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DOE OSTI2020

Accomplishments and Mid-Year Performance Report: Wind Energy Program Fiscal Year 2020

Through transformative science and innovation, the U.S. Department of Energy's (DOE's) National Wind Technology Center (NWTC) at the National Renewable Energy Laboratory's (NREL's) Flatirons Campus helps lead the way to a sustainable energy future that powers America with significant levels of reliable, low-cost, and accessible wind energy. Since 1976, NREL has provided an ideal environment for the research and development (R&D) of advanced energy technologies through: Wind energy resource assessments, World-class research facilities, High-performance computing and modeling, Data and technology analysis, Manufacturing breakthroughs, Environmental analysis and wildlife conservation efforts, Education and training programs for a future workforce. These activities enable the innovations needed to advance U.S. wind systems, address market and deployment barriers, and drive down the cost of wind energy with more efficient, more reliable, and more predictable wind energy systems. This report provides an overview of the achievements NREL delivered on behalf of DOE's Wind Energy Technologies Office (WETO) and other partners during the first and second quarters of Fiscal Year (FY) 2020.

49 EE - Wind and Water Power Program - Wind (EE-4W↗

DOE OSTI2021

Subtask 2.6 – Optimization of Aerosol Mitigation Technology for Postcombustion CO 2 Capture

Growing concerns over the impact of CO 2 emissions from combustion sources on global climate change have prompted numerous research and development projects aimed at developing cost-effective technologies for CO 2 capture. One family of technologies being demonstrated at pilot and full scale globally is postcombustion CO 2 capture (PCCC) systems that employ amine-based solvents. The captured CO 2 can be compressed and permanently stored underground or used for enhanced oil recovery (EOR). The proximity of North Dakota’s lignite-fired fleet of power plants to potential CO 2 storage options creates a unique atmosphere for PCCC within the state. However, the aerosols present in lignite flue gas present a challenge for large-scale PCCC at North Dakota power plants. Aerosols can negatively impact the long-term performance of amine-based solvents for CO 2 capture. Amine-based solvents are volatile, and aerosols provide nucleation sites where amine vapors can condense. Because aerosols cannot be easily captured at the column outlet using conventional technologies, the amine-laden aerosols escape the system and lead to accelerated solvent losses. Moreover, aerosol components can chemically react with amines to form degradation products that can permanently deactivate the amine, cause fouling, and lead to hazardous emissions. Many of the elements that have been shown to catalyze solvent degradation are present in lignite coals and can exacerbate solvent replacement economics. Understanding this issue is critical to the implementation of solvent-based CO 2 capture systems as applied to lignite-fired generation systems. The Energy & Environmental Research Center (EERC) designed and carried out this project to optimize aerosol mitigation technology for PCCC at a lignite-fired power plant. To meet the goal of this project, the following objectives were identified: Determine the effectiveness of a wet electrostatic precipitator (WESP) on collection of aerosols at a low-rank coal-fired power station. Determine the impact of aerosols on the efficiency and degradation products of amine-based carbon capture systems fired with low-rank fuels. Work was conducted at Minnkota Power Cooperative’s (MPC’s) Milton R. Young (MRY) Station Unit 2 using a slipstream of flue gas from the outlet of the plant’s flue gas desulfurization (FGD) unit. To gather initial data for sizing and specifying a WESP for this system, a temporary pilot-scale WESP was rented and installed on-site. Several different conditions were tested to examine the impact of flow rate, voltage, and current on WESP performance. The WESP was effective at removing large particulate (>200 nm) but caused an increase in fine particulate (<75 nm). Fine particulate material at the inlet and outlet of the WESP was collected, analyzed, and showed that crystalline sulfates carried over from the plant’s FGD unit were being converted to fine aerosols and SO 2 was being converted to SO 3 through the WESP. Additionally, the high moisture content of the flue gas stream at this sample location also contributed to an overall increase in aerosol mass under some of the test conditions. Using the results from the rented WESP, the project team installed a smaller-scale WESP upstream of the EERC’s slipstream CO 2 capture system. Flue gas was routed through a pilot-scale FGD unit to remove SO 2 to very low levels (~1 ppm) and then through a direct contact cooler (DCC) to further cool the gas and to remove moisture. The gas exiting the DCC was then routed through the new WESP before passing to the CO 2 absorber columns. Fluor’s amine-based solvent was used to scrub CO 2 from the slipstream through a set of two absorber columns. The rich solvent was regenerated in a stripper column by heating to drive off captured CO 2 . The system operated using a catch-and-release method where the CO 2 was separated to provide data on the process, but the captured CO 2 was released back into the host site stack. Aerosols and sulfur species were measured at multiple locations throughout the pilot-scale system. The inlet FGD and DCC removed much of the particulate matter and gaseous sulfur upstream of the WESP. With this configuration, the WESP achieved >95% particulate capture. The new WESP did not show any of the increases in SO 3 or other aerosol species that had been consistently observed with the larger-scale WESP installed immediately downstream of the plant’s full-scale FGD unit. Particulate samples captured and analyzed from the WESP inlet did not show any presence of crystalline sulfate materials, indicating that the pilot-scale FGD and DCC were efficient at reducing carryover from the plant’s full-scale FGD unit. A set of parametric tests were conducted on the new WESP to assess the impacts of flow rate, voltage, number of online WESP fields, and gas-phase sulfur content on aerosol and sulfur transformations. The results showed that the WESP performed similarly well at all sets of conditions. Sulfur and particulate matter exiting the WESP were further reduced through the absorber column as the amine-based solvent captured some of the residual contaminants. Solvent analysis showed that these species were slowly concentrating in the solvent over the duration of the test. When the WESP was taken offline and the sulfur slip through the FGD allowed to rise, the sulfate content in the solvent rose sharply, showing that the extra FGD and WESP were effective at reducing sulfate and cation uptake. This would be expected to extend amine-based solvent life by slowing the formation of heat-stable salts and other degradation products. This subtask was cofunded through the EERC–U.S. Department of Energy Joint Program on Research and Development for Fossil Energy-Related Resources Cooperative Agreement No. DE-FE0024233. Nonfederal funding was provided by the North Dakota Industrial Commission and MPC.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2022

