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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

UAS remote sensing (AltaX platform): Red-green-blue (RGB) imagery, thermal infrared (TIR) imagery, and canopy reflectance (Level 0 data), Seward Peninsula, Alaska, 2022

Airborne remote sensing data collected using Brookhaven National Laboratory’s (BNL) heavy-lift unoccupied aerial system (UAS) AltaX platform on the Seward Peninsula, Alaska, USA. This package includes raw (L0) data from 10 flights flown over the NGEE-Arctic Kougarok mile marker 64 (MM64) and Teller MM 27 sites in July, 2022. The AltaX is a multi-sensor UAS platform that simultaneously measures very high spatial resolution optical red/green/blue (RGB) and thermal infrared (TIR) surface “skin” temperature imagery, as well as near-surface surface reflectance at 1 nm intervals in the visible to near-infrared spectral range from ~350 – 1000 nm. This package provides the Level 0 (raw, unprocessed) data collected by the platform and sensors. Ancillary aircraft data, flight mission parameters, and general flight conditions provided by the onboard flight and data collection computers are also included. Data and metadata are provided as text (*.txt, *.json), binary (*.dat, *.ulg), tabular (*.csv), geospatial (*.kml. *kmz), image (*.jpg, *.png, *.tif) and pdf formats. The metadata documents contain flight campaign, platform, sensor, flight and file metadata, along with a description of the L0 data and the file naming scheme.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Predicting Trends in VOC Through Rapid, Multimodal Characterization of State-of-the-Art p-i-n Perovskite Devices

Perovskite photovoltaic technologies are approaching commercial deployment, yet single junction and tandem architectures both still have significant room to improve power conversion efficiency and stability. The ability to perform rapid screening of material quality after altering processing conditions is critical to accelerating the optimization and commercialization of perovskite-based technologies. Currently, researchers utilize a wide range of stand-alone metrology tools to isolate sources of power loss throughout a device stack, which can be slow and labor intensive. Here, we demonstrate the use of a multimodal metrology approach to rapidly determine the maximum achievable and predicted open circuit voltages of >100 perovskite devices during fabrication. Acquisition of these different data is facilitated by combining them into a single integrated measurement platform. We show that these data and automated analysis can be used to rapidly understand and ultimately predict quantitative trends in open circuit voltages of state-of-the-art device architectures. The data and automated analysis workflow presented provides a reliable approach to quickly identify absorber and charge transport layer combinations that can lead to improved open circuit voltages.

14 SOLAR ENERGY↗
DOE OSTI2019

GridAPPS-D™

Distribution utilities need better tools to get the full benefit of the abundance of data within their systems. GridAPPS-D™ is a free, open-source, data integration and application development platform. It enables development of portable, advanced applications by standardizing the data exchange interfaces between devices in the field, distributed apps in the systems, and applications in the control room. Contact: Ron Melton, ron.melton@pnnl.gov. For developers: https://github.com/GRIDAPPSD.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Increasing shot and data collection rates of the Shock/Shear experiment at the National Ignition Facility

Updates to the Los Alamos laser-driven high-energy-density Shock/Shear mixing- layer experiment are reported, which have collectively increased the platform's shot and data acquisition rates. Also, the strategies employed have included a move from two-strip to four-strip imagers (allowing four times to be recorded per shot instead of two), the implementation of physics-informed rules of engagements allowing for the maximum flexibility in a shot's total energy and symmetry performance, and by splitting the laser's main drive pulse from a monolithic single pulse equal to all beams into a triply-segmented pulse which minimizes optics damage.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

Interactive XCMS Online: Simplifying Advanced Metabolomic Data Processing and Subsequent Statistical Analyses

