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DOE OSTI2024

Search for resonant production of dark quarks in the dijet final state with the ATLAS detector

This paper presents a search for a new Z' resonance decaying into a pair of dark quarks which hadronise into dark hadrons before promptly decaying back as Standard Model particles. This analysis is based on proton-proton collision data recorded at $\sqrt{s}$ = 13 TeV with the ATLAS detector at the Large Hadron Collider between 2015 and 2018, corresponding to an integrated luminosity of 139 fb –1 . After selecting events containing large-radius jets with high track multiplicity, the invariant mass distribution of the two highest-transverse-momentum jets is scanned to look for an excess above a data-driven estimate of the Standard Model multijet background. No significant excess of events is observed and the results are thus used to set 95% confidence-level upper limits on the production cross-section times branching ratio of the Z' to dark quarks as a function of the Z' mass for various dark-quark scenarios.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Trends in Light and Temperature Sensitivity Recommendations among Licensed Biotechnology Drug Products

Inherent structural and functional properties of biotechnology-derived therapeutic biologics make them susceptible to light- and temperature-induced degradation and consequently can influence their quality. Photosensitivity of therapeutic proteins continues to be examined, but the commonalities and trends of storage conditions and information about light and temperature sensitivity among currently licensed therapeutic proteins has not been previously surveyed. Using a comprehensive and relational database approach, we conducted a scientific survey of all licensed biotechnology-derived drug products with the goal of providing evidence-based information about recommended storage conditions of formulations sorted by light- and temperature-related attributes as described for each product at licensure. We report the prevalence of indications for light and temperature sensitivity in formulations categorized by their presentation type, number of doses, container type, dosage form and active molecule type. We also report the storage temperature range across formulations and diluents for reconstitution and dilution. Formulations with excipients that potentially facilitate light-induced and thermal degradation were also noted. The result of our analysis indicates that light and temperature sensitivity are prevalent across therapeutic protein formulations. However, when a formulation is reconstituted or diluted, both light and temperature sensitivity are less clear. In addition, light and temperature sensitivity are more well defined in liquid formulations than lyophilized powder formulations, and more well defined in products manufactured in autoinjectors, prefilled-syringes, and pens than products in vials. Overall, our report provides a data-driven summary of storage conditions among therapeutic protein formulations to support the development of future biologic drug products.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Machine learning from RANS and LES to inform coarse grid simulations

Nuclear system thermal hydraulic analysis has historically relied on computationally inexpensive 1D codes. However, such tools are unable to capture multiscale multidimensional effects in large nuclear reactor enclosures. On the other hand, simulations with higher fidelity can be too expensive for such purposes. One of the ways to reduce computational cost is to perform simulations on a coarse grid, which, unfortunately, introduces large discretization errors. In this paper, two high-to-low data-driven approaches are investigated: (1) a coarse grid turbulence model to predict eddy viscosity and (2) correction of errors in coarse grid velocity fields. The approaches aim to reduce grid- and turbulence model-induced errors in coarse grid Reynolds-averaged Navier–Stokes (RANS) simulations. Two sources of high-fidelity data, RANS and large eddy simulations (LES), are explored. To extract the eddy viscosity from the LES data, an inverse optimization problem is solved. However, the LES eddy viscosity is shown to be comparable to the RANS eddy viscosity in terms of error reduction. Therefore, the directly available RANS eddy viscosity was used to develop a coarse grid data-driven turbulence model. Additionally, error correction in velocity is used to reduce the remaining uncertainties and bring the results closer to reality. In conclusion, the performance of the frameworks is demonstrated for a scaled upper plenum of a gas-cooled reactor facility.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Dynamic mode decomposition of nonequilibrium electron-phonon dynamics: accelerating the first-principles real-time Boltzmann equation

Abstract Nonequilibrium dynamics governed by electron–phonon ( e -ph) interactions plays a key role in electronic devices and spectroscopies and is central to understanding electronic excitations in materials. The real-time Boltzmann transport equation (rt-BTE) with collision processes computed from first principles can describe the coupled dynamics of electrons and atomic vibrations (phonons). Yet, a bottleneck of these simulations is the calculation of e –ph scattering integrals on dense momentum grids at each time step. Here we show a data-driven approach based on dynamic mode decomposition (DMD) that can accelerate the time propagation of the rt-BTE and identify dominant electronic processes. We apply this approach to two case studies, high-field charge transport and ultrafast excited electron relaxation. In both cases, simulating only a short time window of ~10% of the dynamics suffices to predict the dynamics from initial excitation to steady state using DMD extrapolation. Analysis of the momentum-space modes extracted from DMD sheds light on the microscopic mechanisms governing electron relaxation to a steady state or equilibrium. The combination of accuracy and efficiency makes our DMD-based method a valuable tool for investigating ultrafast dynamics in a wide range of materials.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Decoding the age–chemical structure of the Milky Way disc: an application of copulas and elicitable maps

