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All sources NASA NTRS DOE OSTI

DOE OSTI2021

Muon reconstruction and identification efficiency in ATLAS using the full Run 2 $pp$ collision data set at $\sqrt{s}=13$ TeV

This article documents the muon reconstruction and identification efficiency obtained by the ATLAS experiment for 139 fb$^{-1}$ of $pp$ collision data at $\sqrt{s}=13$ TeV collected between 2015 and 2018 during Run 2 of the LHC. The increased instantaneous luminosity delivered by the LHC over this period required a reoptimisation of the criteria for the identification of prompt muons. Improved and newly developed algorithms were deployed to preserve high muon identification efficiency with a low misidentification rate and good momentum resolution. The availability of large samples of $Z\to\mu\mu$ and $J/\psi\to\mu\mu$ decays, and the minimisation of systematic uncertainties, allows the efficiencies of criteria for muon identification, primary vertex association, and isolation to be measured with an accuracy at the per-mille level in the bulk of the phase space, and up to the percent level in complex kinematic configurations. Excellent performance is achieved over a range of transverse momenta from 3 GeV to several hundred GeV, and across the full muon detector acceptance of $|\eta|<2.7$.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS2005

Fault Diagnosis in HVAC Chillers

Modern buildings are being equipped with increasingly sophisticated power and control systems with substantial capabilities for monitoring and controlling the amenities. Operational problems associated with heating, ventilation, and air-conditioning (HVAC) systems plague many commercial buildings, often the result of degraded equipment, failed sensors, improper installation, poor maintenance, and improperly implemented controls. Most existing HVAC fault-diagnostic schemes are based on analytical models and knowledge bases. These schemes are adequate for generic systems. However, real-world systems significantly differ from the generic ones and necessitate modifications of the models and/or customization of the standard knowledge bases, which can be labor intensive. Data-driven techniques for fault detection and isolation (FDI) have a close relationship with pattern recognition, wherein one seeks to categorize the input-output data into normal or faulty classes. Owing to the simplicity and adaptability, customization of a data-driven FDI approach does not require in-depth knowledge of the HVAC system. It enables the building system operators to improve energy efficiency and maintain the desired comfort level at a reduced cost. In this article, we consider a data-driven approach for FDI of chillers in HVAC systems. To diagnose the faults of interest in the chiller, we employ multiway dynamic principal component analysis (MPCA), multiway partial least squares (MPLS), and support vector machines (SVMs). The simulation of a chiller under various fault conditions is conducted using a standard chiller simulator from the American Society of Heating, Refrigerating, and Air-conditioning Engineers (ASHRAE). We validated our FDI scheme using experimental data obtained from different types of chiller faults.

Choi, Kihoon↗

NASA NTRS2008

Thermal (Silicon Diode) Data Acquisition Systems

Marshall Space Flight Center s X-ray Cryogenic Facility (XRCF) has been performing cryogenic testing to 20 Kelvin since 1999. Two configurations for acquiring data from silicon diode temperature sensors have been implemented at the facility. The facility's environment is recorded via a data acquisition system capable of reading up to 60 silicon diodes. Test article temperature is recorded by a second data acquisition system capable of reading 150+ silicon diodes. The specifications and architecture of both systems will be presented.

Wright, Ernest↗

NASA NTRS2008

Thermal (Silicon Diode) Data Acquisition System

Marshall Space Flight Center's X-ray Calibration Facility (XRCF) has been performing cryogenic testing to 20 Kelvin since 1999. Two configurations for acquiring data from silicon diode temperature sensors have been implemented at the facility. The facility's environment is recorded via a data acquisition system capable of reading up to 60 silicon diodes. Test article temperature is recorded by a second data acquisition system capable of reading 150+ silicon diodes. The specifications and architecture of both systems will be presented.

