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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Data-driven analysis to understand GPU hardware resource usage of optimizations

With heterogeneous systems, the number of GPUs per chip increases to provide computational capabilities for solving science at a nanoscopic scale. However, low utilization for single GPUs defies the need to invest more money in expensive accelerators. Although related work develops optimizations to improve application performance, none studies how these optimizations impact hardware resource usage or average GPU utilization. Here, this paper takes a data-driven analysis approach in addressing this gap by (1) characterizing how hardware resource usage affects device utilization, execution time, or both, (2) presenting a multiobjective metric to identify important application-device interactions that can be optimized to improve device utilization and application performance jointly, (3) studying hardware resource usage behaviors of several optimizations for a benchmark application, and finally (4) identifying optimization opportunities for several scientific proxy applications based on their hardware resource usage behaviors. Furthermore, we demonstrate the applicability of our methodology by applying the identified optimizations to a proxy application, which improves the execution time, device utilization, and power consumption by up to 29.6%, 5.3% and 26.5% respectively.

Computer science↗
DOE OSTI2023

Reverse engineering environmental metatranscriptomes clarifies best practices for eukaryotic assembly

Abstract Background Diverse communities of microbial eukaryotes in the global ocean provide a variety of essential ecosystem services, from primary production and carbon flow through trophic transfer to cooperation via symbioses. Increasingly, these communities are being understood through the lens of omics tools, which enable high-throughput processing of diverse communities. Metatranscriptomics offers an understanding of near real-time gene expression in microbial eukaryotic communities, providing a window into community metabolic activity. Results Here we present a workflow for eukaryotic metatranscriptome assembly, and validate the ability of the pipeline to recapitulate real and manufactured eukaryotic community-level expression data. We also include an open-source tool for simulating environmental metatranscriptomes for testing and validation purposes. We reanalyze previously published metatranscriptomic datasets using our metatranscriptome analysis approach. Conclusion We determined that a multi-assembler approach improves eukaryotic metatranscriptome assembly based on recapitulated taxonomic and functional annotations from an in-silico mock community. The systematic validation of metatranscriptome assembly and annotation methods provided here is a necessary step to assess the fidelity of our community composition measurements and functional content assignments from eukaryotic metatranscriptomes.

Krinos, Arianna I. (ORCID:0000000197678392)↗
DOE OSTI2023

Transient Pressure Interference During CO2 Injection in Saline Aquifers

Abstract CO2 injection in subsurface geological formations (e.g. deep saline aquifers) causes pressure perturbations over a large area surrounding the injection well. Observation wells are widely considered in geologic CO2 storage (GCS) projects where the pressure perturbation induced by CO2 injection is measured. In this work, we use analytical and numerical modeling tools along with field data to examine the pressure behavior in GCS projects before and after CO2 arrival at an observation well. Prior to CO2 arrival, a baseline pressure trend is established which corresponds to single-phase brine flow across the observation well (approximated by Theis solution). Therefore, analysis of early-time pressure data is straightforward, provides the single-phase flow characteristics (mobility and storativity), and helps establishing a baseline pressure change that can be extended beyond the single-phase flow period at the observation well. Upon CO2 arrival, a departure from this baseline trend is expected. For the pressure to detect the CO2 arrival at an observation well, the departure from baseline pressure behavior must be significant and well above the background noise levels. We use existing analytical models to determine the strength of the expected pressure departure signal from the baseline trend upon CO2 arrival. The strength of the expected pressure departure is found to be directly proportional to the mobility ratio. Accordingly, we establish a criterion to determine whether the pressure at an observation well can detect the CO2 arrival. We present an analysis approach through application to synthetic and field data and show the characteristic pressure behavior before and after CO2 arrival. We show that while generally the pressure can be either above or below the expected baseline pressure trend, it would be likely above the baseline upon CO2 arrival. This is because the mobility ratio becomes less than unity after CO2 arrival. We show that depending on the reservoir characteristics, changes in the pressure trend may or may not be sufficient to detect the CO2 arrival.

