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At least 1,135 records · Page 63

On Wings of the Minimum Induced Drag: Spanload Implications for Aircraft and Birds

For nearly a century Ludwig Prandtl's lifting-line theory remains a standard tool for understanding and analyzing aircraft wings. The tool, said Prandtl, initially points to the elliptical spanload as the most efficient wing choice, and it, too, has become the standard in aviation. Having no other model, avian researchers have used the elliptical spanload virtually since its introduction. Yet over the last half-century, research in bird flight has generated increasing data incongruous with the elliptical spanload. In 1933 Prandtl published a little-known paper presenting a superior spanload: any other solution produces greater drag. We argue that this second spanload is the correct model for bird flight data. Based on research we present a unifying theory for superior efficiency and coordinated control in a single solution. Specifically, Prandtl's second spanload offers the only solution to three aspects of bird flight: how birds are able to turn and maneuver without a vertical tail; why birds fly in formation with their wingtips overlapped; and why narrow wingtips do not result in wingtip stall. We performed research using two experimental aircraft designed in accordance with the fundamentals of Prandtl's second paper, but applying recent developments, to validate the various potentials of the new spanload, to wit: as an alternative for avian researchers, to demonstrate the concept of proverse yaw, and to offer a new method of aircraft control and efficiency.

aerodynamics↗

Core/Combustor-Noise Measurements and Source Separation for the DGEN Aeropropulsion Research Turbofan

Acoustic data obtained using a small turbofan engine are further analyzed with the aim to ultimately enable improved prediction methods and mitigation techniques for turbofan core/combustor noise. The relative impact of this propulsion-noise source for civilian-transport aircraft on airport community noise is expected to significantly increase in the future. Simultaneous high-data-rate acoustic measurements acquired using a circumferential infinite-tube-probe array at the core-nozzle exit in conjunction with a far-field microphone array are processed. The test matrix contains engine operational points from idle to engine-maximum setting, with measurements repeated for different circumferential array configurations, as well as for redundancy. The combustor-noise contribution to the far-field noise signature is obtained using an advanced source-separation method that correlates far-field microphone measurements with a modal decomposition of the unsteady pressure field at the core-nozzle exit. The advantages of the present approach compared to the classical two-signal source-separation method are discussed.

Aeroacoustics↗

Energy Systems Integration Facility (ESIF): World-Class Systems Integration Capabilities and Research

The Energy Systems Integration Facility (ESIF), located at the National Renewable Energy Laboratory (NREL) South Table Mountain campus, is a world-renowned user facility for research and development of modern, advanced, and clean energy technologies. ESIF is distinguished by its continuously evolving, highly integrated systems that span throughout the building, connecting research capabilities across multiple laboratories and test areas. The primary ESIF research systems include: [1] data, cyber, and control networks, [2] research electrical distribution buses (REDB), [3] thermal integration infrastructure, and [4] hydrogen systems. The data, cyber, and control networks provide monitoring, control, communication, automation, visualization, and time series data storage and tagging capabilities for research projects and ESIF systems, including facility safety functions. The REDB system consists of four dedicated AC and DC electrical power networks that can connect devices located across the facility through versatile, automatic circuit configuration to support complex power electronics experiments up to the megawatt-scale. The thermal integration infrastructure consists of three temperature-conditioned water loops that provide heating and cooling interfaces and capabilities for thermal energy research. The hydrogen systems provide megawatt-scale hydrogen production, drying, compression, high-pressure storage, and delivery to laboratory end uses, including hydrogen fuel cell vehicle fueling. The ESIF research systems interconnect and extend throughout the various lab areas of the facility to create elaborate networks composed of diverse technologies for cutting-edge research. The ESIF capabilities are operated and stewarded by the ESIF Research Operations group, who also actively upgrade and advance the systems to ensure they remain ahead of anticipated research - enabling the success of many pioneering energy integration projects. The poster, created by members of the ESIF Research Operations team, highlights and summarizes the four core integrated systems at ESIF. The poster was first presented at the internal NREL Energize Forum on May 13th, 2024, and received the "Best Poster" award.

capabilities↗

Trust Not Verify? The Critical Need for Data Curation Standards in Materials Informatics

