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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

ACTIVE GALACTIC NUCLEUS PAIRS FROM THE SLOAN DIGITAL SKY SURVEY. II. EVIDENCE FOR TIDALLY ENHANCED STAR FORMATION AND BLACK HOLE ACCRETION

Active galactic nuclei (AGNs) are occasionally seen in pairs, suggesting that tidal encounters are responsible for the accretion of material by both central supermassive black holes (BHs). In Paper I of this series, we selected a sample of AGN pairs with projected separations r{sub p} < 100 h{sup -1}{sub 70} kpc and velocity offsets <600 km s{sup -1} from the Seventh Data Release of the Sloan Digital Sky Survey and quantified their frequency. In this paper, we address the BH accretion and recent star formation properties in their host galaxies. AGN pairs experience stronger BH accretion, as measured by their [O III] {lambda}5007 luminosities (corrected for contribution from star formation) and Eddington ratios, than do control samples of single AGNs matched in redshift and host-galaxy stellar mass. Their host galaxies have stronger post-starburst activity and younger mean stellar ages, as indicated by stronger H{delta} absorption and smaller 4000 A break in their spectra. The BH accretion and recent star formation in the host galaxies both increase with decreasing projected separation in AGN pairs, for r{sub p} {approx}< 10-30 h{sup -1}{sub 70} kpc. The intensity of BH accretion, the post-starburst strength, and the mean stellar ages are correlated between the two AGNs in a pair. The luminosities and Eddington ratios of AGN pairs are correlated with recent star formation in their host galaxies, with a scaling relation consistent with that observed in single AGNs. Our results suggest that galaxy tidal interactions enhance both BH accretion and host-galaxy star formation in close AGN pairs, even though the majority of low-redshift AGNs are not coincident with on-going interactions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

AN INTERNET RACK MONITOR-CONTROLLER FOR APS LINAC RF ELECTRONICS UPGRADE

To support the research and development in APS LINAC area, the existing LINAC rf control performance needs to be much improved, and thus an upgrade of the legacy LINAC rf electronics becomes necessary. The proposed upgrade plan centers on the concept of using a modern, network-attached, rackmount digital electronics platform –Internet Rack Monitor-Controller (or IRMC) to achieve the goal of modernizing the rf electronics at a lower cost. The system model of the envisioned IRMC is basically a 3-tier stack with a high-performance DSP in the mid-layer to perform the core tasks of real-time rf data processing and controls. The Digital Front-End (DFE) attachment layer at bottom bridges the applicationspecific rf front-ends to the DSP. A network communication gateway, together with an embedded event receiver (EVR) in the top layer merges the Internet Rack MonitorController node into the networks of the accelerator controls infrastructure. Although the concept is very much in trend with today’s Internet-of-Things (IoT), this implementation has actually been used in the accelerators for over two decades.

Ma, Hengjie↗
DOE OSTI2015

SU-E-T-226: Correction of a Standard Model-Based Dose Calculator Using Measurement Data

Purpose: To propose a hybrid method that combines advantages of the model-based and measurement-based method for independent dose calculation. Modeled-based dose calculation, such as collapsed-cone-convolution/superposition (CCCS) or the Monte-Carlo method, models dose deposition in the patient body accurately; however, due to lack of detail knowledge about the linear accelerator (LINAC) head, commissioning for an arbitrary machine is tedious and challenging in case of hardware changes. On the contrary, the measurement-based method characterizes the beam property accurately but lacks the capability of dose disposition modeling in heterogeneous media. Methods: We used a standard CCCS calculator, which is commissioned by published data, as the standard model calculator. For a given machine, water phantom measurements were acquired. A set of dose distributions were also calculated using the CCCS for the same setup. The difference between the measurements and the CCCS results were tabulated and used as the commissioning data for a measurement based calculator. Here we used a direct-ray-tracing calculator (ΔDRT). The proposed independent dose calculation consists of the following steps: 1. calculate D-model using CCCS. 2. calculate D-ΔDRT using ΔDRT. 3. combine Results: D=D-model+D-ΔDRT. Results: The hybrid dose calculation was tested on digital phantoms and patient CT data for standard fields and IMRT plan. The results were compared to dose calculated by the treatment planning system (TPS). The agreement of the hybrid and the TPS was within 3%, 3 mm for over 98% of the volume for phantom studies and lung patients. Conclusion: The proposed hybrid method uses the same commissioning data as those for the measurement-based method and can be easily extended to any non-standard LINAC. The results met the accuracy, independence, and simple commissioning criteria for an independent dose calculator.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Analyzing shear band formation with high resolution X-ray diffraction