Engineering↗
DOE OSTI2020

Demand Driven Cycamore Archetypes

Future nuclear fuel cycle options may present advantages over today’s once-through fuel cycle. Nuclear fuel cycle simulation tools assess the performance of those fuel cycles as well as the dynamics of long-term technology transitions. In many nuclear fuel cycle simulation tools, it has historically been the responsibility of the user to manually define facility deployment schemes and all facility parameters. While this is straightforward in simple fuel cycles, transitions from one fuel cycle to another can be more complex. In particular, deployment schemes for supportive fuel cycle facilities beyond the reactor become complex if the analyst desires to avoid gaps in the nuclear fuel and power supply chain during those transition scenarios. As nuclear fuel cycle analysis approaches questions regarding the feasibility and performance of the deployment schemes and technology choices during technology transition, automation of this historically manual process is necessary. The main objective of this work was to develop and demonstrate Cyclus automation capabilities toward key nuclear fuel cycle transition scenarios. While deploying reactors to meet power demand is trivial, and existed in the earliest versions of CYCLUS, automated, predictive deployment and decommissioning of other facilities is more complex. These include mining, milling, enrichment, fuel fabrication, reprocessing, and others. For example, a balanced closed fuel cycle may require ensuring that there is enough fast reactor fuel for their operation and may drive deployment of a fleet of light water reactors. This concern comprises the main challenge that drove the project effort. The Demand-Driven Cycamore Archetype project (NEUP-FY16-10512) aimed to develop CYCAMORE demand-driven deployment capabilities and thereby automate transition scenario definition. The developed software package, d3ploy, in the form of a CYCLUS Institution agent, deploys Facilities to meet the front-end and back-end demands of the fuel cycle. The University of South Carolina and the University of Illinois applied multiple algorithmic approaches to this challenge. This project developed an in situ demand-driven development schedule calculation through non-optimizing, deterministic-optimizing, and stochastic-optimizing algorithms as CYCLUS archetypes and demonstrated these new archetypes in program-supporting fuel cycle transition scenarios. Both objectives were achieved. This report documents the results and deliverables obtained toward these achievements in detail.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Boride-Carbon Hybrid Technology to Produce Ultra-Wear and Corrosion Resistant Surfaces for Applications in Harsh Conditions (Final Technical Report)

Engineered functional surfaces play an important role to enable new products and manufacturing processes that can endure harsh service conditions such as high impact and contact loads, highly abrasive wear, extreme temperatures, and corrosive environments. Engineered surfaces can also be instrumental to improving the efficient use of energy by reducing frictional losses and extending service life. The main objective of this project was to develop a hybrid surface engineering technology that combines the advantages of a novel ultrafast boriding process with the next generation of superhard carbon coatings. The hypothesis was that this hybrid process will offer an unprecedented combination of wear and corrosion resistance, low frictional losses and affordability for treated parts so that it can be utilized in many applications. During this project, a duplex process was developed that combines the advantages of ultra-fast electrochemical boriding with those of hard tetrahedral amorphous carbon coatings. Both technologies can be combined to form a hybrid technology that is characterized by low friction and wear properties combined with corrosion and fatigue resistance. Good adhesion of both layers to each other was one main goal of this project, that has been achieved with HF1 adhesion through the Rockwell-C adhesion test. In this project, the mechanical properties of the hybrid coating were modeled through a finite-element analysis approach. We can conclude that the FEA model resembles the actual samples and be utilized to predict mechanical behavior under impacts. Based on this model, application-oriented load conditions can be simulated for optimal layer design regarding thickness and mechanical properties. To exemplify, one conclusion that can be drawn from the nanoindentation model is a boride layer thickness of 50 µm is sufficient to effectively support the carbon coating on the identified AISI 1045 low carbon steel substrate material. The duplex treatment yields wear rates as low as 6 x 10 -8 mm 3 N -1 m -1 and a coefficient of friction of 0.14 when tested against a steel counter face in a ball-on-disk test setup. On the other hand, the wear rate of the only-borided AISI 1045 steel was 5 x 10 -5 mm 3 N -1 m -1 , about three orders of magnitude higher than the duplex coating. At the same time, duplex treated samples experience corrosion resistance, which could not be achieved with single-layer carbon coatings. The developed surface treatment withstands a 3-hour exposure to 15% HCl, while the only carbon coated counter sample shows severe delamination of the coating due to pin hole corrosion. The boride layer is chemically stable and pin hole free because it is formed through an electrochemical process under high current densities (700 mA/cm 2 ) and high temperature. Additionally, the hybrid coating led to at least 3x increase in fatigue strength of the steel substrate, which exceeds the target performance of 30% improvement. There are numerous potential applications for the duplex coatings. A representative application is bearing ball coatings for off-shore windmills. Compared to currently employed surface technologies in this field, the initial costs of applying our technology might be higher due to more process steps but the performance benefits lead to an increased life time of treated parts, which will lower the maintenance and replacement costs in the long-term. To validate the technology for this specific application, the team is currently investigating the process of white etching crack initiation of the duplex coating in collaboration with ANL. Overall, this project successfully validated that the boride-carbon hybrid technology can withstand harsh conditions. One possible approach to commercialization under consideration is to transfer the technology to a startup or an existing coatings company.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Risk-Based Dynamic Contingency Analysis Applied to Puerto Rico Electric Infrastructure

