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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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At least 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Effect of Assimilating SMAP Soil Moisture on CO2 and CH4 Fluxes through Direct Insertion in a Land Surface Model

Soil moisture impacts the biosphere–atmosphere exchange of CO2 and CH4 and plays an important role in the terrestrial carbon cycle. A better representation of soil moisture would improve coupled carbon–water dynamics in terrestrial ecosystem models and could potentially improve model estimates of large-scale carbon fluxes and climate feedbacks. Here, we investigate using soil moisture observations from the Soil Moisture Active Passive (SMAP) satellite mission to inform simulated carbon fluxes in the global terrestrial ecosystem model LPJ-wsl. Results suggest that the direct insertion of SMAP reduces the bias in simulated soil moisture at in situ measurement sites by 40%, with a greater improvement at temperate sites. A wavelet analysis between the model and measurements from 26 FLUXNET sites suggests that the assimilated run modestly reduces the bias of simulated carbon fluxes for boreal and subtropical sites at 1–2-month time scales. At regional scales, SMAP soil moisture can improve the estimated responses of CO2 and CH4 fluxes to extreme events such as the 2018 European drought and the 2019 rainfall event in the Sudd (Southern Sudan) wetlands. The simulated improvements to land–surface carbon fluxes using the direct insertion of SMAP are shown across a variety of timescales, which suggests the potential of SMAP soil moisture in improving the model representation of carbon–water coupling.

SMAP↗
NASA NTRS2023

Nonlinear Fast Magnetosonic Waves in Solar Prominence Pillars

We investigate the properties of nonlinear fast magnetosonic (NFM) waves in a solar prominence, motivated by recent high-resolution and high-cadence Hinode/SOT observations of small-scale oscillations in a prominence pillar. As an example, we analyze the details of the 2012 February 14 Hinode/SOT observations of quasi-periodic propagating features consistent with NFM waves, imaged in emission in Ca II and in the far blue wing of Hα. We perform wavelet analysis and find oscillations in the 1-3 min period range. Guided by these observations, we model the NFM waves with a three-dimensional magnetohydrodynamics (3D MHD) model, extending previous 2.5D MHD studies. The new model includes the structure of the high-density, low-temperature material of the prominence pillar embedded in the hot corona, in both potential and non-force-free sheared magnetic field configurations. The nonlinear model demonstrates the effects of mode coupling and the propagating density compressions associated with linear and NFM waves. The guided fast magnetosonic waves, together with density compressions and currents, are reproduced in the 3D pillar structure. We demonstrate or the first time the dynamic effects of the Lorentz force due to the magnetic shear in the non-force-free field on the pillar structure and on the propagation of the waves. The insights gained from the 3D MHD modeling are useful for improving coronal seismology of prominence structures that exhibit fast MHD wave activity.

Leon Ofman↗
NASA NTRS2022

Spectroscopic Study of Solar Transition Region Oscillations in the Quiet-Sun Observed By IRIS Using the Si IV Spectral Line

In this paper, we use the Si IV 1393.755 Å spectral line observed by the Interface Region Imaging Spectrograph (IRIS) in the quiet-Sun (QS) to determine the physical nature of the solar transition region (TR) oscillations. We analyse the properties of these oscillations using wavelet tools (e.g. power, cross-power, coherence, and phase difference) along with the stringent noise model (i.e. power law + constant). We estimate the period of the intensity and Doppler velocity oscillations at each chosen location in the QS and quantify the distribution of the statistically significant power and associated periods in one bright region and two dark regions. In the bright TR region, the mean periods in intensity and velocity are 7 min and 8 min, respectively. In the dark regions, the mean periods in intensity and velocity are 7 min and 5.4 min, respectively. We also estimate the phase difference between the intensity and Doppler velocity oscillations at each location. The statistical distribution of the phase difference is estimated, which peaks at −119° ± 13°, 33° ± 10°, 102° ± 10° in the bright region and at −153° ± 13°, 6° ± 20°, 151° ± 10° in the dark regions. The statistical distribution reveals that the oscillations are caused by propagating slow magneto-acoustic waves encountered with the TR. Some of these locations may also be associated with standing slow waves. Moreover, in the given time domain, several locations exhibit the presence of both propagating and standing oscillations at different frequencies.

