findings from this study provide diesel vehicle and catalyst companies valuable information to develop more cost effective emission control catalysts which helps to expand the use of more fuel efficient diesel power. The fundamental modeling and experimental tools and findings from this project can be applied to catalyst technologies used in the energy and chemical industries. The project led to 14 publications in the peer-reviewed literature with 2 additional currently under review. Finally, the project also led to training of several doctoral students who were placed in research jobs in industry and academia. Specifically, Mugdha Ambast (UH) has joined Cummins, Kevin Gu (UVa) has joined GM, and Abhay Gupta (UH) is to join Caterpillar.

42 ENGINEERING↗
DOE OSTI2021

A Case Study on Condenser Water Supply Temperature Optimization with a District Cooling Plant

District cooling (DC) continues to proliferate due to increasing global cooling demands and economies of scale benefits; however, most district-scale modeling has focused on heating, and to the best of our knowledge, researchers have yet to model cooling plants featuring waterside economizers in DC settings. With the popular Modelica Buildings library expanding its capabilities to district scale, this study is one of the first to demonstrate how the open-source models can be used for detailed energy and control analysis of a DC plant. For a real-world case study, we developed and calibrated high-fidelity models for a DC system central plant at a college campus in Colorado, USA, and we optimized the condenser water supply temperature (CWST) setpoint for a DC plant across multiple time horizons using the Optimization library in Dymola. Results indicate that annual CWST optimization saves 4.7% annual plant energy, with less than 1% of additional energy savings gained through daily optimization. This confirms previous studies' findings that high frequency CWST optimizations are not necessary for the studied system.

chiller plant↗
DOE OSTI2021

Tafel Slope Analyses for Homogeneous Catalytic Reactions

Tafel analysis of electrocatalysts is essential in their characterization. This paper analyzes the application of Tafel-like analysis to the four-electron nonelectrochemical oxidation of water by the stoichiometric homogeneous 1-electron oxidant [Ru(bpy)3]3+ to dioxygen catalyzed by homogeneous catalysts, [Ru4O4(OH)2(H2O)4(γ-SiW10O36)2]10− (Ru4POM) and [Co4(H2O)2(PW9O34)2]10– (Co4POM). These complexes have slow electron exchange rates with electrodes due to the Frumkin effect, which precludes the use of known electrochemical methods to obtain Tafel plots at ionic strengths lower than 0.5 M. The application of an electron transfer catalyst, [Ru(bpy)3]3+/2+, increases the rates between the Ru4POM and electrode, but a traditional Tafel analysis of such a complex system is precluded due to a lack of appropriate theoretical models for 4-electron processes. Here, we develop a theoretical framework and experimental procedures for a Tafel-like analysis of Ru4POM and Co4POM, using a stoichiometric molecular oxidant [Ru(bpy)3]3+. The dependence of turnover frequency (TOF) as a function of electrochemical solution potential created by the [Ru(bpy)3]3+/[Ru(bpy)3]2+ redox couple (an analog of the Tafel plot) was obtained from kinetics data and interpreted based on the suggested reaction mechanism.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Integrated Molten Salt Reactor Modeling Capabilities in NEAMS Thermal Hydraulics Tools