The U.S. Department of Energy’s (DOE) Office of Electricity and Office of Energy Efficiency & Renewable Energy have funded DOE National Laboratories to perform modeling, analysis, and high-level design of resilience-enhancement options for the power grid of the Commonwealth of Puerto Rico. The Pacific Northwest National Laboratory (PNNL) is one of the national laboratories contributing to the DOE effort. Under this funding, PNNL completed Phase I of their analysis in 2018, which identified high-priority transmission enhancements derived from detailed dynamic cascading analysis of severe contingencies, including a hurricane scenario example. This report describes additional analysis completed under Phase II, which was performed over 2019. The PNNL team applied decades of experience making complex power systems more resilient, reliable, secure, flexible, affordable and sustainable, with partners in government and industry. In this report, PNNL presents the Phase II analysis, in which a risk-based dynamic contingency analysis approach to evaluate impact of several hurricane scenarios was developed. This approach was used to identify high-priority enhancements and test resilience mitigation actions, including the evaluation of high-solar scenarios developed as a result of the 2019 Puerto Rico Integrated Resource Plan (IRP).

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Search for an Excess of Electron Neutrino Interactions in MicroBooNE Using Multiple Final State Topologies (submitted to PRL)

We present a measurement of ν e interactions from the Fermilab Booster Neutrino Beam using the MicroBooNE liquid argon time projection chamber to address the nature of the excess of low energy interactions observed by the MiniBooNE collaboration. Three independent ν e searches are performed across multiple single electron final states, including an exclusive search for two-body scattering events with a single proton, a semi-inclusive search for pionless events, and a fully inclusive search for events containing all hadronic final states. With differing signal topologies, statistics, backgrounds, reconstruction algorithms, and analysis approaches, the results are found to be consistent with the nominal ν e rate expectations from the Booster Neutrino Beam and no excess of ν e events is observed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Gimballed Tracking Mount Pointing Angle Qualification

Tonopah Test Range (TTR), in support of its testing mission and modernization effort acquired a fleet of new gimballed tracking mounts (GTMs) manufactured by BAE Systems. The new GTMs can be operated remotely during flight tests and provide near real-time target tracking data. Furthermore, test vehicle Time-Space-Position-Information (TSPI) is evaluated using post-test synchronized imagery and pointing angle measurements acquired from each tracking mount. To comply with the Nuclear Enterprise Assurance Program (NEAP), all measurements devices must be certified. In keeping with the NEAP program, qualification of the new GTMs have been assessed to confirm that their pointing angle measurements produce acceptable TSPI results. This study only evaluated the four GTMs as a stand-alone solution and found that the GTMs meet their performance requirement of 0.006 degrees RMS error (or less) for post-processed pointing angles and produced TSPI solution with error volumes on the order of one meter or less. The new GTMs will be utilized in combination with existing optical tracking mounts, which will only improve the accuracy of the resulting TSPI data product. Details regarding the approach, analysis, summary results, and conclusions are presented.

