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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Performance of the CMS high-level trigger during LHC Run 2

The CERN LHC provided proton and heavy ion collisions during its Run 2 operation period from 2015 to 2018. Proton-proton collisions reached a peak instantaneous luminosity of 2.1 $\times$ 10$^{34}$ cm$^{-2}$s$^{-1}$, twice the initial design value, at $\sqrt{s}$ = 13 TeV. The CMS experiment records a subset of the collisions for further processing as part of its online selection of data for physics analyses, using a two-level trigger system: the Level-1 trigger, implemented in custom-designed electronics, and the high-level trigger, a streamlined version of the offline reconstruction software running on a large computer farm. This paper presents the performance of the CMS high-level trigger system during LHC Run 2 for physics objects, such as leptons, jets, and missing transverse momentum, which meet the broad needs of the CMS physics program and the challenge of the evolving LHC and detector conditions. Sophisticated algorithms that were originally used in offline reconstruction were deployed online. Highlights include a machine-learning b tagging algorithm and a reconstruction algorithm for tau leptons that decay hadronically.

high energy physics↗
NASA NTRS1980

Synoptic thermal and oceanographic parameter distribution in the New York Bight Apex on June 23, 1977

Concurrent surface water measurements made from a moving oceanographic research vessel were used to calibrate and interpret remotely sensed data collected over a plume in the New York Bight Apex on June 23, 1977. Multiple regression techniques were used to develop equations to subsequently map synoptic distributions of water quality parameters, chlorophyll A and total suspended matter, in the remotely sensed scene. Thermal (which did not have surface calibration values) and water quality parameter distributions indicated a cold mass of upwelled water in the Bight Apex with an overflowing nutrient-rich warm water plume that originated in the Sandy Hook Bay and flowed south near the New Jersey shoreline. Additional comparison of remotely sensed thermal and optical properties of the water with shipboard measurements indicated that remotely sensed data may be particularly useful for studying physical and biological processes in the top several meters of surface water at the plume boundaries.

Johnson, R. W.↗
NASA NTRS1981

Synoptic thermal and oceanographic parameter distributions in the New York Bight Apex

Concurrent surface water measurements made from a moving oceanographic research vessel were used to calibrate and interpret remotely sensed data collected over a plume in the New York Bight Apex on 23 June 1977. Multiple regression techniques were used to develop equations to map synoptic distributions of chlorophyll a and total suspended matter in the remotely sensed scene. Thermal (which did not have surface calibration values) and water quality parameter distributions indicated a cold mass of water in the Bight Apex with an overflowing nutrient-rich warm water plume that originated in the Sandy Hook Bay and flowed south near the New Jersey shoreline. Data analysis indicates that remotely sensed data may be particularly useful for studying physical and biological processes in the top several metres of surface water at plume boundaries.

Johnson, R. W.↗
NASA NTRS1988

A simulation study of data link message exchange - Flight deck perspective

Data link message exchange for Air Traffic Control (ATC) offers the potential of increasing flight safety and efficiency by reducing communication errors and enabling more information to be exchanged between aircraft and ground facilities. Digital communication also presents an opportunity to relieve the overloading of air traffic control radio frequencies, which hampers efficient message exchange during peak traffic periods in many busy terminal areas. A study has been completed at NASA Langley Research Center to compare the use of data link, for tactical ATC messages and ATIS reports, to conventional voice communication. The results indicate that flight crews prefer the data link message exchange for routine communication with air traffic control facilities. The data link communication process, however, entailed delays related to the physical characteristics of the link and the nature of the interface explored in this study. Consequently, the crews preferred voice radio when exchanging time critical messages.

