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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 OSTI2024

WELLBASE - An Interactive Platform for Wellbore Material Assessment

This project seeks to build an open-source wellbore material data repository with adequate material performance and contextual data to support Geological Carbon Storage (GCS). By appropriately evaluating the data types as mentioned earlier made available by the WELLBASE tool, stakeholders can make more informed decisions regarding well selections, risk assessment, and economic analysis for geologic carbon storage projects. Advanced Natural Language Processing models and other custom python scripts will be deployed in an automated process to extract unstructured data from documents, reports, and web applications and subsequently parse to more usable formats. The processed data will then be integrated into a robust and comprehensive database architecture, optimizing data accessibility, and usability for analytical purposes. The final data products will be accessible through a user-friendly visualization platform that will allow users to query and visualize the data, as well as download data in usable formats.

Tetteh, Daniel A.↗
DOE OSTI2025

Leveraging AI and Spatial Data to Unlock Pipeline Integrity Insights: NETL’s Advanced Infrastructure Integrity Model (AIIM)

Maintaining the integrity of natural gas infrastructure plays a critical role in ensuring energy security. Robust, data-driven foundational AI models for pipeline integrity can help address risk management and mitigation issues. Trusted foundational models can help with industry adoption and accelerate innovation by enhancing integrity predictions, reduce costs, and informing infrastructure build-out. The AIIM dashboard was released in 2022 and utilizes multi-ML models for ensemble-type insights. It was expanded to include analytics on reported incidents. It was developed as an ESRI Dashboard to support data visualization & interrogation and contains pipeline data and model results.

Advanced Infrastructure Integrity Model (AIIM)↗
DOE OSTI2026

UCB-GLOBES: An open-access mass spectral database of identified and unidentified atmospheric organic compounds

Chemical characterization of atmospheric organic aerosols using gas chromatography with 70 eV electron ionization mass spectrometry (GC/EI-MS) has been used for decades in advancing molecular marker detection and identification, though primarily through suspect screening and/or targeted analyses. To advance non-targeted analyses of environmental samples, we have catalogued approximately 27 000 mass spectra (MS) of the trimethylsilyl derivatives of semi-volatile organic aerosol (OA) analytes in the open-access University of California Berkeley Goldstein Library of Organic Biogenic Environmental Spectra (UCB-GLOBES). Analytes were observed in ambient samples from the U.S. and the Central Amazon and/or laboratory simulations of secondary OA (SOA) formation. These samples are representative of OA under urban and biomass burning influences as well as SOA derived from biogenic precursors (e.g., isoprene, monoterpenes, sesquiterpenes) and biomass burning intermediates. MS are documented in UCB-GLOBES without regard to known chemical identity, annotated with extensive metadata such as sample source/experimental conditions, any structural information gained from MS analyses, and predicted chemical properties such as average carbon oxidation state and carbon number. UCB-GLOBES MS are compatible for importing into the NIST MS Search program, and we have also provided a Jupyter Notebook for MS visualization and comparisons. We demonstrate the utility of UCB-GLOBES through MS reanalyses of prior analytes observed in ambient data, finding a 20 % reduction in the number of analytes assigned to OA source categories reliant solely on time series correlation and an overall 11 % increase in new MS-based OA source categorization for the Southeast U.S. For 1513 analytes observed previously in the Central Amazon, we found 375 MS matches using UCB-GLOBES vs. 136 MS matches during prior analyses, representing a 14 % gain in newly confirmed or newly categorized OA species. While OA from laboratory oxidation experiments in UCB-GLOBES are highly diverse chemically, on average only 29 % of UCB-GLOBES MS have a mass spectral match to another MS entry in UCB-GLOBES and/or in databases of known compounds (i.e. NIST MS Database, Adams Essential Oil, MANE Flavor and Fragrance Company). This indicates that roughly 70 % of UCB-GLOBES MS are unique thus far, not observed more than once among the laboratory oxidation samples and ambient data in UCB-GLOBES MS. Further, only 18 % can be positively identified using these databases or known authentic standards. This points to a large gap between these laboratory simulations and ambient OA. Overall, the UCB-GLOBES database can be utilized for improving confidence in OA source categorization and/or identification, novel chemical marker discovery, tracking chemical diversity, de novo structure and properties prediction, and improving MS search and matching algorithms. This can ultimately inform future research priorities for the chemical characterization of atmospheric organic samples.