In the Milky Way, the distribution of stars in the [α/Fe] versus [Fe/H] and [Fe/H] versus age planes holds essential information about the history of star formation, accretion, and dynamical evolution of the Galactic disc. We investigate these planes by applying novel statistical methods called copulas and elicitable maps to the ages and abundances of red giants in the Apache Point Observatory Galactic Evolution Experiment survey. We find that the high- and low-α disc stars have a clean separation in copula space and use this to provide an automated separation of the α sequences using a purely statistical approach. This separation reveals that the high-α disc ends at the same [α/Fe] and age at high [Fe/H] as the low-[Fe/H] start of the low-α disc, thus supporting a sequential formation scenario for the high- and low-α discs. We then combine copulas with elicitable maps to precisely obtain the correlation between stellar age τ and metallicity [Fe/H] conditional on Galactocentric radius R and height z in the range 0 < R < 20 kpc and |z| < 2 kpc. The resulting trends in the age–metallicity correlation with radius, height, and [α/Fe] demonstrate a ≈0 correlation wherever kinematically cold orbits dominate, while the naively expected negative correlation is present where kinematically hot orbits dominate. This is consistent with the effects of spiral-driven radial migration, which must be strong enough to completely flatten the age–metallicity structure of the low-α disc.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2014

Flexible and Scalable Data Fusion using Proactive Schemaless Information Services

Exascale data environments are fast approaching, driven by diverse structured and unstructured data such as system and application telemetry streams, open-source information capture, and on-demand simulation output. Storage costs having plummeted, the question is now one of converting vast stores of data to actionable information. Complicating this problem are the low degrees of awareness across domain boundaries about what potentially useful data may exist, and write-once- read-never issues (data generation/collection rates outpacing data analysis and integration rates). Increasingly, technologists and researchers need to correlate previously unrelated data sources and artifacts to produce fused data views for domain-specific purposes. New tools and approaches for creating such views from vast amounts of data are vitally important to maintaining research and operational momentum. We propose to research and develop tools and services to assist in the creation, refinement, discovery and reuse of fused data views over large, diverse collections of heterogeneously structured data. We innovate in the following ways. First, we enable and encourage end-users to introduce customized index methods selected for local benefit rather than for global interaction (flexible multi-indexing). We envision rich combinations of such views on application data: views that span backing stores with different semantics, that introduce analytic methods of indexing, and that define multiple views on individual data items. We specifically decline to build a big fused database of everything providing a centralized index over all data, or to export a rigid schema to all comers as in federated query approaches. Second, we proactively advertise these application-specific views so that they may be programmatically reused and extended (data proactivity). Through this mechanism, both changes in state (new data in existing view collected) and changes in structure (new or derived view exists) are made known. Lastly, we embrace found data heterogeneity by coupling multi-indexing to backing stores with appropriate semantics (as opposed to a single store or schema).

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2026

High Temperature High Vacuum Mechanical Property Assessment of Zirconium Nuclear Fuel Cladding

This report presents the mechanical characterization of a specific Zry-4 cladding batch serving as the foundation for a diverse range of fuel performance research at Oak Ridge National Laboratory (ORNL). This effort supports research needs for the U.S. Department of Energy (DOE), particularly regarding evaluating accident tolerant fuel (ATF) cladding coating concepts, expanding understanding of cladding response to loss-of-coolant accidents (LOCA) transients, refining post-critical heat flux (CHF) limits (t@T), and upcoming irradiation campaigns. The central objective was to define the baseline performance of the substrate Zircaloy-4 (Zry-4) material leveraged across ORNL Advanced Fuel Campaign (AFC) efforts through controlled high-temperature vacuum tensile testing. This work begins to address gaps in existing models where implementation based on nominal heat-treatment labels, such as stress relief annealed (SRA), often fail to capture the interplay of recovery, recrystallization, and grain growth. To quantify this, data was benchmarked against the Pacific Northwest National Laboratory (PNNL) stress strain model to determine where this material falls in comparison to assumed values for materials in the same heat treatment regime. Analysis of the tensile data revealed that this specific SRA batch exhibits a transitional microstructural state best described by an effective cold-work (CW) parameter of 0.09, diverging from the previous estimation of 0.5 for SRA materials. Additionally, comparative testing of Cr coated specimens demonstrated no distinct difference in axial strength relative to the bare substrate. This suggests that the strengthening benefits of Cr coatings observed in burst scenarios are driven by residual stress mechanisms acting solely in the hoop direction.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Carbon Utilization and Storage Partnership of the Western United States

