Results for “active data” — Page 25

DOE OSTI2014

Model Evaluation Report for Corrective Action Unit 98: Frenchman Flat, Nevada National Security Site, Nye County, Nevada, Revision 1

Model evaluation focused solely on the PIN STRIPE and MILK SHAKE underground nuclear tests’ contaminant boundaries (CBs) because they had the largest extent, uncertainty, and potential consequences. The CAMBRIC radionuclide migration experiment also had a relatively large CB, but because it was constrained by transport data (notably Well UE-5n), there was little uncertainty, and radioactive decay reduced concentrations before much migration could occur. Each evaluation target and the associated data-collection activity were assessed in turn to determine whether the new data support, or demonstrate conservatism of, the CB forecasts. The modeling team—in this case, the same team that developed the Frenchman Flat geologic, source term, and groundwater flow and transport models—analyzed the new data and presented the results to a PER committee. Existing site understanding and its representation in numerical groundwater flow and transport models was evaluated in light of the new data and the ability to proceed to the CR stage of long-term monitoring and institutional control.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1995

Status of LDEF radiation modeling

The current status of model prediction and comparison with LDEF radiation dosimetry measurements is summarized with emphasis on major results obtained in evaluating the uncertainties of present radiation environment model. The consistency of results and conclusions obtained from model comparison with different sets of LDEF radiation data (dose, activation, fluence, LET spectra) is discussed. Examples where LDEF radiation data and modeling results can be utilized to provide improved radiation assessments for planned LEO missions (e.g., Space Station) are given.

Watts, John W.↗

DOE OSTI2016

Low Activity Waste Glass Waste Form Data Package for Hanford's Integrated Disposal Facility Performance Assessment - 16480

A Direct Feed Low Activity Waste (DFLAW) flowsheet has been initiated to begin immobilization of Hanford tank wastes. In the DFLAW configuration, a pretreatment system will segregate the high level and the low activity wastes (LAW). The LAW will be immobilized in the Waste Treatment Plant (WTP) and disposed of in Hanford's Integrated Disposal Facility (IDF). To obtain authorization to dispose of wastes in the IDF, a Performance Assessment (PA) must be completed in accordance with DOE Order 435.1. The PA shall include calculations of potential doses to representative future members of the public and potential releases from the facility to provide a reasonable expectation that performance objectives will not be exceeded as a result of operation and closure of the facility. To support the development of the PA, data from ILAW glass testing are needed to model the long term release of contaminants over the compliance period and verify that impacts to groundwater quality will be within performance objective limits. Washington River Protection Solutions is conducting an ILAW glass testing program to produce the required data. This program is a combined effort involving National Laboratories with known expertise in glass waste form research. The work involves accelerated testing to develop parameters that can be used in computer simulations of glass corrosion and contaminant release in the IDF environment. The results of this work will be compiled in an ILAW glass waste form performance data package for the IDF PA. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2019

