Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “exploration risk”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Assessing Low-Temperature Geothermal Play Types: Relevant Data and Play Fairway Analysis Methods

This data catalog contains information on low temperature geothermal play types. The U.S. Department of Energy (DOE) Geothermal Technologies Office (GTO) supports the Geothermal Heating and Cooling Geospatial Datasets and Analysis project, conducted by the National Renewable Energy Laboratory (NREL). This project is part of a broader effort to demonstrate the multifaceted value of integrating geothermal power and geothermal heating and cooling technologies into national decarbonization strategies and community energy plans. There is a need to establish baseline low-temperature geothermal resource data sets and evaluate methods for deploying these technologies. This project aims to reduce exploration risk of low temperature geothermal systems by collecting baseline datasets that can be used for Play Fairway Analysis methodologies. This data catalog contains links to publicly available datasets from different sources that can be relevant for the low temperature geothermal systems. This submission contains data catalogs for Alaska, Hawaii, and the Conterminous United States, as well as a technical report on the methods used to classify and asses the geothermal play types.

15 GEOTHERMAL ENERGY↗
NASA NTRS2010

LCROSS - Lunar Impactor: Pioneering Risk-Tolerant Exploration in Search for Water on the Moon

The Lunar CRater Observation and Sensing Satellite (LCROSS) was launched with the Lunar Reconnaissance Orbiter (LRO) on June 18, 2009 to determine the presence of water-ice in a permanently shadowed crater on the south pole of the Moon. However, an equally important purpose was to pioneer low-cost, quick-turnaround NASA missions that could accept a higher-than-normal-level of technical risk. When the LCROSS mission proposal was competitively selected by the NASA Exploration Systems Mission Directorate to design, build, and launch a spacecraft in 31 months with a $79M cost-capped budget and a fixed mass allocation, NASA Ames Research Center and its industry partner, Northrop-Grumman, needed a game-changing approach to be successful. That approach was a ground-breaking combination of having a risk-tolerant NASA Class D mission status and finding the right balance point between the inflexible elements of cost and schedule and the newly-flexible element of technical capability.

Andrews, Daniel R.↗
NASA NTRS2020

Chemical Reactivity of In-Situ Lunar Dust

Is in-situ lunar dust less, the same, or more chemically reactivity reactive than the Apollo samples currently stored in curation facilities here on Earth? This question has been debated extensively across the community of scientists that have focused on lunar dust biotoxicity. A definitive answer will further mitigate exploration risks to future human explorers on the Moon and will provide useful information for astrobiologists and space biologists as well.

Lunar Dust↗
NASA NTRS2021

Chemical Reactivity of In-Situ Lunar Dust for Biotoxicity Assessment

How does the chemical reactivity of in-situ lunar dust compare to Apollo samples currently stored in curation facilities here on Earth? Essential investigations of this question will help us to further mitigate exploration risks for future human explorers on the Moon and will also provide critical information for astrobiologists and space biologists using the Moon for scientific inquiry. Apollo 14 dust biotoxicity studies, carried out by the NASA Lunar Airborne Dust Toxicity Assessment Group (LADTAG), included numerous cellular and animal experiments. Intratracheal instillation and inhalation studies in rats both showed Apollo 14 dust to be intermediate in toxicity compared to low-tox titanium dusts and high-tox quartz dusts of similar particle sizes. The collective results were used in models to establish a safe exposure limit for astronauts. Although LADTAG took extensive steps to preserve what chemical reactivity may still have existed in the samples, it is simply unknown if they possessed true in-situ chemical reactivity or if that reactivity has decayed. Initial gas loss on collection and other alterations, and even intermittent exposure to Earth-normal conditions during subsequent decades of handling, obscure a forensic reconstruction of the initial state. Because a mineral dust’s chemical reactivity influences its biotoxicity, researchers have developed methods to “activate” lunar dust and simulants. Past studies that modeled impact processes and radiation in the lunar environment suggest that in-situ lunar dust is likely to be more chemically reactive than Earth-exposed samples. Because of these results, in-situ measurements are warranted. Since the lunar surface is heterogeneous, dust biotoxicity is expected to vary from site to site due to particle size, mineralogy, physical characteristics, degree of space weathering, and chemical reactivity. This circumstance dictates dust assessments at a suite of lunar sites enabled by CLPS opportunities. Dose, location, and duration of particle exposure will also affect biological responses. In-situ chemical reactivity measurements can inform cross-cutting collaborative research campaigns such as astrobiology studies examining regolith interactions with organisms and its ability to preserve chemical and structural biomarkers, as well as space biology investigations that examine regolith-microbe interactions relating to life support systems, plant growth, biomining, and development of regolith biocomposites.

