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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
NASA NTRS2015

Atmospheric Mining in the Outer Solar System: Aerial Vehicle Mission and Design Issues

Atmospheric mining in the outer solar system has been investigated as a means of fuel production for high energy propulsion and power. Fusion fuels such as Helium 3 (3He) and deuterium can be wrested from the atmospheres of Uranus and Neptune and either returned to Earth or used in-situ for energy production. Helium 3 and deuterium were the primary gases of interest with hydrogen being the primary propellant for nuclear thermal solid core and gas core rocket-based atmospheric flight. A series of analyses were undertaken to investigate resource capturing aspects of atmospheric mining in the outer solar system. This included the gas capturing rate, storage options, and different methods of direct use of the captured gases. While capturing 3He, large amounts of hydrogen and 4He are produced. With these two additional gases, the potential for fueling small and large fleets of additional exploration and exploitation vehicles exists. The mining aerospacecraft (ASC) could fly through the outer planet atmospheres, for global weather observations, localized storm or other disturbance investigations, wind speed measurements, polar observations, etc. Analyses of orbital transfer vehicles (OTVs), landers, and in-situ resource utilization (ISRU) mining factories are included. Preliminary observations are presented on near-optimal selections of moon base orbital locations, OTV power levels, and OTV and lander rendezvous points.

orbital mechanics↗
NASA NTRS2016

Atmospheric Mining in the Outer Solar System: Outer Planet Orbital Transfer and Lander Analyses

Atmospheric mining in the outer solar system has been investigated as a means of fuel production for high energy propulsion and power. Fusion fuels such as Helium 3 (3He) and deuterium can be wrested from the atmospheres of Uranus and Neptune and either returned to Earth or used in-situ for energy production. Helium 3 and deuterium were the primary gases of interest with hydrogen being the primary propellant for nuclear thermal solid core and gas core rocket-based atmospheric flight. A series of analyses were undertaken to investigate resource capturing aspects of atmospheric mining in the outer solar system. This included the gas capturing rate, storage options, and different methods of direct use of the captured gases. While capturing 3He, large amounts of hydrogen and 4He are produced. Analyses of orbital transfer vehicles (OTVs), landers, and the issues with in-situ resource utilization (ISRU) mining factories are included. Preliminary observations are presented on near-optimal selections of moon base orbital locations, OTV power levels, and OTV and lander rendezvous points. For analyses of round trip OTV flights from Uranus to Miranda or Titania, a 10-Megawatt electric (MWe) OTV power level and a 200-metric ton (MT) lander payload were selected based on a relative short OTV trip time and minimization of the number of lander flights. A similar optimum power level is suggested for OTVs flying from low orbit around Neptune to Thalassa or Triton. Several moon base sites at Uranus and Neptune and the OTV requirements to support them are also addressed.

rocket propulsion↗
NASA NTRS2016

Atmospheric Mining in the Outer Solar System: Outer Planet Orbital Transfer and Lander Analyses

Atmospheric mining in the outer solar system has been investigated as a means of fuel production for high energy propulsion and power. Fusion fuels such as Helium 3 (3He) and deuterium can be wrested from the atmospheres of Uranus and Neptune and either returned to Earth or used in-situ for energy production. Helium 3 and deuterium were the primary gases of interest with hydrogen being the primary propellant for nuclear thermal solid core and gas core rocket-based atmospheric flight. A series of analyses were undertaken to investigate resource capturing aspects of atmospheric mining in the outer solar system. This included the gas capturing rate, storage options, and different methods of direct use of the captured gases. While capturing 3He, large amounts of hydrogen and 4He are produced. Analyses of orbital transfer vehicles (OTVs), landers, and the issues with in-situ resource utilization (ISRU) mining factories are included. Preliminary observations are presented on near-optimal selections of moon base orbital locations, OTV power levels, and OTV and lander rendezvous points. For analyses of round trip OTV flights from Uranus to Miranda or Titania, a 10- Megawatt electric (MWe) OTV power level and a 200 metricton (MT) lander payload were selected based on a relative short OTV trip time and minimization of the number of lander flights. A similar optimum power level is suggested for OTVs flying from low orbit around Neptune to Thalassa or Triton. Several moon base sites at Uranus and Neptune and the OTV requirements to support them are also addressed.

lunar mission design↗
DOE OSTI2010

ADVANCED RADIOISOTOPE HEAT SOURCE AND PROPULSION SYSTEMS FOR PLANETARY EXPLORATION

The exploration of planetary surfaces and atmospheres may be enhanced by increasing the range and mobility of a science platform. Fundamentally, power production and availability of resources are limiting factors that must be considered for all science and exploration missions. A novel power and propulsion system is considered and discussed with reference to a long-range Mars surface exploration mission with in-situ resource utilization. Significance to applications such as sample return missions is also considered. Key material selections for radioisotope encapsulation techniques are presented.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Computational Workflow for Accelerated Molecular Design Using Quantum Chemical Simulations and Deep Learning Models

