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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Budget Period 2 Summary Report Part 1: Hywind Maine Project

This project was performed under the Work for Others—Funds in Agreement FIA-14-1793 between Statoil and the Alliance for Sustainable Energy, manager and operator of the National Renewable Energy Laboratory (NREL). To support the development of a 6-MW spar-mounted offshore wind turbine, Statoil funded NREL to perform tasks in the following three categories: 1. Design and analysis 2. Wake modeling 3. Concept resource assessment. This summarizes the document reports on the design and analysis work package, which built a FAST [Jonkman & Buhl, 2005] computer model of the Hywind 6-MW floating wind turbine system and uses this tool to evaluate the performance of a set of design load cases (DLCs). The FAST model was also used in Work Package 2: Wake Modeling .

17 WIND ENERGY↗
DOE OSTI2014

Studies in Quantum Field Theory and Astroparticle Physics

OVERVIEW OF THE RESEARCH PLAN: Task 1: Theoretical Research in High Energy Physics Prof. Alford investigated the phases and properties of matter at the highest densities achieved in nature. His main focus was on quark matter and its manifestations and signatures in neutron stars. His work is directly relevant to both the Cosmic and the Energy Frontiers. Prof. Bender developed PT-symmetric quantum theory, which is a complex generalization of conventional Hermitian quantum theory. During the grant period, he constructed and analyzed new PT-symmetric field theoretic models that exhibit symmetry breaking. He also worked on other fundamental problems in theoretical physics. His work falls under the Intensity Frontier classification. Prof. Bernard continued to work on lattice gauge theory and associated issues in chiral perturbation theory. His research focused primarily on the calculation of weak matrix elements relevant to tests of the standard model and the search for new physics at the Intensity Frontier. This work included both direct numerical simulations in QCD and the development of theoretical tools to analyze the simulations and facilitate the comparison with Intensity-Frontier experiments. Prof. Ferrer continued working on dark matter and other topics in theoretical particle cosmology. His research focused on weakly interacting massive particles relevant to both the Cosmic and the Energy Frontiers, and on dark photons relevant to the Intensity Frontier. Prof. Ogilvie continued his work on lattice gauge theory and the phase structure of gauge theories. His research on the properties of confined, conformal and Higgs phases is relevant to the Energy Frontier, as well as addressing fundamental issues of broad theoretical interest. Task 2: Experimental Research at the Cosmic Frontier in High Energy Physics. Over the three year grant period, Profs. Buckley and Krawczynski continued research under the Cosmic Frontier. Prof. Buckley's group worked on two Cosmic Frontier projects (VERITAS and LZ), and conducted research in dark matter science and other areas of particle astrophysics. Buckley’s group worked on the conceptual design of the next generation ground-based gamma-ray experiment (CTA) and on the development of camera electronics for a 2-mirror SCT prototype telescope. Buckley's group also continued work on GaN photodetector detector development as part of the Instrumentation Frontier (Detector R&D) including work with the LAPPD group on the development robust amorphous photocathodes for very large area MCP-based photodetectors and ongoing development of deep UV photosensors for liquid Ar and Xenon Direct Dark matter detection. Prof. Krawczynski's group worked on two Cosmic Frontier topics: astrophysical tests of Lorentz Invariance and CPT symmetries at the Planck energy scale with the VERITAS gamma-ray experiment, and the search for dark matter particles with indirect detection experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2014

LABORATORY OPTIMIZATION TESTS OF TECHNETIUM DECONTAMINATION OF HANFORD WASTE TREATMENT PLANT LOW ACTIVITY WASTE OFF-GAS CONDENSATE SIMULANT

