Search NASA⌕ Search

SEARCH · Search NASA

Results for “Distributed Estimation”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,297 records · Page 72
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Stopping criteria for ending autonomous, single detector radiological source searches

While the localization of radiological sources has traditionally been handled with statistical algorithms, such a task can be augmented with advanced machine learning methodologies. The combination of deep and reinforcement learning has provided learning-based navigation to autonomous, single-detector, mobile systems. However, these approaches lacked the capacity to terminate a surveying/search task without outside influence of an operator or perfect knowledge of source location (defeating the purpose of such a system). Two stopping criteria are investigated in this work for a machine learning navigated system: one based upon Bayesian and maximum likelihood estimation (MLE) strategies commonly used in source localization, and a second providing the navigational machine learning network with a “stop search” action. A convolutional neural network was trained via reinforcement learning in a 10 m × 10 m simulated environment to navigate a randomly placed detector-agent to a randomly placed source of varied strength (stopping with perfect knowledge during training). The network agent could move in one of four directions (up, down, left, right) after taking a 1 s count measurement at the current location. During testing, the stopping criteria for this navigational algorithm was based upon a Bayesian likelihood estimation technique of source presence, updating this likelihood after each step, and terminating once the confidence of the source being in a single location exceeded 0.9. A second network was trained and tested with similar architecture as the previous but which contained a fifth action: for self-stopping. The accuracy and speed of localization with set detector and source initializations were compared over 50 trials of MLE-Bayesian approach and 1000 trials of the CNN with self-stopping. The statistical stopping condition yielded a median localization error of ~1.41 m and median localization speed of 12 steps. The machine learning stopping condition yielded a median localization error of 0 m and median localization speed of 17 steps. This work demonstrated two stopping criteria available to a machine learning guided, source localization system.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2015

Stochastic Modeling of Radioactive Material Releases

Nonreactor nuclear facilities operated under the approval authority of the U.S. Department of Energy use unmitigated hazard evaluations to determine if potential radiological doses associated with design basis events challenge or exceed dose evaluation guidelines. Unmitigated design basis events that sufficiently challenge dose evaluation guidelines or exceed the guidelines for members of the public or workers, merit selection of safety structures, systems, or components or other controls to prevent or mitigate the hazard. Idaho State University, in collaboration with Idaho National Laboratory, has developed a portable and simple to use software application called SODA (Stochastic Objective Decision-Aide) that stochastically calculates the radiation dose associated with hypothetical radiological material release scenarios. Rather than producing a point estimate of the dose, SODA produces a dose distribution result to allow a deeper understanding of the dose potential. SODA allows users to select the distribution type and parameter values for all of the input variables used to perform the dose calculation. SODA then randomly samples each distribution input variable and calculates the overall resulting dose distribution. In cases where an input variable distribution is unknown, a traditional single point value can be used. SODA was developed using the MATLAB coding framework. The software application has a graphical user input. SODA can be installed on both Windows and Mac computers and does not require MATLAB to function. SODA provides improved risk understanding leading to better informed decision making associated with establishing nuclear facility material-at-risk limits and safety structure, system, or component selection. It is important to note that SODA does not replace or compete with codes such as MACCS or RSAC, rather it is viewed as an easy to use supplemental tool to help improve risk understanding and support better informed decisions. The work was funded through a grant from the DOE Nuclear Safety Research and Development Program.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2016

SSHAC Level 1 Probabilistic Seismic Hazard Analysis for the Idaho National Laboratory

