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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

MASS CALIBRATION AND COSMOLOGICAL ANALYSIS OF THE SPT-SZ GALAXY CLUSTER SAMPLE USING VELOCITY DISPERSION σ {sub v} AND X-RAY Y {sub X} MEASUREMENTS

We present a velocity-dispersion-based mass calibration of the South Pole Telescope Sunyaev-Zel'dovich effect survey (SPT-SZ) galaxy cluster sample. Using a homogeneously selected sample of 100 cluster candidates from 720 deg{sup 2} of the survey along with 63 velocity dispersion (σ {sub v}) and 16 X-ray Y {sub X} measurements of sample clusters, we simultaneously calibrate the mass-observable relation and constrain cosmological parameters. Our method accounts for cluster selection, cosmological sensitivity, and uncertainties in the mass calibrators. The calibrations using σ {sub v} and Y {sub X} are consistent at the 0.6σ level, with the σ {sub v} calibration preferring ∼16% higher masses. We use the full SPT{sub CL} data set (SZ clusters+σ {sub v}+Y {sub X}) to measure σ{sub 8}(Ω{sub m}/0.27){sup 0.3} = 0.809 ± 0.036 within a flat ΛCDM model. The SPT cluster abundance is lower than preferred by either the WMAP9 or Planck+WMAP9 polarization (WP) data, but assuming that the sum of the neutrino masses is ∑m {sub ν} = 0.06 eV, we find the data sets to be consistent at the 1.0σ level for WMAP9 and 1.5σ for Planck+WP. Allowing for larger ∑m {sub ν} further reconciles the results. When we combine the SPT{sub CL} and Planck+WP data sets with information from baryon acoustic oscillations and Type Ia supernovae, the preferred cluster masses are 1.9σ higher than the Y {sub X} calibration and 0.8σ higher than the σ {sub v} calibration. Given the scale of these shifts (∼44% and ∼23% in mass, respectively), we execute a goodness-of-fit test; it reveals no tension, indicating that the best-fit model provides an adequate description of the data. Using the multi-probe data set, we measure Ω{sub m} = 0.299 ± 0.009 and σ{sub 8} = 0.829 ± 0.011. Within a νCDM model we find ∑m {sub ν} = 0.148 ± 0.081 eV. We present a consistency test of the cosmic growth rate using SPT clusters. Allowing both the growth index γ and the dark energy equation-of-state parameter w to vary, we find γ = 0.73 ± 0.28 and w = –1.007 ± 0.065, demonstrating that the expansion and the growth histories are consistent with a ΛCDM universe (γ = 0.55; w = –1)

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Generalized Brønsted‐Evans‐Polanyi Relationships for Reactions on Metal Surfaces from Machine Learning

Abstract Brønsted‐Evans‐Polanyi (BEP) relationships, i. e., a linear scaling between reaction and activation energies, lie at the core of computational design of heterogeneous catalysts. However, BEPs are not general and often require reparameterization for each class of reactions. Here we construct generalized BEPs (gBEPs), which can predict activation energies for a diverse dataset of reactions of C, O, N and H containing molecules on metal surfaces. In a first step we develop a set of descriptors based on scaling relationships that can capture the change in chemical identity of reactants during the reaction. Subsequently, we use the reaction energy, these descriptors and a single descriptor for the surface structure to parameterize machine learning based regression approaches for the prediction of activation energies. The best approach we developed shows a Mean Absolute Error (MAE) of 0.11 eV for the training set (80 % of the data set) and 0.23 eV for the test set (20 % of the data set). The methodology presented here allows to calculate activation energies within fractions of seconds on a typical personal computer and due to its generality, accuracy and simplicity in application it might prove to be useful in transition metal catalyst design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

High-Fidelity Simulation of Flow-Induced Vibrations in Helical Steam Generators for Small Modular Reactors

Flow-induced vibration (FIV) is a widespread problem in energy systems as they rely on fluid movement for energy conversion. Vibrating structures may be damaged as fatigue or wear occur. Given the importance of reliable components in the nuclear industry, FIV has long been a major concern in the safety and operation of nuclear reactors. In particular, nuclear fuel rods and steam generators have been known to suffer from FIV and related failures. In this paper we discuss the use of the computational fluid dynamics code Nek5000 coupled to the structural code Diablo to simulate the flow in helical coil heat exchangers and associated FIV. In particular, one-way coupled calculations are performed, where pressure and tractions data are loaded into the structural model. The main focus of this paper is on validation of this capability. Fluid-only Nek5000 large eddy simulations are first compared against dedicated high-resolution experiments. Then, one-way coupled calculations are performed with Nek5000 and Diablo for two data sets that provide FIV data for validation. These calculations were aimed at simulating available legacy FIV experiments in helical steam generators in the turbulent buffeting regime. In this regime one-way coupling is judged sufficient since the pressure loads do not cause substantial displacements. It is also the most common source of vibration in helical steam generators at the low flows expected in integral pressurized water reactors. We discuss validation of two-way coupled experiments and benchmarks toward the simulation of fluid elastic instability. We briefly discuss the application of these methods to grid-to-rod fretting.

