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All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Distributions of Mesoscale Periodic Structures in the Elemental and Ionic Composition of the Solar Wind

Multiple statistical and event studies based on in situ observations have shown that the solar wind contains mesoscale (∼ 100 – 10,000 Mm) periodic structures in the proton number density. Remote observations of such structures and event studies of concurrent variations in composition have demonstrated that they can form in the solar atmosphere and be preserved while advecting outwards through the heliosphere. Viall, Kepko, and Spence (J. Geophys. Res. (Space Phys.) 113, A07101, 2008; J. Geophys. Res. (Space Phys.) 114, A01201, 2009) and Kepko, Viall, and Wolfinger (J. Geophys. Res. (Space Phys.) 125(8), e28037, 2020) have reported that periodic proton density structures preferentially occur at specific radial length scales and have published their distributions from Wind measurements near L1. Here, we conduct a statistical study of 14 years (1998 – 2011) of 12-minute composition data measured by the Solar Wind Ion Composition Spectrometer instrument aboard the Advanced Composition Explorer spacecraft. We found that the elemental and ionic composition also contain statistically significant mesoscale periodic structures and, for the first time, present occurrence distributions for elemental abundance ratios with low, intermediate, and high first ionization potentials as well as for key solar wind charge states. These distributions set important constraints on solar wind formation in general and the formation of periodic mesoscale solar wind structures specifically, as the elemental and ionic composition are known to be determined at the Sun and to not evolve during advection.

Solar wind↗
NASA NTRS1996

ACTS Propagation Measurements in Maryland and Virginia

Rapid growth in new satellite services incorporating very small aperture terminals (VSAT) and ultra small aperture terminals (USAT) is expected in the coming years. Small size terminals allow for widespread use of satellite services in small business and domestic applications. Due to congestion of lower frequency bands such as C and Ku, most of these services will use Ka-band (2/20 GHz) frequencies. Propagation impairments produced by the troposphere is a limiting factor for the effective use of the 20/30 GHz band and the use of smaller Earth terminals makes it difficult to provide sufficient link margins for propagation related outages. In this context, reliable prediction of propagation impairments for low margin systems becomes important. Due to the complexity of propagation phenomena propagation modeling is mainly attempted on an empirical basis. As such, the availability of reliable measured data that extend to probability levels well in excess of the traditional limit of 1 percent is of great importance in the development, validation, and refinement of propagation models. The beacon payload on the Advanced Communications Technology Satellite (ACTS) together with the propagation measurement terminals developed under the NASA ACTS propagation program provide an excellent opportunity to collect such data on a long-term basis. This paper presents the results of ACTS propagation measurements conducted in the Washington, DC metropolitan area by COMSAT Laboratories.

Dissanayake, Asoka↗
DOE OSTI2020

Normal or abnormal? Machine learning for the leakage detection in carbon sequestration projects using pressure field data

The international commitments for atmospheric carbon reduction will require a rapid increase in carbon capture and storage (CCS) projects. The key to any successful CCS project lies in the long term storage and prevention of leakage of stored carbon dioxide (CO 2 ). In addition to being a greenhouse gas, CO 2 leaks reaching the surface can accumulate in low-lying areas resulting in a serious health risk. Among several alternatives, some of the more promising CCS storage formations are depleted oil and gas reservoirs, where the reservoirs had good geological seals prior to hydrocarbon extraction. With more CCS wells coming online, it is imperative to implement permanent, automated monitoring tools. We apply machine learning models to automate the leakage detection process in carbon storage reservoirs using rates of (CO 2 ) injection and pressure data measured by simple harmonic pulse testing (HPT). To validate the feasibility of this machine learning based workflow, we use data from HPT experiments carried out in the Cranfield oil field, Mississippi, USA. The data consist of a series of pulse tests conducted with baseline parameters and with an artificially introduced leak. Here, in this study, we pose the leakage detection task as an anomaly detection problem where deviation from the predicted behavior indicates leaks in the reservoir. Results show that different machine learning architectures such as multi-layer feed forward network, Long Short-Term Memory, and convolutional neural network are able to identify leakages and can provide early warning. These warnings can then be used to take remedial measures.