Active Subspace Methods for Data-Intensive Inverse Problems

Fostered by advances in computing, simulation and data science have emerged as the third and fourth paradigms enabling scientific discovery. Many mathematical challenges arise when integrating these paradigms to produce new knowledge. One of the most important challenges is the inverse or calibration problem: find the inputs of a simulation such that its outputs agree with a given set of measurements. As both the cost of the complex simulations and the volume of measurement data sets increase, intuition-driven trial-and-error methods for calibration quickly become untenable; modern scientists need more computationally efficient and mathematically rigorous methods. When measurements and simulated predictions contain uncertainty, the Bayesian perspective on statistical inference provides a popular mathematical framework for developing computational methods to solve inverse problems. Markov chain Monte Carlo (MCMC) methods are a mainstay of this framework; they characterize a probability distribution on the space of model inputs that is conditioned on the given measurements. However, despite recent advances, MCMC methods remain impractical for expensive simulations with more than a handful of inputs. At its heart, MCMC searches the space of inputs for corresponding outputs that match measurements, and so it is afflicted with the curse of dimensionality. Additionally, MCMC methods become less efficient as the volume of the measurement data increases, particularly if the measurements are redundant. To make MCMC—and Bayesian methods, in general—amenable to today’s computer models with millions of inputs and terabytes of measurement data, one must devise methods to reduce the dimension of both the simulation inputs and the measurements. For many models used in practice, the outputs vary primarily on a low-dimensional manifold of the nominally high-dimensional input space. Recently developed active subspace methods discover this low-dimensional manifold and exploit it to enable analyses typically suited for low-dimensional settings (e.g., statistical inversion of complex systems). We hypothesize that active subspace methods can provide the necessary dimension reduction, enabling existing Bayesian methods to find solutions for a large class of otherwise intractable statistical inverse problems. We will test the hypothesis with a series of mathematical analyses and computational experiments that will proceed along three research veins: (i) integrating active subspace methods into methods for statistical inversion, (ii) addressing current challenges in active subspace methods, and (iii) incorporating methods for nonlinear dimensionality reduction into active subspace methods. We will apply the methods we develop to real inverse problems in chemical kinetics and turbulent flame modeling. Our team combines expertise in methods for inverse problems, dimension reduction, and reduced order modeling. With a history of active collaboration and thoughtful engagement, we bring enthusiasm and fresh perspectives to a well studied subject. We are excited by the potential opportunity to work together to extend and apply active subspace methods to data-intensive inverse problems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Active Subspace Methods for Data-Intensive Inverse Problems (Final Report)

Fostered by advances in computing, simulation and data science have emerged as the third and fourth paradigms enabling scientific discovery. Many mathematical challenges arise when integrating these paradigms to produce new knowledge. One of the most important challenges is the inverse or calibration problem: find the inputs of a simulation such that its outputs agree with a given set of measurements. As both the cost of the complex simulations and the volume of measurement data sets increase, intuition-driven trial-and-error methods for calibration quickly become untenable; modern scientists need more computationally efficient and mathematically rigorous methods. When measurements and simulated predictions contain uncertainty, the Bayesian perspective on statistical inference provides a popular mathematical framework for developing computational methods to solve inverse problems. Markov chain Monte Carlo (MCMC) methods are a mainstay of this framework; they characterize a probability distribution on the space of model inputs that is conditioned on the given measurements. However, despite recent advances, MCMC methods remain impractical for expensive simulations with more than a handful of inputs. At its heart, MCMC searches the space of inputs for corresponding outputs that match measurements, and so it is afflicted with the curse of dimensionality. Additionally, MCMC methods become less efficient as the volume of the measurement data increases, particularly if the measurements are redundant. To make MCMC—and Bayesian methods, in general—amenable to today’s computer models with millions of inputs and terabytes of measurement data, one must devise methods to reduce the dimension of both the simulation inputs and the measurements. For many models used in practice, the outputs vary primarily on a low-dimensional manifold of the nominally high-dimensional input space. Recently developed active subspace methods discover this low-dimensional manifold and exploit it to enable analyses typically suited for low-dimensional settings (e.g., statistical inversion of complex systems). We hypothesize that active subspace methods can provide the necessary dimension reduction, enabling existing Bayesian methods to find solutions for a large class of otherwise intractable statistical inverse problems. We will test the hypothesis with a series of mathematical analyses and computational experiments that will proceed along three research veins: (i) integrating active subspace methods into methods for statistical inversion, (ii) addressing current challenges in active subspace methods, and (iii) incorporating methods for nonlinear dimensionality reduction into active subspace methods. We will apply the methods we develop to real inverse problems in chemical kinetics and turbulent flame modeling. Our team combines expertise in methods for inverse problems, dimension reduction, and reduced order modeling. With a history of active collaboration and thoughtful engagement, we bring enthusiasm and fresh perspectives to a well studied subject. We are excited by the potential opportunity to work together to extend and apply active subspace methods to data-intensive inverse problems.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2012

Inter-Calibration of EIS, XRT and AIA using Active Region and Bright Point Data

Certain limitations in our solar instruments have created the need to use several instruments together for long term and/or large field of view studies. We will, therefore, present an intercalibration study of the EIS, XRT and AIA instruments using active region and bright point data. We will use the DEMs calculated from EIS bright point observations to determine the expected AIA and XRT intensities. We will them compare to the observed intensities and calculate a correction factor. We will consider data taken over a year to see if there is a time dependence to the correction factor. We will then determine if the correction factors are valid for active region observations.