Flow-Induced Vibration↗
DOE OSTI2023

Optimizing Patient-Specific Medication Regimen Policies Using Wearable Sensors in Parkinson’s Disease

Effective treatment of Parkinson’s disease (PD) is a continual challenge for healthcare providers, and providers can benefit from leveraging emerging technologies to supplement traditional clinic care. We develop a data-driven reinforcement learning (RL) framework to optimize PD medication regimens through wearable sensors. We leverage a data set of n = 26 PD patients who wore wrist-mounted movement trackers for two separate six-day periods. Using these data, we first build and validate a simulation model of how individual patients’ movement symptoms respond to medication administration. We then pair this simulation model with an on-policy RL algorithm that recommends optimal medication types, timing, and dosages during the day while incorporating human-in-the-loop considerations on medication administration. The results show that the RL-prescribed medication regimens outperform physicians’ medication regimens, despite physicians having access to the same data as the RL agent. To validate our results, we assess our wearable-based RL medication regimens using n = 399 PD patients from the Parkinson’s Progression Markers Initiative data set. We show that the wearable-based RL medication regimens would lead to significant symptom improvement for these patients, even more so than training RL policies directly from this data set. In doing so, we show that RL models from even small data sets of wearable data can offer novel, generalizable clinical insights and medication strategies, which may outperform those derived from larger data sets without wearable data.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Overview and Meteorological Validation of the Wind Integration National Dataset toolkit

The Wind Integration National Dataset (WIND) Toolkit described in this report fulfills these requirements, and constitutes a state-of-the-art national wind resource data set covering the contiguous United States from 2007 to 2013 for use in a variety of next-generation wind integration analyses and wind power planning. The toolkit is a wind resource data set, wind forecast data set, and wind power production and forecast data set derived from the Weather Research and Forecasting (WRF) numerical weather prediction model. WIND Toolkit data are available online for over 116,000 land-based and 10,000 offshore sites representing existing and potential wind facilities.

17 WIND ENERGY↗
DOE OSTI2018

ORPC RivGen Hydrokinetic Turbine Wake Characterization

Field measurements of mean flow and turbulence parameters at the Kvichak river prior to and after the deployment of ORPC's RivGen hydrokinetic turbine. Data description and turbine wake analysis are presented in the attached manuscript "Wake measurements from a hydrokinetic river turbine" by Guerra and Thomson (recently submitted to Renewable Energy). There are three data sets: NoTurbine (prior to deployment), Not_Operational_Turbine (turbine underwater, but not operational), and Operational_Turbine. The data has been quality controlled and organized into a three-dimensional grid using a local coordinate system described in the paper. All data sets are in Matlab format (.mat). Variables available in the data sets are: qx: X coordinate matrix (m) qy: Y coordinate matrix (m) z : z coordinate vector (m) lat : grid cell latitude (degrees) lon: grid cell longitude (degrees) U : velocity magnitude (m/s) Ux: x velocity (m/s) Vy: y velocity (m/s) W: vertical velocity (m/s) Pseudo_beam.b_i: pseudo-along beam velocities (i = 1 to 4) (m/s) (structure with raw data within each grid cell) beam5.b5: 5th-beam velocity (m/s) (structure with raw data within each grid cell) tke: turbulent kinetic energy (m2/s2) epsilon: TKE dissipation rate (m2/s3) Reynolds stresses: uu, vv, ww, uw, vw (m2/s2) Variables from the Not Operational Turbine data set are identified with _T Variables from the Operational Turbine data set are identified with _TO

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

FAIRification, Quality Assessment, and Missingness Pattern Discovery for Spatiotemporal Photovoltaic Data

The ongoing growth of the photovoltaic market has pushed the demand for power forecasting and performance evaluation for a huge population of PV power plants. Through access to a large number of time series data sets from different power plants, we have found common issues that impede the modeling process. Namely, the time series data are hard to transfer between groups due to differences in variable nomenclature, and the quality of the data sets can vary. We address the issue of variable nomenclature by FAIRifying spatiotemporal PV time series data. Through the creation of a solar power plant ontology, we propose standards for the naming and structure of metadata used to describe the data from these power plants. Using the structure from this ontology, we have developed both R and Python packages for the automation of the FAIRification process. We have also developed an R package that automates the analysis of the quality of a data set through the designation of letter grades. With access to large time series data sets across many power plants, we can utilize spatiotemporal coherence between the sites in order to improve the quality of our data. To solve the issue of data missingness, we propose the use of Spatiotemporal-GNN autoencoders to detect and impute missing values from a data set by utilizing data from power plants nearby.