MHD – Sun: oscillations↗
NASA NTRS2023

Large-Scale Groundwater Monitoring in Brazil Assisted With Satellite-Based Artificial Intelligence Techniques

Here, we develop and test an artificial intelligence (AI)-based approach to monitor major Brazilian aquifers. The approach combines Gravity Recovery and Climate Experiment (GRACE) data and ground-based hydrogeological measurements from Brazil’s Integrated Groundwater Monitoring Network at hundreds of wells distributed in twelve aquifers across the country. We tested model ensembles based on three AI approaches: Extreme Gradient Boost, Light Gradient Boosting Model and CatBoost, followed by a Linear Regression (LR) step. The approach is further boosted with wavelet and seasonal decomposition processes applied to GRACE data. To determine the AI-based model’s sensitivity to data availability, we propose four experiments combining hydrogeological measurements from different aquifers. Groundwater storage estimates from the Global Land Data Assimilation System (GLDAS) are used as benchmark. A sensitivity analysis shows that the LR-based model ensemble is the best suited and to reproduce groundwater storage change in all studied Brazilian aquifers. Results show that the proposed approach outperforms GLDAS in all experiments, with an RMSE value of 2.68cm for the experiment that covers all monitored wells in Brazil. GLDAS resulted in RMSE=6.76cm. Using our AI model outputs, we quantified the groundwater storage change of two major aquifers, Urucuia and Bauru-Caiuá, over the past two decades: -31km 3 and -6km 3 , respectively. Water loss is driven by a prolonged drought across most of the country and intensification of groundwater pumping for irrigation. This study demonstrates that combining satellite data and AI can be a cost-effective alternative to monitor poorly equipped aquifers at the continental scale, with possible global replicability.

GRACE↗
NASA NTRS2023

The Impact of Dimensionality Reduction of Ion Counts Distributions on Preserving Moments, With Applications to Data Compression

The field of space physics has a long history of utilizing dimensionality reduction methods to distill data, including but not limited to spherical harmonics, the Fourier Transform, and the wavelet transform. Here, we present a technique for performing dimensionality reduction on ion counts distributions from the Multiscale Mission/Fast Plasma Investigation (MMS/FPI) instrument using a data-adaptive method powered by neural networks. This has applications to both feeding low-dimensional parameterizations of the counts distributions into other machine learning algorithms, and the problem of data compression to reduce transmission volume for space missions. The algorithm presented here is lossy, and in this work, we present the technique of validating the reconstruction performance with calculated plasma moments under the argument that preserving the moments also preserves fluid-level physics, and in turn a degree of scientific validity. The method presented here is an improvement over other lossy compressions in loss-tolerant scenarios like the Multiscale Mission/Fast Plasma Investigation Fast Survey or in non-research space weather applications.

D. da Silva↗
DOE OSTI2011

Automatic Diabetic Macular Edema Detection in Fundus Images Using Publicly Available Datasets

Diabetic macular edema (DME) is a common vision threatening complication of diabetic retinopathy. In a large scale screening environment DME can be assessed by detecting exudates (a type of bright lesions) in fundus images. In this work, we introduce a new methodology for diagnosis of DME using a novel set of features based on colour, wavelet decomposition and automatic lesion segmentation. These features are employed to train a classifier able to automatically diagnose DME. We present a new publicly available dataset with ground-truth data containing 169 patients from various ethnic groups and levels of DME. This and other two publicly available datasets are employed to evaluate our algorithm. We are able to achieve diagnosis performance comparable to retina experts on the MESSIDOR (an independently labelled dataset with 1200 images) with cross-dataset testing. Our algorithm is robust to segmentation uncertainties, does not need ground truth at lesion level, and is very fast, generating a diagnosis on an average of 4.4 seconds per image on an 2.6 GHz platform with an unoptimised Matlab implementation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2012

Hawaii Solar Integration Study: Solar Modeling Developments and Study Results; Preprint