The importance of data curation has been recognized in multiple areas of research; however, the discussion of this important issue is only beginning to emerge in materials science. In this Perspective, we highlight the benefits of using the standardized data curation protocols in materials science and discuss current gaps in accurate and reproducible data reporting using case studies drawn from high-impact materials science papers and well-known databases such as the Crystallography Open Database (COD) and the Cambridge Structural Database (CSD). We argue that both experimental and computational materials scientists need to embrace a culture of rigorous data curation as part of modern research data management. We propose a sample data curation pipeline for materials chemistry and illustrate its use by creating two new materials chemistry databases. Here, we hope that this perspective will serve to catalyze further discussion and promote the continuous development of rigorous data curation practices within the materials science research community. We posit that adherence to best practices of data curation will promote and enhance the reliability, reproducibility, and integrity of materials research and enable the development of reliable AI and machine learning models that critically depend on the use of quality data.

Chemical structure↗

Multilateral Biomedical Data Sharing in the One-year Joint US-Russian Mission on the International Space Station

The One Year Mission (1YM) by two astronauts on the International Space Station (ISS), starting in March 2015, offers a unique opportunity to expand multilateral collaboration by sharing data and resources among the partner agencies in preparation for planned space exploration missions beyond low Earth orbit. Agreements and protocols will be established for the collection, distribution, analysis and reporting of both research and clinical data. Data will be shared between the agencies sponsoring the investigators, and between the research and clinical medicine communities where common interests are identified. The assignment of only two astronauts, one Russian and the other American, to the 1YM necessitated creativity in bilateral efforts to maximize the biomedical return from the opportunity. Addition of Canadian, European and Japanese investigations make the effort even more integrative. There will be three types of investigations: joint, cross‐participation and data‐exchange. The joint investigations have US and Russian coprincipal investigators, and the data acquired will be their common responsibility. The other two types must develop data sharing agreements and processes specific to their needs. A multilateral panel of ISS partner space agencies will develop policies for international exchange of scientific information to meet their science objectives and priorities. They will promote archiving of space flight data and will inform each other and the scientific community at large about the results obtained from space life sciences studies. Integration tasks for the 1YM are based on current experience from the ISS and previous efforts on the Russian space station Mir. Closer coordination between international partners requires more common approaches to remove barriers to multilateral resource utilization on the ISS. Greater integration in implementation should increase utilization efficiency to benefit all participants in spaceflight human research. This presentation will describe the overarching principles for multilateral data collection, analysis and sharing and for data security for medical and research data shared between ISS partners prior to release in public forums.

Charles, John B.↗

A systematic review of machine learning in groundwater monitoring

With increasing concerns about water scarcity, groundwater has become crucial since this resource provides most of the freshwater needs. However, various human and natural activities often contaminate the groundwater, making it unsuitable for use. Over the years, scientists and engineers have used many methods to predict and track groundwater contamination as part of environmental monitoring. Consequently, there is an urgent need for improved methods, particularly in the face of increasing contamination. Machine learning has sometimes been used to monitor groundwater, air quality, and climate. Traditional methods must be improved due to the complexity and large amount of environmental data. This includes using hybrid models that combine traditional and new techniques. Despite the use of machine learning in many scientific areas, there is a lack of comprehensive reviews focusing on its use in environmental monitoring, especially groundwater monitoring. We aim to fill this gap by exploring machine-learning applications in groundwater monitoring. We discuss relevant methods, their limitations, and future potential. We summarize research on automating data processing and model training using groundwater sensor data. Our research underscores the transformative potential of machine learning to revolutionize long-term groundwater monitoring and contamination detection, providing valuable insights for future research and practical applications.

AI/ML↗

RTe3_data

The purpose of this dataset is to provide data availability for a research paper. It contains experimental data used to analyze and present the results discussed in the research paper.