58 GEOSCIENCES↗
DOE OSTI2021

Fault Detection Utilizing Convolution Neural Network on Timeseries Synchrophasor Data From Phasor Measurement Units

An end-to-end supervised learning method is proposed for fault detection in the electric grid using Big Data from multiple Phasor Measurement Units (PMUs). The approach consists of preprocessing steps aimed at reducing data noise and dimensionality, followed by utilization of six classification models considered for detecting faults. Three of the models were variants of Convolutional Neural Network (CNN) architectures that consider a single type of measurement (voltage, current or frequency) at all PMUs or all types together also at all PMUs. CNN based models were compared to traditional methods of Logistic Regression (LR), Multi-layer Perceptron (MLP) and Support Vector Machine (SVM). Evaluation was conducted on two-year data measured by PMUs at 37 locations in a large electric grid. Here, the response variable for classification were extracted from the grid-wide outage event log. Experiments show that CNN-based models outperformed traditional methods on one year out-of-sample outage detection over the entire grid.

42 ENGINEERING↗
DOE OSTI2022

Data-Driven Dynamic Modeling in Power Systems: A Fresh Look on Inverter-Based Resource Modeling

Here, this article introduces ways to identify dynamic system models using measurement data. In power system analysis, a static model represents the time-invariant input-output relationship of a system, while a dynamic model describes the behavior of the system over time. For example, how will a system transit from one steady-state operation point to another?

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1983

Spatial resolution estimation of LANDSAT-4 TM and MSS data

In order to verify that the LANDSAT-4 sensors are operating within specifications, it is useful to estimate the system parameters by analysis of the measured data. One parameter of particular interest is the sensor point-spread function (PSF) which determines the resolution of the system. A method of estimating the PSF is described that utilizes data obtained during scanning of ground elements having identifiable geometric and radiometric structure. These data are then processed in such a manner as to recover either the PSF itself or to estimate the parameters of an assumed functional representation of the PSF.

Mcgillem, C. D.↗
DOE OSTI2024

Validation of Local Structural Loads Computed by OpenFAST Against Measurements From the Focal Experimental Campaign: Preprint

This work presents the validation of the local structural load modeling capability in OpenFAST for floating substructures based on data from the FOCAL experimental campaign. Previously, OpenFAST could only represent the floating substructure as a rigid body, and though this approach can model the global response of the floater in most cases, it is not able to capture the structural loads within its individual members. Consideration of local substructure loads is important for some floating designs, as the pursuit of cost reduction often results in lighter and more flexible structures. To address this limitation, the HydroDyn (hydrodynamics) and SubDyn (substructure dynamics) modules of OpenFAST have been recently extended to account for the flexibility of floating substructures. To validate this new capability, we compare the results obtained by OpenFAST with data measured during the FOCAL experimental campaign, which analyzed a 1:70 scale performance-matched model of the IEA 15-MW reference turbine atop a modified University of Maine VolturnUS-S semisubmersible in a wave basin under the action of both wind and waves. For the purposes of the present work, the most important feature of the experiment is the presence of load cells at the root of each pontoon, and our objective is to assess how well those loads are reproduced by OpenFAST. To model the distributed hydrodynamic and hydrostatic loads along the floating substructure, we adopt a strip-theory approach based on the Morison equation, and we discuss the impact of different hydrodynamic modeling options (wave stretching, MacCamy-Fuchs correction, and second-order wave kinematics) on both motions and loads. For simplicity, we focus on wave-only conditions, both regular and irregular. The results demonstrate good overall agreement for the loads at the root of the pontoons for the waves analyzed in this work, especially given the assumptions and simplifications inherent to a simple strip-theory model.

