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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
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↗
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]↗
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↗
DOE OSTI2016

Thermal etching of silver: Influence of rolling defects

Silver is well known to be thermally etched in an oxygen-rich atmosphere and has been extensively studied in the laboratory to understand thermal etching and to limit its effect when this material is used as a catalyst. Yet, in many industrial applications the surface of rolled silver sheets is used without particular surface preparation. Here, it is shown by combining FIB-tomography, FIB-SIMS and analytical SEM that the kinetics of thermal etch pitting are significantly faster on rolled Ag surfaces than on polished surfaces. This occurs due to range of interacting phenomena including (i) the reaction of subsurface carbon-contamination with dissolved oxygen to form pores that grow to intersect the surface, (ii) surface reconstruction around corrosion pits and surface scratches, and (iii) sublimation at low pressure and high temperature. A method to identify subsurface pores is developed to show that the pores have (111) and (100) internal facets and may be filled with a gas coming from the chemical reaction of oxygen and carbon contamination. - Highlights: Thermal etching of industrial silver sheets vs. polished silver sheets Effect of annealing atmosphere on the thermal etching of silver: surface and subsurface characterization Link between etch pitting and defects induced by rolling. FIB-tomography coupled with EBSD for determining crystal planes of the facets of subsurface pores. FIB-SIMS characterization to probe the gas confined inside subsurface pores.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Controlling Subsurface Fractures and Fluid Flow: A Basic Research Agenda

From beneath the surface of the earth, we currently obtain about 80-percent of the energy our nation consumes each year. In the future we have the potential to generate billions of watts of electrical power from clean, green, geothermal energy sources. Our planet’s subsurface can also serve as a reservoir for storing energy produced from intermittent sources such as wind and solar, and it could provide safe, long-term storage of excess carbon dioxide, energy waste products and other hazardous materials. However, it is impossible to underestimate the complexities of the subsurface world. These complexities challenge our ability to acquire the scientific knowledge needed for the efficient and safe exploitation of its resources. To more effectively harness subsurface resources while mitigating the impacts of developing and using these resources, the U.S. Department of Energy established SubTER – the Subsurface Technology and Engineering RD&D Crosscut team. This DOE multi-office team engaged scientists and engineers from the national laboratories to assess and make recommendations for improving energy-related subsurface engineering. The SubTER team produced a plan with the overall objective of “adaptive control of subsurface fractures and fluid flow.”This plan revolved around four core technological pillars—Intelligent Wellbore Systems that sustain the integrity of the wellbore environment; Subsurface Stress and Induced Seismicity programs that guide and optimize sustainable energy strategies while reducing the risks associated with subsurface injections; Permeability Manipulation studies that improve methods of enhancing, impeding and eliminating fluid flow; and New Subsurface Signals that transform our ability to see into and characterize subsurface systems. The SubTER team developed an extensive R&D plan for advancing technologies within these four core pillars and also identified several areas where new technologies would require additional basic research. In response, the Office of Science, through its Office of Basic Energy Science (BES), convened a roundtable consisting of 15 national lab, university and industry geoscience experts to brainstorm basic research areas that underpin the SubTER goals but are currently underrepresented in the BES research portfolio. Held in Germantown, Maryland on May 22, 2015, the round-table participants developed a basic research agenda that is detailed in this report. Highlights include the following: -A grand challenge calling for advanced imaging of stress and geological processes to help understand how stresses and chemical substances are distributed in the subsurface—knowledge that is critical to all aspects of subsurface engineering; -A priority research direction aimed at achieving control of fluid flow through fractured media; -A priority research direction aimed at better understanding how mechanical and geochemical perturbations to subsurface rock systems are coupled through fluid and mineral interactions; -A priority research direction aimed at studying the structure, permeability, reactivity and other properties of nanoporous rocks, like shale, which have become critical energy materials and exhibit important hallmarks of mesoscale materials; -A cross-cutting theme that would accelerate development of advanced computational methods to describe heterogeneous time-dependent geologic systems that could, among other potential benefits, provide new and vastly improved models of hydraulic fracturing and its environmental impacts; -A cross-cutting theme that would lead to the creation of “geo-architected materials” with controlled repeatable heterogeneity and structure that can be tested under a variety of thermal, hydraulic, chemical and mechanical conditions relevant to subsurface systems; -A cross-cutting theme calling for new laboratory studies on both natural and geo-architected subsurface materials that deploy advanced high-resolution 3D imaging and chemical analysis methods to determine the ;rates and mechanisms of fluid-rock processes, and to test predictive models of such phenomena. Many of the key energy challenges of the future demand a greater understanding of the subsurface world in all of its complexity. This greater under- standing will improve the ability to control and manipulate the subsurface world in ways that will benefit both the economy and the environment. This report provides specific basic research pathways to address some of the most fundamental issues of energy-related subsurface engineering.