The Hanford Waste Treatment and Immobilization Plant (WTP) Low Activity Waste (LAW) vitrification facility will generate an aqueous condensate recycle stream (LAW Off-Gas Condensate) from the off-gas system. The baseline plan for disposition of this stream is to send it to the WTP Pretreatment Facility, where it will be blended with LAW, concentrated by evaporation and recycled to the LAW vitrification facility again. Alternate disposition of this stream would eliminate recycling of problematic components, and would enable de-coupled operation of the LAW melter and the Pretreatment Facilities. Eliminating this stream from recycling within WTP would also decrease the LAW vitrification mission duration and quantity of glass waste. This LAW Off-Gas Condensate stream contains components that are volatile at melter temperatures and are problematic for the glass waste form. Because this stream recycles within WTP, these components accumulate in the Condensate stream, exacerbating their impact on the number of LAW glass containers that must be produced. Approximately 32% of the sodium in Supplemental LAW comes from glass formers used to make the extra glass to dilute the halides to acceptable concentrations in the LAW glass, and diverting the stream reduces the halides in the recycled Condensate and is a key outcome of this work. Additionally, under possible scenarios where the LAW vitrification facility commences operation prior to the WTP Pretreatment facility, identifying a disposition path becomes vitally important. This task examines the potential treatment of this stream to remove radionuclides and subsequently disposition the decontaminated stream elsewhere, such as the Effluent Treatment Facility (ETF), for example. The treatment process envisioned is very similar to that used for the Actinide Removal Process (ARP) that has been operating for years at the Savannah River Site (SRS), and focuses on using mature radionuclide removal technologies that are also compatible with longterm tank storage and immobilization methods. For this new application, testing is needed to demonstrate acceptable treatment sorbents and precipitating agents and measure decontamination factors for additional radionuclides in this unique waste stream. The origin of this LAW Off-Gas Condensate stream will be the liquids from the Submerged Bed Scrubber (SBS) and the Wet Electrostatic Precipitator (WESP) from the LAW melter off-gas system. The stream is expected to be a dilute salt solution with near neutral pH, and will likely contain some insoluble solids from melter carryover. The soluble components are expected to be mostly sodium and ammonium salts of nitrate, chloride, and fluoride. This stream has not been generated yet and will not be available until the WTP begins operation, but a simulant has been produced based on models, calculations, and comparison with pilot-scale tests. One of the radionuclides that is volatile and expected to be in greatest abundance in this LAW Off-Gas Condensate stream is Technetium-99 ({sup 99}Tc). Technetium will not be removed from the aqueous waste in the Hanford WTP, and will primarily end up immobilized in the LAW glass by repeated recycle of the off-gas condensate into the LAW melter. Other radionuclides that are low but are also expected to be in measurable concentration in the LAW Off-Gas Condensate are {sup 129}I, {sup 90}Sr, {sup 137}Cs, {sup 241}Pu, and {sup 241}Am. These are present due to their partial volatility and some entrainment in the off-gas system. This report discusses results of optimized {sup 99}Tc decontamination testing of the simulant. Testing examined use of inorganic reducing agents for {sup 99}Tc. Testing focused on minimizing the quantity of sorbents/reactants added, and minimizing mixing time to reach the decontamination targets in this simulant formulation. Stannous chloride and ferrous sulfate were tested as reducing agents to determine the minimum needed to convert soluble pertechnetate to the insoluble technetium dioxide. The reducing agents were tried with and without sorbents. The sorbents, hydroxyapatite and sodium oxalate, were expected to sorb the precipitated technetium dioxide and facilitate removal. The Phase 1 tests examined a broad range of conditions and used the initial baseline simulant. The Phase 2 tests narrowed the conditions based on Phase 1 results, and used a slightly modified simulant. Test results indicate that excellent removal of {sup 99}Tc was achieved using SnCl{sub 2} as a reductant, and was effective with or without sorption onto hydroxyapatite. This reaction worked even in the presence of air (which could oxidize the stannous ion) and at room temperature. This process was very effective at neutral pH, with a Decontamination Factor (DF) >199 in one hour with only 1 g/L of SnCl{sub 2}. Prior work had shown that it was much less effective at alkaline pH. The only deleterious effect observed was that the chromium co-precipitates with the {sup 99}c during the SnCl{sub 2} reduction. This effect was anticipated, and would have to be considered when managing disposition paths of this stream. Reduction using FeSO{sub 4} was not effective at removing {sup 99}Tc, but did remove the Cr. Chromium is present due to partial volatility and entrainment in the off-gas, and is highly oxidizing, so would be expected to react with reducing agents more quickly than pertechnetate. Testing showed that sufficient reducing agent must be added to completely reduce the chromium before the technetium is reduced and removed. Other radionuclides are also present in this off-gas condensate stream. To enable sending this stream to the Hanford ETF, and thereby divert it from the recycle where it impacts the LAW glass volume, several of these also need to be removed. Samples from optimized conditions were also measured for actinide removal in order to examine the effect of the Tc-removal process on the actinides. Plutonium was also removed by the SnCl{sub 2} precipitation process. Results of this separation testing indicate that sorption/precipitation is a viable concept and has the potential to decontaminate the {sup 99}Tc from the stream, allowing it to be diverted away from WTP and thus eliminating the impact of the recycled halides and sulfate on the LAW glass volume. Based on the results, a possible treatment scenario could involve the use of a reductive precipitation agent (SnCl{sub 2}) with or without sorbent at neutral pH to remove the Tc. Although hydroxyapatite was not necessary to effect the {sup 99}Tc removal, it may be beneficial in solid-liquid separations. Other testing will examine removal of the other radionuclides. This testing was the second phase of testing, which aimed at optimizing the process by examining the minimum amount of reductant needed and the minimum reaction time. Although results indicated that SnCl{sub 2} was effective, further work on a pH-adjusted Fe(SO{sub 4}) mixture are needed. Additional tasks are needed to examine removal of the other radionuclides, solid-liquid separation technologies, slurry rheology measurements, composition variability impacts, corrosion and erosion, and slurry storage and immobilization.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Forward Pixel Detector for the Phase 2 Upgrade of CMS And Module Testing at UIC