XCMS Online (xcmsonline.scripps.edu) is a cloud-based informatic platform designed to process and visualize mass-spectrometry-based, untargeted metabolomic data. Initially, the platform was developed for two-group comparisons to match the independent, “control” versus “disease” experimental design. Here, we introduce an enhanced XCMS Online interface that enables users to perform dependent (paired) two-group comparisons, meta-analysis, and multigroup comparisons, with comprehensive statistical output and interactive visualization tools. Newly incorporated statistical tests cover a wide array of univariate analyses. Multigroup comparison allows for the identification of differentially expressed metabolite features across multiple classes of data while higher order meta-analysis facilitates the identification of shared metabolic patterns across multiple two-group comparisons. Given the complexity of these data sets, we have developed an interactive platform where users can monitor the statistical output of univariate (cloud plots) and multivariate (PCA plots) data analysis in real time by adjusting the threshold and range of various parameters. On the interactive cloud plot, metabolite features can be filtered out by their significance level (p-value), fold change, mass-to-charge ratio, retention time, and intensity. The variation pattern of each feature can be visualized on both extracted-ion chromatograms and box plots. The interactive principal component analysis includes scores, loadings, and scree plots that can be adjusted depending on scaling criteria. The utility of XCMS functionalities is demonstrated through the metabolomic analysis of bacterial stress response and the comparison of lymphoblastic leukemia cell lines.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Impact of Domain Knowledge on the Property Prediction of Specialized Machine Learning Models

Developing transferable machine learning models is trending in data-driven materials research. However, how to apply such models to a specific research domain remains unclear. Here, in this work, we choose high-entropy materials as a platform with a specialized data set containing 145,323 DFT-relaxed materials. This data set is used to explore the role of domain-specific knowledge in training effective models. Our tests with three representative graph neural network architectures indicate the model complexity has much smaller influence on performance than the data itself. Specifically, the consideration of low-energy atomic ordering, structures with diverse elemental coverage, and high-order interactions significantly influences the model performance. We also find that domain knowledge-driven sampling can greatly enhance unsupervised learning techniques. This research highlights that developing specialized data sets is more beneficial than further complicating deep learning architectures. Additionally, physics-inspired sampling algorithms are crucially needed for better machine learning models for a specific materials research domain.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Data Structure Alchemy

In an increasingly more data-driven world, the project set out to uncover the first principles of data-structure design, chart the immense design space they form, and build automation that can synthesize an optimal structure, or even a whole storage engine, for any given workload, hardware platform, and cost target. Data structures are at the center of every computational system and are directly responsible for its performance. Two core technical thrusts were defined: 1) Mapping design spaces for key data-centric abstractions (filters, hash functions, storage-engine layouts, neural-network topologies, blockchain protocols, image layouts, etc.). 2) Developing search & synthesis algorithms, initially analytical cost models, later neural-guided bi-level optimisers that navigate sextillions of candidate designs in seconds and materialise the best one as ready‐to-run code. This report distills the key insights, accomplishments, and impact.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

DOE BSSD Performance Management Metrics Report Q1

Microbes play key roles in our biosphere, from driving global nutrient cycling to impacting plant, animal and human health and disease. Complex data from microbial genomes, proteins, and metabolites provide a window into these tiny engines that drive life on our planet. Yet these data are dispersed among researchers’ laboratories and various repositories, making it difficult to access. This calls for new ways of managing data, improving data interoperability, advancing community standards, and creating an infrastructure where data are shared efficiently. We have built the National Microbiome Data Collaborative (NMDC) to advance how scientists create, use, and reuse data to redefine the way we understand and harness the power of microbes. The vision of the National Microbiome Data Collaborative (NMDC) is to drive a microbiome data sharing network connecting data, people, and ideas to advance microbiome innovation and discovery. The NMDC was launched in 2019 and brought together DOE National Laboratories to collaborate across resources, capabilities, and expertise. The NMDC team was strategically assembled to include software developers, microbial researchers, metadata experts, and multi-omics specialists. The diversity of the NMDC team reflects the inherently interdisciplinary nature of microbiome science, and we leverage the strengths of the DOE National Laboratory system. Towards BER’s goal of advancing an iterative systems biology approach to the understanding of microbial genomes, the NMDC serves as a foundation for infrastructure, data standards, and community building. Together with the flagship DOE User Facilities, the Joint Genome Institute (JGI) and the Environmental Molecular Sciences Laboratory (EMSL), we are developing core capabilities in metadata standards for environmental descriptors and sample handling and processing; standardized bioinformatic workflows; an interface for data search and access; and robust community engagement activities. The NMDC production platform supports long-term data infrastructure and community building for BER’s bioenergy and environmental research goals. Our approach leverages lessons learned and an ambitious framework for collaborative, interdisciplinary data infrastructure to support microbiome research. The NMDC supports data, information, and knowledge access through three defined software tools – the Submission Portal, NMDC EDGE, and the Data Portal – driven by community needs. Herein, we describe the value proposition for the microbiome research community, our overarching strategy, and challenges and opportunities for developing the NMDC as both an infrastructure and community engagement program.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Lessons Learned Using Iridium to Communicate with a CubeSat in Low Earth Orbit