Kegley, Jeffrey↗

DOE OSTI2018

Mining data in a dynamic PRA framework

Computational (also known as Dynamic) Probabilistic Risk Assessment (PRA) methods employs system simulation codes coupled with stochastic analysis tools in order to determine probabilities of certain outcomes such as system failure. In contrast to classical PRA methods (i.e., Event-Tree and Fault-Tree) in which timing and sequencing of events is set by the analyst, accident progression is dictated by the system control logic and its interaction with system temporal evolution. Due to the nature of the problem, Simulation-Based PRA methods require large amount of computationally expensive simulation runs. In addition, these methods usually generate a large number of simulation runs (database storage may be on the order of gigabytes or higher). In this paper we investigate and apply several methods and algorithms to analyze these large amounts of time-dependent data. Furthermore, the scope of this article is to present a broad overview of methods and algorithms that can be used to improve data quality and to analyze and extract information from large data sets containing time dependent data. In this context, “extracting information” means constructing input-output correlations, finding commonalities, and identifying outliers.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Unified architecture for data-driven metadata tagging of building automation systems

This article presents a Unified Architecture (UA) for automated point tagging of Building Automation System (BAS) data, based on a combination of data-driven approaches. Advanced energy analytics applications—including fault detection and diagnostics and supervisory control—have emerged as a significant opportunity for improving the performance of our built environment. Effective application of these analytics depends on harnessing structured data from the various building control and monitoring systems, but typical BAS implementations do not employ any standardized metadata schema. While standards such as Project Haystack and Brick Schema have been developed to address this issue, the process of structuring the data, i.e., tagging the points to apply a standard metadata schema, has, to date, been a manual process. This process is typically costly, labor-intensive, and error-prone. In this work we address this gap by proposing a UA that automates the process of point tagging by leveraging the data accessible through connection to the BAS, including time-series data and the raw point names. The UA intertwines supervised classification and unsupervised clustering techniques from machine learning and leverages both their deterministic and probabilistic outputs to inform the point tagging process. Furthermore, we extend the UA to embed additional input and output data-processing modules that are designed to address the challenges associated with the real-time deployment of this automation solution. We test the UA on two datasets for real-life buildings: (i) commercial retail buildings and (ii) office buildings from the National Renewable Energy Laboratory (NREL) campus. We report the proposed methodology correctly applied 85–90% and 70–75% of the tags in each of these test scenarios, respectively for two significantly different building types used for testing UA's fully-functional prototype. The proposed UA, therefore, offers promising approach for automatically tagging BAS data as it reaches close to 90% accuracy. Further building upon this framework to algorithmically identify the equipment type and their relationships is an apt future research direction to pursue.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1995

What's new, Voyager: The discoveries continue

The twin Voyager spacecraft, launched nearly two decades ago, continue to operate and are now searching for the edge of our solar system, the heliopause. Voyager's giant-planet flybys of Jupiter, Saturn, Uranus, and Neptune have provided data that are likely to remain the definitive data set for the foreseeable future and have led to many ongoing discoveries. As the spacecraft move toward the heliopause, they are also providing data on the structure of the heliosphere. This article discusses the discoveries resulting from the flyby and heliosphere data that have been made within the past five years.

Miner, Ellis D.↗

DOE OSTI2022

MINE 2.0: enhanced biochemical coverage for peak identification in untargeted metabolomics

Abstract Summary Although advances in untargeted metabolomics have made it possible to gather data on thousands of cellular metabolites in parallel, identification of novel metabolites from these datasets remains challenging. To address this need, Metabolic in silico Network Expansions (MINEs) were developed. A MINE is an expansion of known biochemistry which can be used as a list of potential structures for unannotated metabolomics peaks. Here, we present MINE 2.0, which utilizes a new set of biochemical transformation rules that covers 93% of MetaCyc reactions (compared to 25% in MINE 1.0). This results in a 17-fold increase in database size and a 40% increase in MINE database compounds matching unannotated peaks from an untargeted metabolomics dataset. MINE 2.0 is thus a significant improvement to this community resource. Availability and implementation The MINE 2.0 website can be accessed at https://minedatabase.ci.northwestern.edu. The MINE 2.0 web API documentation can be accessed at https://mine-api.readthedocs.io/en/latest/. The data and code underlying this article are available in the MINE-2.0-Paper repository at https://github.com/tyo-nu/MINE-2.0-Paper. MINE 2.0 source code can be accessed at https://github.com/tyo-nu/MINE-Database (MINE construction), https://github.com/tyo-nu/MINE-Server (backend web API) and https://github.com/tyo-nu/MINE-app (web app). Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2001