The Hawaii Solar Integration Study (HSIS) is a follow-up to the Oahu Wind Integration and Transmission Study completed in 2010. HSIS focuses on the impacts of higher penetrations of solar energy on the electrical grid and on other generation. HSIS goes beyond the island of Oahu and investigates Maui as well. The study examines reserve strategies, impacts on thermal unit commitment and dispatch, utilization of energy storage, renewable energy curtailment, and other aspects of grid reliability and operation. For the study, high-frequency (2-second) solar power profiles were generated using a new combined Numerical Weather Prediction model/ stochastic-kinematic cloud model approach, which represents the 'sharp-edge' effects of clouds passing over solar facilities. As part of the validation process, the solar data was evaluated using a variety of analysis techniques including wavelets, power spectral densities, ramp distributions, extreme values, and cross correlations. This paper provides an overview of the study objectives, results of the solar profile validation, and study results.

14 SOLAR ENERGY↗
DOE OSTI2014

2D Seismic Reflection Data across Central Illinois

In a continuing collaboration with the Midwest Geologic Sequestration Consortium (MGSC) on the Evaluation of the Carbon Sequestration Potential of the Cambro-Ordovician Strata of the Illinois and Michigan Basins project, Schlumberger Carbon Services and WesternGeco acquired two-dimensional (2D) seismic data in the Illinois Basin. This work included the design, acquisition and processing of approximately 125 miles of (2D) seismic reflection surveys running west to east in the central Illinois Basin. Schlumberger Carbon Services and WesternGeco oversaw the management of the field operations (including a pre-shoot planning, mobilization, acquisition and de-mobilization of the field personnel and equipment), procurement of the necessary permits to conduct the survey, post-shoot closure, processing of the raw data, and provided expert consultation as needed in the interpretation of the delivered product. Three 2D seismic lines were acquired across central Illinois during November and December 2010 and January 2011. Traversing the Illinois Basin, this 2D seismic survey was designed to image the stratigraphy of the Cambro-Ordovician sections and also to discern the basement topography. Prior to this survey, there were no regionally extensive 2D seismic data spanning this section of the Illinois Basin. Between the NW side of Morgan County and northwestern border of Douglas County, these seismic lines ran through very rural portions of the state. Starting in Morgan County, Line 101 was the longest at 93 miles in length and ended NE of Decatur, Illinois. Line 501 ran W-E from the Illinois Basin – Decatur Project (IBDP) site to northwestern Douglas County and was 25 miles in length. Line 601 was the shortest and ran N-S past the IBDP site and connected lines 101 and 501. All three lines are correlated to well logs at the IBDP site. Originally processed in 2011, the 2D seismic profiles exhibited a degradation of signal quality below ~400 millisecond (ms) which made interpretation of the Mt. Simon and Knox sections difficult. The data quality also gradually decreased moving westward across the state. To meet evolving project objectives, in 2012 the seismic data was re-processed using different techniques to enhance the signal quality thereby rendering a more coherent seismic profile for interpreters. It is believed that the seismic degradation could be caused by shallow natural gas deposits and Quaternary sediments (which include abandoned river and stream channels, former ponds, and swamps with peat deposits) that may have complicated or changed the seismic wavelet. Where previously limited by seismic coverage, the seismic profiles have provided valuable subsurface information across central Illinois. Some of the interpretations based on this survey included, but are not limited to: - Stratigraphy generally gently dips to the east from Morgan to Douglas County. - The Knox Supergroup roughly maintains its thickness. There is little evidence for faulting in the Knox. However, at least one resolvable fault penetrates the entire Knox section. - The Eau Claire Formation, the primary seal for the Mt. Simon Sandstone, appears to be continuous across the entire seismic profile. - The Mt. Simon Sandstone thins towards the western edge of the basin. As a result, the highly porous lowermost Mt. Simon section is absent in the western part of the state. - Overall basement dip is from west to east. - Basement topography shows evidence of basement highs with on-lapping patterns by Mt. Simon sediments. - There is evidence of faults within the lower Mt. Simon Sandstone and basement rock that are contemporaneous with Mt. Simon Sandstone deposition. These faults are not active and do not penetrate the Eau Claire Shale. It is believed that these faults are associated with a possible failed rifting event 750 to 560 million years ago during the breakup of the supercontinent Rodinia.