Waller, Marvin C.↗
NASA NTRS1992

Aeronomical modeling of the outer planets

The purpose is twofold: to report the programmatic issues of the project as to accomplishments and anticipated work statement for the next six months and to report the technical findings of the project. From the programmatic point of view the study was a clear success. Four talks were given on the work: (1) a talk on electron Bremsstrahlung X ray production at the annual American Astronomical Society, Division of Planetary Studies meeting in October of 1990 in Charlottesville, Virginia; (2) a poster on proton precipitation at the May, 1991 American Geophysical Union in Baltimore, Maryland; (3) an invited paper on multispectral time dependent modeling at the International Union of Geodosy and Geomagnetism in Vienna in August of 1991; and (4) a paper at the annual Division of Planetary Studies of the American Astronomical Society held in Palo Alto in November of 1991 entitled 'Jovian Bremsstrahlung X Rays: A Ulysses Prediction' which predicted the Jovian auroral X ray flux that should be measured by the Ulysses Gamma Ray Burst experiment during the Ulysses spacecraft's closest encounter with Jupiter in February 1992. The Ulysses' prediction paper generated much interest from people studying the Jovian aurora and from experimenters on the Ulysses spacecraft. As a result of this interest two things happened: (1) a paper of the same title was submitted and accepted for publication in the January issue of the Geophysical Research Letters; and (2) a massive observing campaign was organized to provide supporting ultraviolet and infrared observations at the time of the Ulysses encounter. This observational interest allowed personnel at the Southwest Research Institute (SwRI) (Alan Stern, PI; Hunter Waite Co-I) to obtain director's discretionary time on the Hubble Space Telescope to support the Ulysses encounter by obtaining ultraviolet observations. Once the multiple wavelength observations are obtained in February, the auroral model developed under this project will be used to analyze the data with hopes of providing new insight into the physical processes responsible for the Jovian aurora.

Waite, J. Hunter, Jr.↗
NASA NTRS1992

An investigation of the solar cycle response of odd-nitrogen in the thermosphere

This annual report covers the first year of funding for the study of the solar cycle variations of odd-nitrogen (N((sup 2)D), N((sup 4)S), NO) in the Earth's thermosphere. The study uses the extensive data base generated by the Atmosphere Explorer (AE) satellites, and the Solar Mesosphere Explorer Satellite. The AE data are being used, for the first time, to define the solar variability effect on the odd-nitrogen species through analysis of the emissions at 520 nano-m from N((sup 2)D) and the emission from O(+)((sup 2)P). Additional AE neutral and ion density data are used to help define and quantify the physical processes controlling the variations. The results from the airglow study will be used in the next two years of this study to explain the solar cycle changes in NO measured by the Solar Mesosphere Explorer.

Rusch, David W.↗
NASA NTRS2001

Global Lidar Measurements of Clouds and Aerosols from Space Using the Geoscience Laser Altimeter System (GLAS)

The Geoscience Laser Altimeter System (GLAS) is scheduled for launch on the ICESat satellite as part of the NASA EOS mission in 2002. GLAS will be used to perform high resolution surface altimetry and will also provide a continuously operating atmospheric lidar to profile clouds, aerosols, and the planetary boundary layer with horizontal and vertical resolution of 175 and 76.8 m, respectively. GLAS is the first active satellite atmospheric profiler to provide global coverage. Data products include direct measurements of the heights of aerosol and cloud layers, and the optical depth of transmissive layers. In this poster we provide an overview of the GLAS atmospheric data products, present a simulated GLAS data set, and show results from the simulated data set using the GLAS data processing algorithm. Optical results from the ER-2 Cloud Physics Lidar (CPL), which uses many of the same processing algorithms as GLAS, show algorithm performance with real atmospheric conditions during the Southern African Regional Science Initiative (SAFARI 2000).

Hlavka, Dennis L.↗
NASA NTRS1976

Composite Golf Clubs

Babcock & Wilcox Co. under a partnership with Marshall Space Flight Center, produced composite materials, originally from the shuttle program, for improving golf clubs. Company used Marshall Space Flight Center's data summary file summarizing typical processing techniques and mechanical and physical properties of graphite and boron- reinforced composite materials. Reinforced composites provide combination of shaft rigidity and flexibility that provide maximum distance.

Source record↗
NASA NTRS2014

Federated Giovanni: A Distributed Web Service for Analysis and Visualization of Remote Sensing Data