Jon C Rask↗
NASA NTRSDate not supplied

Chemical Reactivity of In-Situ Lunar Dust for Biotoxicity Assessment

Introduction: How does the chemical reactivity of in-situ lunar dust compare to Apollo samples currently stored in curation facilities here on Earth? Essential investigations of this question will help us to further mitigate exploration risks for future human explorers on the Moon and will also provide critical information for astrobiologists and space biologists using the Moon for scientific inquiry. Discussion: Apollo 14 dust biotoxicity studies, carried out by the NASA Lunar Airborne Dust Toxici-ty Assessment Group (LADTAG), included numerous physiochemical studies[1] and cellular and animal ex-periments. Intratracheal instillation [2] and inhalation studies [3] in rats both showed Apollo 14 dust to be intermediate in toxicity compared to low-tox titanium dusts and high-tox quartz dusts of similar particle siz-es. The collective results were used in models [4] to establish a safe exposure limit for astronauts [5]. Alt-hough LADTAG took extensive steps to preserve what chemical reactivity may still have existed in the sam-ples, it is simply unknown if they possessed true in-situ chemical reactivity or if that reactivity has de-cayed. Initial gas loss on collection and other altera-tions, and even intermittent exposure to Earth-normal conditions during subsequent decades of handling, obscure a forensic reconstruction of the initial state. Because a mineral dust’s chemical reactivity influ-ences its biotoxicity [6], researchers have developed methods to “activate” lunar dust and simulants [7][8]. Past studies that modeled impact processes and radia-tion [9] in the lunar environment suggest that in-situ lunar dust is likely to be more chemically reactive than Earth-exposed samples. Because of these results, in-situ measurements are warranted [10]. Other studies have examined the hydroxyl generating capability of iron bearing mineral phases [11][12] and further em-phasize the role iron plays in chemical reactivity of lunar material, as well as decay of chemical reactivity in mineral dusts [12]. Recent observations of the lunar surface reveal the presence of hematite [13], a finding that further supports the hypothesis that in-situ lunar dust is reactive. Since the lunar surface is heterogene-ous, dust biotoxicity is expected to vary from site to site [14] due to particle size, mineralogy, physical characteristics, degree of space weathering, and chemi-cal reactivity (Figure 1). This circumstance dictates dust assessments at a suite of lunar sites enabled by upcoming NASA and commercial lunar payload ser-vices (CLPS) opportunities. Dose, location, and dura-tion of particle exposure will also affect biological responses. In-situ chemical reactivity measurements can inform cross-cutting collaborative research cam-paigns such as astrobiology studies examining regolith interactions with organisms and its ability to preserve chemical and structural biomarkers, as well as space biology investigations that examine regolith-microbe interactions relating to life support systems, plant growth, biomining, and development of regolith bio-composites. Figure 1: Environment conditions on the lunar surface that may alter regolith reactivity. Summary A series of in-situ measurements of lu-nar dust free radical chemistry at future Artemis and CLPS landing sites, combined with LADTAG-like studies of freshly collected lunar dust specimens, will reveal the true chemical reactivity of in-situ lunar dust and generate scientific data that can be compared to the chemical reactivity and biotoxicity of samples from Apollo landing sites. Furthermore, results from in situ measurements and biotoxicity studies of freshly col-lected specimens can also be used to validate, or re-quire revision of, the current astronaut permissible exposure limit [15]. References: [1] McKay D et al (2015), Acta As-tronaut 107:163–176. [2] Rask J et al (2013), LPSC, p 3062. [3] Lam CW et al (2013), Inhal Toxicol 25:661–678. [4] James JT, et. al. (2013) , Inhal Toxicol 25:243–256. [5] Scully RR, et.al. (2013), Inhal Toxi-col 25:785–793. [6] Porter, D. W., et.al., (2002), Tox-icology 175, 63–71. [7] Wallace WT, et.al., (2009), Meteorit Planet Sci 44:961–970. [8] Wallace WT, et.al., (2010), Earth Planet Sci Lett 295:571–577. [9] Loftus D, Rask J, et.al., (2010), Earth Moon Planet 107:95–105. [10] Rask J, et.al., (2009) LEAG p 57. [11] Turci F, et.a., (2015), Astrobiology. 2015;15(5):371-380. [12] Hendrix DA, et.al., (2019), Geohealth. 2019;3(1):28-42. [13] Li, S., et.al., (2020), Science advances, 6(36), p.eaba1940. [14] Rask J. (2018), In: Cudnik B. (eds) Encyclopedia of Lunar Science. Springer, Cham. [15] Rask, J, (2020), LPI, Artemis III Sci. def. paper 2120.