42 ENGINEERING↗
DOE OSTI2023

ECAR-6564 MARVEL Project Primary Coolant System ASME BPVC Section III Division 5 Design by Analysis

This report demonstrates that the MARVEL Reactor Primary Coolant Boundary (herein referred to as the Primary Coolant System, or PCS) is designed to meet ASME BPVC Section III Division 5 elevated temperature service design-by-analysis criteria. The analysis approach that delineates division of responsibilities to meet project objectives is discussed herein. In short, Design and Service Level A, B, and C code calculations are detailed in ECAR-6580 for the majority of the PCS with complex geometry, while the Lower Downcomers, Bottom Head, and Reactor Core Barrel are analyzed in ANL-23/56. Service Level D code calculations are detailed in this document.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

SBND Analysis using ML Reconstruction Chain

As part of the Short Baseline Neutrino (SBN) Program at Fermilab, the Short Baseline Near Detector (SBND) is positioned in the Booster Neutrino Beam (BNB) and explores neutrino-argon interactions with unprecedented statistics. SBND is a Liquid Argon Time Projection Chamber (LArTPC). Electrons produced through ionization drift toward three wire planes, providing signals that form 2D images of particle trajectories. I introduce the Scalable Particle Imaging using Neural Embeddings (SPINE) framework, which employs a Machine Learning (ML)-based 3D reconstruction using a series of neural networks. Here, we present SPINE’s reconstruction chain, analysis approaches, and results from our latest simulation samples.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

A Dark Photon Dark Matter Search with a Widely-Tunable SRF Cavity

The SERAPH (SupERconducting Axion and Paraphoton Haloscope) experiment is a family of superconducting haloscopes being developed by the Superconducting Quantum Materials and Systems (SQMS) Center to search for wavelike dark matter. This presentation will focus on preliminary results from our dark photon dark matter search using a widely-tunable SRF cavity operating between 4-7 GHz, nicknamed the “plunger cavity.” I will present the cavity design and characterization, analyze the impact of microphonics on system performance and haloscope sensitivity, and describe our tuning methodology for this Q~10^8 cavity. The presentation will cover our haloscope analysis approach and discuss sensitivity limits achieved within this frequency range. Finally, I will outline lessons learned from this first search, implications for future axion searches, and proposed improvements for subsequent SERAPH experiments.

Cervantes, Raphael [Fermilab]↗
DOE OSTI2025

Explicit Dynamic Impact Analysis of the Building 3525 Packages

This report describes the methodology used for the safety basis analysis of the Building (Bldg.) 3525 casks and presents preliminary results for a selected geometry. The specific problem analyzed is a free fall load case onto an unyielding target at various orientations. The various orientations were analyzed to ensure that the packages were subjected to the orientation that causes the most damage. The analysis approach, material modeling, finite element modeling, and results for the selected case are presented, along with a brief discussion of conservatism and uncertainties. The results for the analyzed load cases show that through selected administrative controls, no breach of the container or loss of shielding will occur in the package.

42 ENGINEERING↗
DOE OSTI2025

A Dark Photon Dark Matter Search with a Widely-Tunable SRF Cavity

The SERAPH (SupERconducting Axion and Paraphoton Haloscope) experiment is a family of superconducting haloscopes being developed by the Superconducting Quantum Materials and Systems (SQMS) Center to search for wavelike dark matter. This presentation will focus on preliminary results from our dark photon dark matter search using a widely-tunable SRF cavity operating between 4-7 GHz, nicknamed the "plunger cavity." I will present the cavity design and characterization, analyze the impact of microphonics on system performance and haloscope sensitivity, and describe our tuning methodology for this Q~10^8 cavity. The presentation will cover our haloscope analysis approach and discuss sensitivity limits achieved in this frequency range. Finally, I will outline lessons learned from this first search, implications for future axion searches, and proposed improvements for subsequent SERAPH experiments.