The Geospatial Interactive Online Visualization and Analysis Interface (Giovanni) is a popular tool for users of the Goddard Earth Sciences Data and Information Services Center (GES DISC) and has been in use for over a decade. It provides a wide variety of algorithms and visualizations to explore large remote sensing datasets without having to download the data and without having to write readers and visualizers for it. Giovanni is now being extended to enable its capabilities at other data centers within the Earth Observing System Data and Information System (EOSDIS). This Federated Giovanni will allow four other data centers to add and maintain their data within Giovanni on behalf of their user community. Those data centers are the Physical Oceanography Distributed Active Archive Center (PO.DAAC), MODIS Adaptive Processing System (MODAPS), Ocean Biology Processing Group (OBPG), and Land Processes Distributed Active Archive Center (LP DAAC). Three tiers are supported: Tier 1 (GES DISC-hosted) gives the remote data center a data management interface to add and maintain data, which are provided through the Giovanni instance at the GES DISC. Tier 2 packages Giovanni up as a virtual machine for distribution to and deployment by the other data centers. Data variables are shared among data centers by sharing documents from the Solr database that underpins Giovanni's data management capabilities. However, each data center maintains their own instance of Giovanni, exposing the variables of most interest to their user community. Tier 3 is a Shared Source model, in which the data centers cooperate to extend the infrastructure by contributing source code.

Giovanni↗
NASA NTRS2022

Exploring the Relationship Between Temperature Forecast Errors and Earth System Variables

Accurate subseasonal weather forecasts, from two weeks up to a season, can help reduce costs and impacts related to weather and corresponding extremes. The quality of weather forecasts has improved considerably in recent decades as models represent more details of physical processes, and they benefit from assimilating comprehensive Earth observation data as well as increasing computing power. However, with ever–growing model complexity, it becomes increasingly difficult to pinpoint weaknesses in the forecast models’ process representations which is key to improving forecast accuracy. In this study, we use a comprehensive set of observation–based ecological, hydrological and meteorological variables to study their potential for explaining temperature forecast errors at the weekly time scale. For this purpose, we compute Spearman correlations between each considered variable and the forecast error obtained from the ECMWF subseasonal–to–seasonal (S2S) reforecasts at lead times of 1–6 weeks. This is done across the globe for the time period 2001–2017. The results show that temperature forecast errors globally are most strongly related with climate–related variables such as surface solar radiation and precipitation, which highlights the model’s difficulties in accurately capturing the evolution of the climate–related variables during the forecasting period. At the same time, we find particular regions in which other variables are more strongly related to forecast errors. For instance, in central Europe, eastern North America and southeastern Asia, vegetation greenness and soil moisture are relevant, while in western South America and central North America, circulation–related variables such as surface pressure relate more strongly with forecast errors. Overall, the identified relationships between forecast errors and independent Earth observations reveal promising variables on which future forecasting system development could focus by specifically considering related process representations and data assimilation.

Temperature↗
DOE OSTI2026

A practical guide to unbinned unfolding

Unfolding, in the context of high-energy particle physics, refers to the process of removing detector distortions in experimental data. The resulting unfolded measurements are straightforward to use for direct comparisons between experiments and a wide variety of theoretical predictions. For decades, popular unfolding strategies were designed to operate on data formatted as one or more binned histograms. In recent years, new strategies have emerged that use machine learning to unfold datasets in an unbinned manner, allowing for higher-dimensional analyses and more flexibility for current and future users of the unfolded data. This guide comprises recommendations and practical considerations from researchers across a number of major particle physics experiments who have recently put these techniques into practice on real data.

Canelli, Florencia [Univ. of Zurich (Switzerland)]↗
DOE OSTI2026

A Brief Survey of Data Streaming Technologies

Streaming data is data that is emitted at variable volumes in a continuous, incremental manner with the goal of low-latency processing often at a different physical location. Network infrastructure is used to facilitate the connection between data sources and sinks, and must be robust to handle the requirements of the workflow. The U.S. Department of Energy Office of Science (DOE SC) a federal agency supporting fundamental scientific research for energy and the Nation’s largest supporter of basic research in the physical sciences. DOE SC has the responsibility for operating $\mathbf{1 0}$ National Laboratories, and 28 scientific user facilities supporting advanced supercomputers, particle accelerators, large x-ray light sources, neutron scattering sources, and other specialized facilities for nanoscience and genomics. This paper investigates the state of streaming data workfows, and details some of the approaches to this challenging problem.