14 SOLAR ENERGY↗
DOE OSTI2020

Development of Gamma Background Radiation Digital Twin with Machine Learning Algorithms: Application of Unsupervised Machine Learning to Detection of Anomalies and Nuisances in Gamma Background Radiation Environmental Screening Data

Environmental screening of gamma radiation consists of detecting weak nuisance and anomaly signal in the presence of strong and highly varying background. In a typical scenario, a mobile detector-spectrometer continuously measures gamma radiation spectra in short, e.g., one-second, signal acquisition intervals. The measurement data is a 2D matrix, where one dimension is gamma ray energy, and the other dimension is the number of measurements or total time. In principle, gamma radiation sources can be detected and identified from the measured data by their unique spectral lines. Detecting sources from data measured in a search scenario is difficult due to the highly varying background because of naturally occurring radioactive material (NORM), and low signal-to-noise ratio (S/N) of spectral signal measured during one-second acquisition intervals. The objective of this work is to explore unsupervised machine learning (ML) algorithms for development of a digital twin of gamma radiation background, and for detection and identification of weak nuisances and anomalies events in the presence of highly fluctuating background. In one segment of work, we developed a gamma background estimation model using a Longshort term memory (LSTM) network for one-step CPS time series prediction. The LSTM model was validated with two data sets of measurements from two independent NaI detectors positioned on a mobile platform. The data sets contained background radiation only and no orphan isotope sources. The LSTM model was constructed and tested using data from one of the detectors. Performance of the LSTM model was validate through one-step prediction of CPS time series of another NaI detector without re-training. This approach allows to create a digital twin for nuclear background estimation. Using LSTM, it could be possible to detect a source through subtraction of the estimated counts from the measured background. In another segment of work, we investigated detection of gamma emitting sources in the presence of complex background using unsupervised machine learning. Spectral lines of isotopes are difficult to observe in one-second measurements. Averaging over the entire measurement campaign data set reveals spectral lines of most common background isotopes. Spectral lines of orphan sources, which might appear only in a few measurements during the campaign, will be washed out if averaging is performed over the entire measurement data set. The approach we have explored consists of extracting one-second measurements containing weak spectral features through data clustering. Averaging one-second spectra in a cluster should reveal the presence of anomaly sources. We created two ML models using K-means clustering and Neural Network Self-organizing Map (SOM). Performance of these ML models was benchmarked using search data. One data set contained 137 Cs source, and another dataset contained 131 I source.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Analysis Code - Data Analysis in 'Leveraging Multiple Statistical Methods for Inverse Prediction in Nuclear Forensics Applications' (LMSMIPNFA) v. 1.0

R code that performs the analysis of a data set presented in the paper ‘Leveraging Multiple Statistical Methods for Inverse Prediction in Nuclear Forensics Applications’ by Lewis, J., Zhang, A., Anderson-Cook, C. It provides functions for doing inverse predictions in this setting using several different statistical methods. The data set is a publicly available data set from a historical Plutonium production experiment.

Lewis, John↗
DOE OSTI2023

COMBLE Radar b1 Processing: Corrections, Calibrations, and Processing Report

The U.S. Department of Energy’s (DOE) Atmospheric Radiation Measurement (ARM) user facility recently concluded its Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE), with its campaign emphasis on marine boundary-layer clouds and mixed-phase clouds during cold-air outbreaks. The COMBLE campaign featured the deployment of the first ARM Mobile Facility (AMF1) to northern Scandinavia (Andenes, Norway), including its standard complement of ARM cloud radars. In keeping with user demands stemming from the previous AMF Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina, a post-campaign radar mentor effort was initiated for COMBLE. This activity was intended to improve the usability of the ARM cloud radar data sets in response to overall demands for calibrated, corrected data sets for downstream studies and retrieval applications. In addition to the terrain complexities previously important to CACTI data sets (e.g., clutter designation and/or removal), COMBLE presented new challenges to existing ARM radar mentor capabilities. These included the extension of existing methodologies (e.g., relative calibration adjustment [RCA] target techniques) to frozen environments and the potential issues with their applicability when considering mixed-phase precipitation conditions. As in the previous CACTI documentation (Hardin et al. 2020), the overall calibration and conditioning process in ARM nomenclature is referred to as generating a “b1” datastream. For the radars, these “b1” standards refer to a datastream that has been calibrated (and cross-calibrated), with effort to deliver the highest-quality (well-characterized) data possible. The “b1” radar mentor reporting (this current document) is intended to detail (i) the status/quality of the original “a1” (raw) data sets during the COMBLE AMF campaign, (ii) the corrections and calibrations that are applied to generate the b1 datastreams available on ARM’s Data Discovery, and (iii) the details of the applied methods, e.g., how radar offset/calibration numbers were determined.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