Localization of crystallographic slip into shear bands during uniaxial compression of a copper single crystal is studied using very far-field high-energy diffraction microscopy (vff-HEDM). Diffracted intensity was collected in-situ as the crystal deformed using a unique mobile detector stage that provided access to multiple diffraction peaks with high-angular resolution. From the diffraction data, single crystal orientation pole figures (SCPFs) were generated and are used to track the evolution of the distribution of lattice orientation that develops as slip localizes. To aid the identification of 'signatures' of shear band formation and analyze the SCPF data, a model of slip-driven lattice reorientation within shear bands is introduced. Confidence is built in conclusions drawn from the SCPF data about the character of internal slip localization through comparisons with strain fields on the sample surface measured simultaneously using digital image correlation. From the diffraction data, we find that the active slip direction and slip plane are not directly aligned with the orientation of the shear bands that formed. In fact, by extracting the underlying slip system activity from the SCPF data, we show that intersecting shear bands measured on the surface of the sample arise from slip primarily on the same underlying single slip system. These new vff-HEDM results raise significant questions on the use of surface measurements for slip system activity estimation. (C) 2018 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Optimal sensor placement for reconstructing wind pressure field around buildings using compressed sensing

Deciding how to optimally deploy sensors in a large, complex, and spatially extended structure is critical to ensure that the surface pressure field is accurately captured for subsequent analysis and design. In some cases, reconstruction of missing data is required in downstream tasks such as the development of digital twins. Here, this paper presents a data-driven sparse sensor selection algorithm, aiming to provide the most information contents for reconstructing aerodynamic characteristics of wind pressures over tall building structures parsimoniously. The algorithm first fits a set of basis functions to the training data, then applies a computationally efficient QR algorithm that ranks existing pressure sensors in order of importance based on the state reconstruction to this tailored basis. The findings of this study show that the proposed algorithm successfully re- constructs the aerodynamic characteristics of tall buildings from sparse measurement locations, generating stable and optimal solutions across a range of conditions. As a result, this study serves as a promising first step toward leveraging the success of data-driven and machine learning algorithms to supplement traditional genetic algorithms currently used in wind engineering.

42 ENGINEERING↗
DOE OSTI2025

Quantum frequency resampling

In signal processing, resampling algorithms can modify the number of resources encoding a collection of data points. Downsampling reduces the cost of storage and communication, while upsampling interpolates new data from limited one, e.g., when resizing a digital image. We present a toolset of quantum algorithms to resample data encoded in the probabilities of a quantum register, using the quantum Fourier transform to adjust the number of high-frequency encoding qubits. We discuss advantage over classical resampling algorithms.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Harmonized Database of Western U.S. Water Rights (HarDWR) v.1

Abstract In the arid and semi-arid Western U.S., access to water is regulated through a legal system of water rights. Individuals, companies, organizations, municipalities, and tribal entities have documents that declare their water rights. State water regulatory agencies collate and maintain these records, which can be used in legal disputes over access to water. While these records are publicly available data in all Western U.S. states, the data have not yet been readily available in digital form from all states. Furthermore, there are many differences in data format, terminology, and definitions between state water regulatory agencies. Here, we have collected water rights data from 11 Western U.S. state agencies, harmonized terminology and use definitions, formatted them for consistency, and tied them to a Western U.S.-wide shapefile of water administrative boundaries.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