Legato: Personal Computer Software for Analyzing Pressure-Sensitive Paint Data

'Legato' is personal computer software for analyzing radiometric pressure-sensitive paint (PSP) data. The software is written in the C programming language and executes under Windows 95/98/NT operating systems. It includes all operations normally required to convert pressure-paint image intensities to normalized pressure distributions mapped to physical coordinates of the test article. The program can analyze data from both single- and bi-luminophore paints and provides for both in situ and a priori paint calibration. In addition, there are functions for determining paint calibration coefficients from calibration-chamber data. The software is designed as a self-contained, interactive research tool that requires as input only the bare minimum of information needed to accomplish each function, e.g., images, model geometry, and paint calibration coefficients (for a priori calibration) or pressure-tap data (for in situ calibration). The program includes functions that can be used to generate needed model geometry files for simple model geometries (e.g., airfoils, trapezoidal wings, rotor blades) based on the model planform and airfoil section. All data files except images are in ASCII format and thus are easily created, read, and edited. The program does not use database files. This simplifies setup but makes the program inappropriate for analyzing massive amounts of data from production wind tunnels. Program output consists of Cartesian plots, false-colored real and virtual images, pressure distributions mapped to the surface of the model, assorted ASCII data files, and a text file of tabulated results. Graphical output is displayed on the computer screen and can be saved as publication-quality (PostScript) files.

Schairer, Edward T.↗

DOE OSTI2021

Enabling Scalable and Extensible Memory-mapped Datastores in Userspace

Exascale workloads are expected to incorporate data-intensive processing in close coordination with traditional physics simulations. These emerging scientific, data-analytics and machine learning applications need to access a wide variety of datastores in flat files and structured databases. Programmer productivity is greatly enhanced by mapping datastores into the application process's virtual memory space to provide a unified “in-memory” interface. Currently, memory mapping is provided by system software primarily designed for generality and reliability. However, scalability at high concurrency is a formidable challenge on exascale systems. Also, there is a need for extensibility to support new datastores potentially requiring HPC data transfer services. In this article, we present UMap , a scalable and extensible userspace service for memory-mapping datastores. Furthermore, through decoupled queue management, concurrency aware adaptation, and dynamic load balancing, UMap enables application performance to scale even at high concurrency. We evaluate UMap in data-intensive applications, including sorting, graph traversal, database operations, and metagenomic analytics. Our results show that UMap as a userspace service outperforms an optimized kernel-based service across a wide range of intra-node concurrency by 1.22-1.9 × . We performed two case studies to demonstrate UMap 's extensibility. First, a new datastore residing in remote memory is incorporated into UMap as an application-specific plugin. Second, we present a persistent memory allocator Metall built atop UMap for unified storage/memory.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Soil Organic Matter (SOM): Mass Spectrometry Data Analysis

Considered a general purpose tool for characterization of samples at the molecular level in environmental and life sciences, mass spectrometry (MS) is commonly used for measuring organic matter in air, water and soil. Broad base of commercially available instrumentation and data analysis tools provide functional analytical platforms for molecular characterization for a variety of applications and samples. MS instrumentation with ultrahigh resolving power (m/ΔmFWHM > 200 000) is necessary for broad identification of thousands of molecular components present in organic matter samples. This article discusses basic principles of data analysis for ultrahigh resolution MS measurements of small molecules of organic matter.

Environmental Molecular Sciences Laboratory↗

DOE OSTI2013

Depollution benchmarks for capacitors, batteries and printed wiring boards from waste electrical and electronic equipment (WEEE)

Highlights: • We’ve analysed data on the dismantling of electronic and electrical appliances. • Ten years of mass balance data of more than recycling companies have been considered. • Percentages of dismantled batteries, capacitors and PWB have been studied. • Threshold values and benchmarks for batteries and capacitors have been identified. • No benchmark for the dismantling of printed wiring boards should be set. - Abstract: The article compiles and analyses sample data for toxic components removed from waste electronic and electrical equipment (WEEE) from more than 30 recycling companies in Switzerland over the past ten years. According to European and Swiss legislation, toxic components like batteries, capacitors and printed wiring boards have to be removed from WEEE. The control bodies of the Swiss take back schemes have been monitoring the activities of WEEE recyclers in Switzerland for about 15 years. All recyclers have to provide annual mass balance data for every year of operation. From this data, percentage shares of removed batteries and capacitors are calculated in relation to the amount of each respective WEEE category treated. A rationale is developed, why such an indicator should not be calculated for printed wiring boards. The distributions of these de-pollution indicators are analysed and their suitability for defining lower threshold values and benchmarks for the depollution of WEEE is discussed. Recommendations for benchmarks and threshold values for the removal of capacitors and batteries are given.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRS2000