Cervantes, Raphael [Fermilab]↗
DOE OSTI2023

Density functional modeling of the binding energies between aluminosilicate oligomers and different metal cations

Interactions between negatively charged aluminosilicate species and positively charged metal cations are critical to many important engineering processes and applications, including sustainable cements and aluminosilicate glasses. In an effort to probe these interactions, here we have calculated the pair-wise interaction energies (i.e., binding energies) between aluminosilicate dimer/trimer and 17 different metal cations M n+ (M n+ = Li + , Na + , K + , Cu + , Cu 2+ , Co 2+ , Zn 2+ , Ni 2+ , Mg 2+ , Ca 2+ , Ti 2+ , Fe 2+ , Fe 3+ , Co 3+ , Cr 3+ , Ti 4+ and Cr 6+ ) using a density functional theory (DFT) approach. Analysis of the DFT-optimized structural representations for the clusters (dimer/trimer + M n+ ) shows that their structural attributes (e.g., interatomic distances) are generally consistent with literature observations on aluminosilicate glasses. The DFT-derived binding energies are seen to vary considerably depending on the type of cations (i.e., charge and ionic radii) and aluminosilicate species (i.e., dimer or trimer). A survey of the literature reveals that the difference in the calculated binding energies between different M n+ can be used to explain many literature observations associated with the impact of metal cations on materials properties (e.g., glass corrosion, mineral dissolution, and ionic transport). Analysis of all the DFT-derived binding energies reveals that the correlation between these energy values and the ionic potential and field strength of the metal cations are well captured by 2nd order polynomial functions ( R 2 values of 0.99–1.00 are achieved for regressions). Given that the ionic potential and field strength of a given metal cation can be readily estimated using well-tabulated ionic radii available in the literature, these simple polynomial functions would enable rapid estimation of the binding energies of a much wider range of cations with the aluminosilicate dimer/trimer, providing guidance on the design and optimization of sustainable cements and aluminosilicate glasses and their associated applications. Finally, the limitations associated with using these simple model systems to model complex interactions are also discussed.

Gong, Kai↗
DOE OSTI2022

Physical Controls on Irrigation Return Flow Contributions to Stream Flow in Irrigated Alluvial Valleys

Irrigation can be a significant source of groundwater recharge in many agricultural regions, particularly in arid and semi-arid climates. Once infiltrated, irrigation recharge can travel via subsurface flowpaths that return to the river system in a lagged manner, supplementing natural streamflow weeks, months, or even years from when the irrigation was applied. In regions that experience low flows during summer and early fall, return flows can be a significant source of supplementary streamflow. Many water planning and operations models either ignore return flows or roughly approximate them with analytical solutions. Thus, return flows represent an important but often overlooked component of the hydrological exchange and overall water balance in agricultural regions. This study uses groundwater models to explore a wide range of factors that control irrigation return flow timing in irrigated alluvial valleys. A sensitivity analysis approach is used to assess how factors such as the extent of irrigated land adjacent to a stream, irrigation recharge rate, aquifer hydraulic conductivity, aquifer thickness, water table configuration, and seasonal fluctuations in stream stage control the timing of subsurface return flows. Modeling is conducted using MODFLOW models representing an irrigated alluvial valley adjacent to a stream. While a simplification of the full complexity in real systems, the models are a significant advancement from the analytical solution and provide new insight into the timescales of return flows over a broad range of possible conditions. To contextualize our modeling results, they are compared to an analytical solution commonly used for approximating return flows to evaluate its performance. Our findings show what factors and conditions influence return flow timing and control whether they contribute to streamflow over short term (months) or longer term (seasonal) time scales.

Ferencz, Stephen B.↗
DOE OSTI2023

Can strong substorm-associated MeV electron injections be an important cause of large radiation belt enhancements?

