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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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At least 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Data as a Key Resource in Catalysis: A Community Account

The deployment of artificial intelligence (AI) is transforming the scientific fields central to interdisciplinary catalysis research. By enabling more effective use of data, AI (including simpler machine learning and data science tools) holds great promise for accelerating discoveries. However, progress has so far been modest, largely due to the lack of standardized, machine-readable, and openly shared catalysis data. This perspective, accounting for community insights emerging at conferences, analyses the underlying reasons for these challenges and proposes solutions to a future whereFAIR data management becomes an integral part of research in catalysis. In the short-term, we deem that mandatory FAIR data depositing prior to scientific publications along with consensualized top-down guidelines on data sharing powered by ease-to-use tools can make the necessary step change happen to catalyse data as key resource in our community.

36 - MATERIALS SCIENCE↗
DOE OSTI2021

The Phase-2 Upgrade of the CMS Data Acquisition

The High Luminosity LHC (HL-LHC) will start operating in 2027 after the third Long Shutdown (LS3), and is designed to provide an ultimate instantaneous luminosity of 7:5 × 10$^{34}$ cm$^{-2}$ s$^{-1}$, at the price of extreme pileup of up to 200 interactions per crossing. The number of overlapping interactions in HL-LHC collisions, their density, and the resulting intense radiation environment, warrant an almost complete upgrade of the CMS detector. The upgraded CMS detector will be read out by approximately fifty thousand highspeed front-end optical links at an unprecedented data rate of up to 80 Tb/s, for an average expected total event size of approximately 8 - 10 MB. Following the present established design, the CMS trigger and data acquisition system will continue to feature two trigger levels, with only one synchronous hardware-based Level-1 Trigger (L1), consisting of custom electronic boards and operating on dedicated data streams, and a second level, the High Level Trigger (HLT), using software algorithms running asynchronously on standard processors and making use of the full detector data to select events for offline storage and analysis. The upgraded CMS data acquisition system will collect data fragments for Level-1 accepted events from the detector back-end modules at a rate up to 750 kHz, aggregate fragments corresponding to individual Level- 1 accepts into events, and distribute them to the HLT processors where they will be filtered further. Events accepted by the HLT will be stored permanently at a rate of up to 7.5 kHz. This paper describes the baseline design of the DAQ and HLT systems for the Phase-2 of CMS.

Badaro, Gilbert↗
DOE OSTI2021

Coreset Clustering on Small Quantum Computers

Many quantum algorithms for machine learning require access to classical data in superposition. However, for many natural data sets and algorithms, the overhead required to load the data set in superposition can erase any potential quantum speedup over classical algorithms. Recent work by Harrow introduces a new paradigm in hybrid quantum-classical computing to address this issue, relying on coresets to minimize the data loading overhead of quantum algorithms. We investigated using this paradigm to perform k-means clustering on near-term quantum computers, by casting it as a QAOA optimization instance over a small coreset. We used numerical simulations to compare the performance of this approach to classical k-means clustering. We were able to find data sets with which coresets work well relative to random sampling and where QAOA could potentially outperform standard k-means on a coreset. However, finding data sets where both coresets and QAOA work well—which is necessary for a quantum advantage over k-means on the entire data set—appears to be challenging.

42 ENGINEERING↗
DOE OSTI2025

Groundwater elevation data for monitoring wells within the East and Taylor River basins, Colorado (USA)