Remote sensing and human health: new sensors and new opportunities

Since the launch of Landsat-1 28 years ago, remotely sensed data have been used to map features on the earth's surface. An increasing number of health studies have used remotely sensed data for monitoring, surveillance, or risk mapping, particularly of vector-borne diseases. Nearly all studies used data from Landsat, the French Systeme Pour l'Observation de la Terre, and the National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiometer. New sensor systems are in orbit, or soon to be launched, whose data may prove useful for characterizing and monitoring the spatial and temporal patterns of infectious diseases. Increased computing power and spatial modeling capabilities of geographic information systems could extend the use of remote sensing beyond the research community into operational disease surveillance and control. This article illustrates how remotely sensed data have been used in health applications and assesses earth-observing satellites that could detect and map environmental variables related to the distribution of vector-borne and other diseases.

Satellite Communications↗

DOE OSTI2012

Systematic approach to verification and validation: High explosive burn models

Most material models used in numerical simulations are based on heuristics and empirically calibrated to experimental data. For a specific model, key questions are determining its domain of applicability and assessing its relative merits compared to other models. Answering these questions should be a part of model verification and validation (V and V). Here, we focus on V and V of high explosive models. Typically, model developers implemented their model in their own hydro code and use different sets of experiments to calibrate model parameters. Rarely can one find in the literature simulation results for different models of the same experiment. Consequently, it is difficult to assess objectively the relative merits of different models. This situation results in part from the fact that experimental data is scattered through the literature (articles in journals and conference proceedings) and that the printed literature does not allow the reader to obtain data from a figure in electronic form needed to make detailed comparisons among experiments and simulations. In addition, it is very time consuming to set up and run simulations to compare different models over sufficiently many experiments to cover the range of phenomena of interest. The first difficulty could be overcome if the research community were to support an online web based database. The second difficulty can be greatly reduced by automating procedures to set up and run simulations of similar types of experiments. Moreover, automated testing would be greatly facilitated if the data files obtained from a database were in a standard format that contained key experimental parameters as meta-data in a header to the data file. To illustrate our approach to V and V, we have developed a high explosive database (HED) at LANL. It now contains a large number of shock initiation experiments. Utilizing the header information in a data file from HED, we have written scripts to generate an input file for a hydro code, run a simulation, and generate a comparison plot showing simulated and experimental velocity gauge data. These scripts are then applied to several series of experiments and to several HE burn models. The same systematic approach is applicable to other types of material models; for example, equations of state models and material strength models.

36 MATERIALS SCIENCE↗

NASA NTRS2003

A Climatology of Tropospheric CO over the Central and Southeastern United States and the Southwestern Pacific Ocean Derived from Space, Air, and Ground-based Infrared Interferometer Spectra

This final report summarizes all research activities and publications undertaken as part of NASA Atmospheric Chemistry and Modeling Analysis Program (ACMAP) Grant NAG-1-2022, 'A Climatology of Tropospheric CO over the Central and Southeastern United States and the Southwestern Pacific Ocean Derived from Space, Air, and Ground-based Infrared Interferometer Spectra'. Major project accomplishments include: (1) analysis of more than 300,000 AERI spectra from the ARM SGP site yielding a 5-year (1998-2002) timeseries of CO retrievals from the Lamont, OK AERI; (2) development of a prototype CO profile retrieval algorithm for AERI spectra; (3) validation and publication of the first CO retrievals from the Scanning High-resolution Interferometer Sounder (SHIS); and (4) development of a prototype AERI tropospheric O3 retrieval algorithm. Compilation and publication of the 5-year Lamont, OK timeseries is underway including a new collaboration with scientists at the Lawrence Berkeley National Laboratory. Public access to this data will be provided upon article submission. A comprehensive CO analysis of the archive of HIS spectra of remains as the only originally proposed activity with little progress. The greatest challenge faced in this project was motivating the University of Wisconsin Co-Investigators to deliver their archived HIS and AERIOO data along with the requisite temperature and water vapor profiles in a timely manner. Part of the supplied HIS dataset from ASHOE may be analyzed as part of a Master s Thesis under a separate project. Our success with the SAFARI 2000 SHIS CO analysis demonstrates the utility of such aircraft remote sensing data given the proper support from the instrument investigators. In addition to the PI and Co-I s, personnel involved in this CO climatology project include one Post Doctoral Fellow, one Research Scientist, two graduate students, and two undergraduate students. A total of fifteen presentations regarding research related to this project were delivered at eleven different scientific meetings. Thus far, three publications have resulted from this project with another five in preparation. No subject inventions resulted from this research project.