Mass spectrometry↗
DOE OSTI2017

Helium Ion Microscopy for Imaging and Quantifying Porosity at the Nanoscale

Nanoporous materials are essential components in a vast number of applications from energy to drug delivery and to agriculture. Yet, the number of ways to analytically quantify the salient features of these materials, for example: surface structure, pore shape, and size, remain limited. The most common approach is gas absorption, where volumetric gas absorption and desorption are measured. This technique has some fundamental drawbacks such as low sample throughput and a lack of direct surface visualization. In this work, we demonstrate Helium Ion Microscopy (HIM) as a tool for imaging and quantification of pores in industrially relevant SiO 2 catalyst supports. We start with the fundamental principles of ion-sample interaction, and build on this knowledge to experimentally observe and quantify surface pores by using the HIM and image data analytics. We contrast our experimental results to gas absorption and demonstrate full statistical agreement between two techniques. The principles surrounding the theoretical, experimental, and analytical framework presented herein offer an automated framework for visualization and quantification of pore structures in a wide variety of materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

Data-Intensive Visual Analysis for Cyber Security

Protecting communications networks against attacks where the aim is to steal information, disrupt order, or harm critical infrastructure can require the collection and analysis of staggering amounts of data. The ability to detect and respond to threats quickly is a paramount concern across sectors, and especially for critical government, utility and financial networks. Yet detecting emerging or incipient threats in immense volumes of network traffic requires new computational and analytic approaches. Network security increasingly requires cooperation between human analysts able to spot suspicious events through means such as data visualization and automated systems that process streaming network data in near real-time to triage events so that human analysts are best able to focus their work.

Pike, William A.↗
DOE OSTI2023

Field validation of isotropic analytical models for simulating fabric shades

Fabric roller shades are common shading materials used in commercial and residential buildings. Accurately characterizing and modeling shades helps practitioners select the appropriate product and its control strategy based on climate and occupants' priorities, such as visual comfort and view to outdoors. Previous studies established a generalized method for modeling complex fenestration systems using data-driven tabulated bidirectional scattering distribution functions. However, deploying such a method at scale to all fabric shading products on the market is too costly and time-consuming. Analytical models that are based on a limited set of measurements (e.g., normal-normal and normal-hemispherical visible transmittance and reflectance, and directional cut-off angles) can be used to model the wide variety of shading products on the market. This study evaluates the performance of two isotropic analytical models, Roos-Wienold and Modified-Kotey, for modeling fabric roller shades, with a focus on the model's ability to predict occupant visual comfort. The performance evaluation was conducted through laboratory and field measurements and simulations. The results showed that both models are sufficient for predicting vertical illuminance at seated eye-level. Roos-Wienold model was able to predict binary visual comfort classification (glare/no-glare) under a wide range of luminance conditions, while Modified-Kotey model did not perform as well under high-contrast low-adaptation conditions. Both models are insufficient in predicting visual comfort at a four-point scale (e.g., imperceptible, perceptible, disturbing, intolerable). The two isotropic models become less accurate when the fabric exhibits high anisotropy.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

HunStat – a simple and low-cost potentiostat for analytical and educational purposes

We have developed a truly low-cost (15 USD), simple do-it-yourself (DIY) potentiostat with compact dimensions. The output potential range of this device is between ±1.65 V. The developed instrument takes advantage of a Seeeduino XIAO microcontroller equipped with 10 bit digital-to-analog (D/A) and 12 bit analog-to-digital (A/D) converters and supports various voltammetry techniques, including cyclic voltammetry (CV), differential pulse voltammetry (DPV), and chronoamperometry (CA). Interested users are provided with circuit diagrams, bill of materials, and design files. Additionally, software components are also provided free of charge, including an Arduino sketch and control software. The software enables easy manipulation of electrochemical parameters and visualization of results. The presented design introduces a simple and low-cost DIY potentiostat recommended for both analytical and educational purposes.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Visualizing Millisecond Atomic Dynamics of Nanocrystals in Liquid

Atomic structures of nanomaterials are inherently dynamic and continuously reshaped through interactions with chemical species and external stimuli. Such dynamics are further amplified as the size and dimensionality of nanomaterials decrease. Despite advances in analytical methods, it remains challenging to capture the structural dynamics of nanomaterials in reactive environments with both atomic spatial resolution and commensurate temporal resolution. Here, in this study, we directly visualize atomic-scale dynamics of gold (Au) nanocrystals in reactive liquid environments with millisecond-speed liquid-cell electron microscopy (EM) and deep-learning denoising. We uncover reversible fluctuations in the local crystallinity of Au nanocrystals dependent on the surrounding chemical environment. These transient fluctuations, driven by interactions at nanocrystal–liquid interfaces, critically influence the dissolution kinetics and grain boundary relaxation. By overcoming the spatiotemporal limitations in conventional liquid-cell EM, our findings provide insights into how transient nanoscale structures dictate the stability and reactivity of nanomaterials.