A Probabilistic Seismic Hazard Analysis (PSHA) was completed for the Materials and Fuels Complex (MFC), Advanced Test Reactor (ATR), and Naval Reactors Facility (NRF) at the Idaho National Laboratory (INL). The PSHA followed the approaches and procedures for Senior Seismic Hazard Analysis Committee (SSHAC) Level 1 study and included a Participatory Peer Review Panel (PPRP) to provide the confident technical basis and mean-centered estimates of the ground motions. A new risk-informed methodology for evaluating the need for an update of an existing PSHA was developed as part of the Seismic Risk Assessment (SRA) project. To develop and implement the new methodology, the SRA project elected to perform two SSHAC Level 1 PSHAs. The first was for the Fuel Manufacturing Facility (FMF), which is classified as a Seismic Design Category (SDC) 3 nuclear facility. The second was for the ATR Complex, which has facilities classified as SDC-4. The new methodology requires defensible estimates of ground motion levels (mean and full distribution of uncertainty) for its criteria and evaluation process. The INL SSHAC Level 1 PSHA demonstrates the use of the PPRP, evaluation and integration through utilization of a small team with multiple roles and responsibilities (four team members and one specialty contractor), and the feasibility of a short duration schedule (10 months). Additionally, a SSHAC Level 1 PSHA was conducted for NRF to provide guidance on the potential use of a design margin above rock hazard levels for the Spent Fuel Handling Recapitalization Project (SFHP) process facility.

58 GEOSCIENCES↗
DOE OSTI2017

Optical Sky Brightness and Transparency during the Winter Season at Dome A Antarctica from the Gattini-All-Sky Camera

The summit of the Antarctic plateau, Dome A, is proving to be an excellent site for optical, near-infrared, and terahertz astronomical observations. Gattini is a wide-field camera installed on the PLATO instrument module as part of the Chinese-led traverse to Dome A in 2009 January. We present here the measurements of sky brightness with the Gattini ultra-large field of view (90{sup ∘}×90{sup ∘}) in the photometric B-, V-, and R-bands; cloud cover statistics measured during the 2009 winter season; and an estimate of the sky transparency. A cumulative probability distribution indicates that the darkest 10% of the nights at Dome A have sky brightness of S {sub B} = 22.98, S {sub V} = 21.86, and S {sub R} = 21.68 mag arcsec{sup −2}. These values were obtained during the year 2009 with minimum aurora, and they are comparable to the faintest sky brightness at Maunakea and the best sites of northern Chile. Since every filter includes strong auroral lines that effectively contaminate the sky brightness measurements, for instruments working around the auroral lines, either with custom filters or with high spectral resolution instruments, these values could be easily obtained on a more routine basis. In addition, we present example light curves for bright targets to emphasize the unprecedented observational window function available from this ground-based site. These light curves will be published in a future paper.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Probabilistic Cosmological Mass Mapping from Weak Lensing Shear

We infer gravitational lensing shear and convergence fields from galaxy ellipticity catalogs under a spatial process prior for the lensing potential. We demonstrate the performance of our algorithm with simulated Gaussian-distributed cosmological lensing shear maps and a reconstruction of the mass distribution of the merging galaxy cluster Abell 781 using galaxy ellipticities measured with the Deep Lens Survey. Given interim posterior samples of lensing shear or convergence fields on the sky, we describe an algorithm to infer cosmological parameters via lens field marginalization. In the most general formulation of our algorithm we make no assumptions about weak shear or Gaussian-distributed shape noise or shears. Because we require solutions and matrix determinants of a linear system of dimension that scales with the number of galaxies, we expect our algorithm to require parallel high-performance computing resources for application to ongoing wide field lensing surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Black Hole Growth Is Mainly Linked to Host-galaxy Stellar Mass Rather Than Star Formation Rate