This technical report documents research conducted under DOE Award No. DE-FE0031837 focused on evaluating the feasibility of carbon capture, utilization, and storage (CCUS) systems in the central and western United States. The project integrated geologic characterization, reservoir simulation, infrastructure modeling, and economic analysis to assess CO₂ storage potential near industrial sources and develop strategies for transport and sequestration. The work included subsurface modeling, risk assessment, monitoring and verification (MRV) planning, and evaluation of regulatory pathways such as EPA Underground Injection Control (UIC) Class VI permitting and IRS 45Q tax credit eligibility. Results demonstrate the viability of multiple storage approaches, including saline formations, enhanced coalbed methane recovery, and basalt mineralization, supported by data-driven workflows and regional analyses. The project also produced permitting templates, technology transfer activities, and stakeholder engagement efforts to support deployment readiness. These findings contribute to the development of scalable, economically viable CCUS systems and provide a repeatable framework for future carbon management projects.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2026

Extending Component Lifetime And Improving Inverter Reliability (ECLAIIR)

Inverter reliability remains one of the most persistent challenges limiting the performance, availability, and economic viability of utility‑scale photovoltaic (PV) plants. Industry data consistently show that inverters account for the highest share of corrective maintenance events and unplanned outages across PV fleets. These failures result in energy losses, increased O&M costs, and reduced confidence in long‑term solar asset performance. Motivated by these challenges, this project—Extending Component Lifetime and Improving Inverter Reliability (ECLAIIR)—was undertaken to systematically investigate inverter degradation and failure mechanisms, develop predictive maintenance capabilities, and establish data‑driven pathways to improve service life and reduce the Levelized Cost of Energy (LCOE) for large‑scale PV systems. The primary goal of the project was to identify pre‑failure signatures in string inverters using both lab‑based accelerated lifetime testing and field‑based data and to develop predictive maintenance algorithms that can anticipate inverter faults before they occur. Through collaboration with inverter testing laboratory, solar PV plant owner, and failure‑analysis experts, the project advanced the technical understanding of inverter reliability. By instrumenting inverters with thermistors, humidity sensors, power‑quality meters, and acoustic sensors, the research established how multiple sensing modalities can reliably detect deviations from normal behavior hours to days before failure. These findings substantially enhance scientific understanding of inverter failure kinetics and provide the PV industry with the most comprehensive cross‑OEM characterization of early‑stage failure indicators reported to date. Technically, the project demonstrated the effectiveness of predictive maintenance by developing and validating the PreDICT (Predictive Diagnostics of PV Inverters Using Condition Monitoring and Trend Analysis) framework—a multi‑layer diagnostic architecture combining peer‑to‑peer analytics, historical trend modeling, and advanced machine‑learning techniques such as the Sequential Conditional Variational Autoencoder (SCVAE). This predictive model achieved more than 90% accuracy in detecting pre‑failure conditions and provided up to four days of lead time before inverter failure in field scenarios. Economically, the project’s LCOE analysis showed that predictive maintenance can reduce lifetime energy losses and minimize corrective maintenance interventions. Modeling indicated that, depending on inverter failure rates and replacement timelines, predictive maintenance can significantly reduce LCOE impacts associated with inverter downtime: from as high as 19.4% under conventional maintenance strategies to 0.1%–10.17% when predictive analytics are adopted. These results confirm that predictive maintenance is both technically feasible and economically advantageous for utilities and plant operators. The project’s findings also have broad public benefit. By improving inverter reliability and reducing downtime, predictive maintenance directly increases electricity generation from existing PV assets. Enhanced reliability lowers operational costs for utilities, which can translate over time into lower energy costs for consumers. Furthermore, the project’s technical publications, conference presentations, and industry workshops ensure that knowledge gained is shared broadly across the solar industry, supporting workforce development and enabling utilities of all sizes to adopt modern asset‑health monitoring practices. The retrofitting case study and service‑life prediction framework further support informed decision‑making for aging PV fleets, helping operators extend system life and reduce electronic waste. In summary, the ECLAIIR project significantly advanced the state of knowledge on inverter degradation, demonstrated the technical and economic value of predictive maintenance, and delivered actionable tools and insights that support more reliable, cost‑effective, and sustainable PV plant operation. The outcomes of this project will continue to inform utility practices, guide inverter design improvements, and strengthen the long‑term performance of solar assets nationwide.

