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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

The GEOS Retrospective Data Assimilation System: The 6-hour lag case

The fixed-lag Kalman smoother (FLKS) has been proposed as a framework to construct data assimilation procedures capable of producing high-quality climate research datasets. Fixed-lag Kalman smoother-based systems, referred to as retrospective data assimilation systems, are an extension to three-dimensional filtering procedures with the added capability of incorporating observations not only in the past and present time of the estimate, but also at future times. A variety of simplifications are necessary to render retrospective assimilation procedures practical. In this article, we present an FLKS-based retrospective data assimilation system implementation for the Goddard Earth Observing System (GOES) Data Assimilation System (DAS). The practicality of this implementation comes from the practicality of its underlying (filter) analysis system, i.e., the physical-space statistical analysis system (PSAS). The behavior of two schemes is studied here. The first retrospective analysis (RA) scheme is designed simply to update the regular PSAS analyses with observations available at times ahead of the regular analysis times. Although our GEOS DAS implementation is general, results are only presented for when observations 6-hours ahead of the analysis time are used to update the PSAS analyses and thereby to calculate the so-called lag-1 retrospective analyses. Consistency tests for this RA scheme show that the lag-1 retrospective analyses indeed have better 6-hour predictive skills than the predictions from the regular analyses. This motivates the introduction of the second retrospective analysis scheme which, at each analysis time, uses the 6-hour retrospective analysis to replace the first-guess normally used in the PSAS analysis, and therefore allows the calculation of a revised (filter) PSAS analysis. Since in this scheme the lag-1 retrospective analyses influence the filter results, this procedure is referred to as the retrospective-based iterative analysis (RIA) scheme. Results from the RIA scheme indicate its potential for improving the overall quality of the assimilation.

Zhu, Yan-Qiu↗
DOE OSTI2020

A data mining approach to investigate food groups related to incidence of bladder cancer in the BLadder cancer Epidemiology and Nutritional Determinants International Study

Abstract At present, analysis of diet and bladder cancer (BC) is mostly based on the intake of individual foods. The examination of food combinations provides a scope to deal with the complexity and unpredictability of the diet and aims to overcome the limitations of the study of nutrients and foods in isolation. This article aims to demonstrate the usability of supervised data mining methods to extract the food groups related to BC. In order to derive key food groups associated with BC risk, we applied the data mining technique C5.0 with 10-fold cross-validation in the BLadder cancer Epidemiology and Nutritional Determinants study, including data from eighteen case–control and one nested case–cohort study, compromising 8320 BC cases out of 31 551 participants. Dietary data, on the eleven main food groups of the Eurocode 2 Core classification codebook, and relevant non-diet data (i.e. sex, age and smoking status) were available. Primarily, five key food groups were extracted; in order of importance, beverages (non-milk); grains and grain products; vegetables and vegetable products; fats, oils and their products; meats and meat products were associated with BC risk. Since these food groups are corresponded with previously proposed BC-related dietary factors, data mining seems to be a promising technique in the field of nutritional epidemiology and deserves further examination.

Nutrition & Dietetics↗
DOE OSTI2021

Where’s Swimmy?: Mining unique color features buried in galaxies by deep anomaly detection using Subaru Hyper Suprime-Cam data

Abstract We present the Swimmy (Subaru WIde-field Machine-learning anoMalY) survey program, a deep-learning-based search for unique sources using multicolored (grizy) imaging data from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). This program aims to detect unexpected, novel, and rare populations and phenomena, by utilizing the deep imaging data acquired from the wide-field coverage of the HSC-SSP. This article, as the first paper in the Swimmy series, describes an anomaly detection technique to select unique populations as “outliers” from the data-set. The model was tested with known extreme emission-line galaxies (XELGs) and quasars, which consequently confirmed that the proposed method successfully selected $\sim\!\! 60\%$–$70\%$ of the quasars and $60\%$ of the XELGs without labeled training data. In reference to the spectral information of local galaxies at z = 0.05–0.2 obtained from the Sloan Digital Sky Survey, we investigated the physical properties of the selected anomalies and compared them based on the significance of their outlier values. The results revealed that XELGs constitute notable fractions of the most anomalous galaxies, and certain galaxies manifest unique morphological features. In summary, deep anomaly detection is an effective tool that can search rare objects, and, ultimately, unknown unknowns with large data-sets. Further development of the proposed model and selection process can promote the practical applications required to achieve specific scientific goals.