This paper presents the design and approval process for operating an Iridium transceiver on orbit and provide on-orbit performance data obtained from a CubeSat platform in Low Earth Orbit (LEO) (500 km orbit). On-orbit data demonstrates that use of a commercial, low-cost Iridium transceiver can serve as a valuable communication approach for low volume telemetry with less than a 30-minute lag for approximately 90% of the time. In this work, we also demonstrate that a radial differential velocity of 7 km/sec corresponding to about a 37.5kHz doppler shift and a distance of less than 2,000 km can be used for mission planning.

42 ENGINEERING↗
DOE OSTI2022

OpenPATH and Itinerum Integration: A Case Study of Merging Open-Source Projects: Preprint

Throughout the transportation sector, multiple mobility tracking platforms have been developed independently, in parallel, with similar use cases and infrastructure. Maintaining such platforms on an individual level can result in duplicated effort. Innovation within the field could be slowed by the limited resource of people working on extremely similar projects. We provide a case study in how such platforms can be combined with low effort and provide an example in the mobility domain of what the resulting integration points, modules and ownership may look like. OpenPATH (formerly e-mission) and Itinerum are two examples of similar independently developed human mobility tracking platforms that extend beyond standard traffic data collection. Due to the open source nature of the projects, members from both teams decided to merge the functionality, thereby only hosting a single data collection process and limiting maintenance of separate intake platforms. On the front-end, specific questionnaire functionality from Itinerum, mainly the ability to display and return questions and results focused on sociodemographic attributes, was integrated into the OpenPATH user interface. On the back-end, an export pipeline stage was created to transport collected data back to the Itinerum database. Upon completion, the OpenPATH application retained its original functionality while adding the ability to export data for future data sharing and creating an avenue to incorporate Itinerum hosted questions and answers. Itinerum benefits from the rich feature set and infrastructure of OpenPATH without losing needed elements such as the questionnaire component. This case study, within the transportation sector, illustrates the various configurations in which open source software can be developed, maintained and deployed.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2015

Portable File Format (PFF) specifications

Created at Sandia National Laboratories, the Portable File Format (PFF) allows binary data transfer across computer platforms. Although this capability is supported by many other formats, PFF files are still in use at Sandia, particularly in pulsed power research. This report provides detailed PFF specifications for accessing data without relying on legacy code.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Demonstrating Reliable High Level Waste Slurry Sampling Techniques to Support Hanford Waste Processing

The Hanford Tank Operations Contractor (TOC) and the Hanford Waste Treatment and Immobilization Plant (WTP) contractor are both engaged in demonstrating mixing, sampling, and transfer system capability using simulated Hanford High-Level Waste (HL W) formulations. This work represents one of the remaining technical issues with the high-level waste treatment mission at Hanford. The TOC must demonstrate the ability to adequately mix and sample high-level waste feed to meet the WTP Waste Acceptance Criteria and Data Quality Objectives. The sampling method employed must support both TOC and WTP requirements. To facilitate information transfer between the two facilities the mixing and sampling demonstrations are led by the One System Integrated Project Team. The One System team, Waste Feed Delivery Mixing and Sampling Program, has developed a full scale sampling loop to demonstrate sampler capability. This paper discusses the full scale sampling loops ability to meet precision and accuracy requirements, including lessons learned during testing. Results of the testing showed that the Isolok(R) sampler chosen for implementation provides precise, repeatable results. The Isolok(R) sampler accuracy as tested did not meet test success criteria. Review of test data and the test platform following testing by a sampling expert identified several issues regarding the sampler used to provide reference material used to judge the Isolok's accuracy. Recommendations were made to obtain new data to evaluate the sampler's accuracy utilizing a reference sampler that follows good sampling protocol.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