We investigate the dependence of black hole accretion rate (BHAR) on host-galaxy star formation rate (SFR) and stellar mass (M {sub *}) in the CANDELS/GOODS-South field in the redshift range of 0.5⩽z<2.0. Our sample consists of ≈18,000 galaxies, allowing us to probe galaxies with 0.1M{sub ⊙}yr{sup −1}≲SFR≲100 M{sub ⊙} yr{sup −1} and/or 10{sup 8}M{sub ⊙}≲M{sub ∗}≲10{sup 11} M{sub ⊙}. We use sample-mean BHAR to approximate long-term average BHAR. Our sample-mean BHARs are derived from the Chandra Deep Field-South 7 Ms observations, while the SFRs and M {sub *} have been estimated by the CANDELS team through spectral energy distribution fitting. The average BHAR is correlated positively with both SFR and M {sub *}, and the BHAR–SFR and BHAR–M {sub *} relations can both be described acceptably by linear models with a slope of unity. However, BHAR appears to be correlated more strongly with M {sub *} than SFR. This result indicates that M {sub *} is the primary host-galaxy property related to supermassive black hole (SMBH) growth, and the apparent BHAR–SFR relation is largely a secondary effect due to the star-forming main sequence. Among our sources, massive galaxies (M{sub ∗}≳10{sup 10}M{sub ⊙}) have significantly higher BHAR/SFR ratios than less massive galaxies, indicating that the former have higher SMBH fueling efficiency and/or higher SMBH occupation fraction than the latter. Our results can naturally explain the observed proportionality between M{sub BH} and M {sub *} for local giant ellipticals and suggest that their M{sub BH}/M{sub ∗} is higher than that of local star-forming galaxies. Among local star-forming galaxies, massive systems might have higher M{sub BH}/M{sub ∗} compared to dwarfs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Estimating β eff from static, late-time Feynman neutron noise measurements

The effective delayed neutron fraction, β eff , is a kinematic parameter describing the contribution of delayed neutrons to the effective multiplication factor, $\kappa$ eff , and is of great interest to the nuclear criticality safety community and beyond. In fact, $\kappa$ eff presently must be inferred (as opposed to directly measured) since variables like β eff must be obtained from simulation, reference tables, or a dynamic or pulsed measurement where the assembly of fissile material is perturbed and not in steady state. Several recent works have proposed neutron noise techniques to estimate β eff from static measurements (such as benchmark experiments). This work experimentally investigates an approach based on looking at late-time (up to thousands of seconds) correlated neutrons in Feynman histograms. This work utilizes measurements of weapons grade plutonium and highly enriched uranium to construct late-time Feynman histograms and investigate a proposed methodology of estimating β eff from a fit of the distribution. Data is shown from both He-3 detectors and an organic scintillator array. This work is preliminary in nature, demonstrating some experimental feasibility and proposing improvements for future investigations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

Estimating consistent biased source probabilities over arbitrary bins - 25621

This paper describes computational algorithms for estimating consistent biased source probabilities for the CADIS and FW-CADIS methods that are compatible with most of the general fixed-source capabilities of the MCNP continuous-energy transport code. The basic algorithm is a novel Monte Carlo sampling technique that enables the straightforward estimation of biased source probabilities over arbitrarily complex distribution trees. In recent work, a method has been developed for generating correct biased spatial source probabilities in the presence of rejection from an arbitrary number of geometry cells. Two numerical test problems that demonstrate the correctness of this new algorithm are presented, including a simplified model of a cooling water system with an N-17 neutron source. (authors)

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2024

A CHIL Validation of Machine Learning-Assisted Methods for Real-Time Controls of Solar PV for Grid Services: Preprint

Recent research has highlighted the potential for solar to act as a zero-marginal-cost and zero-emission flexibility resource on the bulk power system when operated with advanced control systems. To increase the performance of these systems, leading technologies, including machine learning (ML) and hierarchical inverter set point allocation, have been proposed; however, these technologies lack comprehensive validation under real-world application scenarios. This paper addresses this gap by designing and developing a controller-hardware-in-the-loop framework to evaluate the performance of different flexible solar technologies in responding to automatic generation control signals in a closed-loop fashion. Simulation results indicate the superior performance of an ML-based approach compared to the conventional reference-control grouping-based approach, showcasing its potential to support grid stability and operational efficiency.

closed-loop validation↗
DOE OSTI2021

Radiation GRMHD simulations of M87: funnel properties and prospects for gap acceleration

