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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1997

Advanced Algorithms and High-Performance Testbed for Large-Scale Site Characterization and Subsurface Target Detecting Using Airborne Ground Penetrating SAR

A team of US Army Corps of Engineers, Omaha District and Engineering and Support Center, Huntsville, let Propulsion Laboratory (JPL), Stanford Research Institute (SRI), and Montgomery Watson is currently in the process of planning and conducting the largest ever survey at the Former Buckley Field (60,000 acres), in Colorado, by using SRI airborne, ground penetrating, Synthetic Aperture Radar (SAR). The purpose of this survey is the detection of surface and subsurface Unexploded Ordnance (UXO) and in a broader sense the site characterization for identification of contaminated as well as clear areas. In preparation for such a large-scale survey, JPL has been developing advanced algorithms and a high-performance restbed for processing of massive amount of expected SAR data from this site. Two key requirements of this project are the accuracy (in terms of UXO detection) and speed of SAR data processing. The first key feature of this testbed is a large degree of automation and a minimum degree of the need for human perception in the processing to achieve an acceptable processing rate of several hundred acres per day. For accurate UXO detection, novel algorithms have been developed and implemented. These algorithms analyze dual polarized (HH and VV) SAR data. They are based on the correlation of HH and VV SAR data and involve a rather large set of parameters for accurate detection of UXO. For each specific site, this set of parameters can be optimized by using ground truth data (i.e., known surface and subsurface UXOs). In this paper, we discuss these algorithms and their successful application for detection of surface and subsurface anti-tank mines by using a data set from Yuma proving Ground, A7, acquired by SRI SAR.

Fijany, Amir↗
DOE OSTI2017

PFLOTRAN-E4D: A parallel open source PFLOTRAN module for simulating time-lapse electrical resistivity data

Time-lapse electrical resistivity tomography (ERT) is finding increased application for remotely monitoring processes occurring in the near subsurface in three-dimensions (i.e. 4D monitoring). However, there are few codes capable of simulating the evolution of subsurface resistivity and corresponding tomographic measurements arising from a particular process, particularly in parallel and with an open source license. Herein we describe and demonstrate an electrical resistivity tomography module for the PFLOTRAN subsurface simulation code, named PFLOTRAN-E4D. The PFLOTRAN-E4D module operates in parallel using a dedicated set of compute cores in a master-slave configuration. At each time step, the master processes receives subsurface states from PFLOTRAN, converts those states to bulk electrical conductivity, and instructs the slave processes to simulate a tomographic data set. The resulting multi-physics simulation capability enables accurate feasibility studies for ERT imaging, the identification of the ERT signatures that are unique to a given process, and facilitates the joint inversion of ERT data with hydrogeological data for subsurface characterization. PFLOTRAN-E4D is demonstrated herein using a field study of stage-driven groundwater/river water interaction ERT monitoring along the Columbia River, Washington, USA. Results demonstrate the complex nature of changes subsurface electrical conductivity, in both the saturated and unsaturated zones, arising from water table changes and from river water intrusion into the aquifer. The results also demonstrate the sensitivity of surface based ERT measurements to those changes over time. PFLOTRAN-E4D is available with the PFLOTRAN development version with an open-source license at https://bitbucket.org/pflotran/pflotran-dev .

Environmental Molecular Sciences Laboratory↗
DOE OSTI2017

EPRI Report: Review of Geostatistical Approaches to Characterization of Subsurface Contamination - 17442

The key metric in the final release of a nuclear facility from radiological controls following plant decommissioning is the potential long-term radiation exposure to future users of the site. This is estimated from modeling of the exposure that could conceivably result from residual contamination remaining on the site in buildings, soil and groundwater after the completion of decontamination, dismantlement and remediation activities. A key input to such modelling is characterization data obtained from surveys and sampling. To date, the regulatory guidance for conducting the final status survey required to show compliance with regulator approved site release limits (i.e., radionuclide concentrations or contamination levels) has been limited to surface contamination. EPRI has completed a project that provides guidance for use of geo-statistics in the development of a site final status survey that will address the impact of dose to a future user of the site from subsurface contamination. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Characterization of Subsurface Microstructural Alterations Induced by Hard Turning of Inconel 718

