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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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DOE OSTI2021

Sensor impact evaluation and verification for fault detection and diagnostics in building energy systems: A review

Sensors are the key information source for fault detection and diagnostics (FDD) in buildings. However, sensors are often not properly designed, installed, calibrated, located, and maintained, which negatively impacts FDD performance. Several sensor-related FDD topics have been widely studied, covering a wide range of fault types and applications. However, it is difficult to get a clear picture of the technical development of sensor-related topics in FDD. A systematic review of sensor topics is needed to summarize the existing research in a logical way, draw conclusions on the current development, and predict the future development of sensors in building FDD. To address this gap, we conducted a comprehensive literature review of more than 100 FDD-sensor-related papers. In this article, we subdivide the FDD tasks into building-level, system-level, and component-level FDD, and review sensor-related topics in each category. Our major conclusions are: (a) current data-driven FDD research focuses more on FDD algorithms than sensors, (b) sensor “hardware” research topics are less studied than sensor “software” topics, (c) very few papers focus on sensor engineering as an integral aspect of FDD development, and (d) some important sensor topics, such as sensor cost-effectiveness and sensor schema/layout/location, are not well studied. Finally, we discuss the need for a systematic framework of FDD sensors and models to integrate sensor design/selection, sensor data analysis/mining, feature selection, physics-based or data-driven algorithm development, sensor fault detection, sensor calibration, and sensor maintenance. Finally, expert interviews are conducted to validate the above findings and conclusions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Real-Time, Adaptive Radiological Anomaly Detection and Isotope Identification Using Non-Negative Matrix Factorization

Spectroscopic anomaly detection and isotope identification algorithms are integral components in nuclear nonproliferation applications such as search operations. The task is especially challenging in the case of mobile detector systems because the observed gamma-ray background changes more than for a static detector system, and a pretrained background model can easily find itself out of domain. The result is that algorithms may exceed their intended false alarm rate or sacrifice detection sensitivity to maintain the desired false alarm rate. Non-negative matrix factorization (NMF) is a powerful tool for spectral anomaly detection and identification, but, like many similar algorithms that rely on data-driven background models, in its conventional implementation, it is unable to update in real time to account for environmental changes that affect the background spectroscopic signature. Here, we have developed a novel NMF-based algorithm that periodically updates its background model to accommodate changing environmental conditions. The adaptive NMF algorithm involves fewer assumptions about its environment, making it more generalizable than existing NMF-based methods while maintaining or exceeding detection performance on simulated and real-world datasets.

Anomaly detection↗
DOE OSTI2019

Cesium Ion Exchange Testing Using Crystalline Silicotitanate with Hanford Tank Waste 241-AP-107 - 19104

Pacific Northwest National Laboratory has been tasked with demonstrating the unit operations for the Low-Activity Waste Pretreatment System (LAWPS) with Hanford tank waste. Washington River Protection Solutions requested that Hanford tank waste collected from tank 241-AP-107 be processed using conceived pretreatment steps (suspended solids removal by filtration, Cs removal by ion exchange), then vitrified. Bench-scale ion exchange testing with approximately 9 L of filtered AP-107 supernate was conducted using crystalline silicotitanate (CST) ion exchange media. Batch contact testing was performed to evaluate CST performance on tank waste prior to processing it in the LAWPS. Subsequently, column testing was performed using prototypic LAWPS operations in a lead-lag column arrangement using 10-mL CST beds. Loading continued until the lag column reached the Hanford Tank Waste Treatment and Immobilization Plant waste acceptance criteria (WAC) for receiving supernatant waste for vitrification (a function of the Na and {sup 137}Cs concentrations). The Cs effluent from the lag column reached the WAC after processing half the feed volume. To keep the subsequent product effluent below the WAC, a new lag column was needed to continue processing and the original lag column was moved to the lead position. Greater than 99% of the Cs processed through the ion exchange system was collected on the CST in the columns. The AP-107 feed and column processing effluent underwent extensive characterization to better define waste characteristics and assess fractionation to the CST. Selected batch contact and lead column samples were analyzed to assess selected metal analyte load characteristics. Results of these analyses will be discussed. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

A Hybrid AI/ML and Computational Mechanics Based Approach for Time-Series State and Fatigue Life Estimation of Nuclear Reactor Components