Here, we use the public code EBHLIGHT to carry out 3D radiative general relativistic magnetohydrodynamics (GRMHD) simulations of accretion on to the supermassive black hole in M87. The simulations self-consistently evolve a frequency-dependent Monte Carlo description of the radiation field produced by the accretion flow. We explore two limits of accumulated magnetic flux at the black hole (SANE and MAD), each coupled to several subgrid prescriptions for electron heating that are motivated by models of turbulence and magnetic reconnection. We present convergence studies for the radiation field and study its properties. We find that the near-horizon photon energy density is an order of magnitude higher than is predicted by simple isotropic estimates from the observed luminosity. The radially dependent photon momentum distribution is anisotropic and can be modeled by a set of point-sources near the equatorial plane. We draw properties of the radiation and magnetic field from the simulation and feed them into an analytic model of gap acceleration to estimate the very high energy (VHE) γ-ray luminosity from the magnetized jet funnel, assuming that a gap is able to form. We find luminosities of $\rm \sim 10^{41} \, erg \, s^{-1}$ for MAD models and $\rm \sim 2\times 10^{40} \, erg \, s^{-1}$ for SANE models, which are comparable to measurements of M87’s VHE flares. The time-dependence seen in our calculations is insufficient to explain the flaring behaviour. Our results provide a step towards bridging theoretical models of near-horizon properties seen in black hole images with the VHE activity of M87.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Exploration of a Potential DOOR Endpoint for Hospital-acquired Bacterial Pneumonia and Ventilator-associated Bacterial Pneumonia Using Six Registrational Trials for Antibacterial Drugs

Abstract Background Desirability of outcome ranking (DOOR) is an innovative approach to clinical trial design and analysis that uses an ordinal ranking system to incorporate the overall risks and benefits of a therapeutic intervention into a single measurement. Here we derived and evaluated a disease-specific DOOR endpoint for registrational trials for hospital-acquired bacterial pneumonia and ventilator-associated bacterial pneumonia (HABP/VABP). Methods Through comprehensive examination of data from nearly 4000 participants enrolled in six registrational trials for HABP/VABP submitted to the Food and Drug Administration (FDA) between 2005 and 2022, we derived and applied a HABP/VABP specific endpoint. We estimated the probability that a participant assigned to the study treatment arm would have a more favorable overall DOOR or component outcome than a participant assigned to comparator. Results DOOR distributions between treatment arms were similar in all trials. DOOR probability estimates ranged from 48.3% to 52.9% and were not statistically different. There were no significant differences between treatment arms in the component analyses. Although infectious complications and serious adverse events occurred more frequently in ventilated participants compared to non-ventilated participants, the types of events were similar. Conclusions Through a data-driven approach, we constructed and applied a potential DOOR endpoint for HABP/VABP trials. The inclusion of syndrome-specific events may help to better delineate and evaluate participant experiences and outcomes in future HABP/VABP trials and could help inform data collection and trial design.

Immunology↗
DOE OSTI2016

A tracking system to calculate patient skin dose in real-time during neurointerventional procedures using a biplane x-ray imaging system

Purpose: Neurovascular interventional procedures using biplane fluoroscopic imaging systems can lead to increased risk of radiation-induced skin injuries. The authors developed a biplane dose tracking system (Biplane-DTS) to calculate the cumulative skin dose distribution from the frontal and lateral x-ray tubes and display it in real-time as a color-coded map on a 3D graphic of the patient for immediate feedback to the physician. The agreement of the calculated values with the dose measured on phantoms was evaluated. Methods: The Biplane-DTS consists of multiple components including 3D graphic models of the imaging system and patient, an interactive graphical user interface, a data acquisition module to collect geometry and exposure parameters, the computer graphics processing unit, and functions for determining which parts of the patient graphic skin surface are within the beam and for calculating dose. The dose is calculated to individual points on the patient graphic using premeasured calibration files of entrance skin dose per mAs including backscatter; corrections are applied for field area, distance from the focal spot and patient table and pad attenuation when appropriate. The agreement of the calculated patient skin dose and its spatial distribution with measured values was evaluated in 2D and 3D for simulated procedure conditions using a PMMA block phantom and an SK-150 head phantom, respectively. Dose values calculated by the Biplane-DTS were compared to the measurements made on the phantom surface with radiochromic film and a calibrated ionization chamber, which was also used to calibrate the DTS. The agreement with measurements was specifically evaluated with variation in kVp, gantry angle, and field size. Results: The dose tracking system that was developed is able to acquire data from the two x-ray gantries on a biplane imaging system and calculate the skin dose for each exposure pulse to those vertices of a patient graphic that are determined to be in the beam. The calculations are done in real-time with a typical graphic update time of 30 ms and an average vertex separation of 3 mm. With appropriate corrections applied, the Biplane-DTS was able to determine the entrance dose within 6% and the spatial distribution of the dose within 4% compared to the measurements with the ionization chamber and film for the SK150 head phantom. The cumulative dose for overlapping fields from both gantries showed similar agreement. Conclusions: The Biplane-DTS can provide a good estimate of the peak skin dose and cumulative skin dose distribution during biplane neurointerventional procedures. Real-time display of this information should help the physician manage patient dose to reduce the risk of radiation-induced skin injuries.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