Data acquisition system for the Baikal-GVD neutrino telescope

The objective of the Baikal-GVD project is the construction of a km{sup 3}-scale neutrino telescope in Lake Baikal. The Gigaton Volume Detector consists of a large three-dimensional array of photo-multiplier tubes. The first GVD-cluster has been deployed and commissioned in April 2015. The data acquisition system (DAQ) of the detector takes care of the digitization of the photo-multiplier tube signals, data transmission, filtering and storage. The design and the implementation of the data acquisition system are described.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

TListSpectrum

TListSpectrum is a C++ class developed inside the CERN high-energy physics analysis C++ framework ROOT. This class structure was developed to assist in the processing, visualization, and analysis of list-mode or time-stamped radiation spectroscopy data. The class structure currently contains parsing and functionality to synthesize list-mode data from CAEN and Mirion Lynx radiation spectroscopy digital acquisition systems along with feature functionality to post-process data sets and build coincident data sets from the instrument.

Pierson, Bruce↗
DOE OSTI2012

United States Industrial Sector Energy End Use Analysis

The United States Department of Energy’s (DOE) Energy Information Administration (EIA) conducts the Manufacturing Energy Consumption Survey (MECS) to provide detailed data on energy consumption in the manufacturing sector. The survey is a sample of approximately 15,000 manufacturing establishments selected from the Economic Census - Manufacturing Sector. MECS provides statistics on the consumption of energy by end uses (e.g., boilers, process, electric drives, etc.) disaggregated by North American Industry Classification System (NAICS) categories. The manufacturing sector (NAICS Sector 31-33) consists of all manufacturing establishments in the 50 States and the District of Columbia. According to the NAICS, the manufacturing sector comprises establishments engaged in the mechanical, physical, or chemical transformation of materials, substances, or components into new products. The establishments are physical facilities such as plants, factories, or mills. For many of the sectors in the MECS datasets, information is missing because the reported energy use is less than 0.5 units or BTUs, or is withheld to avoid disclosing data for individual establishments, or is withheld because the standard error is greater than 50%. We infer what the missing information likely are using several approximations techniques. First, much of the missing data can be easily calculated by adding or subtracting other values reported by MECS. If this is not possible (e.g. two data are missing), we look at historic MECS reports to help identify the breakdown of energy use in the past and assume it remained the same for the current MECS. Lastly, if historic data is also missing, we assume that 3 digit NAICS classifications predict energy use in their 4, 5, or 6 digit NAICS sub-classifications, or vice versa. Along with addressing data gaps, end use energy is disaggregated beyond the specified MECS allocations using additional industry specific energy consumption data. The result is a completed table of energy end use by sector with mechanical drives broken down by pumps, fans, compressed air, and drives.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Harmonized Database of Western U.S. Water Rights (HarDWR)

From Lisk et al. (in review): "In the arid and semi-arid western U.S., access to water is regulated through a legal system of water rights. Individuals, companies, organizations, municipalities, and tribal entities have documents that declare their water rights. State water regulatory agencies collate and maintain these records, which can be used in legal disputes over access to water. While these records are publicly available data in all western U.S. states, the data have not yet been readily available in digital form from all states. Furthermore, there are many differences in data format, terminology, and definitions between state water regulatory agencies. Here, we have collected water rights data from 11 western U.S. state agencies, harmonized terminology and use definitions, formatted them consistently, and tied them to a western U.S.-wide shapefile of water administrative boundaries. We demonstrate how these data enable consistent regional-scale western U.S. hydrologic and economic modeling."

Economics↗
DOE OSTI2024

Harmonized Database of Western U.S. Water Rights (HarDWR)

From Lisk et al. (2024): "In the arid and semi-arid western U.S., access to water is regulated through a legal system of water rights. Individuals, companies, organizations, municipalities, and tribal entities have documents that declare their water rights. State water regulatory agencies collate and maintain these records, which can be used in legal disputes over access to water. While these records are publicly available data in all western U.S. states, the data have not yet been readily available in digital form from all states. Furthermore, there are many differences in data format, terminology, and definitions between state water regulatory agencies. Here, we have collected water rights data from 11 western U.S. state agencies, harmonized terminology and use definitions, formatted them consistently, and tied them to a western U.S.-wide shapefile of water administrative boundaries. We demonstrate how these data enable consistent regional-scale western U.S. hydrologic and economic modeling."