Environmental fatigue modeling is a complex problem due to multiple failure modes and their intermixing. The failure modes are function of various underlying causes in addition to the corrosive effect of reactor coolant environment. Some of the major causes are time-dependence of material associated with cyclic loading, load sequence effect associated with random/variable amplitude loading, effect of strain amplitude and rates, effect of varying temperature (along both temporal and spatial directions) and the effect of mean strain and stress. The nonlinear intermixing of failure modes associated with above mentioned causing parameters makes the environmental fatigue modeling is a challenging task. Because of this challenge, fatigue is traditionally being modeled based on experimental data. However, test based empirical approach often requires hundreds of fatigue tests to model the above-mentioned intermixing failure causes even for a single material system. The problem is further exaggerated for reactor component made from multi-material systems such as made from both carbon and stainless-steel base metals and their similar and dissimilar metal welds. With the difficulty of conducting hundreds of fatigue tests to capture the above-mentioned intermixing failure causes, fatigue modeling approaches often depends on empirical models based on limited available test data such as available through ASME code and NUREG 6909. However, these limited test-data-based models may not be enough to accurately predict the life of reactor components. Accurate prediction of life of reactor component would become a necessity, particularly when the license of the reactors to be extended for long-term-operation (LTO) that is for well beyond its original design life of 40 years. The requirement of extending the license of reactor under LTO requires hundreds of fatigue tests to be conducted to understand the mechanism associated with the above-mentioned interdependent failure causes. However, conducting large number of fatigue tests is not a feasibility due to the cost involved. To address this issues Argonne National Laboratory (ANL) with the sponsorship of DOE Light Water Reactor Sustainability (LWRS) program trying to develop a hybrid predictive modeling approach. This is based on limited experiment-data, Artificial-intelligence (AI) – Machine-Learning (ML) - Deep-Learning (DL) based techniques and Multiphysics-computational-mechanics based modeling tools. The hybrid approach not-only can improve the accuracy of the existing stress analysis and fatigue modeling approach but also can reduce the over-dependency on test-based approach. Towards this goal following are some of the major contributions based on ANL’s FY-20 environmental fatigue modeling activities: 1) A cyclic plasticity material model database for 82/182 dissimilar metal weld, which can be readily shared with US nuclear industry and regulatory agency on request. 2) A well validated analytical modeling methodology to perform cycle-by-cycle stress prediction under both constant amplitude fatigue loading and variable amplitude fatigue loading (with load-sequence effect). 3) An AI/ML/DL based methodology to predict unmeasurable cyclic strain based on other available sensor signals. This type of approach can be used for estimating strain in real reactor components from other sensor readings. 4) An AI/ML based approach to improve the US capability on environmental fatigue testing. This is by improving ANL’s existing environmental fatigue testing capacity to conduct ASME required strain-controlled tests (by controlling strain amplitudes and its rate), while not measuring the strain (due to the difficulty of placing an extensometer in a narrow autoclave in a PWR-water-test system). 5) A simulation and experiment based probabilistic modeling methodology for time-series fatigue state and life estimation of reactor metal such as dissimilar metal weld.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

Combined Assay Systems for the Characterisation of Historical ILW at of the Dounreay Shaft and Silo-18140

In 1955 the Dounreay nuclear power development establishment was created on the northern coast of Scotland primarily to pursue the UK Government policy of developing Fast Breeder Reactor (FBR) technology. The site operated then by the United Kingdom Atomic Energy Authority (UKAEA) saw the development, test and operation of 3 nuclear reactors, two of them FBRs, alongside fabrication and reprocessing facilities for the FBR fuel elements. From the late 1950's until 1977 Intermediate Level radioactive Waste (ILW) generated on site was disposed of into a 65 m deep, 4.6 m diameter shaft. In January 1977, an explosion thought to be caused by hydrogen generated from sodium and potassium wastes reacting with water occurred in the upper part of the shaft, thus stopping any further waste disposal. A second ILW storage facility, the ILW-Silo, started operation in 1971 to deal with the increasing volume of intermediate level waste generated on site in the mid-1960's and continued receiving waste until 1998. The decommissioning of the Shaft and Silo facilities is a challenging engineering task due to the complexity and potential hazards linked with the retrieval and handling of historical waste. Following recovery it is intended to shred the majority of the waste and load it into 200-l assay drums. Sludges would be separated and solidified within a special annulus drum to make them suitable for assay. To support the decommissioning strategies put forward by Dounreay Site Restoration Limited (DSRL), Mirion Technologies Canberra UK Ltd have designed and completed the manufacture of a pair of combined non-destructive waste assay (NDA) systems. The two identical automated ILW counters are intended to provide a means to characterise the waste materials by performing active and passive neutron measurement as well as segmented high resolution gamma spectrometry (HRGS) of 200-l sacrificial waste drums. The active neutron technique is based on the Differential Die Away (DDA) technique using a pulsed Deuterium-Tritium neutron generator to interrogate fissile material present in the waste. The active neutron measurements support, as well as the passive neutron coincidence measurements, a matrix correction capability by use of an Add-a-Source mechanism. The publication will describe in broad lines the approach proposed by DSRL in the processing of the waste retrieved from both the Shaft and Silo facilities. It will also present a description of the overall design of the NDA counters, illustrating how the measurements to be performed will allow DSRL to safely characterise the retrieved waste materials. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Model-based Hierarchical Reinforcement Learning for Improved Physical Security Design: A Prototype