Kang, Sungsu [University of Chicago, IL (United St↗
DOE OSTI2026

Holistic energy analysis method for thermal management architectures of data centers

Modern high-performance computing (HPC) data centers (DCs), particularly those supporting energy-intensive artificial intelligence (AI) workloads, face escalating thermal management challenges that degrade performance through thermal throttling and drive up cooling power consumption and operational costs. To address this challenge, many have developed a wide variety of thermal management solutions (single-phase, two-phase, direct, indirect, hybrid, and more) which attempt to cool HPC DCs effectively while attempting to minimize overall system power consumption. However, the analysis of these solutions and methods to effectively compare one with another is lacking. Overall power usage effectiveness (PUE) and total-power usage effectiveness (TUE) provide a metric to quantify power consumption but fail to identify components in the system which require further optimization. To address this, we propose a holistic analytical framework – the waterfall diagram (WFD) – which leverages a waterfall chart methodology, offering a comprehensive visualization of both the thermal management system loop and heat flow pathways from individual server components to the outdoor ambient. Use of the WFD enables graphical estimations of power efficiency and cooling performance across each component of a DC cooling system and complements Sankey-style energy flow visualizations by additionally resolving stage-wise temperature changes and incremental TUE contributions. The framework is used in conjunction with simulation-based approaches, to conduct a detailed pressure drop and flow distribution analysis aimed at identifying the optimal coolant distribution architecture for a single-phase direct-to-chip water-cooled DC, which serves as the baseline for subsequent WFD analysis. Among the evaluated architectures, the 3 U modular coolant distribution architecture is found to demonstrate the best performance, considering minimal pressure drop and uniform flow distribution. In addition, TUE is calculated for each cooling loop component based on its associated pressure drop and corresponding pumping power, which are integrated into the WFD. This correlation between TUE and local temperature offers immediate insight into the power efficiency and thermal performance contributions of individual components, facilitating further development and optimization. Examples of WFD applications are presented under varying thermal loads and ambient conditions, demonstrating reasonable cooling strategies. Notably, the 3 U modular architecture maintains a consistent chip case temperature of 85°C, achieving a TUE of 1.016 at ambient temperature of 47°C, and a TUE of 1.026 at ambient temperature of 52°C. The WFD methodology provides an efficient, holistic, and streamlined framework for DC thermal management architecture assessment and enables design optimization which is important for addressing the thermal-fluidic energy challenges of current and next-generation DCs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Modeling Chemical Evolution of Cement Seals

The prevention of the migration of fluids is a field of study with high demand in both the private industry as well as in government research and development. More particularly, the prevention of carbon dioxide (CO 2 ) migration or return to the atmosphere. This, in turn, causes measurably significant atmospheric climate change that results in immense damage to the environment. In this study, the interaction between 0.5 molar sodium chloride (NaCl) brine solution and a cement interface over extended periods of time is computationally investigated, as well as the interaction of a 33 millimolar CO 2 dissolved in the afore-mentioned brine solution for the same period of time. A 2-meter-long saturated brine solution reacted with an 800-meter-long cement block in PFLOTRAN, an open-source reactive flow transport software whose output was later visualized in ParaView, a separate, open-source, visualization software.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Real-time GIS Programming and Geocomputation

Streaming data generated continuously from sensor networks, mobile devices, social media platforms and other edge devices have posed significant challenges to existing computing platforms for achieving both high throughput and low latency data processing in addition to scalable computing. This entry introduces a real-time computing and programming platform for time-critical GIS (Geographic Information System) applications. In this platform, advanced streaming data processing software, such as Apache Kafka and Spark Streaming, are integrated to enable data analytics in real-time. This computing platform can also be extended to integrate GeoAI (Geospatial Artificial Intelligence) based machine learning models to leverage both historical and streaming data to achieve real-time prediction and intelligent geospatial analytics. Two real-time geospatial applications in terms of flood simulation and climate data visualization are introduced to demonstrate how real-time programming and computing can help tackle real-world problems with important societal impacts.