“Innervated” Pipelines: A New Technology Platform for In-Situ Repair and Embedded Intelligence

The overall vision pursued under the project would ultimately enable capability for real-time operational monitoring of natural gas (and other) pipeline infrastructures through the combination of in-situ repair and rehabilitation methods with embedded fiber optic sensing and associated data analytics methods and platform tools. Benefits to the public include more resilient and robust natural gas pipeline infrastructures with the potential to expand the applications of developed technologies under the program to other important areas of critical energy infrastructure in the future such as H2 pipelines and infrastructure, municipal and other civil infrastructure, as well as subsurface oil, gas, and geothermal infrastructure applications. The program has successfully demonstrated feasibility for all of the critical enabling elements and serves as a foundation for future technology maturation, deployment, and customization for other potential applications and high-priority needs into the future. Several underlying innovations have shown potential for commercial deployment and are the subject of continued development and technology transfer based upon patents submitted during the project duration.

02 PETROLEUM↗
DOE OSTI2024

Enhancing Biopreparedness through a Model System to Understand the Molecular Mechanisms that Lead to Pathogenesis and Disease Transmission: NW-BRaVE

The science of biopreparedness to counter biological threats hinges on understanding the fundamental principles and molecular mechanisms that lead to pathogenesis and disease transmission. Our vision to address this challenge is to create a powerful and user-friendly platform to elucidate the fundamental principles of how molecular interactions drive pathogen-host relationships and host shifts. We will enable groundbreaking discoveries by integrating a wide range of structural, genomics, proteomics, and other advanced omics measurements, along with evolutionary and artificial intelligence predictions. To make sure the system is applicable to real-world problems, we will develop it in the context of a tractable model system, the small, abundant, and accessible photosynthetic cyanobacteria and their constantly co-adapting viral pathogens, cyanophages. This model will maintain the system’s applicability to real-world problems and techniques, but the overall focus will be on elucidating general principles of detecting, assessing, and surveilling molecular interaction, adaptation, and coevolution that are system agnostic and therefore extensible to other viral-host interactions. Our overall objectives are to (1) identify the molecular complexes that comprise the cyanobacteria redox macromolecular subsystem and how they dynamically change with bacteriophage infection in situ, using cryo-electron tomography; (2) profile regulatory changes during infection using proteomics, multiomics, and experimental validation, and integrate the data with in situ structures; (3) use genomics and metagenomics to determine environmental and population factors across time scales that impact the interactions between marine cyanobacteria and their cyanophage parasites, predicting the evolutionary origins of in situ structural and functional interactions, convergence and coevolution; and (4) develop a data integration and transformation platform that facilitates the integration of in situ, proteomic, and evolutionary measurements of molecular interactions to surveil diverse hosts and parasites in various environmental contexts. These objectives address Focus Area 2 Reveal Molecular Interactions Across Biological Scales for Design of Targeted Interventions. Our powerful and user-friendly platform will enhance connections between the often-siloed fields of structure, molecular phenotype, and evolutionary genomics that are key to biopreparedness, but in need of integration (Figure 1). We will build an integrated navigation tool to facilitate the effective use of globally distributed experimental data for integrated analysis and predictive modeling. The project will develop, implement, and test a platform to assess host-pathogen molecular interactions, adaptation to hosts and host shifts, and coevolution between hosts and pathogens, successfully impacting the research community by revolutionizing abilities to study any host-pathogen interaction, encourage diverse community contributions, and gain fundamental insights into how proteins adapt to new contexts. This ability will be critical for designing early interventions to address future threats. We will build surveillance training capability, aiming for a fair and equitable response to future pandemics and biothreats.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Evaluating the Feasibility of Transactive Approach for Voltage Management Using Inverters of a PV Plant