Prior work in FY24 developed an adversarial AI agent aid in path analysis of physical protection systems. This agent, trained using a model-based reinforcement learning algorithm, was able to successfully learn the most vulnerable path in facilities. It was able to extend the current state of practice for physical protection design by exhibiting dynamic behavior based on current environmental conditions. Whereas PathTrace largely performs a static, graph-based analysis, the AI agent was able to make decisions based on relative position in the facility, current conditions (was the adversarial agnet discovered?), and proximity to secondary targets. The agent demonstrated some novel capabilities, but had limitations that need to be resolved before it can be used for production purposes. For example, the adversarial agent generalizes poorly and takes a relatively long time to train. Nonetheless, there is still considerable promise for developing the adversarial agent further in order to explore even richer, more dynamic behaviors (e.g., adversary motivations, environmental debris, and more). This work considers a complementary idea; development of a planning agent. The planning agent is envisioned as an auto-complete-like tool that can help accelerate security system design by human experts. The agent would respect existing barriers and sensors placed by a human expert while offering cost-effective suggestions (i.e., implicitly balancing effectiveness with cost) to improve the design. The goal is for this agent to be part of an expert’s toolbox, not to totally upend the current state-of-practice, or to displace human experts. The ultimate goal would be concurrent training of both the adversarial and planning agent together, to learn entirely through self-play. This would represent an entirely new way of performing system deign. We selected a hierarchical, model-based reinforcement learning algorithm to serve as the planning agent. This is an extension of concepts used in the prior FY24 adversarial agent work. There, we had a single agent acting an environment. Here, we have two different sub-agents (policies), working together, to form a complete agent. There is a manager policy, which can select abstract goals on slower time scales, and a worker, which performs primitive actions to reach goals selected by the manager. It is worth noting that this class of algorithm is challenging to work with. From our understanding, our work is one of the first successful uses of model-based reinforcement learning (MBRL) in nuclear energy1 , and likely the first hierarchical model-based reinforcement learning application in nuclear energy. Further, this work is one of the first known attempts to apply AI to perform a design tasks in nuclear energy. Consequently, there were significant implementation challenges and the bulk of the work was focused on successful implementation and algorithm design. The results presented here are very low technology readiness level as a consequence of the lack of related literature, but still represent a significant step forward in the pursuit of applied AI for design.

42 ENGINEERING↗
DOE OSTI2017

DE-NE0000724 - Research Performance Final Report - Investigation of Thermal Aging Effects on the Evolution of Microstructure and Mechanical Properties of Cast Duplex Stainless Steels

This report details the research activities carried out under DOE-NEUP award number DE-NE0000724 concerning the evolution of structural and mechanical properties during thermal aging of CF–3 and CF–8 cast duplex stainless steels (CDSS). The overall objective of this project was to use state-of-the-art characterization techniques to elucidate trends and phenomena in the mechanical and structural evolution of cast duplex stainless steels (CDSS) during thermal aging. These steels are commonly used as structural materials in commercial light water nuclear power plants, undergoing aging for decades in operation as cooling water pipes, pump casings, valve bodies, etc. During extended exposure to these conditions, CDSS are known to undergo a change in mechanical properties resulting in a loss of ductility, i.e. embrittlement. While it is generally accepted that structural changes within the ferrite phase, such as decomposition into iron (Fe)-rich and chromium (Cr)-rich domains, lead to the bulk embrittlement of the steels, many questions remain as to the mechanisms of embrittlement at multiple length scales. This work is intended to shed insight into the atomic level composition changes, associated kinetic mechanisms, and effects of changing phase structure on micro- and nano-scale deformation that lead to loss of impact toughness and tensile ductility in these steels. In general, this project provides a route to answer some of these major questions using techniques such as 3-dimensional (3-D) atom probe tomography (APT) and real-microstructure finite element method (FEM) modeling, which were not readily available when these steels were originally selected for service in light water reactors. Mechanical properties evaluated by Charpy V-notch impact testing (CVN), tensile testing, and microhardness and nanohardness measurements were obtained for each condition and compared with the initial baseline properties to view trends in deformation behavior during aging. Concurrent analysis of the microstructure and nanostructure by atom probe tomography (APT) and transmission electron microscopy (TEM) provide mechanistic insight into the kinetic and mechanical behavior occurring on the nano-scale. The presence and morphology of the ferrite, austenite, and carbide phases have been characterized, and formation of new phases during aging, including spinodal decomposition products (α- and α'-ferrite) and G-phase, have been observed. The mechanical and structural characterization have been used to create accurate FEM models based on the real micro- and nano-structures of the systems. These models provide new insight into the local deformation behavior of these steels and the effects of each individual phase (including ferrite, austenite, carbides, and spinodal decomposition products) on the evolving bulk mechanical behavior of the system. The project was divided into three major tasks: 1. Initial Microstructure and Mechanical Property Survey and Initiate Heat Treatment; 2. Microstructural Characterization and Mechanical Property Testing During Aging; and 3. Microstructure-based Finite Element Modeling. Each of these tasks was successfully executed, resulting in reliable data and analysis that add to the overall body of work on the CDSS materials. Baseline properties and aging trends in mechanical data confirm prior observations and add new insights into the mechanical behavior of the steels. Structural characterization on multiple length scales provides new information on phase changes occurring during aging and sheds light on the kinetic processes occurring at the atomic scale. Furthermore, a combination of mechanical testing and microstructural characterization techniques was utilized to design FEM models of local deformation behavior of the ferrite and austenite phases, providing valuable new information regarding the effects of each of the microstructural components on the hardening and embrittlement processes. The data and analysis presented in this report and the publication associated with this project (§V) increase the understanding of aging and deformation in CF–3 and CF–8 steels. These results provide valuable information that can be utilized to aid in making informed decisions regarding the ongoing use of these steels in commercial nuclear infrastructure.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Data Summarization and Inference at Scale