floating offshore wind turbine↗
DOE OSTI2023

R -Matrix Analysis and Statistical Properties of Dysprosium Isotopes in the Neutron Energy Ranges Up To A Few Kev

In support of the Nuclear Criticality Safety Program, a set of evaluated resonance parameters was generated for seven dysprosium isotopes in the neutron energy range from thermal up to a few keV. The evaluation methodology used the Reich-Moore approximation to fit, with the R-matrix code SAMMY, the high-resolution capture and transmission measurements on natural and enriched samples recently performed at the Rensselaer Polytechnic Institute Gaerttner LINear ACcelerator facility. Additional transmission data measured on enriched samples by Liou at the Columbia University Nevis synchrocyclotron in the mid-seventies were used to gauge the neutron widths above 15 eV. Thermal constants such as absorption and (in)coherent scattering cross sections and corresponding scattering lengths were calibrated to the National Institute of Standards and Technology’s compilation except for 161,164 Dy isotopes.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2026

Precise 𝐾* ⁡(892) → 𝐾⁡𝜋 branching fractions

Although discovered more than sixty years ago, direct measurement of the 𝐾*⁡(892) → 𝐾⁡𝜋 branching fractions is a formidable challenge that has not been attempted. Typically they are assumed to obey the isospin limit in hundreds of particle data measurements. We show that an abundance of recent amplitude analyses and other data, however, enables recovery of the ratios ℬ⁡(𝐾 *+ → 𝐾 + ⁢𝜋 0 )/ℬ⁡(𝐾 *+ → 𝐾$^0_S$ ⁢𝜋 + ) and 4⁢ℬ⁡(𝐾 *0 → 𝐾$^0_S$ ⁢𝜋 0 )/ℬ⁡(𝐾 *0 → 𝐾 +⁢ 𝜋 − ) at ∼5% precision.

Gligorov, Vladimir V. [Sorbonne Université, Paris ↗
NASA NTRS1989

Geodetic measurement of deformation in California

The very long baseline interferometry (VLBI) measurements made in the western U.S. since 1979 as part of the NASA Crustal Dynamics Project provide discrete samples of the temporal and spatial deformation field. The interpretation of the VLBI-derived rates of deformation requires an examination of geologic information and more densely sampled ground-based geodetic data. In the first two of three related studies, triangulation and trilateration data measured on two regional networks, one in the central Mojave Desert and one in the Coast Ranges east of the San Andreas fault, have been processed. At the spatial scales spanned by these local geodetic networks, auxiliary geologic and geophysical data have been utilized to examine the relation between measured incremental strain and the accommodation of strain seen in local geological structures, strain release in earthquakes, and principal stress directions inferred from in situ measurements. In a third study, the geocentric position vectors from a set of 77 VLBI experiments beginning in October 1982 have been used to estimate the tangential rate of change of station positions in the western U.S. in a North-America-Fixed reference frame.

Sauber, Jeanne↗
NASA NTRS1979

Electrical conductivity anomaly beneath Mare Serenitatis detected by Lunokhod 2 and Apollo 16 magnetometers

Magnetic fluctuations measured by the Lunokhod 2 magnetometer in the Bay Le Monnier are distinctly anisotropic when compared to simultaneous Apollo 16 magnetometer data measured 1100 km away in the Descartes highlands. This anisotropy can be explained by an anomalous electrical conductivity of the upper mantle beneath Mare Serenitatis. A model is presented of anomalously lower electrical conductivity beneath Serenitatis and the simultaneous magnetic data from the Lunokhod 2 site at the mare edge and the Apollo 16 site are compared to the numerically calculated model solutions. This comparison indicates that the anisotropic fluctuations can be modeled by a nonconducting layer in the lunar lithosphere which is 150 km thick beneath the highlands and 300 km thick beneath Mare Serenitatis. A decreased electrical conductivity in the upper mantle beneath the mare may be due to a lower temperature resulting from heat carried out the magma source regions to the surface during mare flooding.