Astronomy & Astrophysics↗
NASA NTRS1998

Fabrication and Testing of Mo-Re Heat Pipes Embedded in Carbon/Carbon

Refractory-composite/heat-pipe-cooled wing an tail leading edges are being considered for use on hypersonic vehicles to limit maximum temperatures to values below material reuse limits and to eliminate the need to actively cool the leading edges. The development of a refractory-composite/heat-pipe-cooled leading edge has evolved from the design stage to the fabrication and testing of heat pipes embedded in carbon/carbon (C/C). A three-foot-long, molybdenum-rhenium heat pipe with a lithium working fluid was fabricated and tested at an operating temperature of 2460 F to verify the individual heat-pipe design. Following the fabrication of this heat pipe, three additional heat pipes were fabricated and embedded in C/C. The C/C heat-pipe test article was successfully tested using quartz lamps in a vacuum chamber in both a horizontal and vertical orientation. Start up and steady state data are presented for the C/C heat-pipe test article. Radiography and eddy current evaluations were performed on the test article.

Glass, David E.↗
DOE OSTI2016

Algorithms for Heterogeneous, Multiple Depot, Multiple Unmanned Vehicle Path Planning Problems

Unmanned vehicles, both aerial and ground, are being used in several monitoring applications to collect data from a set of targets. This article addresses a problem where a group of heterogeneous aerial or ground vehicles with different motion constraints located at distinct depots visit a set of targets. The vehicles also may be equipped with different sensors, and therefore, a target may not be visited by any vehicle. The objective is to find an optimal path for each vehicle starting and ending at its respective depot such that each target is visited at least once by some vehicle, the vehicle–target constraints are satisfied, and the sum of the length of the paths for all the vehicles is minimized. Two variants of this problem are formulated (one for ground vehicles and another for aerial vehicles) as mixed-integer linear programs and a branchand- cut algorithm is developed to compute an optimal solution to each of the variants. Computational results show that optimal solutions for problems involving 100 targets and 5 vehicles can be obtained within 300 seconds on average, further corroborating the effectiveness of the proposed approach.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2017

Measuring fluorescence polarization with a dichrometer

In this article, a method for obtaining fluorescence polarization data from an instrument designed to measure circular and linear dichroism is compared with a previously reported approach. The new method places a polarizer between the sample and a detector mounted perpendicular to the direction of the incident beam and results in determination of the fluorescence polarization ratio, whereas the previous method does not use a polarizer and yields the fluorescence anisotropy. A similar analysis with the detector located axially with the excitation beam demonstrates that there is no frequency modulated signal due to fluorescence polarization in the absence of a polarizer.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2010

The Impact of Radiotherapy Fields in the Treatment of Patients With Choroid Plexus Carcinoma

Purpose: To perform a comprehensive literature review and analysis of cases dealing with choroid plexus carcinoma (CPC) to determine the optimal radiotherapy (RT) treatment field. Methods and Materials: A PubMed search of English language articles from 1979 to 2008 was performed, yielding 33 articles with 56 patients who had available data regarding RT treatment field. The median age at diagnosis was 2.7 years (range, 1 month-53 years). Of 54 patients with data regarding type of surgery, 21 (38.9%) had complete resection. Chemotherapy was delivered to 27 (48%) as part of initial therapy. The RT treatment volume was the craniospinal axis in 38 (68%), whole brain in 9 (16%), and tumor/tumor bed in 9 (16%). Median follow-up for surviving patients was 40 months. Results: The 5-year overall survival and progression-free survival (PFS) rates were 59.5% and 37.2%, respectively. Complete resection (p = 0.035) and use of craniospinal irradiation (CSI; p = 0.025) were found to positively affect PFS. The 5-year PFS for patients who had CSI vs. whole brain and tumor/tumor bed RT were 44.2% and 15.3%. For the 19 patients who relapsed, 9 (47%) had a recurrence in the RT field, 6 (32%) had a recurrence outside the RT field, and 4 (21%) had a recurrence inside and outside the irradiated field. Conclusion: Patients with CPC who received CSI had better PFS compared with those receiving less than CSI. This study supports the use of CSI in the multimodality management of patients with CPC.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Machine Learning Applied to Zeolite Synthesis: The Missing Link for Realizing High-Throughput Discovery