This is the final report for the DOE ASCR grant SC-0022260, Data Summarization and Inference at Scale, PI: Alex Pothen, Purdue University. The goal of the project was to solve data-intensive and compute-intensive problems in the physical sciences, engineering, information science, data science, etc. by designing and implementing new algorithms that could work with a subset of the data. The four subgoals were: (a) The solution of problems where the data is too large to be stored in the memory of a computer. In this streaming model of computation, the data arrives as a stream of elements to the computer, each element is processed as it arrives, and a decision is made to discard the data or to store it; only a small subset of the data proportional to the size of the output solution is stored, and when all the data has been streamed, a solution to the problem is computed from the stored subset. (b) The use of machine learning methods to compute solutions to data-intensive problems. The use of GPUs is critical to obtain high performance on machine learning tasks, but their memory sizes are smaller relative to that of CPUs. For large-scale problems, the data is sampled many times, and small samples are used with repetition, for robustness, to compute solutions to inference tasks. This sampling reduces the memory required to solve the problem, but attention is needed to avoid slow convergence to the solutions, and reduced accuracy of inference. We propose submodular optimization, Large Language Models, and physics-informed neural networks to enable GPU computations here. (c) Modeling and visualization of high-dimensional data using interpretable features. Clinical proteomic data sets from immunology for the detection of cancer and other diseases are temporal and high-dimensional, and algorithms for visualizing these data sets using clinically interpretable features are lacking. We propose methods that compute distances based on the optimal transportation problem and graph edit distances to address this problem. We also propose the use of optimal transport-based distances, spatial statistics, and network structure to classify image data sets, We apply these algorithms to electron micrographs of the peripheral nervous system in the digestive tract. (d) The design of data-intensive algorithms on emerging architectures, specifically, noisy, intermediate-scale quantum (NISQ) devices. Quantum computers offer the possibility of exploring large solution spaces due to the principle of superposition, but current quantum computers are limited by few qubits, short coherence times due to noise, poor interconections among the qubits, etc. We propose the use of the divide and conquer paradigm to solve large-scale problems, wherein collections of small subproblems are solved on the quantum devices, and the solutions to the subproblems are integrated into a solution for the original problem on a classical computer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Transient Unsaturated Zone Modeling of Radioactive Waste Pits at Los Alamos National Laboratory - 87544 - 18432

Waste disposals at Material Disposal Area G (MDA G) at Los Alamos National Laboratory (LANL) occur primarily in a series of long, rectangular pits into which layers of waste have been emplaced beginning in the late 1950's. MDA G is situated in north-central New Mexico on top of Mesita del Buey, a narrow 3 km long mesa in the Parajito Plateau. The mesa is composed primarily of volcanic tuff from two principle volcanic eruptions in the Jemez Mountains which deposited the Otowi and Tshirege Members of the Bandelier Tuff. The Cerros del Rio Basalt underlay the tuff units and extends down for approximately 200 meters. The water table lies within the basalts at around 300 m below the surface of the mesa. The local climate is temperate and semiarid, averaging around 34 cm of precipitation per year. Ambient subsurface moisture conditions are dry, with water content typically below 10%. During disposal operations, open pits are subject to enhanced infiltration compared to the final covered configuration due to the unvegetated disturbed surface and precipitation events. Additionally, water added to the pits during disposal operations for tasks like waste compaction and dust suppression introduces additional moisture to the system during disposal operations that could potentially provide a pathway for contaminants in the waste zone to migrate through the unsaturated zone. Modeling was conducted using the HYDRUS-2D/3D software to evaluate the spatial extent of saturation changes and fluxes across geologic layer interfaces in the unsaturated zone throughout the life cycle of a waste pit. A general 2-dimensional framework was developed that included a cross section of a waste pit and the underlying geologic units. Several geological layerings were developed to represent the variation across the mesa at Area G. The model was run stochastically for hundreds of realizations to understand how variations in hydraulic parameters (e.g., porosity, saturated hydraulic conductivity, Van Genuchten parameters, etc.) and operational parameters (e.g., pit open period duration, pit depth, initial saturation of the waste zone, etc.) affect the extent of saturation changes and fluxes in the deep unsaturated zone. Distributions for these parameters were developed based on available site data. Results of the unsaturated zone modeling indicate that a significant amount of water can reach the lower unsaturated zone under some circumstances, particularly when the amount of water introduced during pit operations is high. A statistical study of modeling results was performed to develop a regression that describes the variations of water flux in time at the geological layer interfaces. These statistical model results will be used to evaluate solute transport in a future effort. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Beyond Widgets: Validated Systems Energy Savings and Utility Custom Incentive Program Systems Trends