A Semiautomated Framework for Integrating Expert Knowledge into Disease Marker Identification

Background. The availability of large complex data sets generated by high throughput technologies has enabled the recent proliferation of disease biomarker studies. However, a recurring problem in deriving biological information from large data sets is how to best incorporate expert knowledge into the biomarker selection process. Objective. To develop a generalizable framework that can incorporate expert knowledge into data-driven processes in a semiautomated way while providing a metric for optimization in a biomarker selection scheme. Methods. The framework was implemented as a pipeline consisting of five components for the identification of signatures from integrated clustering (ISIC). Expert knowledge was integrated into the biomarker identification process using the combination of two distinct approaches; a distance-based clustering approach and an expert knowledge-driven functional selection. Results. The utility of the developed framework ISIC was demonstrated on proteomics data from a study of chronic obstructive pulmonary disease (COPD). Biomarker candidates were identified in a mouse model using ISIC and validated in a study of a human cohort. Conclusions. Expert knowledge can be introduced into a biomarker discovery process in different ways to enhance the robustness of selected marker candidates. Developing strategies for extracting orthogonal and robust features from large data sets increases the chances of success in biomarker identification.

Biomarker Identification↗
DOE OSTI2016

An Assessment of New Satellite Data Products for the Development of a Long-Term Global Solar Resource at 10-100 km

A project representing an effort to reprocess the NASA based solar resource data sets is reviewed. The effort represented a collaboration between NASA, NOAA, NREL and the SUNY-Albany and aimed to deliver a 10 km resolution, 3-hourly data set spanning from 1983 through near-present. Part of the project was to transition project capability to NREL for annual processing to extend data set. Due to delays in the key input project called ISCCP, we evaluate only Beta versions of this data set and also introduce the potential use of another NASA Langley based cloud data set for the CERES project. The CERES project uses these cloud properties to compute global top-of-atmosphere and surface fluxes at the 1x1 degree resolution. Here, we also briefly discuss these data sets in potential usage for solar resource benchmarking.

14 SOLAR ENERGY↗
DOE OSTI2021

Phase-weighted slant stacking for surface wave dispersion measurement

SUMMARY Surface wave retrieval from ambient noise records using seismic interferometry techniques has been widely used for multiscale shear wave velocity (Vs) imaging. One key step during Vs imaging is the generation of dispersion spectra and the extraction of a reliable dispersion curve from the retrieved surface waves. However, the sparse array geometry usually affects the ability for high-frequency (>1 Hz) seismic signals’ acquisition. Dispersion measurements are degraded by array response due to sparse sampling and often present smeared dispersion spectra with sidelobe artefacts. Previous studies usually focus on interferograms’ domain (e.g. cross-correlation function) and attempt to enhance coherent signals before dispersion measurement. We propose an alternative technique to explicitly deblur dispersion spectra through use of a phase-weighted slant-stacking algorithm. Numerical examples demonstrate the strength of the proposed technique to attenuate array responses as well as incoherent noise. Three different field examples prove the flexibility and superiority of the proposed technique: the first data set consists of ambient noise records acquired using a nodal seismometer array; the second data set utilizes distributed acoustic sensing (DAS) and a marine fibre-optic cable to acquire a similar ambient noise data set; the last data set is a vibrator-based active-source surface wave data. The enhanced dispersion measurements provide cleaner and higher-resolution spectra without distortions which will assist both human interpreters as well as ML algorithms in efficiently picking curves for subsequent Vs inversion.