chemical reactivity↗
DOE OSTI2022

Nontechnical Barriers to Geothermal Development

Geothermal energy presents a significant opportunity for the United States (US). The US has the largest known geothermal resource in the world, with over 31 GW of conventional geothermal (i.e., hydrothermal) potential. Despite this, the development of geothermal power plants has lagged other renewable resources. Though some of the gaps stem from technical barriers numerous non-technical barriers are preventing geothermal energy from reaching its full potential. The gaps include a need to reduce the cost impacts of seismic risk, environmental risk, exploration, drilling and permitting cost risk, and reduced summer capacity on plant profitability. This report identifies pathways to overcome these barriers. We find that geothermal energy could increase its market presence by acting as a complement to lower cost renewables, providing flexible or baseload power in low carbon scenarios. We also outline several contractual and operational strategies (including the use of hybrid systems) that plant operators can pursue to improve the value of their resource, such as multi part remuneration mechanisms that guarantee revenue (e.g., availability payments or a Contract for Differences approach) or resource risk hedging approaches (e.g., shaped market products and portfolio resource approaches).

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Risk Assessment Considerations for Underground Hydrogen Storage in Depleted Gas Reservoirs

Underground hydrogen storage (UHS) in depleted reservoirs presents a promising solution for large-scale energy storage as hydrogen demand grows. As the UHS industry emerges, robust risk assessments are critical to ensuring safe operation of the storage facilities and minimizing the risk of accidents. This work explores risk assessment protocols for underground natural gas storage (UGS) in depleted reservoirs and identifies key considerations for repurposing these facilities for UHS. By examining the differences in physical and chemical properties between hydrogen and natural gas, this work highlights new and modified hazards that merits a reevaluation of traditional natural gas risk assessment practices. This investigation synthesizes insights from previous literature reviews and interviews with UGS industry experts and operators to identify key areas for adapting risk assessment methodologies for hydrogen. The insights gleaned from expert interviews indicate that existing risk assessment standards are non-prescriptive, leading to diverse company-specific risk assessment methodologies requiring substantial additions to become practical. Due to a lack of concrete risk assessment requirements, experience, and relevant data, this uncertainty is expected to be magnified considerably when considering hydrogen. This study identifies several areas for potential modification of existing risk assessment practices that could be considered by those developing standards or performing risk assessments for UHS. Specifically, risk assessments may be improved by including risks unique to hydrogen in existing standards, changing the magnitude of different risk factors in existing risk assessment protocols, and improving methods of data collection and communication across the industry to address large areas of uncertainty.