Energy efficiency upgrades to buildings to date through avenues such as utility incentive programs have been largely limited to component-based products (e.g., lamps, RTUs). While some utilities do provide ‘custom’ incentive programs with whole building and system level technical assistance, these programs require deeper levels of analysis, resulting in higher program costs. This results in custom programs being restricted to utilities with greater resources, and are typically applied mainly to large or energy-intensive facilities, leaving much of the market without cost effective access and incentives for these solutions. In addition, with increasingly stringent energy codes, cost effective component-based solutions that achieve significant savings are dwindling. Integrated building systems (e.g., integrated façade, HVAC and/or lighting solutions) can deliver higher savings (Regnier et al. 2017) that translate into large sector-wide savings if deployed at the scale of these programs. However, systems application poses a number of challenges – energy savings assessments are inherently more complex and are not easily suited to the deemed measure model, and implementation of systems can be more disruptive. This paper presents the outcomes of a project to develop validated utility incentive program packages for three specific integrated building systems, in collaboration with ComEd, a consortium of California Public Owned Utilities (CA POUs) (Northern California Power Agency(NCPA) and the Southern California Public Power Authority(SCPPA)), and Xcel Energy (CO, MN). Previous work described the potential for energy savings of these systems at the individual and market level, early test findings for the task-ambient lighting system, and described the work products to develop utility incentive programs (Regnier et al. 2016). This paper presents validated end use energy savings of all three systems showing energy savings of ~20% for automated shading combined with daylight dimming (does not include light fixture retrofit savings); 30-46% for task-ambient lighting systems combined with plug load occupancy controls, depending on technology package and office building size (no daylight dimming impacts); and ~94% in the south-façade daylit zone for workstation specific lighting systems with daylight dimming (including lighting retrofit and task tuning). Using a U.S. Department of Energy (DOE) reference building model (DOE 2018), this translates to whole building savings of 2-5% (large-medium commercial), 12-23% (large-small commercial), and 13-15% (medium-large commercial) for these systems respectively. Early findings from the utilities in deploying these systems through their incentive programs indicate that deployment methods such as direct install programs might be best suited for these integrated systems. Further, early findings from a scoping study across custom utility programs and federal retrofit programs indicate that component level retrofits are still prevalent even in these programs, pointing towards an opportunity for deeper energy savings deployment. High performance retrofit case studies demonstrate that deeper energy savings is possible through integrated approaches. Market stakeholder interviews indicate however that there is generally a low level of awareness of the potential for energy savings of these three integrated systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Recovery Act: Oxy-Combustion Technology Development for Industrial-Scale Boiler Applications. Task 4 - Testing in Alstom's 15 MW th Boiler Simulation Facility

Alstom Power Inc. (Alstom), under U.S. DOE/NETL Cooperative Agreement No. DE-NT0005290, is conducting a development program to generate detailed technical information needed for application of oxy-combustion technology. The program is designed to provide the necessary information and understanding for the next step of large-scale commercial demonstration of oxy combustion in tangentially fired boilers and to accelerate the commercialization of this technology. The main project objectives include: Design and develop an innovative oxyfuel system for existing tangentially-fired boiler units that minimizes overall capital investment and operating costs; Evaluate performance of oxyfuel tangentially fired boiler systems in pilot scale tests at Alstom’s 15 MWth tangentially fired Boiler Simulation Facility (BSF); Address technical gaps for the design of oxyfuel commercial utility boilers by focused testing and improvement of engineering and simulation tools; Develop the design, performance and costs for a demonstration scale oxyfuel boiler and auxiliary systems; Develop the design and costs for both industrial and utility commercial scale reference oxyfuel boilers and auxiliary systems that are optimized for overall plant performance and cost; and, Define key design considerations and develop general guidelines for application of results to utility and different industrial applications. The project was initiated in October 2008 and the scope extended in 2010 under an ARRA award. The project is scheduled for completion by April 30, 2014. Central to the project is 15 MWth testing in the BSF, which provided in-depth understanding of oxy-combustion under boiler conditions, detailed data for improvement of design tools, and key information for application to commercial scale oxy-fired boiler design. Eight comprehensive 15 MWth oxy-fired test campaigns were performed with different coals, providing detailed data on combustion, emissions, and thermal behavior over a matrix of fuels, oxy-process variables and boiler design parameters. Significant improvement of CFD modeling tools and validation against 15 MWth experimental data has been completed. Oxy-boiler demonstration and large reference designs have been developed, supported with the information and knowledge gained from the 15 MWth testing. This report addresses the results from the 15 MWth testing in the BSF.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

Phasor-Measurement-Unit-Based Data Analytics Using Digital Twin and PhasorAnalytics Software