Resolved Molecular Gas Observations of MaNGA Post-starbursts Reveal a Tumultuous Past

Post-starburst (PSB) galaxies have recently and rapidly quenched their star formation; thus, they are an important way to understand how galaxies transition from star-forming late types to quiescent early types. The recent discovery of large cold gas reservoirs in PSB galaxies calls into question the theory that galaxies must lose their gas to become quiescent. Optical Integral Field Spectroscopy (IFS) surveys have revealed two classes of PSB galaxies: central PSB (cPSB) galaxies with central quenching regions and ring PSB (rPSB) galaxies with quenching in their outskirts. We analyze a sample of 13 nearby (z < 0.1) PSB galaxies with spatially resolved optical IFS data from the Mapping Nearby Galaxies at Apache Point Observatory survey and matched resolution Atacama Large Millimeter/submillimeter Array observations of 12 CO(1–0). Disturbed stellar kinematics in 7/13 of our PSB galaxies and centrally concentrated molecular gas is consistent with a recent merger for most of our sample. In galaxies without merger evidence, alternate processes may funnel gas inward and suppress star formation, which may include outflows, stellar bars, and minor mergers or interactions. The star formation efficiencies of the PSB regions in nearly half our galaxies are suppressed while the gas fractions are consistent with star-forming galaxies. Active galactic nucleus (AGN) feedback may drive this stabilization, and we observe AGN-consistent emission in the centers of 5/13 galaxies. Finally, our cPSB and rPSB galaxies have similar properties except the ionized and molecular gas in cPSB galaxies is more disturbed. Overall, the molecular gas in our PSB galaxies tends to be compact and highly disturbed, resulting in concentrated gas reservoirs unable to form stars efficiently.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

NWPEsSe: an Adaptive-Learning Global Optimization Algorithm for Nanosized Cluster Systems