14 SOLAR ENERGY↗

NASA NTRS1992

Spatial and temporal behavior of ULF pulsations observed by the Goose Bay HF radar

Techniques which allow the instantaneous amplitude and phase to be determined as functions of geomagnetic lattitude, longitude, and time are employed to carry out a detailed analysis of HF radar data of a ULF pulsation event in the postmidnight sector on January 11, 1989. Field line resonances with several different frequencies occur simultaneously at different latitudes. These can be associated with cavity mode frequencies of 1.3 mHz, 1.9 mHz, 2.7 mGz, and 3.3 mHz. These frequencies are constant to better than 10 percent over a local time period of nearly 4 hr. The field-aligned currents driven by the resonances can be as large as 5 micro-A/sq m at ionospheric heights. The data support a picture of modes driven by solar wind impulses.

Walker, A. D. M.↗

NASA NTRS1993

A kinematic analysis of the high-latitude thermospheric neutral circulation pattern

'Synthesized' thermospheric neutral wind fields for solar maximum, December solstice conditions for both quiet and active levels of geometric activity, are provided on the basis of averaged measurements from the Dynamics Explorer 2 (DE 2) satellite combined with theoretical model calculations. High-latitude DE 2 wind data obtained from multiorbit averages are merged with modeled winds from the NCAR thermosphere-ionosphere general circulation model. A 'kinematic analysis' is performed to decompose the merged semiempirical wind fields into their respective divergent and nondivergent components at high latitudes and to calculate the corresponding potential and stream functions. It is concluded that the nondivergent component of the high-latitude thermospheric neutral wind is representative of the convection-driven component of the neutral wind at F region altitudes. The nondivergent wind component comprises a large percentage of the total wind field for both quiet and active geomagnetic conditions.

Thayer, J. P.↗

NASA NTRS1995

Some velocity field results from the thermocapillary flow experiment aboard USML-1 spacelab

Results are reported of the Surface Tension Driven Convection Experiment (STDCE) aboard USML-1 Spacelab. Steady and transient thermocapillary flows were investigated in a 10 cm dia. circular container filled with 10 Cs silicone oil. The velocity and temperature fields were studied in detail under various conditions. It is shown in this paper how the Marangoni number affects the velocity field. A numerical analysis of the flows was also conducted and its results were compared to the experimental data. Good agreement is shown.

Kamotani, Y.↗

NASA NTRS2015

High Resolution Nature Runs and the Big Data Challenge

NASA's Global Modeling and Assimilation Office at Goddard Space Flight Center is undertaking a series of very computationally intensive Nature Runs and a downscaled reanalysis. The nature runs use the GEOS-5 as an Atmospheric General Circulation Model (AGCM) while the reanalysis uses the GEOS-5 in Data Assimilation mode. This paper will present computational challenges from three runs, two of which are AGCM and one is downscaled reanalysis using the full DAS. The nature runs will be completed at two surface grid resolutions, 7 and 3 kilometers and 72 vertical levels. The 7 km run spanned 2 years (2005-2006) and produced 4 PB of data while the 3 km run will span one year and generate 4 BP of data. The downscaled reanalysis (MERRA-II Modern-Era Reanalysis for Research and Applications) will cover 15 years and generate 1 PB of data. Our efforts to address the big data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS), a specialization of the concept of business process-as-a-service that is an evolving extension of IaaS, PaaS, and SaaS enabled by cloud computing. In this presentation, we will describe two projects that demonstrate this shift. MERRA Analytic Services (MERRA/AS) is an example of cloud-enabled CAaaS. MERRA/AS enables MapReduce analytics over MERRA reanalysis data collection by bringing together the high-performance computing, scalable data management, and a domain-specific climate data services API. NASA's High-Performance Science Cloud (HPSC) is an example of the type of compute-storage fabric required to support CAaaS. The HPSC comprises a high speed Infinib and network, high performance file systems and object storage, and a virtual system environments specific for data intensive, science applications. These technologies are providing a new tier in the data and analytic services stack that helps connect earthbound, enterprise-level data and computational resources to new customers and new mobility-driven applications and modes of work. In our experience, CAaaS lowers the barriers and risk to organizational change, fosters innovation and experimentation, and provides the agility required to meet our customers' increasing and changing needs

big data analysis↗

DOE OSTI2016

Utilization of the Differential Die-Away Self-Interrogation Technique for Characterization and Verification of Spent Nuclear Fuel