Physics↗

Diagnostic assessment of reservoir response to fracturing: a case study from Hydraulic Fracturing Test Site (HFTS) in Midland Basin

Abstract This paper outlines a data collection and diagnostics case study involving multiple horizontal shale wells. We look at well production profiles using rate transient analysis, differences in near wellbore complexity, geologic variations within the area of interest, as well as compositional differences in the rocks based on cores obtained from within the stimulated reservoir. The Hydraulic Fracturing Test Site is a multi-well experiment involving stimulation of unconventional shale wells in the southeastern Midland portion of the Permian Basin. The targeted formations include both the upper as well as the middle Wolfcamp formations, also referred alternatively as Wolfcamp A and Wolfcamp B. Data integration and analysis shared in this paper help us understand the various geologic controls impacting well productivity, particularly the wide variance observed between the Wolfcamp A and Wolfcamp B formations. Rate transient analysis indicates similar system permeabilities for stimulated wells. However, we observe higher effective fracture half-lengths for upper Wolfcamp wells. Using observations from 3D seismic interpretations (such as pad scale faults) as well as petrophysical and image log data, we highlight the substantial differences in stimulation as we move along the well laterals from the heel toward the toe sections. These differences are further reconciled with observations from zones with high data density at the core locations through stimulated rock, as well as independent data such as microseismic emissions. At the test site, Wolfcamp A was found to be relatively quartz rich with significant heterogeneity whereas Wolfcamp B is richer in clay and organic content. This impacts the geomechanical characteristics of the rock mass with much higher natural fracture density in the shallower interval. Thus, the fracture growth is more uniform in the deeper interval and more heterogeneous with branching likely in upper interval. Increased complexity also leads to consistently better productivity from the wells in the shallower interval as demonstrated from RTA results. This case study is unique because it provides valuable insights from actual sampling of the stimulated zones in hydraulically fractured wells and helps understand impact of various factors that contribute toward variability in well production. The findings from this study provides insights into need for optimization of completion designs in the various Wolfcamp landing zones, such as optimization of cluster or fracture spacing in various Wolfcamp intervals. In addition, it provides a useful template for data collection and research direction in future field test sites of similar nature in unconventional reservoirs.

Energy & Fuels↗

Remote Data Exploration with the Interactive Data Language (IDL)

A difficulty for many NASA researchers is that often the data to analyze is located remotely from the scientist and the data is too large to transfer for local analysis. Researchers have developed the Data Access Protocol (DAP) for accessing remote data. Presently one can use DAP from within IDL, but the IDL-DAP interface is both limited and cumbersome. A more powerful and user-friendly interface to DAP for IDL has been developed. Users are able to browse remote data sets graphically, select partial data to retrieve, import that data and make customized plots, and have an interactive IDL command line session simultaneous with the remote visualization. All of these IDL-DAP tools are usable easily and seamlessly for any IDL user. IDL and DAP are both widely used in science, but were not easily used together. The IDL DAP bindings were incomplete and had numerous bugs that prevented their serious use. For example, the existing bindings did not read DAP Grid data, which is the organization of nearly all NASA datasets currently served via DAP. This project uniquely provides a fully featured, user-friendly interface to DAP from IDL, both from the command line and a GUI application. The DAP Explorer GUI application makes browsing a dataset more user-friendly, while also providing the capability to run user-defined functions on specified data. Methods for running remote functions on the DAP server were investigated, and a technique for accomplishing this task was decided upon.

Galloy, Michael↗

Big Data Analytics and Machine Intelligence Capability Development at NASA Langley Research Center: Strategy, Roadmap, and Progress

In 2014, a team of researchers, engineers and information technology specialists at NASA Langley Research Center developed a Big Data Analytics and Machine Intelligence Strategy and Roadmap as part of Langley's Comprehensive Digital Transformation Initiative, with the goal of identifying the goals, objectives, initiatives, and recommendations need to develop near-, mid- and long-term capabilities for data analytics and machine intelligence in aerospace domains. Since that time, significant progress has been made in developing pilots and projects in several research, engineering, and scientific domains by following the original strategy of collaboration between mission support organizations, mission organizations, and external partners from universities and industry. This report summarizes the work to date in Data Intensive Scientific Discovery, Deep Content Analytics, and Deep Q&A projects, as well as the progress made in collaboration, outreach, and education. Recommendations for continuing this success into future phases of the initiative are also made.