High-Luminosity Large Hadron Collider (HL-LHC) upgrade aims to increase performance. In order to efficiently handle the larger particle flux and increased radiation, an upgrade is planned for the inner Tracker of the Compact Muon Solenoid (CMS) detector, which is located at the innermost part of the CMS. To efficiently reconstruct and track particles, the existing pixel tracker will be upgraded with novel sensors, readout chips, and front-end electronics designed to handle increased data rates. These improvements will allow the detector to track particles more precisely. In the United States, we are working on assembling new sensor modules to be inserted into disks, referred to as TFPX, for installation in the CMS detector for the HL-LHC era. We are currently testing the prototype of TFPX modules to ensure the upgrade is carried out in the best way possible. The University of Ilinois Chicago has been tasked with testing the final versions of the TFPX sensor modules before they are sent to CERN to be installed into the CMS detector. This work is supported by NSF.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Developing Digital Twin Visualizations: A Methodology and Case Study on Chemical Separation Processing

As advances in digital engineering continue to push the technological boundaries, digital twin (DT) visualizations for diagnostics and safeguards advancement become much more feasible and practical. DTs generate large and complex data streams that require effective user interfaces to provide monitoring and diagnostic capabilities. Unfortunately, while these frameworks exist, there is not much research on the systematic documentation of human–computer interaction (HCI) for DT visualization. This work presents a dual-mode visualization methodology (two dimensional [2D] graphical user interface dashboard and 3D mixed reality) designed to support diagnostic tasks in DT systems and building on a validated framework and applying established HCI principles. The methodology is demonstrated through a case study of aqueous processing at Idaho National Laboratory, using experimental data from the chemical solvent extraction runs. Our interfaces display real-time alerts and monitoring to inform users of safeguards anomalies. The interfaces use immersive 3D mixed-reality visualization for further system and experiment investigation. This work demonstrates how the systematic application of HCI principles can inform DT visualization design for diagnostic and safeguards applications. While formal user evaluation studies remain as future work, this paper documents the systematic design methodology and demonstrates a proof-of-concept implementation.