Global optimization constitutes an important and fundamental problem in theoretical studies in many chemical fields, such as catalysis, materials or separations problems. In this paper, a novel algorithm has been developed for the global optimization of large systems including neat and ligated clusters in gas phase, and supported clusters in periodic boundary conditions. The method is based on an updated artificial bee colony (ABC) algorithm method, that allows for adaptive-learning during the search process. The new algorithm is tested against four classes of systems of diverse chemical nature: gas phase Au_55, ligated Au_8^(2+), Au_8 supported on graphene oxide and defected rutile, and a large cluster assembly ?[Co?_6 Te_8 (PEt_3 )_6][C_60 ]_n, with sizes ranging between 1 to 3 nm and containing up to 1300 atoms. Reliable global minima (GMs) are obtained for all cases, either confirming published data or reporting new lower energy structures. The algorithm and interface to other codes in the form of an independent program, Northwest Potential Energy Search Engine (NWPEsSe), is freely available and it provides a powerful and efficient approach for global optimization of nanosized cluster systems. The work described in this publication was performed at Pacific Northwest National Laboratory (PNNL), which is operated by Battelle for the United States Department of Energy (DOE) under Contract DE-AC05-76RL0180. J. Z. and V.-A. G. acknowledge support from DOE, Office of Science, Office of Basic Energy Sci-ences, Chemical, Geological and Biological Sciences Division and computing resources from PNNL’s Research Computing Facility and the National Energy Research Scientific Computing Center.

Zhang, Jun↗

DOE OSTI2021

A Multiport DC Transformer to Enable Flexible Scalable DC as a Service

The rapid adoption of new DC loads and sources, including photovoltaic arrays, DC fast charging of electric vehicle, battery energy storage and data centers, requires a large amount of DC power conversion. A majority of new deployments also necessitate the integration of multiple DC loads and sources at one site. The traditional approach to serve these new applications relies on multiple standard power converters, each dedicated to a source or load, and results in highly customized systems, with challenging control coordination, complex protection strategies, and poor scalability. Instead, this paper proposes the concept of a multiport DC transformer (MDCT) as a modular building block for realizing a flexible, scalable DC as a Service system and address this rapidly growing need. The MDCT uses the S4T to achieve very tight control of cycle-by-cycle energy exchange between multiple ports, with high efficiency. A single multiport converter replaces 4-6 distinct converters, integrates all energy flows and manages protection. As a result, a new layered control architecture is introduced to ensure stability and scalability of the MDCT, with multiple S4T power converter building blocks connected in parallel to realize a fully modular system and reach the target power levels.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2024

Impact of Silicon Impurity on the Hydrometallurgical Recovery of NCM622 Cathode

Hydrometallurgy is one of the best approaches to date for recycling LIBs due to its high efficiency, low energy usage, and industrial scalability. However, impurities have always been a thorny issue because they could have unintended impacts on the recovered cathode materials. This research marks the first systematic investigation into the influence of silicon impurity on the LiNi 0.6 Co 0.2 Mn 0.2 O 2 (NCM622) cathode obtained from hydrometallurgical recycling. Here we find that silicon nanoparticles will be nucleated at the center of the precursor particles during co-precipitation synthesis, and the silicon core will slowly dissolve in the surrounding ammonia, creating a special hollow structure in the particle. More importantly, the dissolution of silicon impurity will eventually lead to the deposition of silicates in the cathode material, which is an unfavorable result. Test data indicate that NCM622 cathode with 5 at% silicon has a capacity of 148.8 mAh g −1 after 100 cycles at 1/3 C, approximately 10 mAh g −1 lower than the virgin. Despite being relatively mild, the adverse influence of silicon impurity in hydrometallurgical recycling still requires attention.

25 ENERGY STORAGE↗

DOE OSTI2024

Development of ceria-supported metal-oxide (MO x /CeO 2 ) catalysts via a one-pot chemical vapor deposition (OP-CVD) technique: Structure and reverse water gas shift reaction study