Ambur, Manjula Y.↗

Open Energy Data Initiative (OpenEDI) - Open Data Access Tools (OEDI-ODAT) [SWR 20-57]

Open Energy Data Initiative (OpenEDI): Advancing Analytics and Research Innovation through Improved Data Access. The Open Energy Data Initiative (OEDI) aims to improve and automate access of high-value energy data sets across the U.S. Department of Energy’s (DOE’s) programs, offices, and national laboratories. Sponsored by the DOE, this platform is being implemented by the National Renewable Energy Laboratory (NREL) to make data actionable and discoverable by researchers and industry to accelerate analysis and advance innovation. Partners at other national laboratories will help utilize the platform for analysis, can be directly involved in determining requirements, and will be resources for providing new datasets that can be shared with the public to expand innovation.

Rager, David↗

Space Flown Rodent Liver RNA Sequencing Data for Machine Learning in Space Biology Research

High-throughput nucleic acid sequencing (DNA-seq, RNA-seq) has become widespread in biomedical research due to the growing availability and affordability of these assays. Data analysis has been accelerated in recent years by the adoption of artificial intelligence (AI) and machine learning (ML) techniques by biomedical researchers. In space biology research, RNAseq datasets from space-flown experimental samples are critical for characterizing the gene expression aberrations associated with exposure to spaceflight stressors. However, space biological experiments tend to be very low sample size, so identifying proper AI/ML algorithms for sequencing data analysis is an ongoing challenge since these algorithms typically require large sample size. The NASA Science Mission Directorate (SMD) has started the “Benchmark Initiative for AI/ML”, focused on creating datasets meant for three main applications: 1) scientific benchmarking, which finds the best algorithm for a specific problem; 2) application benchmarking, which measures algorithm performance against a set of parameters; and 3) system benchmarking, which evaluates performance of hardware and software architecture. These scientific benchmarks consist of an AI-ready dataset and a reference implementation on a specific scientific question. In this work, we focused on generating standardized datasets to allow the scientific community to benchmark AI/ML algorithms in the domain of space biology. We present here a standardized, AI-ready, publicly available benchmark dataset for space biology RNA-seq data as a collaboration between the NASA AI4LS (Artificial Intelligence for Life Sciences) working group. and NASA’s SMD. This dataset consists of space-flown and ground control mouse liver found in the NASA GeneLab omics database. However, to amplify the small sample number (n=112 samples) for ML purposes, we employ Gaussian noise and a generative adversarial network to extend this dataset to 6,000 synthetic samples, matching the original gene expression characteristics.

James Casaletto↗

Enriched Background Isotope Study (EBIS): Analysis of 14C-Enriched Carbon Cycle in Soils and Litter at Forested Oak Ridge and AmeriFlux Sites, 2001-2011

These data provide a record of the multi-year, multi-institutional Enriched Background Isotope Studies (EBIS) projects that ran from 2000 through 2011. Elevated levels of 14C enriched CO2 in the air and soil atmosphere as well as leaf, stem, and root tissues were observed on the Oak Ridge Reservations (ORR) during the summer of 1999, and were attributed to local incinerator activities on and/or near the ORR (Trumbore et al. 2002). The isolated enrichment of the background levels of 14C in local forest ecosystem represented a unique opportunity to study unresolved carbon cycling processes such as the contribution of leaf versus root litter contributions to soil carbon accumulation, the rate of vertical transport of carbon into deep soil storage pools, and the differential contribution of physicochemical versus faunal driven processes to soil carbon cycling and sequestration. Leaf litter from the local enriched forest was transplanted to selected sites on the ORR and to selected AmeriFlux study sites to study soil C cycling across a range of soils and climates. The EBIS research projects provide data on C flux from litter sources to mineral soil sinks for United States eastern hardwood forests necessary for testing process hypotheses and judging efficacy of soil C cycling models. Experimental results from this study are being used to parameterize and refine existing carbon dynamics models, the quantification of the long-term fate of ecosystem carbon inputs and as a means to judge the potential for ecosystem carbon sequestration via enhance litter inputs to soil. EBIS observations support conclusions that intra- and inter-annual soil carbon cycling in hardwood forest soils should be characterized as a least a two-compartment system where surface leaf-litter and belowground root turnover represent primary carbon sources for organic-layer and mineral-soil carbon cycles, respectively. EBIS experiments were conducted to complete enriched litterfall maniplations in upland forests on Ultisol and Inceptisol soils of the Oak Ridge Reservation, Oak Ridge, Tennessee. We also collected additional14C-enriched materials for new experimental applications, and applied those materials to multiple AmeriFlux sites over a range of climatic, edaphic and biological conditions. The research provided data for addressing DOE's goal of understanding mechanisms controlling C flux, and for the improvement of models to be applied to policy discussions regarding the safe levels of greenhouse gases for the earth's system. There are 5 data files provided in comma separated (*.csv) format for vegetation, field litter, soil and air [C] and C isotope data from the EBIS studies and associated environmental data.