This dataset is comprised of temporal variations in groundwater elevation data for the 24 monitoring wells located throughout the East River watershed. Seasonal to annual variations in groundwater elevations are a critical property of mountainous watersheds needed to understand both hydrological and below ground biogeochemical processes. Such data serve as a critical constraint for numerical models describing coupled groundwater-surface water behavior within the watershed. Additionally, the offset between the maximum and minimum groundwater elevations defines the extent of the bedrock weathering zone, with annual excursions in the groundwater hydrographic (i.e., the rising and falling hydrographic limbs) imposing primary controls on bedrock saturation state and redox conditions that govern biogeochemical reactions impacting nitrogen, carbon, and metals cycling. Manufacturer-specific software is used to download pressure data from each transducer, with broadly available spreadsheet software (e.g. Microsoft Excel) used to convert temporal variations in water pressure to elevations in units of meters above mean sea level. As additional monitoring wells are installed within the East River watershed and new groundwater monitoring wells are installed in the Taylor River watershed, temporal groundwater elevation data will be included as a part of this master dataset. Details regarding the metadata associated with each monitoring well location, including well depths, screened intervals, well location coordinates, and bedrock type, are included, as is a standard operating procedure for generating groundwater elevation data from water pressure values recorded by the pressure transducers. This dataset includes: (1) a zip file (East_River_Watershed_Compiled_Groundwater_Elevation_Data_Plots.zip), containing (a) PNG of groundwater hydrographs, (b) a CSV file with groundwater elevation data, and (c) CSV file containing metadata organized by location; (2) an Excel file (East_River_Watershed_Compiled_Groundwater_Elevation_Data_Plots.xlsx) with the groundwater elevation data, groundwater hydrographs, and metadata organized by location; (3) a Word file (Groundwater_elevation_data_protocols.docx) and a PDF file version (Groundwater_elevation_data_protocols.pdf) containing field protocols and methods; (4) a location metadata (locations.csv) file; (5) a file level metadata (flmd.csv); and (6) data dictionary (dd.csv) file.This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Unlocking the benefits of transparent and reusable science for climate-risk management

People around the world seek climate-risk information to guide their decisions. For instance, projections about future flood risk inform where households choose to live, how lenders manage credit risks, and which communities receive federal funding. Yet data limitations and fundamental validation challenges raise important concerns about the reliability of such projections. The principles of transparency and reusability help address these concerns by enabling scrutiny of assumptions and methods, development of foundational data and tools, and consistent application of evaluation standards. While there is ongoing debate about how much transparency commercial climate-risk services should provide, many expect non-commercial actors to lead the way on operationalizing transparency and reusability to fulfill their knowledge-building role in the climate-risk ecosystem. However, despite prominent success stories, we find a substantial gap between principles and practice: only four percent of the most-cited peer-reviewed climate-risk studies in recent years fully share their data and code despite this being a widely accepted minimum standard for transparency. We highlight low-cost measures that non-commercial researchers can take now to improve transparency and reusability. We also emphasize that transformative progress requires substantial investment, cross-sector collaboration, and careful consideration of tradeoffs, data rights, and multiple perspectives on equity. We hope this perspective accelerates both immediate actions and longer-term conversations to improve the ability of science to effectively support timely, evidence-based, and sound climate-risk management.

Open Science↗
DOE OSTI2021

Assessment of task‐based performance from five clinical DBT systems using an anthropomorphic breast phantom

Purpose Digital breast tomosynthesis (DBT) is a limited‐angle tomographic breast imaging modality that can be used for breast cancer screening in conjunction with full‐field digital mammography (FFDM) or synthetic mammography (SM). Currently, there are five commercial DBT systems that have been approved by the U.S. FDA for breast cancer screening, all varying greatly in design and imaging protocol. Because the systems are different in technical specifications, there is a need for a quantitative approach for assessing them. In this study, the DBT systems are assessed using a novel methodology with an inkjet‐printed anthropomorphic phantom and four alternative forced choice (4AFC) study scheme. Method A breast phantom was fabricated using inkjet printing and parchment paper. The phantom contained 5‐mm spiculated masses fabricated with potassium iodide (KI)‐doped ink and microcalcifications (MCs) made with calcium hydroxyapatite. Images of the phantom were acquired on all five systems with DBT, FFDM, and SM modalities where available using beam settings under automatic exposure control. A 4AFC study was conducted to assess reader performance with each signal under each modality. Statistical analysis was performed on the data to determine proportion correct (PC), standard deviations, and levels of significance. Results For masses, overall detection was highest with DBT. The difference in PC was statistically significant between DBT and SM for most systems. A relationship was observed between increasing PC and greater gantry span. For MCs, performance was highest with DBT and FFDM compared to SM. The difference between PC of DBT and PC of SM was statistically significant for all manufacturers. Conclusions This methodology represents a novel approach for evaluating systems. This study is the first of its kind to use an inkjet‐printed anthropomorphic phantom with realistic signals to assess performance of clinical DBT imaging systems.

