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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Innovative Exploration Techniques for Geothermal Assessment at Jemez Pueblo, New Mexico

Surface exploration methods like geological mapping, mineralogical analysis, hydrogeochemistry, magnetotelluric as well as seismic, have been used to analyze the study area and identify a location for a production size exploration well. After that location has been identified in a blind resource scenario, a 5,657 feet deep deviated production size exploration well has been drilled. The surface casing is 13 3/8 inch with open hole starting at 4,136 feet. The well has been designed to be deepened up to 8,000 feet if needed. The first 4,180 feet have been sufficiently analyzed and were considered only of direct use interest prior to drilling. That has been confirmed. The remaining depth couldn't be logged by the time the report has been submitted because the well had an obstruction at 4,180 feet. Currently the power production potential for the drilling location cannot be determined without additional work-over of the well. The seismic conducted prior to drilling was a 100% success, providing a clear image of the subsurface and allowing for geosteering to be fact based and on target. Once the obstruction has been removed, work can continue to determine the power generation potential at that location.

15 GEOTHERMAL ENERGY↗
DOE OSTI2015

Pilgrim Hot Springs geothermal exploration, 2010-2014 Final report [Recovery Act: Validation of Innovative Exploration Techniques, Pilgrim Hot Springs, Alaska]

Drilling and temperature logging campaigns between the late 1970's and early 1980’s measured temperatures at Pilgrim Hot Springs in excess of 90°C. Between 2010 and 2014 the University of Alaska used a variety of methods including geophysical surveys, remote sensing techniques, heat budget modeling, and additional drilling to better understand the resource and estimate the available geothermal energy.

15 GEOTHERMAL ENERGY↗
DOE OSTI2014

Geothermal Exploration of Newberry Volcano, Oregon

Davenport Newberry (Davenport) has completed 8 years of exploration for geothermal energy on Newberry Volcano in central Oregon. Two deep exploration test wells were drilled by Davenport on the west flank of the volcano, one intersected a hydrothermal system; the other intersected isolated fractures with no hydrothermal interconnection. Both holes have bottom-hole temperatures near or above 315°C (600°F). Subsequent to deep test drilling an expanded exploration and evaluation program was initiated. These efforts have included reprocessing existing data, executing multiple geological, geophysical, geochemical programs, deep exploration test well drilling and shallow well drilling. The efforts over the last three years have been made possible through a DOE Innovative Exploration Technology (IET) Grant 109, designed to facilitate innovative geothermal exploration techniques. The combined results of the last 8 years have led to a better understanding of the history and complexity of Newberry Volcano and improved the design and interpretation of geophysical exploration techniques with regard to blind geothermal resources in volcanic terrain.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

A Deep Learning-Driven Sampling Technique to Explore the Phase Space of an RNA Stem-Loop

The folding and unfolding of RNA stem-loops are critical biological processes; however, their computational studies are often hampered by the ruggedness of their folding landscape, necessitating long simulation times at the atomistic scale. Here, we adapted DeepDriveMD (DDMD), an advanced deep learning-driven sampling technique originally developed for protein folding, to address the challenges of RNA stem-loop folding. Although tempering- and order parameter-based techniques are commonly used for similar rare-event problems, the computational costs or the need for a priori knowledge about the system often present a challenge in their effective use. DDMD overcomes these challenges by adaptively learning from an ensemble of running MD simulations using generic contact maps as the raw input. DeepDriveMD enables on-the-fly learning of a low-dimensional latent representation and guides the simulation toward the undersampled regions while optimizing the resources to explore the relevant parts of the phase space. We showed that DDMD estimates the free energy landscape of the RNA stem-loop reasonably well at room temperature. Our simulation framework runs at a constant temperature without external biasing potential, hence preserving the information on transition rates, with a computational cost much lower than that of the simulations performed with external biasing potentials. Here, we also introduced a reweighting strategy for obtaining unbiased free energy surfaces and presented a qualitative analysis of the latent space. This analysis showed that the latent space captures the relevant slow degrees of freedom for the RNA folding problem of interest. Finally, throughout the manuscript, we outlined how different parameters are selected and optimized to adapt DDMD for this system. We believe this compendium of decision-making processes will help new users adapt this technique for the rare-event sampling problems of their interest.