Current synthesis techniques for metal oxide (MO x )-supported catalysts have certain limitations of undesired target loading, ineffective dispersion of active species over the surface, uncontrolled particle size of active species, and complicated synthesis steps. Here, we developed a one-pot chemical vapor deposition (OP-CVD) methodology; by using which a solid metal precursor forms a vapor in a controlled condition and gets supported over the surrounding matrix. The theoretical stability followed by experimental validation using TGA is crucial for selecting the metal precursors. Three simple steps viz. premixing, dispersion, and rapid fixation by calcination are involved in the catalyst development via the OP-CVD approach. This study solely focused on the synthesis of 3d transition MO x over ceria support. The physicochemical characterizations of the prepared catalysts were performed by XRD, ICP-OES, SEM-EDX, CO pulse chemisorption, XANES, and EXAFS analyses to understand the crystal structure of involved species, target metal loading, dispersion, and particle size and prove the feasibility and viability of OP-CVD. The prepared catalysts were further tested for reverse water gas shift (RWGS) reaction to link their structural information with activity. The RWGS reaction data showed that the CO activity and CO selectivity were metal - and metal precursor-dependent. Higher CO activity of > 0.1 mol/h g-cat was observed for Cu and Co-based catalysts, with CO selectivity of ~100 %. This study provides an opportunity to produce efficient supported catalysts in a convenient way, providing effective catalytic activity.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Vehicle Localization in 3D World Coordinates Using Single Camera at Traffic Intersection

Optimizing traffic control systems at traffic intersections can reduce the network-wide fuel consumption, as well as emissions of conventional fuel-powered vehicles. While traffic signals have been controlled based on predetermined schedules, various adaptive signal control systems have recently been developed using advanced sensors such as cameras, radars, and LiDARs. Among these sensors, cameras can provide a cost-effective way to determine the number, location, type, and speed of the vehicles for better-informed decision-making at traffic intersections. In this research, a new approach for accurately determining vehicle locations near traffic intersections using a single camera is presented. For that purpose, a well-known object detection algorithm called YOLO is used to determine vehicle locations in video images captured by a traffic camera. YOLO draws a bounding box around each detected vehicle, and the vehicle location in the image coordinates is converted to the world coordinates using camera calibration data. During this process, a significant error between the center of a vehicle’s bounding box and the real center of the vehicle in the world coordinates is generated due to the angled view of the vehicles by a camera installed on a traffic light pole. As a means of mitigating this vehicle localization error, two different types of regression models are trained and applied to the centers of the bounding boxes of the camera-detected vehicles. The accuracy of the proposed approach is validated using both static camera images and live-streamed traffic video. Based on the improved vehicle localization, it is expected that more accurate traffic signal control can be made to improve the overall network-wide energy efficiency and traffic flow at traffic intersections.

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

Desmearing two-dimensional small-angle neutron scattering data by central moment expansions

Resolution smearing is a critical challenge in the quantitative analysis of two-dimensional small-angle neutron scattering (SANS) data, particularly in studies of soft-matter flow and deformation using SANS. Here, we present a central moment expansion technique to address smearing in anisotropic scattering spectra, offering a model-free desmearing methodology. By accounting for directional variations in resolution smearing and enhancing computational efficiency, this approach reconstructs desmeared intensity distributions from smeared experimental data. Computational benchmarks using interacting hard-sphere fluids and Gaussian chain models validate the accuracy of the method, while simulated noise analyses confirm its robustness under experimental conditions. Experimental validation using rheological SANS data from shear-induced micellar structures demonstrates the practicality and effectiveness of the proposed algorithm. The desmearing technique provides a powerful tool for advancing the quantitative analysis of anisotropic scattering patterns, enabling precise insights into the interplay between material microstructure and macroscopic flow behavior.

anisotropic scattering spectra↗

DOE OSTI2025

Tactical Analysis for Calculating Contextual Risk at Boundaries: Summary of Laboratory Directed Research & Development Effort