Ikejimba, Lynda C.↗
DOE OSTI2024

Nonlinear elasticity with the Shifted Boundary Method

Here, we propose a new unfitted/immersed computational framework for nonlinear solid mechanics, which bypasses the complexities associated with the generation of CAD representations and subsequent body-fitted meshing. This approach allows to speed up the cycle of design and analysis in complex geometry and requires relatively simple computer graphics representations of the surface geometries to be simulated, such as the Standard Tessellation Language (STL format). Complex data structures and integration on cut elements are avoided by means of an approximate boundary representation and a modification (shifting) of the boundary conditions to maintain optimal accuracy. An extensive set of computational experiments in two and three dimensions is included.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Fission gas diffusion and release for Cr 2 O 3 -doped UO 2 : From the atomic to the engineering scale

Here, the anticipated benefits of large grains in Cr 2 O 3 -doped UO 2 pellets include improved mechanical and fission gas retention properties. To support the assessment of fission gas release (FGR) from doped pellets, the impact of doping on fission gas diffusivity for in-reactor conditions must be understood. In this work, we tackle this issue by informing the fission gas model within the BISON fuel performance code using material models developed at the atomic scale. The investigation of intra-granular fission gas diffusivity in Cr 2 O 3 -doped UO 2 is carried out by adapting a cluster dynamics model that, accounting for UO 2 thermochemistry, is capable of describing Xe diffusion under irradiation in undoped UO 2 as the starting point. Using a thermodynamic analysis, it is shown that in stoichiometric UO 2 with additions of Cr 2 O 3 the oxygen potential is defined by the Cr-Cr 2 O 3 two-phase equilibrium. Using the cluster dynamics model, the predicted Xe diffusivity in doped UO 2 was significantly increased in both the intrinsic and irradiation-enhanced regimes compared to undoped UO 2 as a result of higher concentrations of uranium and oxygen vacancies, respectively. This is a consequence of the more oxidizing conditions at high temperature, and more reducing conditions at low temperature, as a result of doping. Arrhenius functions have been fitted to the cluster dynamics results to enable implementation of the new diffusivities in the BISON fission gas behavior model. BISON simulations were carried out, showing the competing effects of the enlarged grains and the new fission gas diffusivity model, which act to suppress and enhance fission gas release, respectively. The new physics-informed model was validated against in-reactor experimental measurements under normal operation. Additionally, benchmarking was carried out for power ramp conditions. The predicted fission gas release agreed well with the experimental data, showing noticeable improvements over the standard UO 2 model.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Radiative transition probabilities of neutral and singly ionized Europium estimated by laser-induced breakdown spectroscopy (LIBS)

Laser-induced breakdown spectroscopy (LIBS) is a versatile technique for compositional analysis for solids, liquids, or gasses. LIBS is an asset for the quantitative or qualitative analysis of resource limited materials like actinides and rare earths because it is quasi-nondestructive. Two Eu 2 O 3 pellets were synthesized to be a test and validation set, respectively. Spectral lines identified from the National Institute of Standards and Technology database with fundamental data reported were used to form Saha-Boltzmann plots. The Saha-Boltzmann plots were used to determine the plasma temperatures and electron densities of the laser-induced plasmas for both samples. These Saha-Boltzmann plots were then used to calculate previously unreported transition probabilities associated with identified peaks. Additionally, the transition probabilities presented in this paper provide the capability for calibration free LIBS to be performed more readily on europium samples and the spectroscopic analysis of stellar bodies. Eight previously unreported transition probabilities are presented in this paper; five for Eu I and three for Eu II. The transition probabilities for Eu I ranged from 0.172 to 7.38 × 10 7 s -1 and those for Eu II ranged from 1.56 to 6.75 × 10 7 s -1 .