Zeolites are microporous crystalline materials with well-defined cavities and pores, which can be prepared under different pore topologies and chemical compositions. Their preparation is typically defined by multiple interconnected variables (e.g., reagent sources, molar ratios, aging treatments, reaction time and temperature, among others), but unfortunately their distinctive influence, particularly on the nucleation and crystallization processes, is still far from being understood. Thus, the discovery and/or optimization of specific zeolites is closely related to the exploration of the parametric space through trial-and-error methods, generally by studying the influence of each parameter individually. In the past decade, machine learning (ML) methods have rapidly evolved to address complex problems involving highly nonlinear or massively combinatorial processes that conventional approaches cannot solve. Considering the vast and interconnected multiparametric space in zeolite synthesis, coupled with our poor understanding of the mechanisms involved in their nucleation and crystallization, the use of ML is especially timely for improving zeolite synthesis. Indeed, the complex space of zeolite synthesis requires drawing inferences from incomplete and imperfect information, for which ML methods are very well-suited to replace the intuition-based approaches traditionally used to guide experimentation. In this Account, we contend that both existing and new ML approaches can provide the “missing link” needed to complete the traditional zeolite synthesis workflow used in our quest to rationalize zeolite synthesis. Within this context, we have made important efforts on developing ML tools in different critical areas, such as (1) data-mining tools to process the large amount of data generated using high-throughput platforms; (2) novel complex algorithms to predict the formation of energetically stable hypothetical zeolites and guide the synthesis of new zeolite structures; (3) new “ab initio” organic structure directing agent predictions to direct the synthesis of hypothetical or known zeolites; (4) an automated tool for nonsupervised data extraction and classification from published research articles. ML has already revolutionized many areas in materials science by enhancing our ability to map intricate behavior to process variables, especially in the absence of well-understood mechanisms. Undoubtedly, ML is a burgeoning field with many future opportunities for further breakthroughs to advance the design of molecular sieves. For this reason, this Account includes an outlook of future research directions based on current challenges and opportunities. We envision this Account will become a hallmark reference for both well-established and new researchers in the field of zeolite synthesis.

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

Multioutput Convolutional Neural Network for Improved Parameter Extraction in Time-Resolved Electrostatic Force Microscopy Data

Time-resolved scanning probe microscopy methods, like time-resolved electrostatic force microscopy (trEFM), enable imaging of dynamic processes ranging from ion motion in batteries to electronic dynamics in microstructured thin film semiconductors for solar cells. Reconstructing the underlying physical dynamics from these techniques can be challenging due to the interplay of cantilever physics with the actual transient kinetics of interest in the resulting signal. Previously, quantitative trEFM used empirical calibration of the cantilever or feed-forward neural networks trained on simulated data to extract the physical dynamics of interest. Both these approaches are limited by interpreting the underlying signal as a single exponential function, which serves as an approximation but does not adequately reflect many realistic systems. Here, we present a multi-branched, multi-output convolutional neural network (CNN) that uses the trEFM signal in addition to the physical cantilever parameters as input. The trained CNN accurately extracts parameters describing both single-exponential and bi-exponential underlying functions, and more accurately reconstructs real experimental data in the presence of noise. This article demonstrates an application of physics-informed machine learning to complex signal processing tasks, enabling more efficient and accurate analysis of trEFM.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

An insurance perspective on U.S. electric grid disruption costs

Large yet infrequent disruptions of electrical power can impact tens of millions of people in a single event, triggering significant economic damages, portions of which are insured. Small and frequent events are also significant in the aggregate. This article explores the role that insurance claims data can play in better defining the broader economic impacts of grid disruptions in the U.S. context. We developed four case studies, using previously unpublished data for specific actual grid disruptions. The cases include the 1977 New York City blackout, the 2003 Northeast blackout, multi-year national annual lightning-related electrical damage and multi-year national line-disturbance events. Insured losses represent between 3 and 64 per cent of total loss costs across the case studies. Here, the household sector emerges as a larger locus of costs than indicated in previous studies, and short-lived events emerge as important sources of loss costs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Shear strain-induced anisotropic domain evolution in mixed-phase BiFeO 3 epitaxial films