Vanian, L. L.↗
DOE OSTI2020

One-Step Ahead Prediction of Thermal Mixing Tee Sensors with Long Short Term Memory (LSTM) Neural Networks

High-temperature advanced reactors under development, such as sodium fast reactors (SFR) and molten salt cooled reactors (MSCR), are expected to offer lower levelized cost of energy (LCOE) compared to existing light water reactor (LWR’s). In the existing light water reactors (LWR’s), operation and maintenance (O&M) expenses constitute the largest fraction of the total operating cost. Some of the O&M costs are related maintenance of sensors which can fail due to exposure to harsh environment in a reactor. The O&M costs of Advanced Reactor (AR)’s are expected to constitute a significant fraction of the total cost as well, because of high temperature and radiation level in AR are likely to cause material fatigue and premature failure of sensors and components. The O&M costs in AR’s could be reduced through integration of advanced informatics of performance-related sensors into a digital twin designed for reactor monitoring. For example, machine learning (ML) could be employed for real-time validation and correction of performance-related sensors, and reducing the number of performance-related physical sensor units through virtual sensing. As part of the effort, we investigate real-time validation of thermal hydraulic sensors through one-step ahead forecasting of sensor values using long short-term memory (LSTM) recurrent neural networks (RNN). The sensors are installed in a flow loop containing a thermal mixing tee, which is a common experimental model to study thermal fatigue in a thermal hydraulic loop. In addition, nonlinear transients generated in a thermal mixing tee constitute a good challenge data set for training and validation of ML algorithms. Sensors in this study include thermocouples, flow meters, and optical fibers for distributed temperature sensing. In one experiment, measurement data sets were obtained for a loop was filled with water, and in another experiment, measurements were performed on a loop filled with liquid metal Galinstan. We have also conducted preliminary investigation of one-step ahead prediction of fiber optics-based distributed temperature sensing with LSTM networks. In predicting fiber-based temperature measurements, we treated each gauge pitch of the fiber as an independent sensor. Accuracy of one-step ahead forecasting was estimated by calculating root mean square error (RMSE) for the test segment of time series of each sensor. RMSE’s for temperature sensors in water loop were, for the most part, lower than for the same sensors in Galinstan loop. The RMSE’s for flow meters were similar for both loops. The RMSE’s for distributed temperature measured with the fiber optic sensor were similar to those of the point sensors. Results of this study demonstrated the capability of LSTM one-step ahead forecasting with RMSE comparable to uncertainty in sensor measurements.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2017

Kepler Planet Detection Metrics: Per-Target Detection Contours for Data Release 25

A necessary input to planet occurrence calculations is an accurate model for the pipeline completeness (Burke et al., 2015). This document describes the use of the Kepler planet occurrence rate products in order to calculate a per-target detection contour for the measured Data Release 25 (DR25) pipeline performance. A per-target detection contour measures for a given combination of orbital period, Porb, and planet radius, Rp, what fraction of transit signals are recoverable by the Kepler pipeline (Twicken et al., 2016; Jenkins et al., 2017). The steps for calculating a detection contour follow the procedure outlined in Burke et al. (2015), but have been updated to provide improved accuracy enabled by the substantially larger database of transit injection and recovery tests that were performed on the final version (i.e., SOC 9.3) of the Kepler pipeline (Christiansen, 2017; Burke Catanzarite, 2017a). In the following sections, we describe the main inputs to the per-target detection contour and provide a worked example of the python software released with this document (Kepler Planet Occurrence Rate Tools KeplerPORTs)1 that illustrates the generation of a detection contour in practice. As background material for this document and its nomenclature, we recommend the reader be familiar with the previous method of calculating a detection contour (Section 2 of Burke et al.,2015), input parameters relevant for describing the data quantity and quality of Kepler targets (Burke Catanzarite, 2017b), and the extensive new transit injection and recovery tests of the Kepler pipeline (Christiansen et al., 2016; Burke Catanzarite, 2017a; Christiansen, 2017).