Economics↗
DOE OSTI2012

Nuclear Concrete Materials Database Phase I Development

The FY 2011 accomplishments in Phase I development of the Nuclear Concrete Materials Database to support the Light Water Reactor Sustainability Program are summarized. The database has been developed using the ORNL materials database infrastructure established for the Gen IV Materials Handbook to achieve cost reduction and development efficiency. In this Phase I development, the database has been successfully designed and constructed to manage documents in the Portable Document Format generated from the Structural Materials Handbook that contains nuclear concrete materials data and related information. The completion of the Phase I database has established a solid foundation for Phase II development, in which a digital database will be designed and constructed to manage nuclear concrete materials data in various digitized formats to facilitate electronic and mathematical processing for analysis, modeling, and design applications.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2018

Moving Beyond Data Paralysis to Effective use of Building Analytics

As digital controls and smart meters become commonplace in commercial buildings, facilities teams have access to overwhelming amounts of data. This data does not typically lead to insights and corrective actions unless it is analyzed and prioritized in automated ways. Analytic software has been commercially available for over 20 years, but only recently have these tools seen increased adoption. This monitoring infrastructure helps uncover hidden operational opportunities, which is enticing for decision makers looking for short paybacks. Rather than single project savings, monitoring-based commissioning (MBCx) provides tools and processes to implement persistent, continuous improvement. What was formerly a data onslaught is now becoming a controlled resource for improved energy management. In 2016, a Research and Industry Partnership program was launched to support the use of analytics and diagnostics technologies and MBCx practices. This program couples technical assistance to owners to help increase adoption with a data collection effort to inform industry. It includes the most comprehensive data available nationally on analytics installation and use. The first-year results are in, with data from 46 organizations representing over 185 million square feet and 2,300 buildings. These organizations have achieved a median year-over-year energy savings of $0.20/sq ft with a median first year technology implementation cost of $0.12/sq ft. The paper summarizes attributes of successful analytics and MBCx installations, and cost-effectiveness results. The research provides insight for utilities considering adopting analytics technologies into incentive programs, for analytics developers to improve the performance of their solutions, and for commercial building facility staff considering these technologies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

SU-F-J-183: Interior Region-Of-Interest Tomography by Using Inverse Geometry System

Purpose: The inverse geometry computed tomography (IGCT) composed of multiple source and small size detector has several merits such as reduction of scatter effect and large volumetric imaging within one rotation without cone-beam artifact, compared to conventional cone-beam computed tomography (CBCT). By using this multi-source characteristics, we intend to present a selective and multiple interior region-of-interest (ROI) imaging method by using a designed source on-off sequence of IGCT. Methods: All of the IGCT sources are operated one by one sequentially, and each projection in the shape of narrow cone-beam covers its own partial volume of full field of view (FOV) determined from system geometry. Thus, through controlling multi source operation, limited irradiation within ROI is possible and selective radon space data for ROI imaging can be acquired without additional X-ray filtration. With this feature, we designed a source on-off sequence for multi ROI-IGCT imaging, and projections of ROI-IGCT were generated by using the on-off sequence. Multi ROI-IGCT images were reconstructed by using filtered back-projection algorithm. All these imaging process of our study has been performed by utilizing digital phantom and patient CT data. ROI-IGCT images of the phantom were compared to CBCT image and the phantom data for the image quality evaluation. Results: Image quality of ROI-IGCT was comparable to that of CBCT. However, the distal axial-plane from the FOV center, large cone-angle region, ROI-IGCT showed uniform image quality without significant cone-beam artifact contrary to CBCT. Conclusion: ROI-IGCT showed comparable image quality and has the capability to provide multi ROI image within a rotation. Projection of ROI-IGCT is performed by selective irradiation, hence unnecessary imaging dose to non-interest region can be reduced. In this regard, it seems to be useful for diagnostic or image guidance purpose in radiotherapy such as low dose target localization and patient alignment. This research was supported by the Mid-career Researcher Program through NRF funded by the Ministry of Science, ICT & Future Planning of Korea (NRF-2014R1A2A1A10050270) and by the Radiation Technology R&D program through the National Research Foundation of Korea funded by the Ministry of Science, ICT & Future Planning (No. 2013M2A2A7038291)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

