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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Geothermal Deep Direct-Use for Low-Carbon Heating: A Case Study at Cornell University

Cornell University is developing a geothermal deep direct-use system to provide reliable, low carbon, and cost-competitive heating for its main campus in Ithaca, New York. This geothermal system, also referred to as Earth Source Heat, would provide the majority of the campus heating load and represents a key pillar for the university to become carbon neutral by 2035. Over the last 10 years, numerous desktop, seismic, and geophysical studies have been undertaken at Cornell to characterize the local subsurface, predict long-term reservoir performance, design the integration into the existing district heating network, and estimate capital and levelized costs. Recently, the university received funding for drilling an observatory well to obtain in-situ subsurface data (e.g., reservoir temperatures, pressures, fluid chemistry, stress field), significantly lowering subsurface uncertainty and de-risking the project. Drilling of the test well is planned for fall 2021. This paper provides an overview and status of the project, including a summary of past studies and a description of the proposed well and planned testing activities.

Cornell University↗
DOE OSTI2021

Analysis of Selected Publicly Available Geothermal Exploration Data Gaps

As part of a United States Department of Energy (DOE) supported retrospective analysis of DOE's Play Fairway Analysis (PFA) projects, the National Renewable Energy Laboratory (NREL) compiled and analyzed publicly available geothermal exploration datasets to identify and highlight data gaps in areas prospective for hosting geothermal resources. The analysis was intended to understand the existing geographic coverage of selected datasets commonly utilized both by the PFA projects and geothermal developers during resource assessments including geologic mapping, temperature gradient drilling, and aeromagnetic, gravimetric, and lidar surveys. Results indicate that broad areas of the western United States estimated to have geothermal potential lack sufficient geologic and geophysical coverage necessary for even regional resource exploration. The study directly informed the recent Geoscience Data Acquisition for Western Nevada, or GeoDAWN - which united DOE's Geothermal Technologies Office (GTO) with the U.S. Geological Survey (USGS) of the U.S. Department of the Interior to assist U.S. needs for energy and critical minerals. The study also has the potential to inform public investment in further data acquisition for characterization of the Earth both for geothermal and other natural resource assessments.

data↗
DOE OSTI2021

Field Experiments and Model Validation: The EGS Collab Project

Implementing enhanced geothermal systems (EGS) will require improvements in understanding stimulation of crystalline rock to create appropriate flow pathways, and the ability to effectively simulate both the stimulation and the flow and transport processes in the resulting fracture network. The US Department of Energy (DOE) is addressing these and other challenges at multiple scales. The EGS Collab project, addressed here, is performing tests and modeling at the 10 m scale. The FORGE project is performing tests at the full reservoir scale. The EGS Collab team created an underground testbed at the Sanford Underground Research Facility (SURF) in Lead, South Dakota, at a depth of approximately 1.5 km to examine hydraulic fracturing (Experiment 1). We are currently building a second testbed at SURF at a depth of about 1.25 km aimed at investigating shear stimulation (Experiment 2). In Experiment 1, we drilled eight boreholes in a well-characterized phyllite and installed geophysical sensors in six of them to create a well-instrumented testbed to allow careful monitoring of stimulation events and flow tests. Numerical simulation was used to answer key experimental design questions, to forecast fracture propagation trajectories and extents, and to analyze and evaluate results both in near-real-time and in detailed process studies. Stimulations performed in this testbed allowed quantification of processes occurring during stimulation and the examination of dynamic flow occurrences. Long-term ambient temperature and chilled water flow tests were performed in addition to many tracer tests to examine system behavior. Our second testbed, targeted at shear stimulation, is currently being built at the SURF Facility at a depth of about 1.25 km in amphibolite under a different set of stress and fracture conditions than Experiment 1.