McMillian, W. Wallace↗

NASA NTRS2019

Remote Sensing of Daytime Water Leaving Reflectances of Oceans and Large Inland Lakes from EPIC onboard the DSCOVR Spacecraft at Lagrange-1 Point

The NASA’s Earth Polychromatic Imaging Camera (EPIC) on board the Deep Space Climate Observatory (DSCOVR) satellite has been making multiple observations of the entire sunlit Earth in a given day from the Sun-Earth Lagrangian L1 point since the summer of 2015. EPIC contains 10 narrow channels in the 317–780 nm solar spectral range. The data acquired with EPIC have already been used in a variety of scientific investigations, including the study of the global ozone levels, aerosol index and aerosol optical depth, UV reflectivity of clouds over land and ocean, cloud height over land and ocean, and vegetation indices. In this article, we report that EPIC data, particularly for the data measured with narrow channels centered near 443, 551, and 680 nm, can also have important applications in remote sensing of ocean color in different geographical regions. We have modified a version of a multi-channel atmospheric correction algorithm for Moderate Resolution Imaging Spectro Radiometer (MODIS) ocean color applications and adapted the algorithm for processing EPIC data. We present three case studies on water leaving reflectance retrievals from EPIC data acquired over a large turbid river, inland lakes, and oceans. We conclude that a future ocean color instrument on board a satellite at the L1 point, which provides continuous view of the full sunlit disk of the Earth, will complement and extend ocean color observations with the low Earth observing polar orbital and geostationary satellite instruments in both the spatial and time domains.

remote sensing↗

NASA NTRS2009

Design and Calibration of a Flowfield Survey Rake for Inlet Flight Research

The Propulsion Flight Test Fixture at the NASA Dryden Flight Research Center is a unique test platform available for use on NASA's F-15B aircraft, tail number 836, as a modular host for a variety of aerodynamics and propulsion research. For future flight data from this platform to be valid, more information must be gathered concerning the quality of the airflow underneath the body of the F-15B at various flight conditions, especially supersonic conditions. The flow angularity and Mach number must be known at multiple locations on any test article interface plane for measurement data at these locations to be valid. To determine this prerequisite information, flight data will be gathered in the Rake Airflow Gauge Experiment using a custom-designed flowfield rake to probe the airflow underneath the F-15B at the desired flight conditions. This paper addresses the design considerations of the rake and probe assembly, including the loads and stress analysis using analytical methods, computational fluid dynamics, and finite element analysis. It also details the flow calibration procedure, including the completed wind-tunnel test and posttest data reduction, calibration verification, and preparation for flight-testing.

Flynn, Darin C.↗

DOE OSTI2014

Monitoring and data quality assessment of the ATLAS liquid argon calorimeter

The liquid argon calorimeter is a key component of the ATLAS detector installed at the CERN Large Hadron Collider. The primary purpose of this calorimeter is the measurement of electron and photon kinematic properties. It also provides a crucial input for measuring jets and missing transverse momentum. An advanced data monitoring procedure was designed to quickly identify issues that would affect detector performance and ensure that only the best quality data are used for physics analysis. This article presents the validation procedure developed during the 2011 and 2012 LHC data-taking periods, in which more than 98% of the proton-proton luminosity recorded by ATLAS at a centre-of-mass energy of 7-8 TeV had calorimeter data quality suitable for physics analysis.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