Smith, Valerie↗
DOE OSTI2015

Networked Gamma Radiation Detection System for Rapid Tactical Response

A networked gamma radiation detection system with directional sensitivity and energy spectral data acquisition capability is being developed by the National Security Technologies, LLC, to support the close and intense tactical engagement of law enforcement who carry out counterterrorism missions. In the proposed design, three clusters of 2' × 4' × 16' sodium iodide crystals (4 each) with digiBASE-E (for list mode data collection) would be placed on the passenger side of a minivan. To enhance localization and facilitate rapid identification of isotopes, advanced smart realtime localization and radioisotope identification algorithms like WAVRAD (wavelet-assisted variance reduction for anomaly detection) and NSCRAD (nuisance-rejection spectral comparison ratio anomaly detection) will be incorporated. We are testing a collection of algorithms and analysis on the problem of radiation detection with a distributed sensor network, examining such a network’s associated characteristics, and also the trade-offs between false positive alarm rates, true positive alarm rates, and time to detect multiple radiation sources in a large area. Empirical and simulation analyses of critical system parameters, such as number of sensors, sensor placement, and sensor response functions, are being examined. This networked system will provide an integrated radiation detection architecture and framework with (i) a large nationally recognized search database equivalent that would help generate a common operational picture in a major radiological crisis; (ii) a robust reach back connectivity for search data to be evaluated by home teams; and, finally, (iii) a possibility of integrating search data from multi-agency responders.

false alarm rate↗
DOE OSTI2015

39 GHz Interferometer System for Measuring Detonation Velocity

A new 39 GHz RF interferometer system is presented for use in velocity measurements of high explosives (HE) detonations. The frequency was chosen to compliment the currently available suite, and provide more spatial information. An RF signal is generated and coupled to a waveguide adapter serving as an antenna. The HE is initially transparent to the RF. When the HE detonates, the detonation front becomes reflective to the RF. This reflection is picked up by the waveguide adapter and mixed with an unperturbed RF signal to give a low frequency signal which can be digitized with an oscilloscope. By comparing the signal with a reference signal, velocity information can be obtained using Fourier Transforms and wavelet models. Bench test results using a “slapper” are shown. The 39 GHz microwave interferometer is used in Deflagration to Detonation shots. The signal is reflected off a moving surface, and the Doppler shift of the reflected signal is used to calculate the velocity.

Ross, Patrick W.↗
DOE OSTI2018

DWT-Based Aggregated Load Modeling and Evaluation for Quasi-Static Time-Series Simulation on Distribution Feeders: Preprint

This paper presents an extension of a previously reported discrete wavelet transform (DWT) based load modeling methodology which targets at modeling time-series load profiles to enable the more effective and realistic quasi-static time-series simulation of distribution feeders. The time-series load model is composed of two major parts: 1) The low-resolution field-measured load data which usually have a resolution of 30 or 15 min., 2) The high-resolution load variability model data extracted from the established variability database which can have resolution up to 1 sec. A load aggregation methodology is developed to aggregate the load profiles so that load profiles at different transformer ratings can be effectively modeled. Validation, evaluation, and analysis of the developed load modeling approach has been performed on the IEEE-123 feeder and an actual utility feeder from California. The analysis completed on the two feeders have demonstrated the effectiveness and revealed the value of the developed model for distribution feeder quasi-static time-series simulation at high temporal resolution.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

A New Mask for An Old Suspect: Testing the Sensitivity of the Galactic Center Excess to the Point Source Mask

The Fermi-LAT collaboration has recently released a new point source catalog, referred to as 4FGL. For the first time, we perform a template fit using information from this new catalog and find that the Galactic center excess is still present. On the other hand, we find that a wavelet-based search for point sources is highly sensitive to the use of the 4FGL catalog: no excess of bright regions on small angular scales is apparent when we mask out 4FGL point sources. We postulate that the 4FGL catalog contains the large majority of bright point sources that have previously been suggested to account for the excess in gamma rays detected at the Galactic center in Fermi-LAT data. Furthermore, after identifying which bright sources have no known counterpart, we place constraints on the luminosity function necessary for point sources to explain the smooth emission seen in the template fit.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Deep Learning for Automated Identification of Eels in Sonar Data