artificial intelligence↗
DOE OSTI2022

Electrical Imaging for Hydrogeology

This book chapter will contribute to a volume on geophysics being developed for the Groundwater Project (https://gw-project.org). The Groundwater Project is committed to contribute to advancement in education by creating and making available online free high-quality groundwater educational material for all. The Groundwater Project is led by Dr. John Cherry, recipient of the 2020 Stockholm Water Prize, and managed by a 11-member international Steering Committee and a larger and globally diverse Advisory Committee. Details on the groundwater project and book volumes may be found at https://gw-project.org/gw-project-background-information.pdf .

groudnwater, electrical resistivity imaging, resis↗
DOE OSTI2023

PARTITIONING AND TRANSMUTATION OF USED NUCLEAR FUEL IN SUPPORT OF GEOLOGICAL WASTE DISPOSAL

Nuclear energy is a low-carbon technology that generates bulk baseload electricity and supports long-lasting eco-friendly sustainability goals. However, it still faces challenges, such as the securing and managing of long-lived nuclear actinides and fission products—most notably the used fuel, which is considered high-level waste (HLW). Direct geological waste disposal would be the most economical option for HLW but requires highly qualified sites whose geophysical behavior will remain stable for 10,000 years. Partitioning and transmutation (P&T) of used fuel reduces radioactivity and decay heat generation, enabling efficient geological waste disposal. P&T research and development, along with the prospects of P&T techniques such as wet/dry separation and accelerator-driven systems, were reviewed to identify critical needs that must be met to foster their successful implementation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

The 2022 Hunga eruption exercises the nuclear explosion monitoring community

The 2022 Hunga eruption was a global event recorded by all three Comprehensive Test Ban Treaty Organization (CTBTO) International Monitoring System (IMS) geophysical waveform sensor technologies: seismic, infrasound, and hydroacoustic. Large volcanic eruptions are rare and additionally present an opportunity to test seismoacoustic detection, location, and identification methods for nuclear explosion monitoring.

58 GEOSCIENCES↗
DOE OSTI2023

PARTITIONING AND TRANSMUTATION OF USED NUCLEAR FUEL IN SUPPORT OF GEOLOGICAL WASTE DISPOSAL

Nuclear energy is a low-carbon technology that generates bulk baseload electricity and supports long-lasting eco-friendly sustainability goals. However, it still faces challenges, such as the securing and managing of long-lived nuclear actinides and fission products—most notably the used fuel, which is considered high-level waste (HLW). Direct geological waste disposal would be the most economical option for HLW but requires highly qualified sites whose geophysical behavior will remain stable for 10,000 years. Partitioning and transmutation (P&T) of used fuel reduces radioactivity and decay heat generation, enabling efficient geological waste disposal. P&T research and development, along with the prospects of P&T techniques such as wet/dry separation and accelerator-driven systems, were reviewed to identify critical needs that must be met to foster their successful implementation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Preliminary report on applications of machine learning techniques to the Nevada play fairway analysis

We are applying machine learning (ML) techniques, including training set augmentation and artificial neural networks, to mitigate key challenges in the Nevada play fairway project. The study area includes ~85 active geothermal systems as potential training sites and >12 geologic, geophysical, and geochemical features. The main goal is to develop an algorithmic approach to identify new geothermal systems in the Great Basin region. Major objectives include: 1) integrate ML techniques into the geothermal community; 2) develop open community datasets, whereby all play fairway and ML datasets and algorithms are publicly released and available for modification by various user groups; 3) identify data acquisition targets with high value for future work; 4) identify new signatures to detect blind geothermal systems; and 5) foster new capabilities for characterizing subsurface temperature and permeability. Initially, ML techniques are being applied to the same play fairway datasets and workflow. ML will then be applied to both enhanced and additional datasets, with modification of the PFA workflow to incorporate the new datasets. Finally, ML will be applied to define new workflows using the enhanced and additional datasets. An algorithmic approach that empirically learns to estimate weights of influence for diverse parameters can potentially scale and perform better than the play fairway analysis. Initial work on this project has involved 1) evaluating potential positive and negative training sites, 2) transformation of datasets into formats suitable for ML, and 3) initial development and testing of ML techniques.