54 ENVIRONMENTAL SCIENCES↗

Packet radio data link applications in the NASA Langley Research Center Transport Systems Research Vehicle

An amateur packet radio system operating in the very high frequency (VHF) range has been implemented in the Transport Systems Research Vehicle at the NASA Langley Research Center to provide an economical, bidirectional, real-time, ground-to-air data link. The packet system has been used to support flight research involving air traffic control (ATC), differential global positioning systems (DGPS), and windshear terminal doppler weather radar (TDWR). A data maximum rate of 2400 baud was used. Operational reliability of the packet system has been very good. Also, its versatility permits numerous specific configurations. These features, plus its low cost, have rendered it very satisfactory for support of data link flight experiments that do not require high data transfer rates.

Easley, Wesley C.↗

FunM2C: A Filter for Uncertainty Visualization of Multivariate Data on Multi-Core Devices

Uncertainty visualization is an emerging research topic in data visualization because neglecting uncertainty in visualization can lead to inaccurate assessments. In this paper, we study the propagation of multivariate data uncertainty in visualization. Although there have been a few advancements in probabilistic uncertainty visualization of multivariate data, three critical challenges remain to be addressed. First, the state-of-the-art probabilistic uncertainty visualization framework is limited to bivariate data (two variables). Second, existing uncertainty visualization algorithms use computationally intensive techniques and lack support for cross-platform portability. Third, as a consequence of the computational expense, integration into production visualization tools is impractical. In this work, we address all three issues and make a threefold contribution. First, we take a step to generalize the state-of-the-art probabilistic framework for bivariate data to multivariate data with an arbitrary number of variables. Second, through utilization of VTK-m’s shared-memory parallelism and cross-platform compatibility features, we demonstrate acceleration of multivariate uncertainty visualization on different many-core architectures, including OpenMP and AMD GPUs. Third, we demonstrate the integration of our algorithms with the ParaView software. We demonstrate the utility of our algorithms through experiments on multivariate simulation data with three and four variables.

Hari, Gautam↗

Deploying Object Oriented Data Technology to the Planetary Data System

How do you provide more than 350 scientists and researchers access to data from every instrument in Odyssey when the data is curated across half a dozen institutions and in different formats and is too big to mail on a CD-ROM anymore? The Planetary Data System (PDS) faced this exact question. The solution was to use a metadata-based middleware framework developed by the Object Oriented Data Technology task at NASA s Jet Propulsion Laboratory. Using OODT, PDS provided - for the first time ever - data from all mission instruments through a single system immediately upon data delivery.

Kelly, S.↗

The utility of Thematic Mapper sensor characteristics for surface mine monitoring

The employment of Landsat Multispectral Scanner (MSS) data for surface coal mine inventory and inspection applications has been extensively investigated. However, in spite of encouraging research results, MSS data have not gained wide acceptance for surface mine monitoring operations. This situation is partly related to MSS spatial resolution (80 m), which is considered insufficient for detailed surface mine inspection. The Thematic Mapper (TM) employed on the Landsat-4 and 5 satellites provides an improved resolution (30 m) and other refinements which are expected to enhance the usefulness of TM data relative to MSS data. The present paper reports research which was conducted to assess the usefulness of actual TM data and to quantitatively evaluate the contribution of individual sensor characteristics to data utility for surface mine monitoring. The obtained results demonstrate that the TM spatial resolution can be of immediate benefit for certain applications such as surface mine monitoring.

Irons, J. R.↗