Freshwater eels, such as the American eel (Anguilla rostrata) present numerous challenges related to safe downstream fish passage at hydroelectric facilities. One of those challenges is effective monitoring of their abundance, movements, and behavior to facilitate design and operation of eel protection and passage facilities. A previous EPRI study documented the ability of human analysts to reliably identify American eels in data obtained with a 1100/1800 kHz, multibeam sonar. This report describes a project to develop deep learning (a subset of artificial intelligence) tools to automate the time-consuming, subjective process of eel identification in multibeam sonar data. The project exploited new data collected in the laboratory and the existing data from the prior EPRI field study to develop and test deep learning and other data analytic tools, including wavelet filtering, differencing for static object removal, and convolutional neural network analysis. The analysis of the laboratory data demonstrated feasibility of the approach, revealed object characteristics observed with the sonar that distinguish eels from similarly sized and shaped acoustic targets, and provided additional data for algorithm selection and training. Deep learning algorithms trained and tested on the laboratory data alone achieved accuracy rates of greater than 98% when classifying acoustic images of eels and similar-sized neutrally buoyant sticks. The algorithm trained and tested on the pre-existing field data alone, and yielded classification accuracy of 9.3% false positives and 13.3% false negatives when distinguishing between eels and sticks/PVC pipes based on video clips (i.e., multiple, consecutive images). This performance is comparable to the classification accuracy achieved by human analysts in the prior study. The deep learning algorithm trained on a combination of video clips obtained in the laboratory and the field and tested on video clips from the field, was able to distinguish eels from sticks and PVC pipes (a river debris analog) of similar size with 100% accuracy. Outreach to the hardware, software, and end-user communities early in the project helped to identify needs and specify the application space. Outreach to those communities at the end of the project communicated project results and opportunities for further development. The project achieved proof of concept for automated identification of eel in multibeam sonar data. Future work should focus on acquisition of additional data for more robust algorithm training and testing; modification of the software tools to accommodate multiple acoustic targets in the acoustic field at a given time; identification of additional object classes; incorporation of motion in the object identification and classification algorithms; operationalizing the software tools, including integration with other existing sonar data analysis tools; and partnering with hardware and software providers for distribution of the software tools with their commercial products.

13 HYDRO ENERGY↗
DOE OSTI2020

Completion design improvement using a deep convolutional network

Maximizing stimulated natural and hydraulic fracture network is one of the primary hydraulic fracturing concerns for economic production from a horizontal shale gas well. Geomechanical facies and preexisting fractures in each stage are identified based on similarities in formation characteristics to optimize the locations of perforation clusters. This often requires analyzing large volumes of drilling, Logging While Drilling (LWD) and Measurement While Drilling (MWD) data. In this paper, we develop a methodology that calculates the mechanical specific energy (MSE) using real-time drill string acceleration signals directly from its definition. High resolution vibration signals have been collected using a tri-axial accerlometer, which was an auxiliary tool included in acoustic borehole imager. This technique provides a cost-efficient solution for engineered completion design. Furthermore, we adopt deep Convolutional Neural Network (CNN) with signal processing to build a data pipeline that effectively extracts patterns from dynamic acceleration signals for rock lateral MSE classification. First, we apply discrete wavelet transform and Short-Time Fourier Transform (STFT) for signal denoising and pattern recognition. Then we construct an image dataset using multi-scale image fusion at pixel level from 3 sensor channels, including axial, lateral acceleration spectrograms and zero-padded revolutions per minute (RPM). The resulted RGB image dataset includes 4,000 images of 5 MSE ranges with various rock strength conditions. Our results demonstrate that the proposed deep learning model can achieve more than 90% classification accuracy. The deep learning results, as a reference source, were applied in selected Marcellus Shale Energy and Environmental Lab (MSEEL) wells engineered completion located in the Marcellus shale gas site.