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

An investigation of hard-disk drive circularity accounting for socio-technical dynamics and data uncertainty

The installed data storage capacity in the U.S. will reach 2.2 Zettabytes by 2025, generating about 50 million units of end-of-life (EOL) hard-disk drives (HDDs) per year. Due to data security concerns, most EOL HDDs are currently shredded (even when still functioning), representing an economic loss. Moreover, raw material extraction linked to the increased demand for storage causes environmental impacts. Besides mitigating the threat posed by sudden restrictions of raw materials, the circular economy (CE) offers to maximize value retention in the economy and reduce the environmental impacts of human activities. Common CE strategies are reusing and recycling products. However, the reuse of hard disk drives is currently burdened by the lack of trust HDD end-users have toward other non-physical means of data removal than shredding. Here, an agent-based modeling (ABM) approach is proposed to explore how techno-economic and social factors affect end-users' decisions to adopt EOL management practices other than shredding. The proposed method also accounts for data uncertainty by applying a semi-quantitative approach. Results demonstrate how increased green procurement and more robust standards could spur end-users' trust toward data-wiping technologies. Even when accounting for uncertainty, HDDs' reuse brings better environmental and economic benefits than HDD shredding followed by material recovery. The semi-quantitative approach proposed in this study could be more universally applied in future ABM, especially given the often-stochastic nature of such models. The developed ABM is also the first to represent several HDD industry stakeholders and demonstrate how the HDD shredding lock-in situation could be resolved.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

ATLAS-MAP: An Automated Test Station for Gated Electronic Transport Measurements

The diversification of electronic materials in devices provides a strong incentive for methods to rapidly correlate device performance with fabrication decisions. In this work, we present a low-cost automated test station for gated electronic transport measurements of field-effect transistors. Utilizing open-source PyMeasure libraries for transparent instrument control, the “ATLAS-MAP” system serves as a customizable interface between sourcemeters and samples under test and is programmed to conduct transfer curve and van der Pauw methods with static and sweeping gate voltages. Zinc oxide transistors of variable thickness (5, 10, and 20 nm) and channel size (50 μm to 3 mm, of equal length and width) were fabricated to validate the design. Standardization of testing procedures and raw data formatting enabled automated data analysis. A detailed list of parts and code files for the system are provided.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Integration of Rucio Metadata in Belle II

Rucio is a Data Management software that has become a de-facto standard in the HEP community and beyond. It allows the management of large volumes of data over their full lifecycle. The Belle II experiment located at KEK (Japan) recently moved to Rucio to manage its data over the coming decade (O(10) PB/year). In addition to its Data Management functionalities, Rucio also provides support for storing generic metadata. Rucio metadata already provides accurate accounting of the data stored all over the sites serving Belle II. Annotating files with generic metadata opens up possibilities for finer-grained metadata query support. We will first introduce some of the new developments aimed at providing good performance that were done to cover Belle II use-cases like bulk insert methods, metadata inheritance, etc. We will then describe the various tests performed to validate Rucio generic metadata at Belle II scale (O(100M) files), detailing the import and performance tests that were made.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Mic-hackathon 2024: hackathon on machine learning for electron and scanning probe microscopy