Understanding and controlling the domain evolution under external stimuli in multiferroic thin films is critical to realizing nano-electronic devices, including for non-volatile memory, data storage, sensors, and optoelectronics. In this article, we studied the shear-strain effect on the domain evolution with temperature in highly strained BiFeO 3 thin films on rhombohedral LaAlO 3 sub-strates using a high-resolution synchrotron X-ray diffraction three dimensional-reciprocal space mapping (3D-RSM) technique. The results revealed significant biaxial, anisotropic, evolution behaviors of the mixed-phase ( M C + R' / T' phases) BiFeO 3 ferro-electric domains along the in-plane [100] and [010] axes. These biaxial, anisotropic, evolution behaviors were attributed to the shear-strain-modulated transition pathways of the mixed-phase ferroelectric domains. This viewpoint was further verified in the BiFeO 3 /LaSrAlO 4 (001) system in which no anisotropic evolution behaviors of the mixed-phase domains were found. This work sheds light on the quantitative analysis of domain evolution in multi-domain systems and demonstrates that the shear-strain effect could act as an effective tool to manipulate the domain behavior and control novel functionalities of ferroelectric thin films.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Monitoring of Wind Turbine Gearbox Condition through Oil and Wear Debris Analysis: A Full-Scale Testing Perspective

Despite the wind industry's dramatic development during the past decade, it is still challenged by premature turbine subsystem/component failures, especially for turbines rated above 1 MW. Because a crane is needed for each replacement, gearboxes have been a focal point for improvement in reliability and availability. Condition monitoring (CM) is a technique that can help improve these factors, leading to reduced turbine operation and maintenance costs and, subsequently, lower cost of energy for wind power. Although technical benefits of CM for the wind industry are normally recognized, there is a lack of published information on the advantages and limitations of each CM technique confirmed by objective data from full-scale tests. This article presents first-hand oil and wear debris analysis results obtained through tests that were based on full-scale wind turbine gearboxes rated at 750 kW. The tests were conducted at the 2.5-MW dynamometer test facility at the National Wind Technology Center at the National Renewable Energy Laboratory. The gearboxes were tested in three conditions: run-in, healthy, and damaged. The investigated CM techniques include real-time oil condition and wear debris monitoring, both inline and online sensors, and offline oil sample and wear debris analysis, both onsite and offsite laboratories. The reported results and observations help increase wind industry awareness of the benefits and limitations of oil and debris analysis technologies and highlight the challenges in these technologies and other tribological fields for the Society of Tribologists and Lubrication Engineers and other organizations to help address, leading to extended gearbox service life.

17 WIND ENERGY↗
DOE OSTI2011

The US-CMS Tier1 center network evolving toward 100Gbps

Fermilab hosts the US Tier-1 Center for the LHC's Compact Muon Collider (CMS) experiment. The Tier-1s are the central points for the processing and movement of LHC data. They sink raw data from the Tier-0 at CERN, process and store it locally, and then distribute the processed data to Tier-2s for simulation studies and analysis. The Fermilab Tier-1 Center is the largest of the CMS Tier-1s, accounting for roughly 35% of the experiment's Tier-1 computing and storage capacity. Providing capacious, resilient network services, both in terms of local network infrastructure and off-site data movement capabilities, presents significant challenges. This article will describe the current architecture, status, and near term plans for network support of the US-CMS Tier-1 facility.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Uncertainty analysis of Raman spectra for measuring ortho-parahydrogen compositions

Determining ortho-parahydrogen compositions at cryogenic temperatures is an important quality control for liquid hydrogen custody exchange. High orthohydrogen compositions lead to an exothermic reaction resulting in increased boil off and increased venting losses of liquid hydrogen product by either the supplier or consumer. Traditional methods for measuring ortho-parahydrogen compositions such as hot-wire anemometry, nuclear magnetic resonance, and infrared spectroscopy are typically inconvenient at best. As the number of cryogenic hydrogen systems continue to increase, there is an increased need for flexible, standardized techniques for post processing composition measurements. A Raman spectrometer implemented in the Cryo-Catalysis Hydrogen Experimental Facility (CHEF) has demonstrated incredible flexibility for composition measurements of cryogenic hydrogen flows through a wide range of temperatures and pressures with the Raman probe in-situ at cryogenic temperatures. This article analyzes the uncertainty of Raman spectroscopy data of cryogenic hydrogen equilibrated at temperatures in IONEX catalyst. Spectra from hydrogen catalyzed to the equilibrium ortho-parahydrogen composition are processed and compared to expected statistical distributions for method verification.