ESTIMATING PHOTOMETRIC REDSHIFTS OF QUASARS VIA THE k-NEAREST NEIGHBOR APPROACH BASED ON LARGE SURVEY DATABASES

We apply one of the lazy learning methods, the k-nearest neighbor (kNN) algorithm, to estimate the photometric redshifts of quasars based on various data sets from the Sloan Digital Sky Survey (SDSS), the UKIRT Infrared Deep Sky Survey (UKIDSS), and the Wide-field Infrared Survey Explorer (WISE; the SDSS sample, the SDSS-UKIDSS sample, the SDSS-WISE sample, and the SDSS-UKIDSS-WISE sample). The influence of the k value and different input patterns on the performance of kNN is discussed. kNN performs best when k is different with a special input pattern for a special data set. The best result belongs to the SDSS-UKIDSS-WISE sample. The experimental results generally show that the more information from more bands, the better performance of photometric redshift estimation with kNN. The results also demonstrate that kNN using multiband data can effectively solve the catastrophic failure of photometric redshift estimation, which is met by many machine learning methods. Compared with the performance of various other methods of estimating the photometric redshifts of quasars, kNN based on KD-Tree shows superiority, exhibiting the best accuracy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

An integrated data management and informatics framework for continuous drug product manufacturing processes: A case study on two pilot plants

The pharmaceutical industry continuously looks for ways to improve its development and manufacturing efficiency. In recent years, such efforts have been driven by the transition from batch to continuous manufacturing and digitalization in process development. To facilitate this transition, integrated data management and informatics tools need to be developed and implemented within the framework of Industry 4.0 technology. Here, in this regard, the work aims to guide the data integration development of continuous pharmaceutical manufacturing processes under the Industry 4.0 framework, improving digital maturity and enabling the development of digital twins. This paper demonstrates two instances where a data integration framework has been successfully employed in academic continuous pharmaceutical manufacturing pilot plants. Details of the integration structure and information flows are comprehensively showcased. Approaches to mitigate concerns in incorporating complex data streams, including integrating multiple process analytical technology tools and legacy equipment, connecting cloud data and simulation models, and safeguarding cyber-physical security, are discussed. Critical challenges and opportunities for practical considerations are highlighted.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

GOOML - Real World Applications of Machine Learning in Geothermal Operations

GOOML (Geothermal Operational Optimization with Machine Learning) is a machine-learning based framework that enables geothermal power plant operators to explore optimization opportunities for their assets in an efficient and robust digital environment. Backed by real-world data sources, thermodynamic constraints and steamfield intelligence, the GOOML environment provides new tools to explore how to best operate steamfields as well as test new scenarios and configurations prior to implementation in the field. To prove the effectiveness of GOOML, we have undertaken optimization experiments using reinforcement learning (RL) to generate operational suggestions using a balance of mass-take targets, sustainability considerations and net generation. Our experiments use the GOOML construct to explore different field parameters and perform multiple reinforcement learning experiments. Like a comprehensive laboratory workbench, we can change out components of a steamfield to perform testing under a variety of conditions (restrict mass, increase pressure, reroute steam, etc.). This flexibility allows us to explore conditions that would require significant infrastructure changes in a real-world setting at a fraction of the cost and time in a digital environment. The results highlight the benefits of using digital twins and advanced data analytics for the geothermal industry.

forecasting↗