A major objective of this project was to apply GE’s commercial machine learning and data analytics toolsets to large-scale, real-world, anonymized Phasor Measurement Unit (PMU) datasets in order to extract signatures, correlated and/or causal factors, and precursor patterns associated with significant power system phenomena. The project had a particular emphasis on extraction of insights relevant to asset health monitoring, real-time load modeling and cybersecurity monitoring. Additionally, the team was directed to undertake a comprehensive data quality analysis for the provided datasets and encouraged to estimate the ‘machine-learning readiness’ of the datasets by documenting any major obstacles to the application of commercial machine learning algorithms. To accomplish the aforementioned objectives, the project team’s work centered around the identification of key event signatures and application of the identified event signatures for event detection and event classification. The industry-validated, semi-supervised machine learning strategy employed for event signature identification involved several major tasks, including data-preprocessing, generation of an overabundance of features, normal data identification, normality modeling, and event signature identification through a methodical, quantitative ranking of features in order of relevance to each studied event type. Throughout the project, data quality issues and mitigation techniques were investigated. In this report, insights are provided regarding the readiness of the provided synchrophasor datasets for application of machine learning and data analytics. The methodologies employed for this technical strategy are summarized in this report. With regards to data preprocessing and feature generation, the provided Training and Test Datasets were ingested into GE’s big data environment. Subsequently, the team applied bad data cleansing and data imputation scripts, event detection scripts, and application programming interfaces (APIs) to the datasets for convenient data access. The project team completed development and validation of dozens of physics-based, statistics-based and transformation-based feature functions used for the extraction of over 60 synchrophasor features. Using a new parallel feature generation technology developed on this project, over 60 features have been rapidly generated for the full two years’ worth of Training and Test Dataset data associated with both the Eastern and Western interconnects. Even accommodating for temporal down-sampling inherent to the feature extraction procedure, this parallel feature generation activity resulted in a massive feature set with a storage requirement approximately equal to that of the raw training dataset itself. With regards to normal data identification and normality modeling, a normality model was built using the feature data extracted from the Training Dataset and iteratively refined subsequent to incremental adjustments and expansions of the Training Dataset feature data. With respect to event characterization and signature identification, an event signature identification pipeline was developed and used in conjunction with the normality model to identify over 15 event signatures for key event categories within the Training Dataset. The identified event signatures were used to characterize hundreds of key events in terms of relative severity, duration, and location of the event. An investigation was undertaken to identify correlated and causal factors involved in transformer events. A separate investigation into temporal trends in ring-down analysis results was undertaken to determine possible associations between system dynamics and various other factors such as loading, season or year. To validate the identified event signatures, additional work was undertaken to develop signature-based anomaly detection and classification tools suitable for convenient application to the synchrophasor datasets. The anomaly detection and classification tools, suitable for online application, were then applied to the entirety of the Eastern Interconnect Training and Test Datasets. Performance of the event detection and classification tools was evaluated upon receipt of the Test Dataset event logs (i.e., the labels for events contained in the Test Dataset), and promising results were obtained despite several challenges (documented herein) associated with application of supervised or semi-supervised machine learning methods to large-scale, anonymized datasets. Finally, the detection and classification tools were used to detect, classify, and characterize thousands of new events not included in the original event logs provided by the DOE within both the Training and Test Datasets.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

HydraGNN_Predictive_GFM_2024 - Ensemble of predictive graph foundation models for ground state atomistic materials modeling

We provide the ensemble of fifteen pre-trained graph foundation models (GFMs) for atomistic materials modeling applications. Each one of the fifteen GFMs has been trained on five open-source datasets that (once aggregated) amount to over 154 million atomistic structures, which cover over two-thirds of the natural elements of the periodic table and that comprises a broad set of organic and inorganic compounds. This vast set of atomistic structures comprises ground state configurations that are dynamically stable (i.e., equilibrated structures with atomic forces approximately close to zero values) as well as dynamically unstable structures (i.e., non-equilibrium structures with non-negligible non-zero values of atomic forces). The ensemble of datasets aggregated does NOT include excited states. The datasets have been curated to remove atomistic structures with spectral norm of the force tensor above 100 eV/angstrom. Moreover, a linear term of the energy was computed for each dataset using a linear regression model that uses the chemical concentration of each natural element as regressor. The linear term predicted by the linear regression model has been subtracted from each original energy value to perform a re-alignment of the energy values across different electronic structures approximation theories performed to generate the diverse multi-source, multi-fidelity datasets. The folder "ADIOS_files" contains the set of pre-processed datasets in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used for the development and training of GFMs in this work. The "ADIOS_files" directory contains 6 sub-directories named as follows: - ANI1x-v3.bp - MPTrj-v3.bp - OC2020-20M-v3.bp - OC2020-v3.bp - OC2022-v3.bp - qm7x-v3.bp Each sub-directory contains the pre-processed datasets converted in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used to the development, training, and performance testing of the ensemble go predictive graph foundation models. Each GFM was developed using HydraGNN (https://github.com/ORNL/HydraGNN) as underlying graph neural network (GNN) architecture. The multi-task learning (MTL) capability of HydraGNN was used to simultaneously train the GFMs on labeled values for direct predictions of energy (a total system property of an atomistic structure that measures the chemical stability) and atomic forces (an atomic level property of an atomistic structure that measures the dynamical stability). The hyper parameters of the GFM have been tuned using scalable hyperparameter optimization (HPO) algorithms implemented in the software DeepHyper (https://github.com/deephyper/deephyper). The pre-training of each HPO trial was performed using distributed data parallelism (DDP) to scale the training across 128 compute nodes of the exascale OLCF supercomputer Frontier. Each HPO trial was trained only for 10 epochs and an early stopping was performed to avoid wasting significant computational resources on GNN architectures that were clearly underperforming. For each HPO trial, the 'omnistat' tool developed by (AMD Research - Advanced Micro Device) was used to measure the total energy consumption in kWh. The ensemble of GFMs was obtained by selecting the fifteen best performing HPO trials. Four models have been selected for their clear advantage in accuracy, and these are the GFMs with IDs 229, 156, 147, 260. Additional eleven models have been selected based on judicious balance between accuracy and energy consumption needed for training, and these are the GFMs with IDs 165, 78, 137, 1, 175, 171, 181, 67, 179, 167, 351. Each selected GFM of the ensemble was continued to cumulate a total of at most 30 epochs. In some cases, the total number of epochs actually performed was les than 30 due to two combined factors: (1) the size of the GFM (i.e., the number of model parameters to train) and (2) the total wall-clock time for which the computational resources could be allocated on OLCF-Frontier. The "Ensemble_of_models" directory contains 15 sub-directories named as follows: - gfm_0.229 - gfm_0.156 - gfm_0.147 - gfm_0.260 - gfm_0.165 - gfm_0.78 - gfm_0.137 - gfm_0.1 - gfm_0.175 - gfm_0.171 - gfm_0.181 - gfm_0.67 - gfm_0.179 - gfm_0.167 - gfm_0.351 Each one of these sub-directories refers to one of the fifteen HPO trials that have been selected to continue the pre-training with at most 30 epochs. With each sub-directory associated with a specific HPO trial, the following files can be found: - config.json: file for argument parsing to develop and train an HydraGNN architecture - gfm_0.ID_epoch_N.pk: file with model parameters for HPO ID trial after N epochs of training The ensemble of fifteen GFM architectures was used for (1) ensemble averaging to stabilize the predictions of energy and atomic forces after pre-training for post-processing analysis and (2) ensemble uncertainty quantification (UQ). The code used to develop, pre-train, and load the pre-trained models for post-processing analysis is available on the ORNL-GitHub at the following link: https://github.com/ORNL/HydraGNN/tree/Predictive_GFM_2024