08 HYDROGEN↗
NASA NTRS2001

Integrating Public Perspectives in Sample Return Planning

Planning for extraterrestrial sample returns, whether from Mars or other solar system bodies, must be done in a way that integrates planetary protection concerns with the usual mission technical and scientific considerations. Understanding and addressing legitimate societal concerns about the possible risks of sample return will be a critical part of the public decision making process ahead. This paper presents the results of two studies, one with lay audiences, the other with expert microbiologists, designed to gather information, on attitudes and concerns about sample return risks and planetary protection. Focus group interviews with lay subjects, using generic information about Mars sample return and a preliminary environmental impact assessment, were designed to obtain an indication of how the factual content is perceived and understood by the public. A research survey of microbiologists gathered information on experts' views and attitudes about sample return, risk management approaches and space exploration risks. These findings, combined with earlier research results on risk perception, will be useful in identifying levels of concern and potential conflicts in understanding between experts and the public about sample return risks. The information will be helpful in guiding development of the environmental impact statement and also has applicability to proposals for sample return from other solar system bodies where scientific uncertainty about extraterrestrial life may persist at the time of mission planning.

Race, Margaret S.↗
DOE OSTI2019

Deep Direct-Use Feasibility Study Numerical Modeling and Uncertainty Analysis using iTOUGH2 for West Virginia University

To reduce the geothermal exploration risk, a feasibility study is performed for a deep direct-use system proposed at the West Virginia University (WVU) Morgantown campus. This study applies numerical simulations to investigate reservoir impedance and thermal production. Because of the great depth of the geothermal reservoir, few data are available to characterize reservoir features and properties. As a result, the study focuses on the following three aspects: 1. model choice for predicting reservoir impedance and thermal breakthrough: after investigating three potential models (one single permeability model and two dual permeability models) for flow through fractured rock, it is decided to use single permeability model for further analysis; 2. well placement (horizontal vs. vertical) options: horizontal well placement seems to be more robust to heterogeneity and the impedance is more acceptable; 3. Prediction uncertainty: the most influential parameters are identified using a First-Order-Second-Moment uncertainty propagation analysis, and the uncertain range of the model predictions is estimated by performing a Monte Carlo simulation. Heterogeneity has a large impact on the perdition, therefore, is considered in the predictive model and uncertainty analysis. The numerical model results and uncertainty analysis are used for economic analysis. The dataset submitted here support the described study. Manuscript is submitted to Geothermics, will be linked once paper is accepted.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