Gupta, Ayush↗
DOE OSTI2020

Micro- and nano-scale techniques to explore bacteria and fungi interactions with silicate minerals

Biogeochemical interfaces such as bacteria-mineral, fungi-mineral, and water-mineral are hot spots for important processes and reactions in all environments. Studying these interfaces are useful over a range of spatial and temporal scales in order to understand elemental cycles, soil formation, restoration, and bioremediation. Advancement of micro- and nano-scale techniques lead to new discoveries about reactions and interactions at the heterogeneous interface of bacteria, fungi, and minerals. The current state-of-art of analytical techniques allows to study solid, liquid, and gas phases and their interactions with biological components and often permits simultaneous study of chemical and physical properties in a site-specific manner. Improved techniques not only increased spatial and temporal resolution with site specificity but also decreased sample preparation, artifact generation, sample size requirements and analytical time. There are limitations and challenges that still need to be overcome while studying the very heterogeneous microbe-mineral interface, but improved accessibility to beam time at the synchrotron facilities and instrument time at the user laboratories offer some solutions. This review chapter focuses on a subset of the available micro- and nano-scale techniques: electron and atomic force microscopy, and X-ray based spectroscopy of traditional and synchrotron-based applications.

Slicate minerals, microscopy, microbes↗
DOE OSTI2015

CUORE and beyond: Bolometric techniques to explore inverted neutrino mass hierarchy

The CUORE (Cryogenic Underground Observatory for Rare Events) experiment will search for neutrinoless double beta decay of 130 Te. With 741 kg of TeO 2 crystals and an excellent energy resolution of 5 keV (0.2%) at the region of interest, CUORE will be one of the most competitive neutrinoless double beta decay experiments on the horizon. With five years of live time, CUORE projected neutrinoless double beta decay half-life sensitivity is 1.6 × 10 26 y at 1σ (9.5 × 10 25 y at the 90% confidence level), which corresponds to an upper limit on the effective Majorana mass in the range 40–100 meV (50–130 meV). Further background rejection with auxiliary light detector can significantly improve the search sensitivity and competitiveness of bolometric detectors to fully explore the inverted neutrino mass hierarchy with 130 Te and possibly other double beta decay candidate nuclei.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Exploring communication techniques across a wide spectrum of groups - 15105

Communication about radionuclide and nuclear waste is one factor in facilitating decisions regarding environmental issues and energy solutions. However, the decision process involves many people with a variety of perceptions that have been formed throughout many years. This paper describes various communication techniques across a spectrum of stakeholders from the general public, students, first responders, analysts, and decisions makers. Techniques include dose assessment tools, assessment training, preparing first responders to confront potential radiological/nuclear situations, web multimedia information, ongoing Department of Energy (DOE) education projects, public talks and presentations, and National Environmental Policy Act (NEPA) participation. Issues are identified concerning perception including units, background radiation, effects of environmental levels of radiation, potential approaches to energy literacy, and risk comparison. These various communication techniques across a spectrum of stakeholders are evaluated with feedback and use statistics concerning aspects of strategies, challenges, and common perceptions. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

Feasibility study of archaeological structures scanning by muon tomography

One of the main concerns in archaeology is to find of a method to study precisely archaeological structures in the least invasive way possible to avoid damage. The requirement of preserving the structures integrity prevents, in the case of pyramids or tumuli, the study of any internal structure (halls or tombs) which are not reachable by existing corridors. One non-invasive method is the muon tomography. By placing a detector which allows to register the muon direction after the structure, it is possible to have an idea of its composition based on the attenuation of the muon flux, which depends on the material length and density that muons have crossed. This technique, alone or together with other exploration techniques as seismic tomography or electrical resistivity tomography, can provide useful information about the internal structure of the archaeological form that can not be obtained by conventional archaeological methods. In this work, the time measurement necessary to obtain a significant result about the composition of an archaeological structure is estimated. To do that, a Monte Carlo simulation framework based on the MUSIC software, properly tuned for this study, has been developed. The particular case of the Kastas Amfipoli Macedonian tumulus has been considered to perform the simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Decision Level Fusion: An Event Driven Approach

This study presents a technique that combines the occurrence of certain events, as observed by different sensors, in order to detect and classify objects. This technique explores the extent of dependence between features being observed by the sensors, and generates more informed probability distributions over the events. Provided some additional information about the features of the object, this fusion technique can outperform other existing decision level fusion approaches that may not take into account the relationship between different features.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Geologic hydrogen: a review of resource potential, subsurface dynamics, exploration, production, transportation, and research opportunities

Hydrogen is a versatile resource with critical roles in decarbonization, industrial manufacturing, and energy integration. However, most hydrogen today is produced from fossil fuels, resulting in high emissions and energy consumption. Although low-carbon hydrogen production methods, such as steam methane reforming with carbon capture and renewable-powered electrolysis, are advancing, their high costs hinder large-scale deployment. Identifying alternative pathways for producing low-cost, low-emission hydrogen is therefore essential. Geologic hydrogen, referring to natural and stimulated hydrogen generated in the Earth's subsurface, has attracted growing attention as a potential source of sustainable, economically viable, and environmentally favorable hydrogen. This paper provides a comprehensive review of geologic hydrogen, covering its resource potential, origins, migration and trapping mechanisms, exploration techniques, production strategies, and pipeline transportation. It also identifies key knowledge gaps and proposes a roadmap for future research. The review indicates that geologic hydrogen has vast resource potential and can leverage existing subsurface technologies and geophysical exploration methods. However, major challenges persist, including uncertain hydrogen generation rates, limited understanding and control of serpentinization processes, costly transportation infrastructure, the lack of validated techno-economic analysis, and potential social and environmental issues. As the field is still in its early stages, progress will require interdisciplinary collaboration spanning geoscience, engineering, economics, environmental science, and policy and regulation.