Liu, Yan↗
DOE OSTI2015

Investigations of element spatial correlation in Mn-promoted Co-based Fischer–Tropsch synthesis catalysts

Making connections between performance and structure in bimetallic catalysts requires knowledge of how the two elements are spatially associated. Elemental maps obtained by analytical TEM methods are an invaluable tool for identifying the location of different elements, but for many samples, visual inspection of elemental maps is insufficient for assessing the degree of element spatial correlation. This is particularly true for beam-sensitive materials where short mapping acquisition times lead to images with high noise and low color depth. In these situations, statistical analysis of elemental maps can be used to identify spatial correlations among the elements in a sample. In this work, the relationship between catalyst performance and bimetallic spatial association was explored using Mn-promoted Co-based Fischer-Tropsch synthesis catalysts prepared by different pretreatment methods. Mn was used as a catalyst additive to suppress methane formation. Catalysts that underwent calcination before reduction produced more methane and fewer long-chain hydrocarbons than catalysts that were directly reduced. The extent to which Co and Mn were spatially associated was assessed using correlation metrics, colocation plots, and histograms generated using data from STEM-EDS maps. Although both catalysts yielded visually similar elemental maps, the results of statistical analysis suggested that the calcined catalyst exhibited greater spatial segregation between the Co and Mn. These findings support the hypothesis that having Mn in close proximity to the Co is essential for the manifestation of Mn promotion effects in Co-based FTS catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Predictive process mapping for laser powder bed fusion: A review of existing analytical solutions

One of the main challenges in the laser powder bed fusion (LPBF) process is making dense and defect-free components. These porosity defects are dependent upon the melt pool geometry and the processing conditions. Power-velocity (PV) processing maps can aid in visualizing the effects of LPBF processing variables and mapping different defect regimes such as lack-of-fusion, under-melting, balling, and keyholing. This work presents an assessment of existing analytical equations and models that provide an estimate of the melt pool geometry as a function of material properties. The melt pool equations are then combined with defect criteria to provide a quick approximation of the PV processing maps for a variety of materials. Finally, the predictions of these processing maps are compared with experimental data from the literature. Here, the predictive processing maps can be computed quickly and can be coupled with dimensionless numbers and high-throughput (HT) experiments for validation. The present work provides a boundary framework for designing the optimal processing parameters for new metals and alloys based on existing analytical solutions.

36 MATERIALS SCIENCE↗
DOE OSTI2016

PB-AM: An open-source, fully analytical linear poisson-boltzmann solver

Here, we present the open source distributed software package Poisson-Boltzmann Analytical Method (PB-AM), a fully analytical solution to the linearized PB equation, for molecules represented as non-overlapping spherical cavities. The PB-AM software package includes the generation of outputs files appropriate for visualization using visual molecular dynamics, a Brownian dynamics scheme that uses periodic boundary conditions to simulate dynamics, the ability to specify docking criteria, and offers two different kinetics schemes to evaluate biomolecular association rate constants. Given that PB-AM defines mutual polarization completely and accurately, it can be refactored as a many-body expansion to explore 2- and 3-body polarization. Additionally, the software has been integrated into the Adaptive Poisson-Boltzmann Solver (APBS) software package to make it more accessible to a larger group of scientists, educators, and students that are more familiar with the APBS framework.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

On the structure of the drifton phase space and its relation to the Rayleigh–Kuo criterion of the zonal-flow stability

The phase space of driftons (drift-wave quanta) is studied within the generalized Hasegawa–Mima collisionless-plasma model in the presence of zonal flows. This phase space is made intricate by the corrections to the drifton ray equations that were recently proposed by Parker [J. Plasma Phys. 82, 595820602 (2016)] and Ruiz et al. [Phys. Plasmas 23, 122304 (2016)]. Contrary to the traditional geometrical-optics (GO) model of the drifton dynamics, it is found that driftons can not only be trapped or passing but also accumulate spatially while experiencing indefinite growth of their momenta. In particular, it is found that the Rayleigh–Kuo threshold known from geophysics corresponds to the regime when such “runaway” trajectories are the only ones possible. On one hand, this analysis helps to visualize the development of the zonostrophic instability, particularly its nonlinear stage, which is studied here both analytically and through wave-kinetic simulations. On the other hand, the GO theory predicts that zonal flows above the Rayleigh–Kuo threshold can only grow; hence, the deterioration of intense zonal flows cannot be captured within a GO model. In particular, this means that the so-called tertiary instability of intense zonal flows cannot be adequately described within the quasilinear wave kinetic equation, contrary to some previous studies.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Grid Data Crossing Platform (GriD-Xing)