It has become well-established that strong outer radiation belt enhancements are due to wave-driven electron energization by whistler-mode chorus waves. However, in this study, we examine strong MeV electron injections on 10 July 2019 and find substantial evidence that such injections may be a crucial contributor to outer radiation belt enhancement events. For such an examination, it is essential to precisely separate temporal flux changes from spatial variations observed as Van Allen Probes move along their orbits. Employing a new “hourly snapshot” analysis approach, we discover unprecedented details of electron flux evolutions that suggest that for this event, the outer belt enhancement was not continuous but instead intermittent, mostly composed of 4 large discrete injection-driven flux increases. The injections appear as sharp flux increases when observed near apogee. Otherwise, by comparing hourly snapshots for different times, we infer injections and infer temporally stable fluxes between injections, despite strong and continuous chorus emission. The fast and intermittent electron flux growth successively extending earthwards implies cumulative outer belt enhancement via a series of repetitive inward transport associated with injection-induced electric fields.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Estimating the Performance Loss Rate of Photovoltaic Systems Using Time Series Change Point Analysis

The accurate quantification of the performance loss rate of photovoltaic systems is critical for project economics. Following the current research activities in the photovoltaic performance and reliability field, this work presents a comparative assessment between common change point methods for performance loss rate estimation of fielded photovoltaic installations. An extensive testing campaign was thus performed to evaluate time series analysis approaches for performance loss rate evaluation of photovoltaic systems. Historical electrical data from eleven photovoltaic systems installed in Nicosia, Cyprus, and the locations’ meteorological measurements over a period of 8 years were used for this investigation. The application of change point detection algorithms on the constructed monthly photovoltaic performance ratio series revealed that the obtained trend might not always be linear. Specifically, thin film photovoltaic systems showed nonlinear behavior, while nonlinearities were also detected for some crystalline silicon photovoltaic systems. When applying several change point techniques, different numbers and locations of changes were detected, resulting in different performance loss rate values (varying by up to 0.85%/year even for the same number of change points). The results highlighted the importance of the application of nonlinear techniques and the need to extract a robust nonlinear model for detecting significant changes in time series data and estimating accurately the performance loss rate of photovoltaic installations.

photovoltaics↗
DOE OSTI2022

Implementing Spatio-Temporal 3D-Convolution Neural Networks and UAV Time Series Imagery to Better Predict Lodging Damage in Sorghum

Unmanned aerial vehicle (UAV)-based remote sensing is gaining momentum in a variety of agricultural and environmental applications. Very-high-resolution remote sensing image sets collected repeatedly throughout a crop growing season are becoming increasingly common. Analytical methods able to learn from both spatial and time dimensions of the data may allow for an improved estimation of crop traits, as well as the effects of genetics and the environment on these traits. Multispectral and geometric time series imagery was collected by UAV on 11 dates, along with ground-truth data, in a field trial of 866 genetically diverse biomass sorghum accessions. We compared the performance of Convolution Neural Network (CNN) architectures that used image data from single dates (two spatial dimensions, 2D) versus multiple dates (two spatial dimensions + temporal dimension, 3D) to estimate lodging detection and severity. Lodging was detected with 3D-CNN analysis of time series imagery with 0.88 accuracy, 0.92 Precision, and 0.83 Recall. This outperformed the best 2D-CNN on a single date with 0.85 accuracy, 0.84 Precision, and 0.76 Recall. The variation in lodging severity was estimated by the best 3D-CNN analysis with 9.4% mean absolute error (MAE), 11.9% root mean square error (RMSE), and goodness-of-fit (R2) of 0.76. This was a significant improvement over the best 2D-CNN analysis with 11.84% MAE, 14.91% RMSE, and 0.63 R2. The success of the improved 3D-CNN analysis approach depended on the inclusion of “before and after” data, i.e., images collected on dates before and after the lodging event. The integration of geometric and spectral features with 3D-CNN architecture was also key to the improved assessment of lodging severity, which is an important and difficult-to-assess phenomenon in bioenergy feedstocks such as biomass sorghum. This demonstrates that spatio-temporal CNN architectures based on UAV time series imagery have significant potential to enhance plant phenotyping capabilities in crop breeding and Precision agriculture applications.

3D-convolution neural networks↗