The DOE neams program supports a full range of computational thermal fluids analysis capabilities and code developments for a broad range of advanced reactor concepts. The research and development approach under the thermal fluids technical area synergistically combines three length and time scales in a hierarchical multi-scale approach. To enable multi-scale thermal fluids capability using these codes, a key joint effort has been underway to develop an integrated system- and engineering-scale thermal fluids analysis capability, through integration of SAM and Pronghorn codes, both based on the MOOSE framework. This report summarizes recent advances in developing an integrated system- and engineering-scale modeling capability for the msr concept, which has gained significant interest in recent years. A consistent framework was established by coupling Pronghorn and SAM through the Saline interface, with thermophysical properties provided by the Molten Salt Thermal Property Database (MSTDB-TP). Further improvements were made to the coupling schemes and domain-overlapping strategies, enhancing the stability and robustness of multi-code simulations. Verification and validation efforts demonstrate the accuracy of this integration across a range of benchmark problems, including one-dimensional heated pipe flows, three-dimensional natural convection loops with evolving isotopic compositions, and \gls{msre} demonstration cases. Within Pronghorn, new capabilities were introduced to model corrosion and noble-metal plating phenomena, supported by an extended thermal-hydraulics framework and refined turbulence treatments. To capture two-phase flow behavior, a multiphase Euler–Euler model was implemented in Pronghorn, including advanced closure relations, high-resolution advection techniques, and capillary force reconstruction. Preliminary verification cases confirm the fidelity of the approach, while planned validation efforts target canonical multiphase benchmarks and application to msr components such as the msre pump bowl. Finally, updates to SAM’s msr mass transfer modeling were extended to consider noble gas migration into porous structures like graphite. The point kinetics model was updated to include reactivity feedback contributions from any defined species, such as xenon. The gas transport model was expanded for applicability to gas mixtures, bubble efflux phenomena, and species transport between liquid and gas phases. A selection of multi-scale Sherwood number correlations from MOSCATO/NekRS and multi-phase correlations from literature have been added for improved accuracy in calculating mass transfer coefficients. A companion effort on developing system-level redox corrosion has also been incorporated into SAM. Collectively, these enhancements strengthen the predictive capability of SAM and Pronghorn for simulating MSR thermal-hydraulics, corrosion, multiphase behavior, and fission-product transport, providing a more complete toolset for design, safety analysis, and licensing support of next-generation \gls{msr}s.

42 - ENGINEERING↗
DOE OSTI2022

Energy Resilience for Mission Assurance: Agile Co-simulation for Cyber Energy System Security (ACCESS), Model Advancements for Resilience Analysis

Agile Co-simulation for Cyber Energy System Security (ACCESS) is a co-simulation platform developed by Lawrence Livermore National Laboratory (LLNL). The primary high-level use-case for ACCESS is to study existing or new cyber-physical critical infrastructure systems, with a heavy emphasis on 1) systems that utilize communication networks, and 2) studies that seek to understand cyber-related system impacts. ACCESS is currently used for several energy system resilience projects at LLNL. In the Energy Resilience for Mission Assurance (ERMA) project, ACCESS is used in the Modeling for Metric Calculation task (specifically, subtask 4.3, Communications and Cyber Modeling) to model and simulate the cyber and communication system aspects of Defense Critical Electric Infrastructure (DCEI) systems, with a focus on computing specific communication system metrics that can impact system resilience and mission performance. Simulated communication system performance will be fed back to other ERMA system components so that mission performance can be evaluated holistically. This report describes several enhancements to the ACCESS platform that were implemented during the execution of the ERMA project in support of reslience analysis. This includes the addition of new models and subsystems, enhancements to existing models, and integration with external systems. The remainder of this report is structured as follows. In Section 2, a brief background description of the ACCESS platform is provided, including an outline of ACCESS components, example usecases, and a set of communication network resilience metrics that can be computed with ACCESS. Section 3 describes the ACCESS model enhancements for ERMA in detail. Finally, Section 4 briefly outlines future integration opportunities between ACCESS and project participant capabilities identified during the progression of the project.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A micromechanical hydro-mechanical-damage coupled model for layered rocks considering multi-scale structures

An anisotropic Hydro-Mechanical-Damage coupled model was established in the framework of thermodynamics for saturated layered rocks, which contain arbitrarily-distributed cracks of smaller scales and orientated bedding planes of much larger scales. Here, the proposed model considers the anisotropic damage growth and frictional sliding of cracks and shear sliding and dilatancy of bedding planes, as well as the interaction between cracks and bedding planes. Numerical implementation of the nonlinear coupled model was developed based on TOUGHREACT, which is a well-established simulator for multiphase fluid flow and reactive transport analysis. The proposed model was first validated with laboratory water injection test results on rock blocks, and then used to study the effects of injection rates, in situ stresses and bedding planes on fluid injection processes at the field scale, demonstrating the importance to consider the variation of multi-scale structures for better understanding the coupled Hydro-Mechanical responses in layered rocks.