Kissel, Ezra↗
DOE OSTI2020

Distributed heterogeneous compute infrastructure for the study of additive manufacturing systems

We present the current status of a large-scale computing framework to address the need of the multidisciplinary effort to study chemical dynamics. Specifically, we are enabling scientists to process and store experimental data, run large-scale computationally expensive high-fidelity physical simulation, and analyze these results using the state-of-the-art data analytics tools, machine learning, and uncertainty quantification methods using heterogeneous computing resources, such as CPU and GPU cluster. The framework can integrate or abstract out multiple domains based on roles. In order to develop this framework, we have leveraged an existing framework coupled with in-house heterogeneous computing resources. We present the results of using this framework on a single metadata triggered workflow to accelerate an additive manufacturing use case.

heterogeneous computing, hpc, workflows, reproduci↗
DOE OSTI2025

Development of a Discrepancy Checker for the Digital Twin in a Supervisory Control System for a Thermal Energy Delivery System

Defined as a virtual representation of a physical object, process, or service, and used to support real-world decision-making, a digital twin (DT) can be utilized to combine classical and novel frameworks in sensors, state predictions, and multi-input/multi-output systems, and to enable optimal autonomous operations. However, a DT’s usefulness largely depends on its ability to adequately mirror the state of its physical counterpart, and this adequacy should be reflected by the level of uncertainty in the underlying simulation models when estimating and predicting quantities of interest (QOIs). Moreover, simulation models in a DT may involve multiple fidelities of representations—ranging from physics-based models to data-driven ones—but classical uncertainty quantification (UQ) methods struggle to handle numerous uncertainty sources, nor are they designed for real-time applications. This work presents a UQ-based discrepancy checking and diagnosis tool for a DT-based supervisory control system applied to a thermal energy delivery system (TEDS) at Idaho National Laboratory. The discrepancy checker was developed using metadata from an automated DT development process, and these metadata included different combinations of physical model forms and model parameters, training data and hyperparameters for surrogate models, and design parameters for supervisory control systems. Next, correlations between the uncertainty results and the metadata were established and then applied to the DT operations. The discrepancy checker evaluates the discrepancies between model predictions from virtual and sensor measurements and backtraces them to the corresponding major sources of uncertainty. The discrepancy checker showed reasonable performance in detecting discrepancies and diagnosing sources of uncertainty in testing scenarios.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2008

Application of an Ensemble Smoother to Precipitation Assimilation

Assimilation of precipitation in a global modeling system poses a special challenge in that the observation operators for precipitation processes are highly nonlinear. In the variational approach, substantial development work and model simplifications are required to include precipitation-related physical processes in the tangent linear model and its adjoint. An ensemble based data assimilation algorithm "Maximum Likelihood Ensemble Smoother (MLES)" has been developed to explore the ensemble representation of the precipitation observation operator with nonlinear convection and large-scale moist physics. An ensemble assimilation system based on the NASA GEOS-5 GCM has been constructed to assimilate satellite precipitation data within the MLES framework. The configuration of the smoother takes the time dimension into account for the relationship between state variables and observable rainfall. The full nonlinear forward model ensembles are used to represent components involving the observation operator and its transpose. Several assimilation experiments using satellite precipitation observations have been carried out to investigate the effectiveness of the ensemble representation of the nonlinear observation operator and the data impact of assimilating rain retrievals from the TMI and SSM/I sensors. Preliminary results show that this ensemble assimilation approach is capable of extracting information from nonlinear observations to improve the analysis and forecast if ensemble size is adequate, and a suitable localization scheme is applied. In addition to a dynamically consistent precipitation analysis, the assimilation system produces a statistical estimate of the analysis uncertainty.

Zhang, Sara↗
DOE OSTI2021

Validation Metrics for Fixed Effects and Mixed-Effects Calibration

The modern scientific process often involves the development of a predictive computational model. To improve its accuracy, a computational model can be calibrated to a set of experimental data. A variety of validation metrics can be used to quantify this process. Some of these metrics have direct physical interpretations and a history of use, while others, especially those for probabilistic data, are more difficult to interpret. In this work, a variety of validation metrics are used to quantify the accuracy of different calibration methods. Frequentist and Bayesian perspectives are used with both fixed effects and mixed-effects statistical models. Through a quantitative comparison of the resulting distributions, the most accurate calibration method can be selected. Two examples are included which compare the results of various validation metrics for different calibration methods. It is quantitatively shown that, in the presence of significant laboratory biases, a fixed effects calibration is significantly less accurate than a mixed-effects calibration. This is because the mixed-effects statistical model better characterizes the underlying parameter distributions than the fixed effects model. The results suggest that validation metrics can be used to select the most accurate calibration model for a particular empirical model with corresponding experimental data.

97 MATHEMATICS AND COMPUTING↗