This article evaluates the feasibility of a double-auction-based Transactive Energy System (TES) for engaging the reactive power (Var) capability of inverters in a utility-scale pho¬tovoltaic (PV) plant for voltage management in distribution feed¬ers. In this approach, a PV plant owner (seller) provides Var supply curves in terms of price-quantity pairs consider¬ing the inverters’ P-Q capability, efficiency, opportunity cost for real power curtail¬ment, losses, etc. At the same time, the utility (buyer) proposes Var demand curves in price-quantity pairs exhibiting the marginal price of Var. Then, market clearing points are obtained from the intersection points of supply and demand curves which are then used to create Var dispatch sig-nals from the PV plant. To conduct a fea¬sibility study of this approach, an Australian distribution network with a utility-scale PV plant (capacity 3.275 MWP) is modeled in RSCAD and simu¬lated in real-time on MATLAB, RSCAD-RTDS co-simulation platform using real historical data. Consid¬ering a use case, namely, conservation voltage reduction (CVR), the operational feasibility along with cost-benefit analysis is demonstrated in software-in-the-loop (SIL) and hardware-in-the-loop (HIL) platform. Another use case, exploring the benefit from tap-change reduction of step-voltage-regulator, is investigated. These studies explore further use cases, e.g., PV hosting capacity, network reconfiguration, etc., to be incorporated in TES for experiencing substantial economic benefits.

Alam, Mollah Rezaul↗
DOE OSTI2018

Effects of Platform Design on the Customer Experience in an Online Solar PV Marketplace

Residential solar photovoltaic (PV) customers are increasingly buying PV systems in online marketplaces, where customers can compare multiple quotes from several installers on quote platforms. In this study, we use data from an online marketplace to explore how quote platform design affects customer experiences. We analyze how four design changes affected customer experiences in terms of factors such as prices. We find that three of the four design changes are associated with statistically significant and robust price reductions, even though none of the changes were implemented specifically to reduce prices. The results suggest that even seemingly small platform design changes can affect PV customer experiences in online marketplaces.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Forecasting Responses of a Northern Peatland Carbon Cycle to Elevated CO 2 and a Gradient of Experimental Warming

The ability to forecast ecological carbon cycling is imperative to land management in a world where past carbon fluxes are no longer a clear guide in the Anthropocene. However, carbon–flux forecasting has not been practiced routinely like numerical weather prediction. This study explored (1) the relative contributions of model forcing data and parameters to uncertainty in forecasting flux– versus pool–based carbon cycle variables and (2) the time points when temperature and CO 2 treatments may cause statistically detectable differences in those variables. We developed an online forecasting workflow (Ecological Platform for Assimilation of Data (EcoPAD)), which facilitates iterative data–model integration. EcoPAD automates data transfer from sensor networks, data assimilation, and ecological forecasting. We used the Spruce and Peatland Responses Under Changing Experiments data collected from 2011 to 2014 to constrain the parameters in the Terrestrial Ecosystem Model, forecast carbon cycle responses to elevated CO 2 and a gradient of warming from 2015 to 2024, and specify uncertainties in the model output. Our results showed that data assimilation substantially reduces forecasting uncertainties. Interestingly, we found that the stochasticity of future external forcing contributed more to the uncertainty of forecasting future dynamics of C flux–related variables than model parameters. However, the parameter uncertainty primarily contributes to the uncertainty in forecasting C pool–related response variables. Given the uncertainties in forecasting carbon fluxes and pools, our analysis showed that statistically different responses of fast–turnover pools to various CO 2 and warming treatments were observed sooner than slow–turnover pools. In conclusion, our study has identified the sources of uncertainties in model prediction and thus leads to improve ecological carbon cycling forecasts in the future.

54 ENVIRONMENTAL SCIENCES↗