15 GEOTHERMAL ENERGY↗
DOE OSTI2017

Environmental Remediation

At Pacific Northwest National Laboratory, we are a leader in subsurface science and remediation. We use our expertise in these areas to characterize subsurface contaminants and assess contaminant transport. We're also using our capabilities to restore the Hanford Site in southeastern Washington State to a safe, clean environment.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Low‐Field Borehole NMR Applications in the Near‐Surface Environment

Core Ideas Low‐field borehole NMR characterizes subsurface heterogeneities from pore to macro scales. Borehole NMR is sensitive to biogeochemical processes and conversions in the subsurface. Development of borehole NMR benefits hydrogeology, soil management, and engineering. The inherent heterogeneity of the near subsurface (<200 m below the ground surface) presents challenges for agricultural water management, hydrogeologic characterization, and engineering, among other fields. Borehole nuclear magnetic resonance (NMR) has the potential not only to describe this heterogeneity in space nondestructively but also to monitor physical and chemical changes in the subsurface with time. Nuclear magnetic resonance is sensitive to parameters of interest like porosity and permeability, saturation, fluid viscosity, and formation mineralogy. Borehole NMR tools have been used to measure soil moisture in model soils, and recent advances in low‐field borehole NMR instrumentation allow estimation of hydraulic properties of unconsolidated aquifers. We also demonstrate the potential for low‐field borehole NMR tools to monitor field‐relevant biogeochemical processes like biofilm accumulation and microbially induced calcite precipitation at laboratory and field scales. Finally, we address some remaining challenges and areas of future research, as well as other possible applications where borehole NMR could provide valuable complementary data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Ultrasonic linear array validation via concrete test blocks

Oak Ridge National Laboratory (ORNL) comparatively evaluated the ability of a number of NDE techniques to generate an image of the volume of 6.5′ X 5.0′ X 10″ concrete specimens fabricated at the Florida Department of Transportation (FDOT) NDE Validation Facility in Gainesville, Florida. These test blocks were fabricated to test the ability of various NDE methods to characterize various placements and sizes of rebar as well as simulated cracking and non-consolidation flaws. The first version of the ultrasonic linear array device, MIRA [version 1], was one of 7 different NDE equipment used to characterize the specimens. This paper deals with the ability of this equipment to determine subsurface characterizations such as reinforcing steel relative size, concrete thickness, irregularities, and inclusions using Kirchhoff-based migration techniques. The ability of individual synthetic aperture focusing technique (SAFT) B-scan cross sections resulting from self-contained scans are compared with various processing, analysis, and interpretation methods using the various features fabricated in the specimens for validation. The performance is detailed, especially with respect to the limitations and implications for evaluation of a thicker, more heavily reinforced concrete structures.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Estimation of hydraulic conductivity in a watershed using sparse multi-source data via Gaussian process regression and Bayesian experimental design

Enhanced water management systems depend on accurate estimation of subsurface hydraulic properties. However, geologic formations can vary significantly, so information from a single source (e.g., widely spaced boreholes) is insufficient in characterizing subsurface aquifer properties. Therefore, multiple sources of information are needed to complement the hydrogeology understanding of a region. Here, this study presents a numerical framework in which information from different measurement sources is combined to characterize the 3D random field in a multi-fidelity prediction model. Coupled with the model, a Bayesian experimental design was used to determine the best future sampling locations. The Upper Sangamon watershed in east-central Illinois was selected as the case study site, where the multi-fidelity Gaussian process model was used to estimate the hydraulic conductivity in the region of interest. Multi-source observation data were obtained from electrical resistivity and borehole pumping tests. The accuracy of the model prediction is dependent on the locations and the distribution of both high- and low-fidelity data. Furthermore, the multi-fidelity model was compared with the single-fidelity model. The uncertainties and confidence in the measurements and parameter estimates were quantified and used to design future cycles of data collection to further improve the confidence intervals.

54 ENVIRONMENTAL SCIENCES↗