58 GEOSCIENCES↗
DOE OSTI2022

Modeling and Analysis of a Micro-reactor using the DireWolf Code Suite

Micro-reactors, specifically those cooled with heat pipes, are tightly coupled systems which require multi-physics tools to accurately model both steady-state and transient behavior. Idaho National Laboratory’s DireWolf code suite is tailor-built to model heat-pipe reactors. DireWolf’s ability to model the coupled steady-state behavior of a heat-pipe micro-reactor are demonstrated herein. The DireWolf modeling methodology is described, and a micro-reactor design is introduced. A full-core model is developed, and the steady-state coupled neutronics-thermal analysis is performed. Results of the analyses, including flux, power, and temperature distributions are presented and discussed. This is a Westinghouse Electric Company (WEC) led publication.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

SAM Code Development for Source Term Modeling in Fluoride-salt-cooled High-temperature Reactors

The SAM code is under development and supported by DOE-NE’s Nuclear Energy Advanced Modeling and Simulation program as a modern system-level modeling and simulation tool for advanced non-light water reactor safety analyses. These advanced reactor concepts incorporate novel and improved approaches to achieve safety and economic feasibility. Many concepts leverage higher operating temperatures for improved efficiency with a variety of coolants and structural materials to support those needs. Such design choices may also pose unique radiological source term risks. There are continuous efforts to incorporate new physics and phenomena relevant to advanced reactor concepts, including capabilities to enable system-level source term modeling. The Pebble-Bed Fluoride-salt-cooled High-temperature Reactor (PB-FHR) is a promising candidate among advanced nuclear reactor concepts with its improved passive safety characteristics and high thermal efficiency. In addition to past efforts to support the development and utilization of SAM for PB-FHR safety analysis, the species transport modeling capabilities in SAM have been extended to simulate source term phenomena in concepts like the PB-FHR. The PB-FHR concept utilizes pebble-form TRISO fuel and FLiBe salt coolant to provide robust barriers to the release of almost all fission products and radiological source terms. However, tritium poses a unique radiological risk due to its significant production from neutron interactions with 6 Li and 9 Be in the FLiBe as well as its high mobility at elevated temperatures where it can permeate through structural metals. Secondly, the use of graphite at elevated temperatures poses the risk of oxidation damage to any structural components or fuel pebbles that are exposed to accidental air ingress. This report summarizes progress made in modeling these phenomena in SAM, which leverages the system-level multi-physics thermal hydraulic simulation to support an effective engineering-scale source term modeling capability. A tritium transport model is developed to simulate the various transport pathways in FHR and MSR concepts and is presented through verification and validation examples as well as demonstrations simulating experimental test loops and reference plant FHR models. A graphite oxidation model is also introduced to calculate local oxidation rates and is presented with initial validation results.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Interpretable machine learning models classify minerals via spectroscopy

Developing methods to identify mineral species confidently and rapidly from Raman spectral analysis is critical to numerous fields. Traditionally, analysis relies on pattern matching the Raman spectrum of an unknown dataset with a supporting library of well-characterized spectral data, which may prove difficult for environmental samples that are poorly crystalline or phase mixtures. Here, we developed interpretable machine learning models that can classify uranium minerals by secondary oxyanion chemistry and other physicochemical properties based solely on Raman spectra. This new ML method produces a mineral profile of physical and chemical properties for an unknown sample and can rapidly classify or identify unknown minerals from Raman data, without the need for an exact pattern match in a spectral library. Training models are validated by 1. Strong correlation of high confidence model regions with published spectroscopic assignments and 2. Correct classification of a mineral not present in training data. Training data are from the Compendium of Uranium Raman and Infrared Experimental Spectra and available crystallographic information files within the open-source Smart Spectral Matching scientific framework. Physically meaningful classifier models can rapidly identify key structural and chemical information about unknown uranium minerals and the overall methodology is broadly applicable for mineral phases.