Mulu-Moore, Fana M.↗

NASA NTRS1982

Flight data file: STS-4 crew activity plan

The STS-4 Crew Activity Plan contains the on-orbit timeline, which is a flight data file article. Various time scales such as Mission Elapsed Time (MET), Greenwich Mean Time (GMT), and time until deorbit ignition as well as crew activities, day/night, orbit position, ground tracking, communication coverage, attitude, and maneuvers are presented in chart form.

Pippert, E. B., Jr.↗

DOE OSTI2019

MOVES Activity Updates Using Fleet DNA Data: Interim Report

The U.S. Environmental Protection Agency's Motor Vehicle Emissions Simulator (MOVES) is a publicly available tool used by researchers and policy makers to help understand motor vehicle emission sources at national, county, and project levels. However, estimates of heavy-duty activity (MOVES2014), have been identified as areas needing improvement. The start activities in MOVES2014 are calculated using a limited data set, prompting the concern that inventory values are not representative. In addition, MOVES2014 is believed to underestimate heavy-duty activity that is not captured in the current drive cycles used to represent on-network activity. For example, MOVES2014 does not account for work-day idling activity that takes place on off-network roads, such as at a distribution center while the truck is queuing or during loading and unloading. Under the guidance and expertise of the EPA, the National Renewable Energy Laboratory has leveraged its extensive Fleet DNA database of heavy-duty vehicles to reinforce the data set behind the next-generation MOVES model and enhance idle activity and start fractions using six heavy-duty vehicle classes. The data available in Fleet DNA from 420 conventional, diesel powered vehicles provided over 120,000 hours of operation. Start fraction, soak fraction, and idle fraction by hour of the day were derived for each source type, state, and vocation, and results were provided in the form of .CSV files representing MOVES table inputs. This midterm report details these results providing graphical analysis and context for the start, soak, and idle distributions.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

Seismic Characterization of the Blue Mountain Geothermal Field

Subsurface characterization is crucial for geothermal energy exploration and production. Yet hydrothermal reservoirs usually reside in highly fractured and faulted zones where accurate characterization is very challenging because of low signal-to-noise ratios of land seismic data and lack of coherent reflection signals. We perform an active-source seismic characterization for the Blue Mountain geothermal field in Nevada using active seismic data to reveal the elastic medium property complexity and fault distribution at this field. We first employ an unsupervised machine learning method to attenuate groundroll and near-surface guided-wave noise and enhance coherent reflection and scattering signals from noisy seismic data. We then build a smooth initial P-wave velocity model based on an existing magnetotellurics survey result, and use 3D first-arrival traveltime tomography to refine the initial velocity model. We then derive a set of elastic wave velocities and anisotropic parameters using elastic full-waveform inversion, and obtain PP and PS images using elastic reverse-time migration. We identify major faults by analyzing the variations of seismic velocities and anisotropy parameters, and reveal mid- to small-scale faults by applying a supervised machine learning method to the seismic migration images. Our characterization reveals complex velocity heterogeneities and anisotropies, as well as faults, with a high spatial resolution. These results can provide valuable information for optimal placement of future injection and production wells to increase geothermal energy production at the Blue Mountain geothermal power plant.

58 GEOSCIENCES↗

NASA NTRS1982

Australia ground data collection 1981-82 crop year, volume 1

Under AgRISTARS management, ground data were collected at 20 agricultural sites within Australia during the crop year 1981-82. The data collection activity is summarized. Specifically, the following information is provided: discussion of data procedures, methods, and products; crop production results; photographs of the Australia agriculture scene, map sheets of segments, LANDSAT full frames, and aerial photographs of data collection areas; and summarizations of district agronomist reports.