42 ENGINEERING↗
DOE OSTI2025

Application of Raman Spectroscopy to Determine Uranium Content in ADUN Solution

The work presented in this report is part of the ongoing efforts to address the nuclear material control and accounting needs for advanced reactor fuel fabrication facilities. This work was supported by the Materials Protection, Accounting, and Control Technologies (MPACT) program under the US Department of Energy Office of Nuclear Energy‘s Nuclear Fuel Cycle and Supply Chain program. The activities and engagements under the MPACT program are designed to support a robust US civilian nuclear energy enterprise. In the work described in this report, we supported MPACT objectives by developing measurement techniques that could be used for material accounting and process monitoring and by working with industry partners to identify existing gaps and areas for improvement. Oak Ridge National Laboratory has been working with commercial tristructural isotropic (TRISO) fuel fabricators such as Standard Nuclear to develop technology for rapid and cost-effective uranium content assessment. This work has focused on demonstrating advanced measurement techniques (e.g., Raman spectroscopy) that can be used for rapid, reliable, and cost-effective routine measurements of uranium content in feed solutions and liquid waste streams as well as for monitoring in-line process measurements and product streams. Specifically, this report explores techniques for accurately determining uranium content in acid-deficient uranyl nitrate (ADUN) solutions and detecting low uranium concentrations in ammonia solutions. Developing such measurement techniques will benefit TRISO fuel fabrication facilities, facilities involved in other parts of the fuel cycle that require online monitoring of aqueous solutions, and potentially molten salt fuel reactors. This work supports developing Raman spectroscopy procedures to determine uranium concentrations in ADUN solutions, which are used as feedstock in the sol–gel process for creating TRISO fuel. Some additional benefits of using Raman spectroscopy for uranium quantification in fabrication facilities include enabling online monitoring of the chemical process, which would provide near real-time feedback; eliminating the need for sample transfers, preparation, or dilution; providing nondestructive measurements; and user friendliness. In this fiscal year, FY25, we determined the identity of the unknown Raman band at approximately 853 cm−1 that was discovered in ADUN Raman spectra in FY24, created calibration curves and determined uranium concentrations of two ADUN solutions, and compared the Raman results to results obtained from inductively coupled plasma mass spectrometry and Davies–Gray titration. Furthermore, we have identified focus areas for experimentation in future fiscal years. A key result is that the accuracy of using Raman can provide accuracy comparable to destructive analysist techniques, With a well-developed calibration curve, using standards and a large number of samples (more than five samples), uncertainty on the order of 1%–3% is achievable. Given that the uncertainties achieved by Raman spectroscopy were on the order of the uncertainties achieved using ICP-MS, we conclude that with a well-developed procedure Raman spectroscopy can be used to determine uranium concentrations in ADUN solutions for NMC&A applications. The benefits of such an approach are that the time and effort will be less than that of comparable destructive analysis techniques, with approximately the same level of technical expertise. This will be attractive to operators of fuel fabrication facilities as it will lower costs and improve efficiencies.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Model Exploration of an Information-Based Healthcare Intervention Using Parallelization and Active Learning

This paper describes the application of a large-scale active learning method to characterize the parameter space of a computational agent-based model developed to investigate the impact of CommunityRx, a clinical information-based health intervention that provides patients with personalized information about local community resources to meet basic and self-care needs. Additionally, the diffusion of information about community resources and their use is modeled via networked interactions and their subsequent effect on agents' use of community resources across an urban population. A random forest model is iteratively fitted to model evaluations to characterize the model parameter space with respect to observed empirical data. We demonstrate the feasibility of using high-performance computing and active learning model exploration techniques to characterize large parameter spaces; by partitioning the parameter space into potentially viable and non-viable regions, we rule out regions of space where simulation output is implausible to observed empirical data. We argue that such methods are necessary to enable model exploration in complex computational models that incorporate increasingly available micro-level behavior data. We provide public access to the model and high-performance computing experimentation code.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