The Tactical Analysis for Calculating Contextual Risk at Boundaries (TACCRAB) tool is an innovative digital twin (DT) platform and automated risk algorithm designed to transform operational decision-making in structured screening environments, with an initial focus on Southern Border Land Ports of Entry (POEs). The invention provides integration points for advanced artificial intelligence, predictive modeling, and real-time data analysis to produce a comprehensive risk management tool that enables proactive, data-informed security strategies. The core inventive features of TACCRAB center on its unique risk algorithm, which dynamically calculates contextual risk by synthesizing historical data, near real-time streaming data from the checkpoints themselves, and AI-generated predictions. Unlike traditional risk assessment methods, TACCRAB utilizes a DT to provide comprehensive operational insights, allowing stakeholders to visualize, simulate, and optimize checkpoint configurations with unprecedented speed and contextual awareness. TACCRAB's key innovation lies in its ability to combine multiple complex inputs - including technology detection probabilities, resource availability, screening pathway characteristics, and threat actor behavioral patterns - into a unified risk calculation and update these inputs based on changing operational and environmental conditions. By leveraging a DT that continuously updates and learns from linked data, TACCRAB can suggest adaptive mitigation strategies that minimize risk while maintaining operational efficiency. Particularly novel is the platform's approach to decision support, which goes beyond static risk assessment. The DT provides dynamic metrics such as wait times, resource allocation effectiveness, and potential emerging threat scenarios, enabling users to view sophisticated, relevant what-if simulations and optimize checkpoint operations in near real-time. The system's architecture allows for generalized application across different screening environments, such as secure facilities, ports of entry, and soft targets, making it a versatile tool for security and operational management. The invention distinguishes itself through its comprehensive integration of predictive modeling, AI-driven pattern discovery, and user-friendly interface design. By combining these elements, TACCRAB transforms complex risk data into actionable insights, supporting decision-makers at various organizational levels - from booth agents making split-second screening decisions to checkpoint managers optimizing the day's resource allocation to strategic planners managing long-term investments.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2022

Development of a water source heat pump hardware-in-the-loop (HIL) testing facility for smart building applications

Over the last decade, the global fight against climate change through electrification has led to an increase in research on building heating, ventilation, and air conditioning (HVAC) systems that utilize intelligent control algorithms to provide demand-side grid service while maintaining the thermal comfort of building occupants. As the pivotalpoint between building electricity consumption and indoor thermal comfort, high-efficiency electrical heatpumps are at the center of these emerging studies, and various grid-interactive and occupant-comfort control algorithms have been developed for them. The impact of these algorithms on the heatpump operation andperformance under different weather, building load, and grid requests calls for investigation and verification via experimental tests with actual heat pumps integrated with real-time building and grid responses. This study presents a Water-Source Heat Pump Hardware-in-The-Loop (HIL) Test Facility developed with the capability to perform such tests. The hardware configuration for this testfacility introduces a hydronic system that emulates the conditions for the heat pump water-side, and a duct system that emulates conditions for the heat pump airside. Both data acquisition and emulator control are implemented through the National Instruments (NI) LabVIEW software running on an NI PXIplatform. The HIL mechanism based on the hardware-software integration that allows the testbed to communicate with a generic simulation environment is also discussed. Currently, the test facility setup includes a single heatpump and virtual building model in EnergyPlus coupled with an occupant behavioral model in MATLAB. Preliminary test results of the current setup demonstrate the building load emulator's ability to track the simulated gone temperature with a Root Mean Square Deviation (RSME) below 0.12°C (0.216°F). An uncertainty analysis based on sensor accuracies shows that the heat pump coefficient of performance (COP) can be measured with a relative uncertainty of 10.4% in cooling and 3.7% in heating. Apartfrom the current testing on a single heat pump, the test facility also provides the flexibility to include additional heat pumps to form a heat pump cluster, as well as coupling the heat pump with active thermal storage to provide enhanced demandflexibility.

Calfa, Caleb↗

DOE OSTI2024

Hydra: Computer Vision for Data Quality Monitoring

Hydra: Computer Vision for Data Quality Monitoring Mar 13, 2024, 4:15 PM 30m Charles B. Wang Center, Stony Brook University Poster Track 1: Computing Technology for Physics Research Poster session with coffee break Speaker Ayan Roy Description Hydra is an extensible framework for training, managing, and deploying machine learning models for near real-time data quality monitoring. It is designed to take some of the burden off of shift crews by providing ‘round-the-clock’ monitoring of plots representing the data being collected. The Hydra system is backed by a database which is leveraged for near push button training and is primarily controlled and viewed through a set of web interfaces. This web interface contains a simple-to-use web based GUI for labeling of the datasets used in training; making it possible to label thousands of images quickly and efficiently. To aid in the analysis of Hydra inferences gradCAM, a method of interpretability, is performed and overlaid on the target image, highlighting regions of interest, making diagnosis of problems much faster. Development began in 2019 for the GlueX Experiment in Hall-D, the Hydra system has grown to encompass all of the experimental halls at Jefferson Laboratory. This talk will focus on the features of Hydra as well as provide details of the challenges present with deploying to disparate experimental halls. With a roadmap of development established, Hydra aims to continue to grow in richness of feature set and expand to encompass other monitoring tasks in differing communities.