Microscopy is one of the primary sources of information on materials structure and functionality at the nanometer and atomic scales. The data generated through microscopy is often contained in well-structured datasets, enriched with extensive metadata and sample histories, although not always with the same level of detail or storage format. The broad incorporation of data management plans by major funding agencies ensures the preservation and accessibility of this data. However, deriving insights from these rich datasets remains challenging due to the lack of established code ecosystems, standardized benchmarks, and integration strategies. Correspondingly, the efficiency of data usage is very low, and time expenditures at the analysis stage are enormous. In addition to post-acquisition data analysis, the emergence of application programming interfaces by major microscope manufacturers now creates opportunities for real-time ML-based data analytics to enable automated decision making, and particularly ML-agent controlled real-time microscope operation. Despite these opportunities, there is a significant gap in integrating the ML community with the broader microscopy community, limiting the value that these methods bring to physics and materials discovery and materials optimization. Hackathons address these challenges by fostering collaboration between ML experts and microscopy professionals, encouraging the development of innovative solutions that leverage ML for microscopy and preparing the workforce of the future both for microscopy-intensive domains areas, instrument manufacturers, and ML scientists interested in real world applications for fundamental research, materials optimization, and manufacturing. The hackathon generated benchmark datasets and digital twins of microscopes that further contribute to the development of the field and establish data analysis ecosystems. All the codes can be found at GitHub(https://github.com/KalininGroup/Mic-hackathon-2024-codes-publication/tree/1.0.0.1) and Zenodo (https://zenodo.org/records/15579940).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Detection of Stealthy False Data Injection Attacks in Unobservable Distribution Networks

In this paper, a composite scheme is proposed for detecting stealthy data manipulation attacks on distribution system which is unobservable with standard least squares based state estimators. This technique has three stages where the process of data imputation, voltage phasor estimation and the bad data detection are carried out in a systematic manner. The proposed approach is then integrated with moving target defense strategies which perturbs the network parameters to reveal stealthy false data injection attacks. The proposed approach is tested is validated on a three-phase, unbalanced 37-node distribution system and its results are presented. It is shown that the proposed approach has the ability to accurately detect the presence of FDI attacks using limited measurements (i.e., the test system is unobservable).

Rajasekaran, James K.↗
DOE OSTI2022

FAIRification, Quality Assessment, and Missingness Pattern Discovery for Spatiotemporal Photovoltaic Data

The growth of the photovoltaic market has pushed the demand for power forecasting and performance evaluation for a huge population of PV power plants. Many of these power plants have spatiotemporal coherence that can be utilized for improving model accuracy. We have demonstrated in this paper the FAIRification of spatiotemporal PV time series data. Through the creation of a solar power plant ontology, we propose standards for the naming and structure of metadata used to describe the data from these power plants. Using the structure from this ontology, we have developed both R and Python packages for the automation of the FAIRification process. Going further, we have also developed an R package that automates the analysis of the quality of a data set through the designation of letter grades. To solve the issue of data missingness, we propose the use of St-GNN autoencoders to detect and impute missing values from a data set by utilizing data from power plants nearby.

14 SOLAR ENERGY↗
DOE OSTI2025

xsearch

xsearch is a utility to index and search for Coupled model Intercomparison (CMIP) datasets from data stores that are in a standard directory structure.

Po-Chedley, StephenD [Lawrence Livermore National ↗
DOE OSTI2025

sourcePy

Pollutant source identification techniques (of which there are many variations) are either locked behind researchers writing their own code for each use case or GUI platforms that are easy to use but inflexible and opaque. The Python package sourcePy brings together many of the pollutant source identification algorithms, giving the user full control out of the box. It aims to create a platform for source identification experiments where the full analysis from beginning to end can be done in Python, with a level of specificity in design that isn't available in the GUI options. sourcePy provides users with a few key features: -A Python interface with HYSPLIT, which can be used to generate trajectories and concentration plumes -Several Python classes which standardize the preparation and processing of data related to source identification experiments -Example scripts and notebooks that allow even new python users to get started with their own experiments quickly -Visualization methods

Arseneau, Isaac [Oak Ridge National Laboratory (OR↗