9Cr steel visualization and predictive modeling

The goals of this work were to develop tools to visualize and characterize materials information, to explore new alloy compositions and/or processing, to better predict tensile strength and other mechanical properties. The 9–12 wt% Cr (9Cr) steel test data were compiled from several sources. About 3000 records with 30 predictor variables represent 82 unique steel alloy compositions, variations of thermo-mechanical processing steps and temperatures (homogenization, normalization and tempering cycles), test conditions and outcomes. Detailed data processing steps such as visualization and exploratory analysis, including univariate and bivariate analysis, different clustering techniques used to segment the data, statistical post-hoc analysis to verify significance of the findings, feature engineering to identify predictors of importance, and predictive modeling with cross-validation were performed. The outcome of analysis at each step was reviewed in the context of the domain knowledge of this class of steel, to see if there were underlying physical mechanisms that explain statistical relationships. Data analytics techniques and their parameters were fine-tuned to facilitate interpretation of the results as aligned with the insights from domain experts. Here, the ensemble predictive modeling using Random Forest regressor and post-hoc means comparison corroborated domain knowledge on the role of Co which is known to increase strength of steel alloys through solid-solution strengthening and to affect diffusion of alloying elements and precipitates. Alloys with Co content of 0.7–8 wt% had significantly higher mean strength than the ones without (while having Cr locked within 10–11 wt% range in either group and keeping other compositional element ratios fixed). End products of the computational techniques exploration are presented as the tools that can be used in iterative workflow of materials development and testing.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Fermilab Booster loss modelling and rebalancing using Bayesian methods

Fermilab Booster is being upgraded for the PIP-II project to support 20Hz ramp rate at higher intensities. Loss trip limits determine the achievable peak power. To meet PIP-II requirements, losses need to be halved as compared to current levels. Losses primarily occur at injection and transition crossing, with both gradually increasing and threshold-like intensity-dependent behaviors. The existing simulation models are not yet good enough for quantitative loss predictions. In practice, it will be necessary to tune up the Booster using iterative methods and operator intuition. In this paper we present an effort to systematically model Booster losses using active learning (Bayesian exploration) techniques, and subsequently to rebalance them for higher trip limit margins. We first created several sets of spatially and temporally isolated orbit and optics knobs, and trained Gaussian process models for each beam loss monitor as well as beam current. This is a complex task due to safety and timing requirements – we discuss mitigations such as uncertainty constraints and approximate fitting. Once models are stable, we perform large-scale single and multi-objective tuning using scalarized objectives made up of critical beam loss locations. Our results demonstrate significant rebalancing of losses, increasing trip margins, as well as an overall improvement in beam transmission efficiency. We are exploring how to combine existing simulations with experimental data and automate the collection procedure so that more advanced surrogate models can be created over time.

Kuklev, Nikita [Fermilab]↗
DOE OSTI2023

Using Dark Fiber and Distributed Acoustic Sensing to Characterize a Geothermal System in the Imperial Valley, Southern California

The Imperial Valley, CA, is a tectonically active transtensional basin located south of the Salton Sea; the area hosts numerous geothermal fields, including significant hidden hydrothermal resources without surface manifestations. Development of inexpensive, rugged, and highly sensitive exploration techniques for undiscovered geothermal systems is critical for accelerating geothermal power deployment as well as unlocking a low-carbon energy future. We present a case study utilizing distributed acoustic sensing (DAS) and ambient noise interferometry for geothermal reservoir imaging, utilizing unlit fiber-optic telecommunication infrastructure (dark fiber). The study exploits two days of passive DAS data acquired in early November 2020 over a ~28-km section of fiber from Calipatria, CA to Imperial, CA. We apply ambient noise interferometry to retrieve coherent signals from DAS records and develop a bin stacking technique to attenuate the effects from persistent localized noise sources and to enhance retrieval of coherent surface waves. As a result, we are able to obtain high-resolution two-dimensional (2D) S wave velocity ($V_s$) structure to 3 km depth, based on joint inversion of both the fundamental and higher overtones. We observe a previously unmapped high $V_s$ and low $V_p$/$V_s$ ratio feature beneath the Brawley geothermal system, which we interpret to be a zone of hydrothermal mineralization and lower porosity. This interpretation is consistent with a host of other measurements including surface heat flow, gravity anomalies, and available borehole wireline data. These results demonstrate the potential utility of DAS deployed on dark fiber for geothermal system exploration and characterization in the appropriate geological settings.

Vp/Vs imaging↗
DOE OSTI2023

Methods for rapid identification of anomalous layers in laser powder bed fusion

In situ process monitoring is a key requirement for increased industry acceptance of powder bed Additive Manufacturing. As sensing technologies increase in maturity, attention must also be given to effective data exploration techniques. These data are often high-resolution and multi-modal, with each build consisting of thousands of layers. Here, in this paper, the authors propose two methods enabling users to rapidly identify layers of interest within a build. Both methods leverage results from deep learning based segmentations of in situ powder bed images. The first method is an unsupervised “reverse layer search” algorithm while the second method uses supervised machine learning.

36 MATERIALS SCIENCE↗