Roy, Ayan↗

DOE OSTI2022

Mitigation Methods for Piperazine Oxidation in Post-Combustion Carbon Capture

Piperazine is a promising second-generation solvent for amine scrubbing in post-combustion CO2 capture. However, the oxidative degradation of PZ can cause environmental problems and economic loss. This work presents the effects of two mitigation methods: carbon treating and N2 sparging, on the PZ oxidation in long-term operations. The species and their respective quantities adsorbed by the activated carbon were tested in a bench-scale device. The carbon was then tested in the High Temperature Oxidation Reactor (HTOR), where the solvent oxidizes at a reasonably fast rate. Pilot plant campaigns were also performed at the UT Austin SRP and the National Carbon Capture Center (NCCC) and the effects were verified. When dissolved Fe is removed by carbon treating or other methods, available soluble Fe in the system dissolves and replaces the dissolved Fe. Therefore, all available Fe and ligands need to be removed for the mitigation to be effective. The sources of soluble Fe include fly ash and the corrosion of stainless steel, and the ligands are degradation products. 8 All PZ degraded solvents have two absorbance peaks at 320 nm and 538 nm. The 320 nm peak is caused by dissolved metals, especially Fe, complexed by degradation products. The 320 nm peak is related to the amine degradation level and can be used as a simple and efficient method to estimate the amine degradation rate. The pilot plant data suggest that NO2 can oxidize PZ significantly, possibly through radical reactions. 0.01 mmol/kg-hr absorption of NO2 increased the PZ oxidation rate from 1.2 mmol/kg-hr to 2.5 mmol/kg-hr.

Yuying, Wu↗

DOE OSTI2024

Critical Assessment of Electronic Structure Descriptors for Predicting Perovskite Catalytic Properties

The discovery and design of materials which can efficiently catalyze the oxygen reduction and evolution reactions at reduced temperatures is important for facilitating the widespread adoption of fuel cell and electrolyzer technologies. Numerous studies have produced correlations between catalytic properties, such as oxygen surface exchange or electrode area specific resistance (ASR), and properties of the catalyst material. However, correlations have historically been limited in scope (e.g., using only a few materials or at a single temperature) and it has been difficult to provide detailed assessments of their robustness. Here, in this study, we assess the ability of the O p-band center electronic structure descriptor, obtained from density functional theory (DFT) calculations, to correlate with oxygen surface exchange rates, diffusivities, and area specific resistances for a large database of perovskite oxide catalytic properties. By data mining the literature, we obtain 747 catalytic property value data points spanning 299 unique perovskite compositions from 313 studies. We assess linear correlations of each property with the O p-band center and find generally modest correlations that are qualitatively useful (prediction mean absolute errors of about 0.5 log units are typical), where the correlations are improved at higher temperatures (e.g., 800 °C vs. 500 °C) and significantly improve when considering fits to the subset of materials which have multiple independent measurements. These findings suggest that the spread of property data is significantly influenced by experimental uncertainty, and subsequent measurements of additional materials will likely improve the O p-band center correlations.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2025

Large field-of-view event-mode camera for high-precision epithermal neutron resonance imaging