Quinones, C. R.↗

NASA NTRS2014

The Importance of Data Visualization: Incorporating Storytelling into the Scientific Presentation

From its inception in 2000, one of the primary tasks of the Biomedical Data Reduction Analysis (BDRA) group has been translation of large amounts of data into information that is relevant to the audience receiving it. BDRA helps translate data into an integrated model that supports both operational and research activities. This data integrated model and subsequent visual data presentations have contributed to BDRA's success in delivering the message (i.e., the story) that its customers have needed to communicate. This success has led to additional collaborations among groups that had previously not felt they had much in common until they worked together to develop solutions in an integrated fashion. As more emphasis is placed on working with "big data" and on showing how NASA's efforts contribute to the greater good of the American people and of the world, it becomes imperative to visualize the story of our data to communicate the greater message we need to share. METHODS To create and expand its data integrated model, BDRA has incorporated data from many different collaborating partner labs and other sources. Data are compiled from the repositories of the Lifetime Surveillance of Astronaut Health and the Life Sciences Data Archive, and from the individual laboratories at Johnson Space Center that support collection of data from medical testing, environmental monitoring, and countermeasures, as designated in the Medical Requirements Integration Documents. Ongoing communication with the participating collaborators is maintained to ensure that the message and story of the data are retained as data are translated into information and visual data presentations are delivered in different venues and to different audiences. RESULTS We will describe the importance of storytelling through an integrated model and of subsequent data visualizations in today's scientific presentations and discuss the collaborative methods used. We will illustrate the discussion with examples of graphs from BDRA's past work supporting operations and/or research efforts.

Babiak-Vazquez, A.↗

NASA NTRS2018

NASA Pilot-Engaged Expert Response Using IBM Watson Technology: Prototype Evaluation of Knowledge Retrieval System

NASA Langley Research Center and IBM have been investigating the use of IBM Watson technology in aerospace research and development. One application of Watson technology is the Pilot-Engaged Expert Response (PEER) use case. The PEER system is envisioned as an in-cockpit advisor that will act as a source of situationally-relevant information for pilots and other flight crew members to assist in decision making about real-time events and situations that arise in the course of aircraft operations. PEER will make available vast stores of knowledge and information quickly and directly, putting important informational resources where they are needed most. IBM has worked with NASA to develop an architecture and articulate a roadmap for the development of the PEER system. That vision is built around Watson Discovery Advisor (WDA) software solution, derived from IBM's Jeopardy!-winning automatic question answering system. PEER makes use of WDA's sophisticated question-answering capabilities as its core, adding important User Interface components and other customizations for the cockpit environment, including communication with flight systems and other external data sources. The development plan for PEER includes four development stages, with the current project constituting the first phase. In this project, a prototype instance of PEER was successfully adapted to the aviation domain, enabling users to ask questions about aviation topics and receive useful and accurate answers to these questions. Major tasks accomplished include the development of procedures for domain adaptation through automatic lexicon extraction from domain glossaries; generation of question-answer training data which was used to train the system; and assessment of the effectiveness of domain adaptation, which showed a dramatic improvement in the ability of the PEER system to answer domain-relevant questions. In addition, the vision for the PEER system was pushed forward by the articulation of a plan for the automatic enhancement of question-answering with contextual information. This initial phase focused on two main goals: 1) the targeted domain adaptation of the underlying WDA system to the aviation domain; and, 2) the design of the software systems needed to leverage flight-contextual data. Domain adaptation of the WDA system proceeds via three main activities: Domain data ingestion, lexical customization and model training. A textual corpus consisting of 1,147 individual documents with more than 7.5 million words of text was ingested into the system and this served as the basis of all further development. A domain lexicon of over 3,500 aviation-domain terms was semi-automatically generated from domain documents and used to train the system. In addition, a set of over 500 question-answer (QA) pairs relevant to the PEER use case was developed; these were used to train and assess the system. These important first steps established the basis for the PEER system. In addition, steps were taken towards the integration of the PEER system into the cockpit environment with the development of a functional design for the Contextual Data Augmentation (CDA) subsystem. This subsystem brings to bear contextual data to improve system responses. It has three main submodules: the Contextual Data Collection module, the Contextual Data Selection module, and the Contextual QA Augmentation module. These modules form a processing pipeline that addresses the problems associated with automatically integrating information from external resources into the knowledge-retrieval mechanism.