Machine learning↗
DOE OSTI2024

SCALE inventory and reactivity analysis as part of the Hermes 2021 PSAR review

The readiness of SCALE for comprehensive studies of pebble-bed reactors has been demonstrated through detailed analysis of a fluoride salt–cooled, high-temperature pebble-bed reactor (PB-FHR). The methods developed for pebble-bed reactor modeling in SCALE, particularly for inventory generation, have proven effective in gaining insights into the reactor physics of this advanced reactor. Excellent agreement with another code package has been observed, further highlighting SCALE’s strong performance. The SCALE results supported the US Nuclear Regulatory Commission’s construction permit application review of the Hermes low-power PB-FHR demonstration reactor. A SCALE model of the Hermes reactor was developed at Oak Ridge National Laboratory using information from the Preliminary Safety Analysis Report (PSAR) and supplemented with publicly available data. SCALE reactivity coefficient simulations reproduced PSAR results within 1σ statistical uncertainties. Sensitivity studies emphasized the importance of graphite specifications for accurate keff predictions.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Physics-Informed Machine Learning from Observations for Clouds, Convection, and Precipitation Parameterizations and Analysis

Fusing learning from Earth observations from space and from the ground (e.g., ARM) with newly developed interpretable and generalizable physics-informed model structures to improve the parameterization of clouds and convection and advance the simulation, understanding and analysis of hydrological extreme events. This covers the prescribed foci of (2) predictive modeling using a hierarchy of models and (3) insight gleaned from complex data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Unlocking the Value of Deep Energy Retrofits

This publication is based on an extensive study conducted on behalf of the New York State Energy Research and Development Authority (NYSERDA) and the U S Department of Energy (DOE) to explore financial and risk products to accelerate the implementation of deep energy retrofit (DER) solutions to reduce costs These financial products are intended to speed market development via three fundamental themes: reallocating risk from building owners and lenders to insurers, quantifying and monetizing previously unrecognized value associated with DERs, and providing building owners and lenders with confidence in the performance of building systems Findings are based on a comprehensive analysis and characterization of 100+ existing DER case studies, and more than 40 qualitative interviews with industry experts, including insurers, researchers, building owners, and policymakers In addition, a quantitative model of overall project economics with baseline, high, and low cases was developed This data informed the analysis that resulted in three recommended financial product solutions, which were evaluated for their potential to raise projected cash flows and finance DERs: 1) Building System Performance and Energy Savings Guarantee; 2) Trade Credit Insurance; 3) Ancillary Revenue Contracts. This report identifies many value streams associated with DERs and introduces these three potential financial products, which aim to reallocate risks and reduce barriers to the adoption of advanced envelope DERs.

ancillary renenue contracts↗
DOE OSTI2025

The molten salt tritium transport experiment: A pumped fluoride salt loop for hydrogen isotope experimentation

Molten salt reactors (MSRs) and fusion reactors propose to use molten salts as coolants and breeder blanket materials, respectively. Tritium, however, poses safety concerns in both reactor types due to its ability to permeate through reactor materials and potential for environmental release. This manuscript addresses the tritium transport phenomena in molten salts and presents the design and analysis of the Molten Salt Tritium Transport Experiment (MSTTE). MSTTE is a forced-convection fluoride salt loop intended to measure hydrogen isotope permeation through structural materials in a flowing salt system. In the first phase, MSTTE will use FLiNaK salt and deuterium as surrogates for FLiBe and tritium, with future plans to utilize tritium and FLiBe. MSTTE couples a Copenhagen Atomics pumped salt loop with an external test section that introduces hydrogen isotopes into the loop and measures transport phenomena. The Hydrogen Injection System (HIS) controls hydrogen isotope introduction into the molten salt loop. Here, the permeation test section measures the permeation rate through stainless steel tubing in contact with flowing salt. Computational fluid dynamics (CFD) analysis ensures fully developed salt flow in the permeation test section. MSTTE is modeled with MELCOR-TMAP to predict the permeation rate as a function of experimental variables such as source term, salt flow rate, and salt temperature. Results indicate that the source term is the only parameter with a significant effect on the permeation rate. Pressure drop analysis suggests that the loop should operate below 200 LPM to maintain a pressure drop below 200 kPa. Additionally, finite-element analysis assesses thermal stress during loop operation to ensure the experiment's safe design. MSTTE will provide semi-integral data on tritium transport phenomena in molten salts and serve as a testbed for advancing molten salt technology.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Machine learning prediction of electron density and temperature from He I line ratios