58 GEOSCIENCES↗
DOE OSTI2020

Machine learning for natural resource assessment: An application to the blind geothermal systems of Nevada

A study is underway to apply machine learning methods to evaluate natural resource potential. In particular, we are considering the search for blind geothermal systems in Nevada. Beginning with the data and experience from the previous Nevada play fairway analysis project, we are building models in TensorFlow/Keras and gaining experience toward predicting the geothermal resource potential as a probability map. During the first year of this project we have encountered several issues particular to using geological and geophysical data sets with these tools. Through an illustrative example we develop a promising workflow for future use as more data become available and are analyzed.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Characterizing Signatures of Geothermal Exploration Data with Machine Learning Techniques: An Application to the Nevada Play Fairway Analysis

We are introducing machine learning methods to the play fairway analysis to generate geothermal potential maps to support the evaluation of geothermal resource potential and the exploration for undiscovered blind geothermal systems in the Nevada Great Basin region. Our project aims to identify new ways to combine the play fairway data and empirically organize relationships between feature weights and labels in an improved workflow. As a means of doing this, we introduce machine learning methods to evaluate the influence of certain geological and geophysical features/feature sets in predicting geothermal favorability. This report highlights promising approaches based on supervised and unsupervised learning methods. First, we demonstrate a filter method applied to supervised classification modeling. The supervised filter method is based on permutation analysis to evaluate every possible feature combination/drop out scenario and rank feature influence based on the performance variance of supervised classification models. Additionally, we present an unsupervised factor analysis based on principal component analysis coupled with a semi-supervised kmeans clustering algorithm. This analysis allows us to identify the optimal number of groups/clusters for training sites and structural settings to identify feature patterns including correlation, variance, and latent and dominant feature relationships. The results from these methods offer a promising avenue for identifying favorable sources of predictive information to identify the locations of blind geothermal systems and furthering our understanding of complex geothermal feature and label relationships in the Great Basin region and beyond.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Global inventory and meta-analysis of offshore geologic carbon storage efforts

Poster for presentation at the American Geophysical Union Fall Meeting 2023 detailing work conducted for the Carbon Storage Data Field Work Proposal. This poster presents an inventory and meta-analysis conducted for Carbon Storage Data Task 4, which includes a review of offshore geologic carbon storage projects worldwide including site characterization, resource estimates, and transport information.

Mark-Moser, Mackenzie K.↗
DOE OSTI2023

Multi factor Assessment for Decarbonization via Technically Viable Carbon Storage

Final poster presenting research under EDX4CCS portfolio, Task 21, on multi-factor assessment for technically viable carbon storage. To date, carbon storage viability assessments have focused on the technically recoverable storage resources to estimate the volume of carbon storage in reservoirs targeted for geologic carbon sequestration. The Carbon Storage Technical Viability Approach (CS TVA) was developed to account for all factors determining the viability of carbon storage at a given site, including socioeconomic and environmental aspects in addition to the classic subsurface geologic assessment. Abstract accepted to the American Geophysical Union Fall Meeting 2023.

Mark-Moser, Mackenzie K.↗
DOE OSTI2023

Simulation tools, first results and experimental status of the MURAVES experiment

The MUon RAdiography of VESuvius (MURAVES) project aims at the study of Mt. Vesuvius, an active and hazardous volcano near Naples, Italy, with the use of muons freely and abundantly produced by cosmic rays. In particular, the MURAVES experiment intends to perform muographic imaging of the internal structure of the summit of Mt. Vesuvius. The challenging measurement of the rock density distribution in its summit by muography, in conjunction with data from other geophysical techniques, can help model possible eruption dynamics. The MURAVES apparatus consists of an array of three independent and identical muon trackers, with a total sensitive area of 3 square meters. In each tracker, a sequence of 4 XY tracking planes made of plastic scintillators is complemented by a 60 cm thick lead wall inserted between the two downstream planes to improve rejection of background from low energy muons. The apparatus is currently acquiring data. This paper presents preliminary results from the analysis of the first data samples acquired with trackers pointing towards Mt. Vesuvius, including the first relative measurement of the density projection of two flanks of the volcano at three different altitudes; we also present the workflow of the simulation chain of the MURAVES experiment and its ongoing developments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Panel Session 100: An Integrated Systems-Based Monitoring Approach