Machine learning↗

DOE OSTI2021

Three dimensional low-mode areal-density non-uniformities in indirect-drive implosions at the National Ignition Facility

To achieve hotspot ignition, an inertial confinement fusion implosion must achieve high hotspot pressure that is inertially confined by a dense shell of DT fuel. This requires a symmetric implosion having high in-flight shell velocity and high areal density at stagnation. The size of the driver and scale of the capsule required can be minimized by maintaining a high efficiency of energy coupling from the imploding shell to the hotspot. Significant 3D low mode asymmetries, however, are commonly observed in indirect-drive implosions and reduce the coupling of shell kinetic energy to the hotspot. To better quantify the magnitudes and impacts of shell density asymmetries, we have developed new analysis techniques and analytic models [Hurricane et al., Phys. Plasmas 27(6), 062704 (2020)]. To build confidence in the underlying data, we have also developed an analytic neutron transport model to cross-compare two independent measurements of asymmetry, which shows excellent agreement across shots for mode-1 (ℓ = 1). This work also demonstrates that asymmetry can introduce potential sampling bias into down-scattered ratio measurements causing the solid-angle-average and uncertainty-weighted-average down-scattered ratios to differ significantly. Diagnosing asymmetries beyond mode-1 (ℓ > 1) presents significant challenges. Using new diagnostic instruments and analysis techniques, however, evidence of significant Legendre mode P2 (ℓ = 2, m = 0) and additional 3D asymmetries (ℓ > 1, m ≠ 0) are beginning to emerge from the high precision activation diagnostic data (real-time nuclear activation detectors) and down-scattered neutron imaging data.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Weighted active space protocol for multireference machine-learned potentials

Multireference methods such as multiconfiguration pair-density functional theory accurately capture electronic correlation in systems with strong multiconfigurational character, but their cost precludes direct use in molecular dynamics. Combining these methods with machine-learned interatomic potentials (MLPs) can extend their reach. However, the sensitivity of multireference calculations to the choice of the active space complicates the consistent evaluation of energies and gradients across structurally diverse nuclear configurations. To overcome this limitation, we introduce the weighted active space protocol (WASP), a systematic approach to assign a consistent active space for a given system across uncorrelated configurations. By integrating WASP with MLPs and enhanced sampling techniques, we propose a data-efficient active learning cycle that enables the training of an MLP on multireference data. We demonstrated the approach on the TiC + -catalyzed C–H activation of methane, a reaction that poses challenges for Kohn–Sham density functional theory due to its significant multireference character. This framework enables accurate and efficient modeling of catalytic dynamics, establishing a paradigm for simulating complex reactive processes beyond the limits of conventional electronic-structure methods.

enhanced sampling↗

NASA NTRS2002

Activity Tracking for Pilot Error Detection from Flight Data

This report presents an application of activity tracking for pilot error detection from flight data, and describes issues surrounding such an application. It first describes the Crew Activity Tracking System (CATS), in-flight data collected from the NASA Langley Boeing 757 Airborne Research Integrated Experiment System aircraft, and a model of B757 flight crew activities. It then presents an example of CATS detecting actual in-flight crew errors.