08 HYDROGEN↗
DOE OSTI2025

Achieving a large transverse emittance ratio in the Relativistic Heavy Ion Collider: Beam experiments, analytical estimates, and implications for the Electron-Ion Collider

The Electron-Ion Collider (EIC), to be built at Brookhaven National Laboratory, will collide a polarized high-energy electron beam with polarized proton and light ion beams in the center-of-mass energy range of 20–140 GeV, achieving a maximum peak luminosity of up to 1 × 10 34 cm − 2 s − 1 for the proton and electron collisions. To achieve such a high luminosity, the EIC will employ small, flat beams at the interaction point, which requires a transverse emittance ratio of about 11:1. Since 2017, we have carried out a series of beam experiments to test the feasibility of achieving such a large emittance ratio in the Relativistic Heavy Ion Collider (RHIC). Thanks to the existing stochastic cooling and decoupling feedback system in RHIC, we successfully demonstrated a large transverse emittance ratio of 11:1 with gold ion beams in 2023. In this article, we will present more details and data analysis for these experiments, along with analytical estimates and numerical multiparticle simulations, and provide an outlook for future EIC coupling compensation. Published by the American Physical Society 2025

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2015

Bunch mode specific rate corrections for PILATUS3 detectors

PILATUS X-ray detectors are in operation at many synchrotron beamlines around the world. This article reports on the characterization of the new PILATUS3 detector generation at high count rates. As for all counting detectors, the measured intensities have to be corrected for the dead-time of the counting mechanism at high photon fluxes. The large number of different bunch modes at these synchrotrons as well as the wide range of detector settings presents a challenge for providing accurate corrections. To avoid the intricate measurement of the count rate behaviour for every bunch mode, a Monte Carlo simulation of the counting mechanism has been implemented, which is able to predict the corrections for arbitrary bunch modes and a wide range of detector settings. This article compares the simulated results with experimental data acquired at different synchrotrons. It is found that the usage of bunch mode specific corrections based on this simulation improves the accuracy of the measured intensities by up to 40% for high photon rates and highly structured bunch modes. For less structured bunch modes, the instant retrigger technology of PILATUS3 detectors substantially reduces the dependency of the rate correction on the bunch mode. The acquired data also demonstrate that the instant retrigger technology allows for data acquisition up to 15 million photons per second per pixel.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

The new ATLAS/LUCID detector

The new ATLAS luminosity monitor has many innovative aspects implemented. Its photomultipliers tubes are used as detector elements by using the Cherenkov light produced by charged particles above threshold crossing the quartz windows. The analog shaping of the readout chain has been improved, in order to cope with the 25 ns bunch spacing of the LHC machine. The main readout card is a quite general processing unit based on 12 bit - 500 MS/s Flash ADC and on FPGAs, delivering the processed data to 1.3 Gb/s optical links. The article will describe all these aspects and will outline future perspectives of the card for next generation high energy physics experiments. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

DeepAndes: A Self-Supervised Vision Foundation Model for Multispectral Remote Sensing Imagery of the Andes

By mapping sites at large scales usingremotely sensed data, archaeologists can generate unique insights into long-term demographic trends, interregional social networks, and human adaptations in the past. Remote sensing surveys complement field-based approaches, and their reach can be especially great when combined with deep learning and computer vision techniques. However, conventional supervised deep learning methods face challenges in annotating fine-grained archaeological features at scale. In addition, while recent vision foundation models have shown remarkable success in learning large-scale remote sensing data with minimal annotations, most off-the-shelf solutions are designed for RGB images rather than multispectral satellite imagery, such as the eight-band data used in our study. In this article, we introduce DeepAndes, a transformer-based vision foundation model trained on three million multispectral satellite images, specifically tailored for Andean archaeology. DeepAndes incorporates a customized DINOv2 self-supervised learning algorithm optimized for eight-band multispectral imagery, marking the first foundation model designed explicitly for the Andes region. We evaluate its image understanding performance through imbalanced image classification, image instance retrieval, and pixel-level semantic segmentation tasks. Our experiments show that DeepAndes achieves superior F1 scores, mean average precision, and Dice scores in few-shot learning scenarios, significantly outperforming models trained from scratch or pretrained on smaller datasets. This underscores the effectiveness of large-scale self-supervised pretraining in archaeological remote sensing.

Guo, Junlin [Vanderbilt Univ., Nashville, TN (Unit↗