3D visualization↗
DOE OSTI2015

Low Cost High-H 2 Syngas Production for Power and Liquid Fuels

This report summarizes the technical progress made of the research project entitled “Low Cost High-H2 Syngas Production for Power and Liquid Fuels,” under DOE Contract No. DE-FE-0011958. The period of performance was October 1, 2013 through July 30, 2015. The overall objectives of this project was to determine the technical and economic feasibility of a systems approach for producing high hydrogen syngas from coal with the potential to reduce significantly the cost of producing power, chemical-grade hydrogen or liquid fuels, with carbon capture to reduce the environmental impact of gasification. The project encompasses several areas of study and the results are summarized here. (1) Experimental work to determine the technical feasibility of a novel hybrid polymer/metal H2-membrane to recover pure H2 from a coal-derived syngas was done. This task was not successful. Membranes were synthesized and show impermeability of any gases at required conditions. The cause of this impermeability was most likely due to the densification of the porous polymer membrane support made from polybenzimidazole (PBI) at test temperatures above 250 °C. (2) Bench-scale experimental work was performed to extend GTI's current database on the University of California Sulfur Recovery Process-High Pressure (UCSRP-HP) and recently renamed Sulfur Removal and Recovery (SR2) process for syngas cleanup including removal of sulfur and other trace contaminants, such as, chlorides and ammonia. The SR2 process tests show >90% H2S conversion with outlet H2S concentrations less than 4 ppmv, and 80-90% ammonia and chloride removal with high mass transfer rates. (3) Techno-economic analyses (TEA) were done for the production of electric power, chemical-grade hydrogen and diesel fuels, from a mixture of coal- plus natural gas-derived syngas using the Aerojet Rocketdyne (AR) Advanced Compact coal gasifier and a natural gas partial oxidation reactor (POX) with SR2 technology. Due to the unsuccessful experimental results with the hybrid polymer/metal H2 membrane, a conventional CO2 capture (single-stage Selexol) and hydrogen purification (PSA) technologies were used in the appropriate cases. In all cases, the integrated system of Advanced Compact coal gasifier, non-catalytic natural gas partial oxidation, and SR2 multicontaminant removal with state-of-the-art auxiliary system provided a 5-25% cost advantage over the base line plants using GEE coal gasifier with conventional Selexol/Claus sulfur removal and recovery. These plants also produce 18-30% less CO2 than with the conventional coal gasification plants.

08 HYDROGEN↗
DOE OSTI2026

NuGraph2 with context-aware inputs: physics-inspired improvements in semantic segmentation

Graph neural networks have recently shown strong promise for event reconstruction tasks in Liquid Argon Time Projection Chambers, yet their performance remains limited for underrepresented classes of particles, such as Michel electrons. In this work, we investigate physics-informed strategies to improve semantic segmentation within the NuGraph2 architecture. We explore three complementary approaches: (i) enriching the input representation with context-aware features derived from detector geometry and track continuity, (ii) introducing auxiliary decoders to capture class-level correlations, and (iii) incorporating energy-based regularization terms motivated by Michel electron energy distributions. Experiments on MicroBooNE public datasets show that physics-inspired feature augmentation yields the largest gains, particularly boosting Michel electron precision and recall by disentangling overlapping latent space regions. In contrast, auxiliary decoders and energy-regularization terms provided limited improvements, partly due to the hit-level nature of NuGraph2, which lacks explicit particle- or event-level representations. Our findings highlight that embedding physics context directly into node-level inputs is more effective than imposing task-specific auxiliary losses, and suggest that future hierarchical architectures such as NuGraph3, with explicit particle- and event-level reasoning, will provide a more natural setting for advanced decoders and physics-based regularization. The code for this work is publicly available on Github at https://github.com/vitorgrizzi/nugraph_phys/tree/main_phys.

Other Experiments↗
DOE OSTI2017

Providing plant data analytics through a seamless digital environment - 211

As technology continues to evolve and become more integrated into a worker's daily routine in the commercial nuclear power industry the need for easy access to data becomes a priority. Not only does the need for data increase but the amount of data collected increases. In most cases the data is collected and stored in various software applications, many of which are legacy systems, which do not offer any other option to access the data except through the application's user interface. Furthermore the data get grouped in 'silos' according to work function and not necessarily by subject. Hence, in order to access all the information needed for a particular task or analysis one may have to access multiple applications to gather all the data needed. The industry and the research community have identified the need for a digital architecture and more importantly the need for a Seamless Digital Environment (SDE). An SDE provides a means to access multiple applications, gather the data points needed, conduct the analysis requested, and present the result to the user with minimal or no effort by the user. The study researches the potential approaches to building an analytics solution for equipment reliability, on a small scale, focusing on either a single piece of equipment or a single system. The analytics solution will consist of a data integration layer, predictive and machine learning layer and the user interface layer that will display the output of the analysis in a straight forward, easy to consume manner. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

Effects of Exhaust Gas Recirculation (EGR) on Turbulent Combustion and Emissions in Advanced Gas Turbine Combustors with High-Hydrogen-Content (HHC) Fuels (Final Report)