58 GEOSCIENCES↗
DOE OSTI2016

Nonparametric estimators for incomplete surveys

Nonparametric estimators, such as the 1/V{sub max} estimator and the C{sup −} estimator, have been applied extensively to estimate luminosity functions (LFs) of astronomical sources from complete, truncated survey data sets. Application of such estimators to incomplete data sets typically requires further truncation of data, separation into subsets of constant completeness, and/or correction for incompleteness-induced bias. In this paper, we derive generalizations of the above estimators designed for use with incomplete, truncated data sets. We compare these generalized nonparametric estimators, investigate some of their simple statistical properties, and validate them using Monte Carlo simulation methods. We apply a nonparametric estimator to data obtained from the extended Baryon Oscillation Spectroscopic Survey to estimate the QSO LF for redshifts 0.68<z<4.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Chandra and very large array observations of the nearby Sd galaxy NGC 45

We present an analysis of high angular resolution observations made in the X-ray and the radio with the Chandra X-ray Observatory and the Karl Jansky Very Large Array (VLA), respectively, of the nearby spiral galaxy NGC 45. This galaxy is the third that we have considered in a study of the supernova remnant (SNR) populations of nearby spiral galaxies and the present work represents the first detailed analysis of the discrete X-ray and radio source populations of this galaxy. We analyzed data sets from the three pointed observations made of this galaxy with Chandra along with a merged data set obtained from combining these data sets: the total effective exposure time of the merged data set is 63515 s. A total of 25 discrete X-ray sources are found in the entire field of view of the ACIS-S3 chip, with 16 sources found within the visual extent of the galaxy. We estimate that as many as half of the sources detected in the entire field of view of the ACIS-S3 chip and seven of the sources detected in the optical extent of NGC 45 may be background sources. We analyzed the spectral properties of the discrete X-ray sources within the galaxy and conclude that the majority of these sources are X-ray binaries. We have searched for counterparts at different wavelengths to the discrete X-ray sources and we find two associations: one with a star cluster and the other with a background galaxy. We have found one source that is clearly variable within one observation and seven that are seen to vary from one observation to another. We also conduct a photometric analysis to determine the near-infrared fluxes of the discrete X-ray sources in Spitzer Infrared Array Camera channels. We constructed a cumulative luminosity function of the discrete X-ray sources seen toward NGC 45: taking into account simultaneously the luminosity function of background sources, the fitted slope of the cumulative luminosity function Γ = –1.3{sub −1.6}{sup +0.7} (all error bounds correspond to 90% confidence intervals). The VLA observations reveal seven discrete radio sources: we find no overlaps between these sources and the X-ray detected sources. Based on their measured spectral indices and their locations with respect to the visible extent of NGC 45, we classify one source as a candidate radio SNR associated with the galaxy and the others as likely background galaxies seen in projection toward NGC 45. Finally, we discuss the properties of a background cluster of galaxies (denoted as CXOU J001354.2–231254.7) seen in projection toward NGC 45 and detected by the Chandra observations. The fit parameters to the extracted Chandra spectra of this cluster are a column density N{sub H} = 0.07(<0.14) × 10{sup 22} cm{sup −2}, a temperature kT = 4.22{sub −1.42}{sup +2.08} keV, an abundance Z = 0.30(<0.75) relative to solar and a redshift z = 0.28 ± 0.14. From the fit parameters we derive an electron number density n{sub e} = 4(±1) × 10{sup −3} cm{sup −3}, an unabsorbed X-ray luminosity L{sub 0.5−7.0 keV} ∼ 8.77(±0.96) × 10{sup 43} erg s{sup −1} for the cluster and an X-ray emitting mass M = 2.32(±1.75) × 10{sup 12} M{sub ⊙}.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

A search for anisotropy in the arrival directions of ultra high energy cosmic rays recorded at the Pierre Auger Observatory

Observations of cosmic ray arrival directions made with the Pierre Auger Observatory have previously provided evidence of anisotropy at the 99% CL using the correlation of ultra high energy cosmic rays (UHECRs) with objects drawn from the Véron-Cetty Véron catalog. In this paper we report on the use of three catalog independent methods to search for anisotropy. The 2pt–L, 2pt+ and 3pt methods, each giving a different measure of self-clustering in arrival directions, were tested on mock cosmic ray data sets to study the impacts of sample size and magnetic smearing on their results, accounting for both angular and energy resolutions. If the sources of UHECRs follow the same large scale structure as ordinary galaxies in the local Universe and if UHECRs are deflected no more than a few degrees, a study of mock maps suggests that these three methods can efficiently respond to the resulting anisotropy with a P-value = 1.0% or smaller with data sets as few as 100 events. Using data taken from January 1, 2004 to July 31, 2010 we examined the 20,30,...,110 highest energy events with a corresponding minimum energy threshold of about 49.3 EeV. The minimum P-values found were 13.5% using the 2pt-L method, 1.0% using the 2pt+ method and 1.1% using the 3pt method for the highest 100 energy events. In view of the multiple (correlated) scans performed on the data set, these catalog-independent methods do not yield strong evidence of anisotropy in the highest energy cosmic rays.

79 ASTRONOMY AND ASTROPHYSICS↗