Conventional machining especially hard turning is commonly used in aerospace industry to produce the final shape of cylindrical parts. However, the process generates damages and microstructural alterations on the surface and at the subsurface layer, in particular the cracking of hard carbides particles. The present paper focuses on investigating the probable mechanisms responsible for carbide cracking and the formation of a softened layer at a depth between 10 and 30 µm below the machined surface. Advanced techniques, such as laser confocal microscopy and field emission gun scanning electron microscope equipped with in situ picoindenter and electron backscatter diffraction, were used to characterize and analyze the evolution of the microstructure in the affected layer and propose the possible governing mechanisms for the observations.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Improving Subsurface Stress Characterization for Carbon Dioxide Storage Projects by Incorporating Machine Learning Techniques

The overall objective of this project is to develop a framework for reliable characterization and prediction of the state of stress in the overburden and underburden (including the basement) in CO 2 storage reservoirs using machine learning and integrated geomechanics and geophysical methods. Specifically, we propose to develop workflow encompassing of technologies and/or methods to predict stress and pressure changes due to CO 2 injection in an active tertiary recovery site and their impacts on subtle fault activation, fractures and occurrence of microseismic events and compare responses to field observations. In this project, we anticipate using dataset from the Farnsworth field Unit (FWU) which is operated by Purdure Petroleum. A novel elastic-waveform VSP inversion technique will be used to estimate high-resolution spatial and temporal changes of elastic moduli in CO 2 storage reservoirs, which will be combined with velocity-stress relationship derived from laboratory tests to obtain subsurface pressure and stress. Clustered microseismic data will be jointly inverted for improved focal mechanisms. Least-squares reverse-time migration of microseismic waveform data will be performed to directly image fracture/fault zones. Additionally, a deep neural network machine learning technique with convolutional and recurrent layers will be used for learning the spectro-temporal structures in microseismic waveforms. The results of this geotechnical data analysis will be integrated to develop a high-resolution 3D mechanical earth model extending from the overburden sealing formations to the underburden including the basement. Mechanical properties will be derived through integration of mechanical logs, tests, available results from chemo-mechanical laboratory tests, and elastic inversion of seismic data using a combination of Bayesian and stochastic methods as well as machine learning technique. Failure features (faults/fractures) will be represented and/or modeled based on seismic and core data analysis. A transient hydrodynamic-geomechanical model will be developed through coupling with the calibrated FWU reservoir simulation model. The full physics coupled model will be used to train a reduced order proxy model using machine learning algorithm for estimating stress which will then be used with appropriate constitutive relationships and forward seismological models to simulate pressure changes and induced microseismicity. An advanced optimization framework will be developed to perform a history match to minimize error between field observations and simulated. The history matched proxy model will be verified against the full-physics equivalent. The field observations that will be used in the coupled model calibration process include pressure/stress inverted from VSP, moment magnitude from microseismic analysis, real time downhole pressure measurements, production and injection data. Parameter sensitivity and uncertainty analysis will be performed to characterize the impact of model parameter uncertainty on stress estimates. The proposed project will have significant impact on future field implementation of the proposed technology. Because the project field site is an ongoing CO 2 EOR development, the value of the new technology will be demonstrated in an operational context and evaluated as a viable risk mitigation strategy. Cost/benefit will be evaluated together with the various commercial incentives for CO 2 sequestration available to oil and gas operators. The extensive available dataset and ongoing data acquisition under the SWP Phase III work plan provides flexibility for investigation of multiple approaches and reduces technical risk.

58 GEOSCIENCES↗
DOE OSTI2012

Atmospheric and soil-gas monitoring for surface leakage at the San Juan Basin CO{sub 2} pilot test site at Pump Canyon New Mexico, using perfluorocarbon tracers, CO{sub 2} soil-gas flux and soil-gas hydrocarbons