This Final Report documents the entire four years of the Cooperative Agreement DE-FE0011822, from October 1, 2013 through September 30, 2017, that was competitively awarded under Funding Opportunity Announcement DE-FOA-0000795 in support of the U.S. Department of Energy’s (DOE) National Energy Technology Laboratory (NETL) University Turbine Systems Research (UTSR) Program. Under the UTSR Program, DOE typically issues Funding Opportunity Announcement (FOA) solicitations every 1-2 years so that U.S. colleges/universities may propose research & development (R&D) projects in specific technical topic areas (e.g., combustion, aerodynamics/heat transfer, advanced materials) that have been identified as both scientifically and industrially relevant to the gas turbine industry. The primary objectives of this Purdue University project (in collaboration with Princeton University through a sub-award) was to develop experimental methods, kinetic models, and numerical tools to help quantify and predict the impact of exhaust gas recirculation (EGR) on NOx and CO emissions, combustion kinetics, radiation heat transfer, turbulent combustion, and combustion instabilities for natural gas and high hydrogen content (HHC) fuels by using laminar and turbulent flow reactors and gas turbine combustors operating at high temperatures and pressures. This project has obtained detailed data for improving chemical kinetic models to study EGR effects and provide insight into the effects of EGR on flame speeds and turbulent flame structure, as well as assessing the impact of EGR on emissions in a high-pressure combustion test rig. The project was divided into six primary tasks: 1) Project Management and Program Planning; 2) Flow Reactor Investigation of EGR Chemical Kinetics; 3) Combustion Bomb Investigation of EGR Effects on Flame Speed; 4) Investigation of EGR Effects on Turbulent Flame Structure in Reactor-Assisted Turbulent Slot (RATS) Burner; 5) Investigation of EGR Effects in Purdue Staged Combustion Test Rig; and 6) Development and Validation of a Large Eddy Simulation (LES) Tool for Gas Turbine Combustion and Emission Modeling using HHC Fuels. Details on the execution and results of each of these tasks are provided in the main report. To assist the reader, each task is described in a chapter-like format with its own separate listing of tables, figures, and references so that the results of each task/subtask (i.e., technical sections covering various experiments and modeling work focused on a specific area) can be quickly obtained while also getting an overall feel of the project execution. This chapter-like format also allows for acknowledgement of support from other programs relative to specific tasks. In this manner, the reader can more conveniently digest the R&D results in the various task/subtask sections that may be of most interest to the reader. In addition to this Final Report, the Purdue/Princeton team was able to publish 7 journal articles and provide 7 presentations at national scientific meetings/conferences to disseminate results. A listing of these journal articles and presentations is provided at the end of this report

03 NATURAL GAS↗
DOE OSTI2022

Reduced Order Models Generation for HTGRs Pebble Shuffling Procedure Optimization Studies

This report provides an initial study for producing reduced-order models (ROMs) of pebble-bed high temperature gas reactor (HTGR) models for the purposes of design optimization. As an initial study, this work is meant to be exploratory---identifying useful workflows and methods for ROM generation---and not meant to be a catch-all analysis of HTGR ROM generation and usage for optimization. This report summarizes three tasks performed in Fiscal Year 2022: 1) the creation of HTGR model, 2) the sensitivity analysis of model design parameters, and 3) an introduction to ROM generation techniques. The representative HTGR model created in this work is a multiphysics equilibrium-core using the BlueCRAB (comprehensive reactor analysis bundle) reactor analysis application, coupling four physical phenomena: neutronics, streamline depletion, porous flow thermal hydraulics, and pebble heat conduction. Part of the model creation was identifying some design parameters and quantities of interest that are relevant in an optimization analysis and adjustable in the model. The sensitivity analysis utilized a polynomial chaos expansion methodology to compute global sensitivity metrics. This analysis showed that thermal hydraulics parameters and quantities of interest had a relatively small impact on simulation results. Finally, the ROM generation work involved exploring three different ROM methodologies: polynomial regression, a Gaussian process, and artificial neural networks. Using a cross-validation technique to characterize ROM performance, the Gaussian process and single-layer artificial neural networks showed the most promising results. Overall, this study was insightful and the lessons learned will be invaluable for the eventual development of an HTGR design optimization workflow.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Quarterly Research Performance Progress Report (Q8)