DEEPEN Global Standardized Categorical Exploration Datasets for Magmatic Plays

DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments. As part of the development of the DEEPEN 3D play fairway analysis (PFA) methodology for magmatic plays (conventional hydrothermal, superhot EGS, and supercritical), weights needed to be developed for use in the weighted sum of the different favorability index models produced from geoscientific exploration datasets. This was done using two different approaches: one based on expert opinions, and one based on statistical learning. This GDR submission includes the datasets used to produce the statistical learning-based weights. While expert opinions allow us to include more nuanced information in the weights, expert opinions are subject to human bias. Data-centric or statistical approaches help to overcome these potential human biases by focusing on and drawing conclusions from the data alone. The drawback is that, to apply these types of approaches, a dataset is needed. Therefore, we attempted to build comprehensive standardized datasets mapping anomalies in each exploration dataset to each component of each play. This data was gathered through a literature review focused on magmatic hydrothermal plays along with well-characterized areas where superhot or supercritical conditions are thought to exist. Datasets were assembled for all three play types, but the hydrothermal dataset is the least complete due to its relatively low priority. For each known or assumed resource, the dataset states what anomaly in each exploration dataset is associated with each component of the system. The data is only a semi-quantitative, where values are either high, medium, or low, relative to background levels. In addition, the dataset has significant gaps, as not every possible exploration dataset has been collected and analyzed at every known or suspected geothermal resource area, in the context of all possible play types. The following training sites were used to assemble this dataset: - Conventional magmatic hydrothermal: Akutan (from AK PFA), Oregon Cascades PFA, Glass Buttes OR, Mauna Kea (from HI PFA), Lanai (from HI PFA), Mt St Helens Shear Zone (from WA PFA), Wind River Valley (From WA PFA), Mount Baker (from WA PFA). - Superhot EGS: Newberry (EGS demonstration project), Coso (EGS demonstration project), Geysers (EGS demonstration project), Eastern Snake River Plain (EGS demonstration project), Utah FORGE, Larderello, Kakkonda, Taupo Volcanic Zone, Acoculco, Krafla. - Supercritical: Coso, Geysers, Salton Sea, Larderello, Los Humeros, Taupo Volcanic Zone, Krafla, Reyjanes, Hengill. **Disclaimer: Treat the supercritical fluid anomalies with skepticism. They are based on assumptions due to the general lack of confirmed supercritical fluid encounters and samples at the sites included in this dataset, at the time of assembling the dataset. The main assumption was that the supercritical fluid in a given geothermal system has shared properties with the hydrothermal fluid, which may not be the case in reality. Once the datasets were assembled, principal component analysis (PCA) was applied to each. PCA is an unsupervised statistical learning technique, meaning that labels are not required on the data, that summarized the directions of variance in the data. This approach was chosen because our labels are not certain, i.e., we do not know with 100% confidence that superhot resources exist at all the assumed positive areas. We also do not have data for any known non-geothermal areas, meaning that it would be challenging to apply a supervised learning technique. In order to generate weights from the PCA, an analysis of the PCA loading values was conducted. PCA loading values represent how much a feature is contributing to each principal component, and therefore the overall variance in the data.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

DEEPEN 3D PFA Weights for Exploration Datasets in Magmatic Environments

DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments. As part of the development of the DEEPEN 3D play fairway analysis (PFA) methodology for magmatic plays (conventional hydrothermal, superhot EGS, and supercritical), weights needed to be developed for use in the weighted sum of the different favorability index models produced from geoscientific exploration datasets. This GDR submission includes those weights. The weighting was done using two different approaches: one based on expert opinions, and one based on statistical learning. The weights are intended to describe how useful a particular exploration method is for imaging each component of each play type. They may be adjusted based on the characteristics of the resource under investigation, knowledge of the quality of the dataset, or simply to reduce the impact a single dataset has on the resulting outputs. Within the DEEPEN PFA, separate sets of weights are produced for each component of each play type, since exploration methods hold different levels of importance for detecting each play component, within each play type. The weights for conventional hydrothermal systems were based on the average of the normalized weights used in the DOE-funded PFA projects that were focused on magmatic plays. This decision was made because conventional hydrothermal plays are already well-studied and understood, and therefore it is logical to use existing weights where possible. In contrast, a true PFA has never been applied to superhot EGS or supercritical plays, meaning that exploration methods have never been weighted in terms of their utility in imaging the components of these plays. To produce weights for superhot EGS and supercritical plays, two different approaches were used: one based on expert opinion and the analytical hierarchy process (AHP), and another using a statistical approach based on principal component analysis (PCA). The weights are intended to provide standardized sets of weights for each play type in all magmatic geothermal systems. Two different approaches were used to investigate whether a more data-centric approach might allow new insights into the datasets, and also to analyze how different weighting approaches impact the outcomes. The expert/AHP approach involved using an online tool (https://bpmsg.com/ahp/) with built-in forms to make pairwise comparisons which are used to rank exploration methods against one-another. The inputs are then combined in a quantitative way, ultimately producing a set of consensus-based weights. To minimize the burden on each individual participant, the forms were completed in group discussions. While the group setting means that there is potential for some opinions to outweigh others, it also provides a venue for conversation to take place, in theory leading the group to a more robust consensus then what can be achieved on an individual basis. This exercise was done with two separate groups: one consisting of U.S.-based experts, and one consisting of Iceland-based experts in magmatic geothermal systems. The two sets of weights were then averaged to produce what we will from here on refer to as the "expert opinion-based weights," or "expert weights" for short. While expert opinions allow us to include more nuanced information in the weights, expert opinions are subject to human bias. Data-centric or statistical approaches help to overcome these potential human biases by focusing on and drawing conclusions from the data alone. More information on this approach along with the dataset used to produce the statistical weights may be found in the linked dataset below.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