Efficient methods for searching the chemical space of molecular compounds are needed to automate and accelerate the design of new functional molecules such as pharmaceuticals. Given the high cost in both resources and time for experimental efforts, computational approaches play a key role in guiding the selection of promising molecules for further investigation. Here, we construct a workflow to accelerate design by combining approximate quantum chemical methods [i.e. density-functional tight-binding (DFTB)], a graph convolutional neural network (GCNN) surrogate model for chemical property prediction, and a masked language model (MLM) for molecule generation. Property data from the DFTB calculations are used to train the surrogate model; the surrogate model is used to score candidates generated by the MLM. The surrogate reduces computation time by orders of magnitude compared to the DFTB calculations, enabling an increased search of chemical space. Furthermore, the MLM generates a diverse set of chemical modifications based on pre-training from a large compound library. We utilize the workflow to search for near-infrared photoactive molecules by minimizing the predicted HOMO-LUMO gap as the target property. Our results show that the workflow can generate optimized molecules outside of the original training set, which suggests that iterations of the workflow could be useful for searching vast chemical spaces in a wide range of design problems.

Blanchard, Andrew↗
DOE OSTI2026

On-Detector Machine Learning for Beam-Induced Background Rejection at a 10 TeV Muon Collider

A 10 TeV Muon Collider is a compelling candidate for a future energy-frontier facility, offering unprecedented opportunities to explore the fundamental laws of particle physics. Muon decays in the collider ring produce intense beam-induced background (BIB) that can overwhelm detector occupancy and exceed readout bandwidth constraints. We investigate the potential of on-detector Machine Learning for BIB rejection in the vertex detector, exploiting pixel cluster shapes to distinguish background from collision products. We study three classes of lightweight neural-network architectures, and evaluate their implementation feasibility using high-level synthesis. Selected architectures achieve 88 to 90% data reduction at 99% signal efficiency, while requiring hardware resources compatible with potential ASIC implementation. These results demonstrate the potential of performing substantial BIB rejection directly in the pixel readout, providing a strategy for meeting the tracker readout requirements at a future Muon Collider.

Abadjiev, Daniel [Chicago U.]↗
DOE OSTI2022

Selective recovery of critical materials in zero-liquid discharge supercritical water desalination

Zero-liquid discharge desalination techniques are being actively studied as alternative means to produce drinkable water and recover valuable resources without polluting the environment. Supercritical water desalination is one of such alternatives that can overcome the technical and environmental challenges in standard desalination techniques. In addition to these attractive features, this work explores the possibility of utilizing supercritical water desalination for the selective recovery of strategic (critical) materials as co-products. Here, to validate the technical and economic feasibility of the process, we prepared model brine solutions that consist of sodium, neodymium, and different anions and conducted a series of desalination experiments. The results were analyzed experimentally and theoretically and were utilized to evaluate the economic feasibility of the process. When a few tens or hundreds of parts-per-million critical materials are dissolved in the feed, the proposed method becomes more cost-effective in producing critical materials than existing technologies. In addition, it does not discharge any concentrated contaminants while producing drinkable water. These results suggest that supercritical water desalination can recover critical materials while producing freshwater in an economically feasible fashion.

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

System and method for configuring a channel

An integrated circuit device comprises pin resources, a memory controller circuit, a network interface controller circuit, and transmitter circuitry. The pin resources comprise pads coupled to off-chip pins of the integrated circuit device. The memory controller circuit comprises a first interface and the network interface controller circuit comprises a second interface. The transmitter circuitry is configurable to selectively couple either a first signal of the first interface or a second signal of the second interface to a first pad of the pin resources based on a pin distribution between the first interface and the second interface.

Keckler, Stephen William↗
NASA NTRS1974

Management of natural resources through automatic cartographic inventory

The author has identified the following significant results. Significant correspondence codes relating ERTS imagery to ground truth from vegetation and geology maps have been established. The use of color equidensity and color composite methods for selecting zones of equal densitometric value on ERTS imagery was perfected. Primary interest of temporal color composite is stressed. A chain of transfer operations from ERTS imagery to the automatic mapping of natural resources was developed.

Rey, P. A.↗
DOE OSTI2023

A parallel, distributed memory implementation of the adaptive sampling configuration interaction method

The many-body simulation of quantum systems is an active field of research that involves several different methods targeting various computing platforms. Many methods commonly employed, particularly coupled cluster methods, have been adapted to leverage the latest advances in modern high-performance computing. Selected configuration interaction (sCI) methods have seen extensive usage and development in recent years. However, the development of sCI methods targeting massively parallel resources has been explored only in a few research works. Here, we present a parallel, distributed memory implementation of the adaptive sampling configuration interaction approach (ASCI) for sCI. In particular, we will address the key concerns pertaining to the parallelization of the determinant search and selection, Hamiltonian formation, and the variational eigenvalue calculation for the ASCI method. Load balancing in the search step is achieved through the application of memory-efficient determinant constraints originally developed for the ASCI-PT2 method. The presented benchmarks demonstrate near optimal speedup for ASCI calculations of Cr 2 (24e, 30o) with 10 6 , 10 7 , and 3 × 10 8 variational determinants on up to 16 384 CPUs. Importantly, to the best of the authors’ knowledge, this is the largest variational ASCI calculation to date.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2011