As part of Task 1, we have started by testing our modeling capabilities by reproducing isothermal DFIT simulations presented in the literature. Once satisfied with the results we have started by targeting the modeling of the DFITs at conducted at well 58-32. We have a identified a specific test (cycle 4 in zone 2) as the most interesting to be model with GEOS hydraulic fracturing module. Thus, we have first produced results with an isothermal model and adjusted model parameters to get a satisfying match with field pressure data. The, we have added thermal effects and compared the modeling results with and without thermal effects to estimate how thermal effects may influence test interpretation. Models seem to suggest that, for small volumes of fluid, thermal effects are moderate. In Task 2, we have adapted GEOS phase-field formulation to be able to simulate near-wellbore hydraulic fracture nucleation and propagation. We have devised a novel formulation that, compared to other existing ones, incorporates rock strengths. We have submitted a journal publication about our work. We are currently employing this phase-field formulation to model the experiments taking place at U Pitt and help us understand the effect of various parameters. In Task 3, we have built a model of the region surround well 16A and have started modeling stage 3 stimulation because of its simpler planar geometry. After calibrating simulation parameters using known analytical solutions, we have simulated the stage 3 stimulation using our isothermal hydraulic fracturing module, varying the permeability field, the stress conditions including different physics to get a better understanding of the numerical challenges and of the effects of varying these parameters on the simulation results. In Task 4 laboratory experimentation, a set of specialized drilling and injection tools has been customized and constructed to accommodate an inclined well with an orientation of up to 30 degrees relative to material anisotropy or principal stress axes. These inclined samples have also undergone thermal stress and hydraulic fracturing at a temperature of 190 degrees Celsius. Furthermore, both vertical and deviated sampling testing setups enable an extended analysis of post-peak pressure behaviors, facilitating post-test pressure analyses such as the G-function, step rate, and fracture reopening measurements. Thus, the key components of in-situ stress estimation can be extracted and validated through our experiment, providing a solid foundation for validating existing in-situ stress estimation theories or proposing new ones. Simultaneously, we are integrating computer vision techniques with traditional experimental fracture observation methods such as multi-overcore/slicing and water-penetration fracture observation. This combination will prove beneficial in populating the hydraulic fracture patterns database, generated under challenging EGS conditions. This approach aims to deepen our understanding of the complexities in EGS reservoirs and pave the way for future data-driven investigations. Additionally, PITT has also equipped the ELE International compression machine, which is now prepared for conducting indirect tensile and fracture toughness tests. These tests will aid in characterizing how rock fabrics influence the resulting fracture patterns. Additionally, we have completed the required personnel training and gained access to Scanning Electron Microscopy (SEM) and Energy Dispersive Spectroscopy (EDS) for conducting more detailed characterization and analysis of rock fabrics, as well as the examination of thermal and hydraulically induced fracture patterns. Thus, the PITT team has effectively demonstrated the capabilities of our experimental apparatuses in exploring the thermal effects, well deviation angles, material anisotropy, and operational choices (such as circulation rate, injection fluid viscosity, and injection rate) and their impact on pressure responses and fracture trajectories under the Utah FORGE conditions.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Machine learning surrogate for charged particle beam dynamics with space charge based on a recurrent neural network with aleatoric uncertainty

In this work, we develop a machine learning (ML) model with aleatoric uncertainty for the low energy beam transport (LEBT) region of the LANSCE linear accelerator in which we model the transport of a space-charge-dominated 750 keV proton beam through a lattice of 22 quadrupole magnets. Our ML model is developed based on data generated by a Kapchinsky–Vladimirsky (KV) envelope model of beam transport. We show that a recurrent neural network can be used as a dynamical surrogate model for fast prediction of the LEBT beam envelope. Furthermore, we endow the model with the prediction of aleatoric uncertainty and compare three different approaches. We demonstrate that the ML-based uncertainty quantification models are well calibrated and produce good estimates of the regions where the model is less certain about its predictions. This ML framework is a necessary step in the development of a real-time virtual diagnostic tool with uncertainty quantification that can be integrated into more complex downstream tasks (e.g., adaptive control or learning flexible control policies via reinforcement learning) for improved efficiency in beam operations. In future work, we plan to expand on this preliminary study by considering more realistic envelope models that include longitudinal momentum spread and dispersive effects in bending magnets, as well as particle tracking codes with 3D space charge (such as and ). Published by the American Physical Society 2024