03 NATURAL GAS↗
DOE OSTI2023

Efficient generalized boundary detection

Fast, efficient, and robust compression-based methods for detecting boundaries in arbitrary datasets, including sequences (1D datasets), are desired. The methods, each employing three simple algorithms, approximate the information distance between two adjacent sliding windows within a dataset. One of the algorithms calculates an initial ordered list of subsequences; while a second algorithm updates the ordered list of subsequences by dropping a first entry and appending a last entry rather than calculating completely new ordered lists with each iteration. Large values in the distance metric are indicative of boundary locations. A smoothed z-score or a wavelet-based algorithm may then be used to locate peaks in the distance metric, thereby identifying boundary locations. An adaptive version of the method employs a collection of window sizes and corresponding weighting functions, making it more amenable to real datasets with unknown, complex, and changing structures.

Ting, Christina↗
DOE OSTI2023

WavPool: A New Block for Deep Neural Networks

Modern deep neural networks comprise many operational layers, such as dense or convolutional layers, which are often collected into blocks. In this work, we introduce a new, wavelet-transform-based network architecture that we call the multi-resolution perceptron: by adding a pooling layer, we create a new network block, the WavPool. The first step of the multi-resolution perceptron is transforming the data into its multi-resolution decomposition form by convolving the input data with filters of fixed coefficients but increasing size. Following image processing techniques, we are able to make scale and spatial information simultaneously accessible to the network without increasing the size of the data vector. WavPool outperforms a similar multilayer perceptron while using fewer parameters, and outperforms a comparable convolutional neural network by ~ 10% on relative accuracy on CIFAR-10.

McDermott, Samuel D.↗
DOE OSTI2012

Inverse transport problem solvers based on regularized and compressive sensing techniques

According to the direct exposure measurements from flash radiographic image, regularized-based method and compressive sensing (CS)-based method for inverse transport equation are presented. The linear absorption coefficients and interface locations of objects are reconstructed directly at the same time. With a large number of measurements, least-square method is utilized to complete the reconstruction. Owing to the ill-posedness of the inverse problems, regularized algorithm is employed. Tikhonov method is applied with an appropriate posterior regularization parameter to get a meaningful solution. However, it's always very costly to obtain enough measurements. With limited measurements, CS sparse reconstruction technique Orthogonal Matching Pursuit (OMP) is applied to obtain the sparse coefficients by solving an optimization problem. This paper constructs and takes the forward projection matrix rather than Gauss matrix as measurement matrix. In the CS-based algorithm, Fourier expansion and wavelet expansion are adopted to convert an underdetermined system to a well-posed system. Simulations and numerical results of regularized method with appropriate regularization parameter and that of CS-based agree well with the reference value, furthermore, both methods avoid amplifying the noise. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2015

A novel application of cosmic rays for spent nuclear fuel monitoring - 15738

Monitoring nuclear waste and controlling spent nuclear fuel at its source is one of the main strategies to minimize the risks of nuclear proliferation and reduce potential homeland threats. Storage of high level waste from a fission reactor typically involves placing spent fuel in sealed dry casks. After the spent nuclear fuel has been placed inside the dry cask, the cask is welded, not allowing for visual inspection. Conventional methods for examining the interior of materials e.g., X-rays, are limited by the fact that they cannot penetrate very dense well-shielded objects. Instead, cosmic ray muons have the potential to allow for assessment of spent nuclear fuel stored within sealed dense dry casks. Objectives: 1.Exploit the ability of cosmic ray muons to penetrate high density materials allowing the distribution of materials within the object to be inferred from the muon tracks. 2. Develop a monitoring system that tracks the path of cosmic ray muons, including their deflection and attenuation, to identify the contents of a closed dry cask. Methods: - Monte-Carlo simulations using Geant4; - Bayesian decision theory for signal classification; - Wavelet analysis for noise cancellation; - Chaotic Neural Networks for signal encryption. Results: 1. Preliminary statistical analysis proved that the method is feasible and has the ability to identify the presence of spent nuclear fuel in a dry cask. 2. Using monoenergetic muons, a dry cask with one fuel assembly missing can be correctly identified with error rate <1%. Broader merit: Once completed, the proposed project for cosmic ray muon transport will enable a new and potentially transformative approach towards efficient, inexpensive and remote cosmic ray muon applications in archaeology, medicine and safeguarding of nuclear materials.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