36 MATERIALS SCIENCE↗
DOE OSTI2013

The feasibility of a regional CTDI{sub vol} to estimate organ dose from tube current modulated CT exams

Purpose: In AAPM Task Group 204, the size-specific dose estimate (SSDE) was developed by providing size adjustment factors which are applied to the Computed Tomography (CT) standardized dose metric, CTDI{sub vol}. However, that work focused on fixed tube current scans and did not specifically address tube current modulation (TCM) scans, which are currently the majority of clinical scans performed. The purpose of this study was to extend the SSDE concept to account for TCM by investigating the feasibility of using anatomic and organ specific regions of scanner output to improve accuracy of dose estimates. Methods: Thirty-nine adult abdomen/pelvis and 32 chest scans from clinically indicated CT exams acquired on a multidetector CT using TCM were obtained with Institutional Review Board approval for generating voxelized models. Along with image data, raw projection data were obtained to extract TCM functions for use in Monte Carlo simulations. Patient size was calculated using the effective diameter described in TG 204. In addition, the scanner-reported CTDI{sub vol} (CTDI{sub vol,global}) was obtained for each patient, which is based on the average tube current across the entire scan. For the abdomen/pelvis scans, liver, spleen, and kidneys were manually segmented from the patient datasets; for the chest scans, lungs and for female models only, glandular breast tissue were segmented. For each patient organ doses were estimated using Monte Carlo Methods. To investigate the utility of regional measures of scanner output, regional and organ anatomic boundaries were identified from image data and used to calculate regional and organ-specific average tube current values. From these regional and organ-specific averages, CTDI{sub vol} values, referred to as regional and organ-specific CTDI{sub vol}, were calculated for each patient. Using an approach similar to TG 204, all CTDI{sub vol} values were used to normalize simulated organ doses; and the ability of each normalized dose to correlate with patient size was investigated. Results: For all five organs, the correlations with patient size increased when organ doses were normalized by regional and organ-specific CTDI{sub vol} values. For example, when estimating dose to the liver, CTDI{sub vol,global} yielded a R{sup 2} value of 0.26, which improved to 0.77 and 0.86, when using the regional and organ-specific CTDI{sub vol} for abdomen and liver, respectively. For breast dose, the global CTDI{sub vol} yielded a R{sup 2} value of 0.08, which improved to 0.58 and 0.83, when using the regional and organ-specific CTDI{sub vol} for chest and breasts, respectively. The R{sup 2} values also increased once the thoracic models were separated for the analysis into females and males, indicating differences between genders in this region not explained by a simple measure of effective diameter. Conclusions: This work demonstrated the utility of regional and organ-specific CTDI{sub vol} as normalization factors when using TCM. It was demonstrated that CTDI{sub vol,global} is not an effective normalization factor in TCM exams where attenuation (and therefore tube current) varies considerably throughout the scan, such as abdomen/pelvis and even thorax. These exams can be more accurately assessed for dose using regional CTDI{sub vol} descriptors that account for local variations in scanner output present when TCM is employed.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TH-AB-209-01: Making Benchtop X-Ray Fluorescence Computed Tomography (XFCT) Practical for in Vivo Imaging by Integration of a Dedicated High-Performance X-Ray Source in Conjunction with Micro-CT Functionality