LA100 Equity Strategies. Chapter 11: Truck Electrification for Improved Air Quality and Health

The LA100 Equity Strategies project integrates community guidance with robust research, modeling, and analysis to identify strategy options that can increase equitable outcomes in Los Angeles' clean energy transition. This report focuses on truck electrification as a means to improve air quality and health in traffic and air quality disadvantaged communities. It also identifies potential strategies to more equitably distribute air quality benefits from electrification of trucks, defined here as heavy-duty vehicles over 8,500 pounds (lbs) gross vehicle weight. Specifically, NREL analyzed 1) baseline air pollutant emissions, 2) emissions reductions associated with incremental increases in electrification of three types of heavy-duty trucks in 2035, and 3) resultant changes to air pollutant concentrations for selected census tracts along major roadways in disadvantaged and non-disadvantaged communities for comparison. In addition, NREL analyzed the impact of estimated pollutant concentrations on several health effects and the distribution of those health effects by disadvantaged community status. NREL's analysis is complemented by a University of California Los Angeles (UCLA) analysis of air quality benefits from transportation electrification, which included light-duty vehicles (Chapter 15) and evaluated regional air-quality changes across Los Angeles. Research was guided by input from the community engagement process, and associated equity strategies are presented in alignment with that guidance.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

A Fast Load Control System Based on Mobile Distribution-Level Phasor Measurement Unit

The increase of renewable penetration in power grids calls for loads to participate in frequency regulation to avoid under-frequency load shedding after large resource contingencies. The motivation of our work is to fulfill a fast residential load control for frequency stability enhancement. For this purpose, a novel Mobile Distribution-level Phasor Measurement Unit (MDPMU) is developed for disturbance event detection, power mismatch estimation and fast load control. First, the systematic design of the MDPMU introduced. The proposed MDPMU has the major advantages of low cost and high frequency measurement accuracy. Second, the distribution-level measurement is utilized for the Rate Of Change Of Frequency (ROCOF) calculation. Upon detection of a power system frequency event, power mismatch is estimated at the early stage of the event. Details of a robust approach to calculate ROCOF and determine the event starting point are presented. Practical issues including the measurement reporting rate and estimation time are also examined. Finally, with the proposed control system, residential load responses can be controlled coordinately based on load availability and compensation prices offered by customers, providing adaptive frequency regulation service after large resource contingencies. The accuracy of frequency measurement and power mismatch estimation are evaluated via experimental analysis. The frequency stability improvement by load control is validated via PSS/E simulation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Physics-Guided Deep Learning for Time-Series State Estimation Against False Data Injection Attacks

The modern power grid is a cyber-physical system. While the grid is becoming more intelligent with emerging sensing and communication techniques, new vulnerabilities are introduced and cyber security becomes a major concern. One type of cyber-attacks - False Data Injection Attacks (FDIAs) - exploits the limitations in traditional power system state estimation, and modifies system states without being detected. In this paper, we propose a physics-guided deep learning (PGDL) approach to defend against FDIAs. The PGDL takes real-time measurements as inputs to neural networks, outputs the estimated states, and reconstructs measurements considering power system physics. A deep recurrent neural network - Long Short-Term Memory (LSTM) - is employed to learn the temporal correlations among states. This hybrid learning model leads to a time-series state estimation method to defend against FDIAs. The simulation results using IEEE 14-bus test system demonstrate the accuracy and robustness of the proposed time-series state estimation under FDIAs.