This panel focused on recent advancements in monitoring and remaining challenges associated with the implementing cost-effective monitoring approaches for complex DOE sites. Monitoring presents one of the largest, long-term costs for environmental remediation and site stewardship within the DOE because contamination at complex sites will likely be present for tens to hundreds of years into the future. Integration of monitoring with predictive modeling is an approach that can reduce long-term monitoring costs as well as support the effective monitoring design and data interpretation for managing a site toward closure and long-term stewardship. Panelists with presentations: Autonomous Electrical Geophysical Monitoring of Contaminated Sites (Tim Johnson); Systems Based Long-Term Monitoring for DOE Legacy Management (David Shafer); Long-Term Systems Based Monitoring (Anderson Ward)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Field Test Bed for Vadose-Zone Monitoring Approaches - 20404

In the Central Plateau at the U.S. Department of Energy Hanford Site, a large inventory of contaminants resides in unsaturated sediments within the approximately 100-meter-thick vadose zone, posing a potential continuing risk to groundwater. Vadose zone remedies used to address these contaminants will require performance monitoring to provide feedback during implementation and for long-term verification that remedial action objectives have been met. Passive approaches may also need long-term monitoring to demonstrate that the flux of contaminants from the vadose zone to the groundwater are below thresholds established to meet groundwater protection goals. Collection of physical (e.g., groundwater or sediment) samples is a common method for identifying contaminant concentration distributions, but this approach is limited by the number of locations and the frequency with which data can be collected. In situ vadose zone measurements have evolved over the past few years to include key measurements of water content, soil water pressure, temperature, and chemical concentration. However, the current generation of sensors is designed for relatively short-term use in near-surface soils or sediments. Geophysical methods have been evolving but are also limited in that they have not been designed for the specific long-term vadose zone monitoring needs at the Hanford Site. Overall, monitoring under unsaturated conditions can be difficult due to the need to install and maintain instrumentation over a large area and depth and the need to identify preferential flow pathways due to geologic and chemical heterogeneities over long periods. Thus, a vadose zone monitoring test bed was initiated to address these challenges and identify cost-effective approaches for implementation and postclosure monitoring of the deep vadose zone. The monitoring test bed is expected to provide valuable field-scale information for the design of vadose zone monitoring systems. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

The German Site Selection Process - Implementation and Challenges - 20528

Federal Company for Radioactive Waste Disposal (Bundesgesellschaft fuer Endlagerung mbH; hereinafter referred to as BGE) is a state-owned company established in September 2016 following the 'Act on the Organisational Restructuring in the Field of Radioactive Waste Storage'. 'Repository Site Selection Act' (hereinafter referred to as StandAG) defines the site selection procedure, aiming to ensure the best possible safety for storing high-level radioactive waste in Germany for the duration of one million years. The selection procedure is a participatory, transparent, learning and self-questioning process based on scientific expertise. Possible repository sites are sought all over Germany, without excluding or predetermining any potential site at the beginning. The procedure consists of three phases. After each phase, the legislative decides on the regions for further consideration in the next phase of the site selection procedure. The current first phase (Phase I) is divided into two steps. Objective of Step 1 is to determine subareas of favorable geological conditions based on available data. Step 2 of Phase I will lead to identification of specific regions that are considered for further surface-based geoscientific and geophysical exploration. Potential host rocks are rock salt, clay rock and crystalline rock. As part of the site selection procedure, an overview of the status of the planning and implementation of R and D activities is given. Topics of particular interest are, for example, the adaptation of the maximum temperature of 100 deg. C at the container surface, the impact of long-term climate variations as well as minimally invasive exploration methods. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