A large-area event-mode camera system coupled with a 6 LiF-ZnS:Ag scintillator is applied for neutron resonance imaging (NRI) on the energy-resolved neutron imaging (ERNI) flight path, also known as Flight Path 5 (FP5), at the Los Alamos Neutron Science Center (LANSCE). This novel neutron imaging system, featuring a 120 x 120 mm 2 field of view, efficiently captures resonance information across the entire image in a single acquisition, significantly reducing beam time requirements compared to conventional energy-resolved neutron imaging systems. High-quality neutron radiographs with enhanced spatial resolution are achieved through the reconstruction of neutron events based on observations of individual photons emitted from the scintillator. The system demonstrates reduced background through neutron/gamma discrimination capabilities while maintaining sharpness across a large fields of view. In the measurements presented here, a spatial resolution of approximately 340 μm was achieved using center-of-gravity photon cluster centroiding. We demonstrate the system’s capability for quantitatively determining isotopic distributions in various thin samples, as well as automatically reconstructing complex scenes with overlapping resonances from diverse samples. These results are obtained using standard data analysis tools, despite the relatively slow 6 LiF-ZnS:Ag scintillator, which may not be optimal for absorption resonance detection. The capabilities demonstrated here offer a valuable, versatile, and cost-effective solution for high spatial and temporal resolution, large field-of-view energy-resolved neutron imaging, with potential applications across various scientific and industrial domains.

36 MATERIALS SCIENCE↗

DOE OSTI2020

All-electron ab initio Bethe-Salpeter equation approach to neutral excitations in molecules with numeric atom-centered orbitals

The Bethe-Salpeter equation (BSE) based on GW quasiparticle levels is a successful approach for calculating the optical gaps and spectra of solids and also for predicting the neutral excitations of small molecules. We here present an all-electron implementation of the GW+BSE formalism for molecules, using numeric atom-centered orbital (NAO) basis sets. We present benchmarks for low-lying excitation energies for a set of small organic molecules, denoted in the literature as “Thiel’s set.” Literature reference data based on Gaussian-type orbitals are reproduced to about one millielectron-volt precision for the molecular benchmark set, when using the same GW quasiparticle energies and basis sets as the input to the BSE calculations. For valence correlation consistent NAO basis sets, as well as for standard NAO basis sets for ground state density-functional theory with extended augmentation functions, we demonstrate excellent convergence of the predicted low-lying excitations to the complete basis set limit. A simple and affordable augmented NAO basis set denoted “tier2+aug2” is recommended as a particularly efficient formulation for production calculations. We finally demonstrate that the same convergence properties also apply to linear-response time-dependent density functional theory within the NAO formalism.

Liu, Chi↗

DOE OSTI2024

AutoCheck: Automatically Identifying Variables for Checkpointing by Data Dependency Analysis

Checkpoint/Restart (C/R) has been widely deployed in numerous HPC systems, Clouds, and industrial data centers, which are typically operated by system engineers. Nevertheless, there is no existing approach that helps system engineers without domain expertise and domain scientists without system fault tolerance knowledge identify those critical variables accounted for correct application execution restoration in a failure for C/R. To address this problem, we propose an analytical model and a tool (AutoCheck) that can automatically identify critical variables to checkpoint for C/R. AutoCheck relies on first, analytically tracking and optimizing data dependency between variables and other application execution state, and second, a set of heuristics that identify critical variables for checkpointing from the refined data dependency graph (DDG). AutoCheck allows programmers to pinpoint critical variables to checkpoint quickly within a few minutes. We evaluate AutoCheck on 13 representative HPC benchmarks, demonstrating that AutoCheck can efficiently identify correct critical variables to checkpoint.

HPC↗

DOE OSTI2026

Cross-domain digital twin architecture for predictive maintenance via machine learning and Large Language Models

This research introduces a comprehensive framework for creating and deploying a digital twin platform for continuous monitoring and predictive maintenance within industrial settings. Through utilizing advanced technologies, including Unreal Engine 5, Unity 3D, the Message Queue Telemetry Transport protocol, Random Forest machine learning algorithms, and Large Language Models (LLMs), we establish a platform that digitally reproduces physical equipment and translates digital controls into real-world actions. This facilitates preventive maintenance approaches and improves operational effectiveness. The digital twin platform gathers sensor data from operational equipment, analyzes it using machine learning, and delivers practical insights to prevent potential malfunctions and enhance equipment performance. Furthermore, the incorporation of a web portal enables efficient monitoring and access to historical data, educational materials, and equipment status information. Preliminary findings indicate that digital twins can transform industrial equipment management and maintenance methodologies.

97 MATHEMATICS AND COMPUTING↗