We propose to utilize machine learning to predict the electron density, ne, and temperature, T e , from He I line intensity ratios. In this approach, training data consist of measured He I line ratios as input and ne and T e measured using other diagnostic(s) as desired output, which is a Langmuir probe in our study. Support vector machine regression analysis is, then, performed with the training data to develop a predictive model for n e and T e , separately. It is confirmed that n e and T e predicted using the developed models agree well with those from the Langmuir probe in the ranges of 0.28 × 10 18 ≤ n e (m -3 ) ≤ 3.8 × 10 18 and 3.2 ≤ T e (eV) ≤ 7.5. The developed models are, further, examined with an evaluation data, which are not included in the training data, and are found to well reproduce absolute values and radial profiles of probe-measured n e and T e .

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

BISON High Burnup Structure Modeling Capabilities Validated with a Selection of the Halden IFA-650 Rods

The U.S. Department of Energy (DOE)’s Nuclear Energy Advanced Modeling and Simulation (NEAMS)program aims to develop predictive capabilities using computational methods for the analysis and design of advanced reactor and fuel cycle systems. This program has been supporting the development of BISON, a high-fidelity and high-resolution fuel performance tool at the engineering scale. This document continues analysis and refinement of capabilities added to BISON early this calendar year in regards to the incorporation of capabilities applicable to extended burnups in response to industry interest. Details are provided on high burnup thermal conductivity models, a refitting of the high-burnup structure (HBS) porosity formation model to include additional data, the coupling of the HBS volume fraction model to thermal conductivity and fine fragmentation models, and validation activities. The IFA-650.4 and IFA-650.9 loss of coolant accident (LOCA) analyses are revisited with the latest developments in this report. A new validation case, IFA-650.14 has also been added to the BISON test suite.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

MindSynchro

This report presents the developments and results of MindSynchro project as part of DOE OE FOA 1861. DOE and Pacific Northwest National Laboratory (PNNL) have made available to FOA awardees datasets containing years of real historical data recorded from various phasor measurement units (PMUs) which are installed in three large US interconnections: Texas (IC A), Western (IC B), and Eastern (IC C). The main goal of the project, which was successfully achieved, was to develop methods for detection and identification of events which are relevant for power grid operation. Tasks performed for achieving the project goals included data exploration and pre-processing, the development and application of physics-based features, data analysis and labeling based on unsupervised learning approaches, training and testing of DSSL models for classification of events which are relevant for power grid operation, and deployment of solutions to cloud environments. The methods developed in the project can potentially provide relevant benefits to power grid asset owners/operators in general in terms of situational awareness. Two main types of outcomes can be provided by these tools: Identification of specific relevant power grid event types: Semi-supervised ML methods developed in the project can adequately employ not only the relatively scarce labeled data but also the large amount of available unlabeled data to train models for detection of specific event types. Such methods enable the application of trained models for the detection of events in a population of PMUs much larger than that associated to the labeled events. Support in data labeling / label validation: Labels are critical for training of models for identification of specific types of events. However, labeling large amounts of data is a manual and tedious process. This means that such process is error prone and is not scalable. Methods developed in the project, based on ensembles of clustering models, have been successfully employed for turning manual labeling into a scalable process. Accurate identification of specific relevant events can provide the operators with immediate situational awareness that could otherwise require hours or days of analysis from domain experts. We envision that such methods could be initially employed in support of post-mortem analysis of events and, as confidence is gained, they could be employed for online/real-time support, providing, among other benefits, insights for avoiding major events which could happen due to a combination of smaller ones. On the longer term, related methods could potentially be employed to improve protection and control.

24 POWER TRANSMISSION AND DISTRIBUTION↗