Planet Detection Metrics↗
DOE OSTI2022

Anomaly Detection in Gamma Spectra Using Hopfield Neural Network with B-SAT and Grover’s Algorithm on a Quantum Computing Simulator

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. In principle, gamma radiation sources can be detected and identified by their unique spectral lines. However, 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. In recent prior work, we have developed a Hopfield Neural Network (HNN) in conjunction with an image processing algorithm to detect a weak signal anomaly hidden among the highly fluctuating background spectra. The objective of this work is to explore quantum computing methods to increase the speed of HNN. The approach is based on the Grover’s search algorithm in conjunction with a 3-SAT problem formalism. The Grover’s algorithm is implemented on a quantum computing simulator using Qiskit software. Performance of HNN algorithm is benchmarked using search data from an environmental screening campaign, where the anomaly is a subset of measurements containing a 137 Cs source. Results indicate that using Grover’s algorithm on a quantum simulator reduces runtime of HNN by two orders of magnitude.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2024

IER-580 CED-3b: Experiment Execution Summary for the Pulsed-Neutron Die-Away Experiments (PNDA) with H 2 O

This report summarizes new experiments performed with H 2 O for IER-580, a testbed for pulsed-neutron die-away (PNDA) experiments at Lawrence Livermore National Laboratory (LLNL). The experiments will become high-quality benchmarksthat serve to optimize and validate thermal neutron scattering laws (TSLs) of H 2 O, which are high-priority nuclear data for the Department of Energy’s Nuclear Criticality Safety Program (NCSP). The report presents the details of the measurements conducted in October of 2023, including the experimental set-up (PNDA equipment and H 2 O targets) and all experimentally measured data. The report gives the fitted decay constants for the die-away curves of each target sample. It also provides the tabulated data for the die-away curves in the appendices.

42 ENGINEERING↗
DOE OSTI2022

A Shallow Strain Tensor Type-Curve Analysis to Characterizing Reservoir Boundaries

Pressure data measured in monitoring wells during well tests are typically used to estimate subsurface boundaries, but the resolution of this technique is often limited. We have shown that the strain tensor at shallow depth can be measured with optical strainmeters, and this signal responds to pressure changes in an underlying aquifer or reservoir. Moreover, these data can be inverted to estimate reservoir properties using analyses similar to classical type-curve analyses. The objective of this investigation is to evaluate the feasibility of extending the type-curve analysis of strain tensor data to include the characterization of aquifer boundaries.

Roudini, Soheil↗
DOE OSTI2024

Validation of the SCALE/Polaris-PARCS Code Procedure with the ENDF/B-VII.1 AMPX 56-Group Library: Pressurized Water Reactor

This study was conducted to validate the SCALE/Polaris v6.3.0–PARCS v3.4.2 code procedure with the Evaluated Nuclear Data File (ENDF)/B-VII.1 AMPX 56-group library for pressurized water reactor (PWR) analysis, by comparing simulated results with measured data for critical experiments and operating PWRs. Uncertainties of the SCALE/Polaris–PARCS code procedure for PWR analysis were evaluated in the validation for the PWR key nuclear parameters such as critical boron concentrations, reactivity, control bank work, temperature coefficients, and pin and assembly power peaking factors.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS1963

Biomedical and Human Factors Requirements for a Manned Earth-Orbiting Station

The study reported here has presented a measurement data pool for the determination of biomedical and behavioral effects of long-term exposure to weightlessness. This includes measures, techniques, equipment, and requirements in terms of weight, power, volume, time, crew activities, subsystem interfaces and experimental programs and designs, and confidence ratings for their effectiveness for determining weightlessness effects.

MANNED ORBITAL SPACE STATION /MOSS/↗