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Efficient Measurement-Driven Eigenenergy Estimation with Classical Shadows

Quantum algorithms exploiting real-time evolution under a target Hamiltonian have demonstrated remarkable efficiency in extracting key spectral information. However, the broader potential of these methods, particularly beyond ground-state calculations, is underexplored. In this work, we introduce the framework of multiobservable dynamic mode decomposition (MODMD), which combines the observable dynamic mode decomposition (DMD), a measurement-driven eigensolver tailored for near-term implementation, with classical shadow tomography. MODMD leverages random scrambling in the classical shadow technique to construct, with exponentially reduced resource requirements, a signal subspace that encodes rich spectral information. Notably, we replace typical Hadamard-test circuits with a protocol designed to predict low-rank observables, thereby broadening the use of classical shadow tomography for predicting many low-rank observables. We establish theoretical guarantees on the spectral approximation from MODMD, taking into account distinct sources of error. In the ideal case, we prove that the spectral error scales as exp (−Δ⁢𝐸⁢𝑡 max ), where Δ⁢𝐸 is the Hamiltonian spectral gap and 𝑡 max is the maximal simulation time. This analysis provides a rigorous justification of the rapid convergence observed across simulations. To demonstrate the utility of our framework, we consider its application to fundamental tasks, such as determining the low-lying, i.e., ground or excited, energies of representative many-body systems. Our work paves the path for efficient designs of measurement-driven algorithms on near-term and early fault-tolerant quantum devices.

quantum algorithms & computation↗
DOE OSTI2023

KEBLM: Knowledge-Enhanced Biomedical Language Models

Pretrained language models (PLMs) have demonstrated strong performance on many natural language processing (NLP) tasks. Despite their great success, these PLMs are typically pretrained only on unstructured free texts without leveraging existing structured knowledge bases that are readily available for many domains, especially scientific domains. As a result, these PLMs may not achieve satisfactory performance on knowledge-intensive tasks such as biomedical NLP. Comprehending a complex biomedical document without domain-specific knowledge is challenging, even for humans. Inspired by this observation, we propose a general framework for incorporating various types of domain knowledge from multiple sources into biomedical PLMs. We encode domain knowledge using lightweight adapter modules, bottleneck feed-forward networks that are inserted into different locations of a backbone PLM. For each knowledge source of interest, we pretrain an adapter module to capture the knowledge in a self-supervised way. We design a wide range of self-supervised objectives to accommodate diverse types of knowledge, ranging from entity relations to description sentences. Once a set of pretrained adapters is available, we employ fusion layers to combine the knowledge encoded within these adapters for downstream tasks. Each fusion layer is a parameterized mixer of the available trained adapters that can identify and activate the most useful adapters for a given input. Our method diverges from prior work by including a knowledge consolidation phase, during which we teach the fusion layers to effectively combine knowledge from both the original PLM and newly-acquired external knowledge using a large collection of unannotated texts. After the consolidation phase, the complete knowledge-enhanced model can be fine-tuned for any downstream task of interest to achieve optimal performance. Extensive experiments on many biomedical NLP datasets show that our proposed framework consistently improves the performance of the underlying PLMs on various downstream tasks such as natural language inference, question answering, and entity linking. These results demonstrate the benefits of using multiple sources of external knowledge to enhance PLMs and the effectiveness of the framework for incorporating knowledge into PLMs. Finally, while primarily focused on the biomedical domain in this work, our framework is highly adaptable and can be easily applied to other domains, such as the bioenergy sector.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2023

Deep Reinforcement Learning Based HVAC Control for Reducing Carbon Footprint of Buildings

In this paper, we present our work on deep reinforcement learning (DRL) based intelligent control of Heating, Ventilation, and Air Conditioning (HVAC) with the goal of reducing carbon emission. We performed this task using 1) Marginal Operating Emission Rates (MOER), where the objective was to shift the demand to the low emission period of the day and 2) Time-Of-Use (TOU) demand-response price where the objective was to shift the demand to low price period of the day. This was achieved by learning an optimal pre-cooing strategy. We found the carbon emission reduction in the range of 6%-16% depending on the opportunity presented by the MOER signal. Similarly, we observed the carbon emission reduction in the range of 23%-29% during the peak price period when TOU price was used. The results clearly demonstrated the applicability of our approach in reducing the carbon footprint of the building.