Cyber security, state estimation, physics-guided d↗
DOE OSTI2021

Solar-to-Grid Public Data File for Utility-scale (UPV) and Distributed Photovoltaics (DPV) Generation, Capacity Credit, and Value for 2012-2020

Lawrence Berkeley National Laboratory (Berkeley Lab) estimates hourly project-level generation data for utility-scale solar projects and hourly county-level generation data for residential and non-residential distributed photovoltaic (PV) systems in the seven organized wholesale markets and 10 additional Balancing Areas. To encourage its broader use, Berkeley Lab has made this data file public here at OEDI, covering the years 2012-2020. The public project-level dataset is updated annually with data from the previous calendar year. For more information about the research project, including a technical report, briefing material, visualizations, and additional data, please visit the project homepage linked in this submission.

annual solar value↗
DOE OSTI2017

Towards optimal extraction of cosmological information from nonlinear data

One of the main unsolved problems of cosmology is how to maximize the extraction of information from nonlinear data. If the data are nonlinear the usual approach is to employ a sequence of statistics (N-point statistics, counting statistics of clusters, density peaks or voids etc.), along with the corresponding covariance matrices. However, this approach is computationally prohibitive and has not been shown to be exhaustive in terms of information content. Here we instead develop a hierarchical Bayesian approach, expanding the likelihood around the maximum posterior of linear modes, which we solve for using optimization methods. By integrating out the modes using perturbative expansion of the likelihood we construct an initial power spectrum estimator, which for a fixed forward model contains all the cosmological information if the initial modes are gaussian distributed. We develop a method to construct the window and covariance matrix such that the estimator is explicitly unbiased and nearly optimal. We then generalize the method to include the forward model parameters, including cosmological and nuisance parameters, and primordial non-gaussianity. We apply the method in the simplified context of nonlinear structure formation, using either simplified 2-LPT dynamics or N-body simulations as the nonlinear mapping between linear and nonlinear density, and 2-LPT dynamics in the optimization steps used to reconstruct the initial density modes. We demonstrate that the method gives an unbiased estimator of the initial power spectrum, providing among other a near optimal reconstruction of linear baryonic acoustic oscillations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Time-lapse seismic data inversion for estimating reservoir parameters using deep learning

Geologic carbon sequestration involves the injection of captured carbon dioxide ([Formula: see text]) into subsurface formations for long-term storage. The movement and fate of the injected [Formula: see text] plume is of great concern to regulators because monitoring helps to identify potential leakage zones and determines the possibility of safe long-term storage. To address this concern, we design a deep-learning framework for [Formula: see text] saturation monitoring to determine the geologic controls on the storage of the injected [Formula: see text]. We use different combinations of porosities and permeabilities for a given reservoir to generate saturation and velocity models. We train the deep-learning model with a few time-lapse seismic images and their corresponding changes in saturation values for a particular [Formula: see text] injection site. The deep-learning model learns the mapping from the change in the time-lapse seismic response to the change in [Formula: see text] saturation during the training phase. We then apply the trained model to data sets comprising different time-lapse seismic image slices (corresponding to different time instances) generated using different porosity and permeability distributions that are not part of the training to estimate the [Formula: see text] saturation values along with the plume extent. Our algorithm provides a deep-learning assisted framework for the direct estimation of [Formula: see text] saturation values and plume migration in heterogeneous formations using the time-lapse seismic data. Our method improves the efficiency of time-lapse inversion by streamlining the large number of intermediate steps in the conventional time-lapse inversion workflow. This method also helps to incorporate the geologic uncertainty for a given reservoir by accounting for the statistical distribution of porosity and permeability during the training phase. Tests on different examples verify the effectiveness of our approach.

Geochemistry & Geophysics↗