Callantine, Todd J.↗

NASA NTRS2009

Overview of Materials International Space Station Experiment 7B

Materials International Space Station Experiment 7B (MISSE 7B) is the most recent in a series of experiments flown on the exterior of International Space Station for the purpose of determining the durability of materials and components in the space environment. A collaborative effort among the Department of Defense, the National Aeronautics and Space Administration, industry, and academia, MISSE 7B will be flying a number of NASA experiments designed to gain knowledge in the area of space environmental effects to mitigate risk for exploration missions. Consisting of trays called Passive Experiment Containers, the suitcase sized payload opens on hinges and allows active and passive experiments contained within to be exposed to the ram and wake or zenith and nadir directions in low Earth orbit, in essence, providing a test bed for atomic oxygen exposure, ultraviolet radiation exposure, charged particle radiation exposure, and thermal cycling. New for MISSE 7B is the ability to monitor experiments actively, with data sent back to Earth via International Space Station communications. NASA?s active and passive experiments cover a range of interest for the Agency. Materials relevant to the Constellation Program include: solar array materials, seal materials, and thermal protection system materials. Materials relevant to the Exploration Technology Development Program include: fabrics for spacesuits, materials for lunar dust mitigation, and new thermal control coatings. Sensors and components on MISSE 7B include: atomic oxygen fluence monitors, ultraviolet radiation sensors, and electro-optical components. In addition, fundamental space environmental durability science experiments are being flown to gather atomic oxygen erosion data and thin film polymer mechanical and optical property data relevant to lunar lander insulation and the James Web Space Telescope. This paper will present an overview of the NASA experiments to be flown on MISSE 7B, along with a summary of the thermal environment to be expected during the 1 yr mission scheduled for launch in 2009.

Jaworske, Donald A.↗

DOE OSTI2019

Formylglycine-generating enzyme binds substrate directly at a mononuclear Cu(I) center to initiate O 2 activation

The formylglycine-generating enzyme (FGE) is required for the posttranslational activation of type I sulfatases by oxidation of an active-site cysteine to C α -formylglycine. FGE has emerged as an enabling biotechnology tool due to the robust utility of the aldehyde product as a bioconjugation handle in recombinant proteins. Here, we show that Cu(I)–FGE is functional in O 2 activation and reveal a high-resolution X-ray crystal structure of FGE in complex with its catalytic copper cofactor. We establish that the copper atom is coordinated by two active-site cysteine residues in a nearly linear geometry, supporting and extending prior biochemical and structural data. The active cuprous FGE complex was interrogated directly by X-ray absorption spectroscopy. These data unambiguously establish the configuration of the resting enzyme metal center and, importantly, reveal the formation of a three-coordinate tris(thiolate) trigonal planar complex upon substrate binding as furthermore supported by density functional theory (DFT) calculations. Critically, inner-sphere substrate coordination turns on O 2 activation at the copper center. These collective results provide a detailed mechanistic framework for understanding why nature chose this structurally unique monocopper active site to catalyze oxidase chemistry for sulfatase activation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2011

Effects of Active Sting Damping on Common Research Model Data Quality

Recent tests using the Common Research Model (CRM) at the Langley National Transonic Facility (NTF) and the Ames 11-foot Transonic Wind Tunnel (11' TWT) produced large sets of data that have been used to examine the effects of active damping on transonic tunnel aerodynamic data quality. In particular, large statistically significant sets of repeat data demonstrate that the active damping system had no apparent effect on drag, lift and pitching moment repeatability during warm testing conditions, while simultaneously enabling aerodynamic data to be obtained post stall. A small set of cryogenic (high Reynolds number) repeat data was obtained at the NTF and again showed a negligible effect on data repeatability. However, due to a degradation of control power in the active damping system cryogenically, the ability to obtain test data post-stall was not achieved during cryogenic testing. Additionally, comparisons of data repeatability between NTF and 11-ft TWT CRM data led to further (warm) testing at the NTF which demonstrated that for a modest increase in data sampling time, a 2-3 factor improvement in drag, and pitching moment repeatability was readily achieved not related with the active damping system.

Acheson, Michael J.↗