Near-surface monitoring and subsurface characterization activities were undertaken in collaboration with the Southwest Regional Carbon Sequestration Partnership on their San Juan Basin coal-bed methane pilot test site near Navajo City, New Mexico. Nearly 18,407 short tons (1.670 × 107 kg) of CO{sub 2} were injected into 3 seams of the Fruitland coal between July 2008 and April 2009. Between September 18 and October 30, 2008, two additions of approximately 20 L each of perfluorocarbon (PFC) tracers were mixed with the CO{sub 2} at the injection wellhead. PFC tracers in soil-gas and in the atmosphere were monitored over a period of 2 years using a rectangular array of permanent installations. Additional monitors were placed near existing well bores and at other locations of potential leakage identified during the pre-injection site survey. Monitoring was conducted using sorbent containing tubes to collect any released PFC tracer from soil-gas or the atmosphere. Near-surface monitoring activities also included CO{sub 2} surface flux and carbon isotopes, soil-gas hydrocarbon levels, and electrical conductivity in the soil. The value of the PFC tracers was demonstrated when a significant leakage event was detected near an offset production well. Subsurface characterization activities, including 3D seismic interpretation and attribute analysis, were conducted to evaluate reservoir integrity and the potential that leakage of injected CO{sub 2} might occur. Leakage from the injection reservoir was not detected. PFC tracers made breakthroughs at 2 of 3 offset wells which were not otherwise directly observable in produced gases containing 20–30% CO{sub 2}. These results have aided reservoir geophysical and simulation investigations to track the underground movement of CO{sub 2}. 3D seismic analysis provided a possible interpretation for the order of appearance of tracers at production wells.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2005

Surface Clutter Removal in Airborne Radar Sounding Data from the Dry Valleys, Antarctica

We have collected roughly 1,000 line-km of airborne radar sounding data over glaciers, rock/ice glaciers, permafrost, subsurface ice bodies, ice-covered saline lakes, and glacial deposits in Taylor and Beacon Valley. These data are being analyzed in order to develop techniques for discriminating between subsurface and off-nadir echoes and for detecting and characterizing subsurface interfaces. The identification of features on Mars exhibiting morphologies consistent with ice/rock mixtures, near-surface ice bodies and near-surface liquid water, and the importance of such features to the search for water on Mars, highlights the need for appropriate terrestrial analogs and analysis techniques in order to prepare for radar sounder missions to Mars. Climatic, hydrological, and geological conditions in the Dry Valleys of Antarctica are analogous in many ways to those on Mars. A crucial first step in the data analysis process is the discrimination of echo sources in the radar data. The goal is to identify all returns from the surface of off-nadir topography in order to positively identify subsurface echoes. This process will also be critical for radar data that will be collected in areas of Mars exhibiting significant topography, so that subsurface echoes are identified unambiguously. The positive detection and characterization of subsurface (including sub-ice) water is a primary goal of NASA's Mars exploration program. Our data over the Dry Valleys provides an opportunity to implement techniques we are developing to accomplish these goals.

Holt, J. W.↗
DOE OSTI2018

Non-Destructive Characterization of Subsurface Residual Stress Fields and Correlation with Microstructural Conditions in a Shot-Peened Inconel Component

Shot-peening is an important surface treatment used in a preventative way to guard against fatigue failures. The residual stress state imparted by shot-peening deters the formation and propagation of surface cracks. In this paper, we describe the measurement of residual stresses in an Inconel, IN100, sample using lattice strains measured using High Energy X-ray Diffraction (HEXD) and a Bi-Scale Optimization Method (BSOM). HEXD enabled rapid, non-destructive lattice strain measurements over a large region of the sample. Subsurface strains were obtained using a conical slit setup. The BSOM utilizes a macroscale representation of the sample and a spherical harmonic-based crystal scale representation of crystal orientation space at each experimental point (diffraction volume). A roughly biaxial stress state was predicted with a von Mises equivalent stress between 300 MPa and 400 MPa near the surface. The layer of material with high residual stress induced by shot-peening was found to be approximately 1 mm thick. Diffraction peak width, EBSD, and microhardness measurements were also made on the same sample, which rendered more qualitative measures of the plasticity-related effects of the shot-peening induced residual stress field. All of these measurements show a dimishing shot-peening plasticity with the increasing depth.

42 ENGINEERING↗
DOE OSTI2024

Rock Characterization for Subsurface Energy Applications

An overview of the Fossil Energy and Carbon Management mission for carbon management was given, along with information on the role the Research and Innovation Center plays in understanding aspect of research needed to accelerate geologic carbon storage nationwide.