This project is to develop a data management and analytics platform called Grid Data Crossing (GriD-Xing) that integrates data processing, storage, analysis, and visualization functionalities for users at industry (e.g., EMS and DMS) and universities. It intends to increase the applicability of large set of multi-source data (e.g., SCADA, PMU, μPMU, AMI, inverter, load/DER forecasting, and cyber data) and eventually improve the resilience, reliability, and security of critical energy infrastructure. GriD-Xing also intends to be a research platform to identify the usefulness of large multi-source data via case studies of interest to industry (e.g., 2011 southwest cold weather and 2016 Blue Cut fire events). In the first year, the team focused on the development and demonstration of large multi-source data (e.g., PMU, μPMU, SCADA, AMI, and cyber data) processing, storage, and analysis. Specifically, the team designed GriD-Xing framework in functional and structural aspects, developed GriD-Xing algorithms for large multi-source grid data processing, storage, and analysis, and finally demonstrated GriD-Xing prototype algorithms performance through a series of tests and case studies using real-world and simulation data.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Residential Solar-Adopter Income and Demographic Trends: 2021 Update [Slides]

The report describes income, demographic, and other socio-economic trends among U.S. residential rooftop solar adopters. The report is based on data for roughly 1.9 million residential rooftop solar systems installed through 2019, representing 82% of all U.S. systems. With its unique size, geographic scope, and level of detail, this report is intended to serve as a foundational reference document for policy-makers, industry stakeholders, and researchers. Key findings include the following: Solar adopters generally skew towards higher incomes, though that trend continues to diminish over time. Solar adopter incomes vary considerably and encompass many low-to-moderate income (LMI) households. Solar-adopter incomes are consistently higher for systems paired with battery storage, for host-owned systems, and for systems installed on single-family homes. Solar adopters differ from the broader U.S. population in terms of a variety of other demographic and socioeconomic measures. State-level comparisons indicate that solar-adopters tend to live in neighborhoods with relatively high non-Hispanic White and Asian populations, and with relatively low Hispanic and Black populations. In conjunction with the report, Berkeley Lab has published an updated accompanying set of online data visualizations that allow users to further explore the underlying data. Berkeley Lab is also offering related analytical support to states, local agencies, and other organizations on issues related to solar adoption among low-to-moderate income households.

14 SOLAR ENERGY↗
DOE OSTI2022

Residential Solar-Adopter Income and Demographic Trends: November 2022 Update [Slides]

The report describes income, demographic, and other socio-economic trends among U.S. residential rooftop solar adopters. The report is based on address-level data for roughly 2.8 million residential rooftop solar systems installed through 2021, representing 86% of all U.S. systems. With its unique size, geographic scope, and level of detail, this report is intended to serve as a foundational reference document for policy-makers, industry stakeholders, and researchers. Key findings include the following: -Median solar adopter income was about $\$110$k/year in 2021, compared to a U.S. median of about $\$63$k/year for all households and $\$79$k/year for all owner-occupied households -The degree of income skew varies significantly across all states, but all states exhibit some positive income skew, with median solar-adopter incomes ranging from 131-168% of the respective county-median income for all households -Notwithstanding the fact that solar adopter incomes skew high, a substantial share of adopters could be considered low-to-moderate income (LMI), with 22% of all 2021 adopters earning less than 80% of area median income, and an additional 21% between 80% and 120% of area median income. -Solar-adopter incomes are declining over time, with median incomes dropping from $\$129$k in 2010 to $\$110$k in 2021, as adoption becomes more proportionately distributed across the population and has started to broaden into low- and middle-income states since 2016. -Solar-adopter incomes are consistently higher for systems paired with battery storage, for host-owned systems, and for systems installed on single-family homes; higher income adopters also consistently install larger systems. -Solar adopters tend to live in Census Tracts not identified as “disadvantaged communities” (using the U.S. Department of Energy’s interim definitions developed March 2022), making up 11% of adopters compared to 18% of U.S. households. -Compared to the broader population, solar adopters tend to: identify as Non-Hispanic White, be primarily English-speaking, have higher education levels, be middle-aged, work in business and finance-related occupations, and live in higher-value homes In conjunction with the report, Berkeley Lab has published an updated accompanying set of online data visualizations that allow users to further explore the underlying data. Berkeley Lab is also offering related analytical support to states, local agencies, and other organizations on issues related to solar adoption among low-to-moderate income households; requests for analytical support may be submitted through this online form.

13 HYDRO ENERGY↗