Purpose: To make benchtop x-ray fluorescence computed tomography (XFCT) practical for routine preclinical imaging tasks with gold nanoparticles (GNPs) by deploying, integrating, and characterizing a dedicated high-performance x-ray source and addition of simultaneous micro-CT functionality. Methods: Considerable research effort is currently under way to develop a polychromatic benchtop cone-beam XFCT system capable of imaging GNPs by stimulation and detection of gold K-shell x-ray fluorescence (XRF) photons. Recently, an ad hoc high-power x-ray source was incorporated and used to image the biodistribution of GNPs within a mouse, postmortem. In the current work, a dedicated x-ray source system featuring a liquid-cooled tungsten-target x-ray tube (max 160 kVp, ∼3 kW power) was deployed. The source was operated at 125 kVp, 24 mA. The tube’s compact dimensions allowed greater flexibility for optimizing both the irradiation and detection geometries. Incident x-rays were shaped by a conical collimator and filtered by 2 mm of tin. A compact “OEM” cadmium-telluride x-ray detector was implemented for detecting XRF/scatter spectra. Additionally, a flat panel detector was installed to allow simultaneous transmission CT imaging. The performance of the system was characterized by determining the detection limit (10-second acquisition time) for inserts filled with water/GNPs at various concentrations (0 and 0.010–1.0 wt%) and embedded in a small-animal-sized phantom. The phantom was loaded with 0.5, 0.3, and 0.1 wt% inserts and imaged using XFCT and simultaneous micro-CT. Results: An unprecedented detection limit of 0.030 wt% was experimentally demonstrated, with a 33% reduction in acquisition time. The reconstructed XFCT image accurately localized the imaging inserts. Micro-CT imaging did not provide enough contrast to distinguish imaging inserts from the phantom under the current conditions. Conclusion: The system is immediately capable of in vivo preclinical XFCT imaging with GNPs. Micro-CT imaging will require optimization of irradiation parameters to improve contrast. Supported by NIH/NCI grant R01CA155446; This investigation was supported by NIH/NCI grant R01CA155446.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

Background: Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022. Methods: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1–4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models’ predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models’ forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models’ past predictive performance. Results: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models’ forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models’ forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models’ forecasts of deaths (N=763 predictions from 20 models). Across a 1–4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models. Conclusions: Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks. Funding: AA, BH, BL, LWa, MMa, PP, SV funded by National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and respectively Virginia Dept of Health Grant VDH-21-501-0141, VDH-21-501-0143, VDH-21-501-0147, VDH-21-501-0145, VDH-21-501-0146, VDH-21-501-0142, VDH-21-501-0148. AF, AMa, GL funded by SMIGE - Modelli statistici inferenziali per governare l'epidemia, FISR 2020-Covid-19 I Fase, FISR2020IP-00156, Codice Progetto: PRJ-0695. AM, BK, FD, FR, JK, JN, JZ, KN, MG, MR, MS, RB funded by Ministry of Science and Higher Education of Poland with grant 28/WFSN/2021 to the University of Warsaw. BRe, CPe, JLAz funded by Ministerio de Sanidad/ISCIII. BT, PG funded by PERISCOPE European H2020 project, contract number 101016233. CP, DL, EA, MC, SA funded by European Commission - Directorate-General for Communications Networks, Content and Technology through the contract LC-01485746, and Ministerio de Ciencia, Innovacion y Universidades and FEDER, with the project PGC2018-095456-B-I00. DE., MGu funded by Spanish Ministry of Health / REACT-UE (FEDER). DO, GF, IMi, LC funded by Laboratory Directed Research and Development program of Los Alamos National Laboratory (LANL) under project number 20200700ER. DS, ELR, GG, NGR, NW, YW funded by National Institutes of General Medical Sciences (R35GM119582; the content is solely the responsibility of the authors and does not necessarily represent the official views of NIGMS or the National Institutes of Health). FB, FP funded by InPresa, Lombardy Region, Italy. HG, KS funded by European Centre for Disease Prevention and Control. IV funded by Agencia de Qualitat i Avaluacio Sanitaries de Catalunya (AQuAS) through contract 2021-021OE. JDe, SMo, VP funded by Netzwerk Universitatsmedizin (NUM) project egePan (01KX2021). JPB, SH, TH funded by Federal Ministry of Education and Research (BMBF; grant 05M18SIA). KH, MSc, YKh funded by Project SaxoCOV, funded by the German Free State of Saxony. Presentation of data, model results and simulations also funded by the NFDI4Health Task Force COVID-19 ( https://www.nfdi4health.de/task-force-covid-19-2 ) within the framework of a DFG-project (LO-342/17-1). LP, VE funded by Mathematical and Statistical modelling project (MUNI/A/1615/2020), Online platform for real-time monitoring, analysis and management of epidemic situations (MUNI/11/02202001/2020); VE also supported by RECETOX research infrastructure (Ministry of Education, Youth and Sports of the Czech Republic: LM2018121), the CETOCOEN EXCELLENCE (CZ.02.1.01/0.0/0.0/17-043/0009632), RECETOX RI project (CZ.02.1.01/0.0/0.0/16-013/0001761). NIB funded by Health Protection Research Unit (grant code NIHR200908). SAb, SF funded by Wellcome Trust (210758/Z/18/Z).

60 APPLIED LIFE SCIENCES↗