carbon emission↗
DOE OSTI2021

Local time stepping for the shallow water equations in MPAS

In this work we assess the performance of a set of local time-stepping (LTS) schemes for the shallow water equations implemented in the Model for Prediction Across Scales (MPAS). The goal of LTS is to speed up the simulation by allowing different time-steps on different regions of the computational grid. The LTS schemes considered here were originally introduced by Hoang et al. (2019) [26], who laid out the mathematical foundation of the methods. Here, the authors take on the task of presenting a fast, efficient and scalable parallel implementation of these LTS methods on high performance computing machines, with the aim to provide a recipe for other climate modeling groups that may be interested in employing LTS algorithms in their codes. As a matter of fact, even if MPAS is our framework of choice, our approach is general enough and could be of interest to other groups beyond the MPAS community. Due to their nature, LTS methods possess an inherent load imbalance that needs to be carefully addressed in order to obtain efficient scalability. Even more important is the far from trivial task of computing the right-hand side terms only on specific LTS regions during the time-stepping procedure. An inefficient handling of this task causes a drastic decay of the CPU time performance, making the LTS algorithms practically of no use. The emphasis of the present work is therefore on the computational and parallel aspects of the LTS methods, whose proper treatment is crucial to make the methods run faster against existing strategies, such as for instance high-order explicit global time-stepping schemes. This is in fact the ultimate goal of using an LTS procedure and it is the one to which we direct all our optimization efforts.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Computing in AEC Education: Hindsight, Insight, and Foresight

In the architecture, engineering, and construction (AEC) fields, computing and information technologies play an increasingly prevalent and complex role in day-to-day work. Consequently, educators must adjust and, in many cases, reimagine curricula and teaching methodologies to adapt to the changing landscape. Past research efforts led by the ASCE Computing Division Education Committee, formerly called the Task Committee on Computing Education of the Technical Council on Computing and Information Technology (TCCIT), have regularly surveyed AEC educators to understand computing trends in AEC curricula, with the latest survey taking place nearly a decade ago. This work presents the results of an updated survey that used this prior work as a springboard, providing timely insights into the computing skills and curricular barriers faced by AEC educators today. The results showed that the technical skills used by students have evolved, but the barriers faced in incorporating new skills into curricula have remained largely the same. In addition to comparisons with prior surveys, this work presents the results of an expanded, open-ended portion of the survey that explores educator perspectives on the future of the AEC workforce in a broader lens than used in previous surveys. Thematic analysis of these open-ended responses revealed themes that were common among responses and provided organization to the findings. For example, educators provided their vision of what competencies the future AEC workforce would need, which were thematically organized into a continuum based on the level of interaction between humans and technology. These results suggest an increasingly complex and evolving relationship between the AEC workforce and emerging technology, highlighting the need for educators to encourage the development of technological adaptability and agility. Overall, this work provides a systematic comparison of current educational practices in AEC computing with a decade ago to illustrate educational shifts and adds a prediction of AEC trends from experts in AEC education, providing crucial discussion of curricular transformations that will better position students for success in the workforce.

42 ENGINEERING↗
DOE OSTI2012

Coherent control and time-dependent density functional theory: Towards creation of wave packets by ultrashort laser pulses

Explicitly time-dependent density functional theory (TDDFT) is a formally exact theory, which can treat very large systems. However, in practice it is used almost exclusively in the adiabatic approximation and with standard ground state functionals. Therefore, if combined with coherent control theory, it is not clear which control tasks can be achieved reliably, and how this depends on the functionals. In this paper, we continue earlier work in order to establish rules that answer these questions. Specifically, we look at the creation of wave packets by ultrashort laser pulses that contain several excited states. We find that (i) adiabatic TDDFT only works if the system is not driven too far from the ground state, (ii) the permanent dipole moments involved should not differ too much, and (iii) these results are independent of the functional used. Additionally, we find an artifact that produces fluence-dependent excitation energies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