Crandall, Dustin [NETL]↗
NASA NTRS2003

Life Detection and Characterization of Subsurface Ice and Brine in the McMurdo Dry Valleys Using an Ultrasonic Gopher: A NASA ASTEP Project

Evidence for the presence of ice and fluids near the surface of Mars in both the distant and recent past is growing with each new mission to the Planet. One explanation for fluids forming springlike features on Mars is the discharge of subsurface brines. Brines offer potential refugia for extant Martian life, and near surface ice could preserve a record of past life on the planet. Proven techniques to get underground to sample these environments, and get below the disruptive influence of the surface oxidant and radiation regime, will be critical for future astrobiology missions to Mars. Our Astrobiology for Science and Technology for Exploring Planets (ASTEP) project has the goal to develop and test a novel ultrasonic corer in a Mars analog environment, the McMurdo Dry valleys, Antarctica, and to detect and describe life in a previously unstudied extreme ecosystem; Lake Vida (Fig. 1), an ice-sealed lake.

Doran, P. T.↗
NASA NTRS2019

Characterization of Subsurface Flow Dynamics for Forecasting of Solar Activity

Evolution of large-scale magnetic field structures in the solar photosphere and corona is controlled by motions beneath the visible surface of the Sun. Subsurface plasma flows play a critical role in formation and evolution of active regions and their activity. We analyze subsurface flow maps provided by the local helioseismology pipeline from the Helioseismic and Magnetic Imager (HMI) data on board the Solar Dynamics Observatory, and investigate links between flow characteristics and magnetic activity. The primary goal is to determine flow descriptors, which can improve solar activity forecasts. In particular, by employing machine learning classifiers, we test how the flow helicity and velocity shear descriptors can improve the prediction of initiation of flares and CME eruptions.

Kosovichev, Alexander↗
DOE OSTI2023

Seismic Characterization of the Blue Mountain Geothermal Field

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

58 GEOSCIENCES↗
DOE OSTI2016

Scalable subsurface inverse modeling of huge data sets with an application to tracer concentration breakthrough data from magnetic resonance imaging

When characterizing subsurface properties is crucial for reliable and cost-effective groundwater supply management and contaminant remediation. With recent advances in sensor technology, large volumes of hydro-geophysical and geochemical data can be obtained to achieve high-resolution images of subsurface properties. However, characterization with such a large amount of information requires prohibitive computational costs associated with “big data” processing and numerous large-scale numerical simulations. To tackle such difficulties, the Principal Component Geostatistical Approach (PCGA) has been proposed as a “Jacobian-free” inversion method that requires much smaller forward simulation runs for each iteration than the number of unknown parameters and measurements needed in the traditional inversion methods. PCGA can be conveniently linked to any multi-physics simulation software with independent parallel executions. In our paper, we extend PCGA to handle a large number of measurements (e.g. 106 or more) by constructing a fast preconditioner whose computational cost scales linearly with the data size. For illustration, we characterize the heterogeneous hydraulic conductivity (K) distribution in a laboratory-scale 3-D sand box using about 6 million transient tracer concentration measurements obtained using magnetic resonance imaging. Since each individual observation has little information on the K distribution, the data was compressed by the zero-th temporal moment of breakthrough curves, which is equivalent to the mean travel time under the experimental setting. Moreover, only about 2,000 forward simulations in total were required to obtain the best estimate with corresponding estimation uncertainty, and the estimated K field captured key patterns of the original packing design, showing the efficiency and effectiveness of the proposed method. This article is protected by copyright. All rights reserved.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2011

Characterization and monitoring of subsurface processes using parallel computing and electrical resistivity imaging

This newsletter discusses recent advancement in subsurface resistivity characterization and monitoring capabilities. The BC Cribs field desiccation treatability test resistivity monitoring data is use an example to demonstrate near-real time 3D subsurface imaging capabilities. Electrical resistivity tomography (ERT) is a method of imaging the electrical resistivity distribution of the subsurface. An ERT data collection system consists of an array of electrodes, deployed on the ground surface or within boreholes, that are connected to a control unit which can access each electrode independently (Figure 1). A single measurement is collected by injecting current across a pair of current injection electrodes (source and sink), and measuring the resulting potential generated across a pair of potential measurement electrodes (positive and negative). An ERT data set is generated by collecting many such measurements using strategically selected current and potential electrode pairs. This data set is then processed using an inversion algorithm, which reconstructs an estimate (or image) of the electrical conductivity (i.e. the inverse of resistivity) distribution that gave rise to the measured data.

99 GENERAL AND MISCELLANEOUS↗