New nondestructive assay techniques are sought to better characterize spent nuclear fuel. One of the NDA instruments selected for possible deployment is differential die-away self-interrogation (DDSI). The proposed DDSI approach for spent fuel assembly assay utilizes primarily the spontaneous fission and (α, n) neutrons in the assemblies as an internal interrogating radiation source. The neutrons released in spontaneous fission or (α,n) reactions are thermalized in the surrounding water and induce fission in fissile isotopes, thereby creating a measurable signal from isotopes of interest that would be otherwise difficult to measure. The DDSI instrument employs neutron coincidence counting with 3He tubes and list-mode-based data acquisition to allow for production of Rossi-alpha distributions (RADs) in post-processing. The list-mode approach to data collection and subsequent construction of RADs has expanded the analytical possibilities, as will be demonstrated throughout this thesis. One of the primary advantages is that the measured signal in the form of a RAD can be analyzed in its entirety including determination of die-away times in different time domains. This capability led to the development of the early die-away method, a novel leakage multiplication determination method which is tested throughout the thesis on different sources in simulation space and fresh fuel experiments. The early die-away method is a robust, accurate, improved method of determining multiplication without the need for knowledge of the (α,n) source term. The DDSI technique and instrument are presented along with the many novel capabilities enabled by and discovered through RAD analysis. Among the new capabilities presented are the early die-away method, total plutonium content determination, and highly sensitive missing pin detection. Simulation of hundreds of different spent and fresh fuel assemblies were used to develop the analysis algorithms and the techniques were tested on a variety of spontaneous fission-driven fresh fuel assemblies at Los Alamos National Laboratory and the BeRP ball at the Nevada National Security Site. The development of the new, improved analysis and characterization methods with the DDSI instrument makes it a viable technique for implementation in a facility to meet material control and safeguards needs.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

Evaluating Physics-Informed Neural Network Performance for Seismic Discrimination between Earthquakes and Explosions

In this article, we evaluate adding a weak physics constraint, that is, a physics‐based empirical relationship, to the loss function with a physics‐informed manner in local distance explosion discrimination in the hope of improving the generalization capability of the machine learning (ML) model. We compare the proposed model with the two‐branch model we previously developed, as well as with a pure data‐driven model. Unexpectedly, the proposed model did not consistently outperform the pure data‐driven model. By varying the level of inconsistency in the training data, we find this approach is modulated by the strength of the physics relationship. In conclusion, this result has important implications for how to best incorporate physical constraints in ML models.

58 GEOSCIENCES↗

NASA NTRS2006

Toward a Framework for Modeling Space Systems Architectures

In this paper we will describe this extended RASDS/RAMSS methodology, the set of viewpoints that we have derived, and describe their relationship to RM-ODP. While this methodology may be directly used in a variety of document driven ways to describe space system architecture, the real power of it will come when there are tools available that will support full description of system architectures that can be captured electronically in a way that permits their analysis, verification, and transformation.

cost savings↗

DOE OSTI2015

Jahn-Teller versus quantum effects in the spin-orbital material LuVO 3

In this article, we report on combined neutron and resonant x-ray scattering results, identifying the nature of the spin-orbital ground state and magnetic excitations in LuVO 3 as driven by the orbital parameter. In particular, we distinguish between models based on orbital-Peierls dimerization, taken as a signature of quantum effects in orbitals, and Jahn-Teller distortions, in favor of the latter. In order to solve this long-standing puzzle, polarized neutron beams were employed as a prerequisite in order to solve details of the magnetic structure, which allowed quantitative intensity analysis of extended magnetic-excitation data sets. The results of this detailed study enabled us to draw definite conclusions about the classical versus quantum behavior of orbitals in this system and to discard the previous claims about quantum effects dominating the orbital physics of LuVO 3 and similar systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2020

Origin and mitigation of wind noise on balloon-borne infrasound microbarometers

High-altitude monitoring of low-frequency acoustic waves (infrasound) on Earth has regained prominence in recent years, primarily driven by improvements in light-weight sensor technology and advances in scientific ballooning techniques. Balloon-borne infrasound monitoring is also being proposed as a remote sensing technique for planetary exploration. Contrary to ground-based infrasound monitoring, the infrasound noise background in the stratosphere as measured by a balloon remains uncharacterized and the efficacy of wind noise mitigation filters has not been investigated. In this study, an analysis of pressure data collected using infrasound microbarometers during the flight of a long-duration zero pressure balloon is presented. Here, a dramatic reduction of background noise in the stratosphere is demonstrated and it is shown that wind noise mitigation filters are not effective at reducing wind noise under these conditions. Results from this study demonstrate stratospheric balloons as a low-noise platform for infrasound monitoring and motivate the development of improved noise mitigation tools.

47 OTHER INSTRUMENTATION↗