DEEPEN 3D PFA Index Models for Exploration Datasets at Newberry Volcano

DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments. As part of the development of the DEEPEN 3D play fairway analysis (PFA) methodology for magmatic plays (conventional hydrothermal, superhot EGS, and supercritical), index models needed to be developed to map values in geoscientific exploration datasets to favorability index values. This GDR submission includes those index models. Index models were created by binning values in exploration datasets into chunks based on their favorability, and then applying a number between 0 and 5 to each chunk, where 0 represents very unfavorable data values and 5 represents very favorable data values. To account for differences in how exploration methods are used to detect each play component, separate index models are produced for each exploration method for each component of each play type. Index models were created using histograms of the distributions of each exploration dataset in combination with literature and input from experts about what combinations of geophysical, geological, and geochemical signatures are considered favorable at Newberry. This is in attempt to create similar sized bins based on the current understanding of how different anomalies map to favorable areas for the different types of geothermal plays (i.e., conventional hydrothermal, superhot EGS, and supercritical). For example, an area of partial melt would likely appear as an area of low density, high conductivity, low vp, and high vp/vs. This means that these target anomalies would be given high (4 or 5) index values for the purpose of imaging the heat source. To account for differences in how exploration methods are used to detect each play component, separate index models are produced for each exploration method for each component of each play type. Index models were produced for the following datasets: - Geologic model - Alteration model - vp/vs - vp - vs - Temperature model - Seismicity (density*magnitude) - Density - Resistivity - Fault distance - Earthquake cutoff depth model

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

DEEPEN Leapfrog Geodata Model Cleaned and Reformatted Exploration Datasets from Newberry Volcano

DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments. As part of the DEEPEN 3D play fairway analysis (PFA) conducted at Newberry Volcano for multiple play types (conventional hydrothermal, superhot EGS, and supercritical), existing geoscientific exploration datasets needed to be acquired, cleaned, reformatted, and assembled in Leapfrog Geothermal. This GDR submission includes all of the cleaned and reformatted (X (m), Y (m), elevation (m), processed data values) datasets used to build the Leapfrog Geodata model. Existing datasets were acquired from the GDR, from AltaRock, and from other sources. This yielded the following datasets: - Digital elevation model produced from LiDAR data by Ramsey and Bard, 2016 - MT surveys from 2006, 2011, 2014, and 2017 (including single inversions) - Gravity surveys from 2006, 2007, and 2011 (including single) - Earthquake catalogs from PNSN, LLNL, and the Newberry EGS Demonstration project - Seismic velocity model from Templeton et al., 2014 - The Frone, 2015 temperature model and a new one produced through extrapolating downhole temperature measurements and the SMU temperature at depth maps. Two versions of the new model are provided: 250 m spacing and 500 m spacing - EarthVision geologic model with alteration from Moser et al., 2016 - Well data from EGS well 55-29, deep geothermal wells, coreholes (GEO N-2 through 5) and several thermal gradient holes - "Newberry Well Data:" Location, simple lithology, directional survey data, and temperature data for the 34 wells and coreholes used in the Newberry PFA Although there are additional 2D datasets available in the area, such as aeromagnetic surveys, these were not included in the analysis. While it may be possible to project these datasets into three dimensions by assuming the surface measurements do not vary with depth, this method is associated with high uncertainty. Preexisting inversions of these data were unavailable, and inverting additional geophysical datasets is outside the scope of this project.

15 GEOTHERMAL ENERGY↗