Solar Thermochemical Hydrogen Production Research (STCH): Thermochemical Cycle Selection and Investment Priority

Eight cycles in a coordinated set of projects for Solar Thermochemical Cycles for Hydrogen production (STCH) were self-evaluated for the DOE-EERE Fuel Cell Technologies Program at a Working Group Meeting on October 8 and 9, 2008. This document reports the initial selection process for development investment in STCH projects, the evaluation process meant to reduce the number of projects as a means to focus resources on development of a few most-likely-to-succeed efforts, the obstacles encountered in project inventory reduction and the outcomes of the evaluation process. Summary technical status of the projects under evaluation is reported and recommendations identified to improve future project planning and selection activities.

08 HYDROGEN↗
DOE OSTI2012

Towards Efficient N - x Contingency Selection Using Group Betweenness Centrality

The goal of N -x contingency selection is to pick a subset of critical cases to assess their potential for causing a severe crippling of an electric power grid. Even for a moderate-sized system there can be an overwhelming number of contingency cases that need to be studied. The number grows exponentially with x. This combinatorial explosion renders any exhaustive search strategy computationally infeasible, even for small to medium sized systems. We propose a novel method for N- x selection for x >1 using group betweenness centrality and show that the amount of computation can be decoupled from the problem size, thus making the contingency analysis for large systems with x > 1 computationally feasible. Consequently, it may be that N - x (for x > 1) contingency selection can be effectively deployed despite the combinatorial explosion of the number of possible N - x contingencies. Our strategy replaces computation with storage of intermediate results and therefore relies on high performance computing resources.

Contingency analysis, Contingency selection, group↗
DOE OSTI2022

Dynamic logistic regression and variable selection: Forecasting and contextualizing civil unrest

Civil unrest can range from peaceful protest to violent furor, and researchers are working to monitor, forecast, and assess such events to allocate resources better. Twitter has become a real-time data source for forecasting civil unrest because millions of people use the platform as a social outlet. Additionally, daily word counts are used as model features, and predictive terms contextualize the reasons for the protest. To forecast civil unrest and infer the reasons for the protest, we consider the problem of Bayesian variable selection for the dynamic logistic regression model and propose using penalized credible regions to select parameters of the updated state vector. This method avoids the need for shrinkage priors, is scalable to high-dimensional dynamic data, and allows the importance of variables to vary in time as new information becomes available. A substantial improvement in both precision and F1-score using this approach is demonstrated through simulation. Finally, we apply the proposed model fitting and variable selection methodology to the problem of forecasting civil unrest in Latin America. Our dynamic logistic regression approach shows improved accuracy compared to the static approach currently used in event prediction and feature selection.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

BioSiting Tool (BioSiting) v2

The BioSiting Tool provides a geospatial interface for analyzing bioeconomy resources and infrastructure across the continental U.S. The tool integrates empirical and modeled data from a broad range of sources. Bioeconomy resources mapped in the tool include agricultural residues, forest residues, municipal solid waste streams, food waste, manure, fats, oils and greases and potential yields of energy crops. Infrastructure mapped in the tool includes biorefineries, material recovery facilities, anaerobic digesters, wastewater treatment plants, combustion plants, district energy systems, crude oil pipelines, petroleum pipelines, natural gas pipelines, railways and freight terminals. Additional data layers include environmental justice indicators at the census tract level and carbon dioxide geologic storage potential. Users can select a location on the map, define a buffer radius in kilometers and generate an inventory of all bioecomony resources within the buffer zone. Data from the tool can be downloaded from individual buffer zones, or at the state or national level.

Huntington, Tyler↗
DOE OSTI2012

Aleutian Pribilof Islands Wind Energy Feasibility Study

Under this project, the Aleutian Pribilof Islands Association (APIA) conducted wind feasibility studies for Adak, False Pass, Nikolski, Sand Point and St. George. The DOE funds were also be used to continue APIA's role as project coordinator, to expand the communication network quality between all participants and with other wind interest groups in the state and to provide continued education and training opportunities for regional participants. This DOE project began 09/01/2005. We completed the economic and technical feasibility studies for Adak. These were funded by the Alaska Energy Authority. Both wind and hydro appear to be viable renewable energy options for Adak. In False Pass the wind resource is generally good but the site has high turbulence. This would require special care with turbine selection and operations. False Pass may be more suitable for a tidal project. APIA is funded to complete a False Pass tidal feasibility study in 2012. Nikolski has superb potential for wind power development with Class 7 wind power density, moderate wind shear, bi-directional winds and low turbulence. APIA secured nearly $\$$1M from the United States Department of Agriculture Rural Utilities Service Assistance to Rural Communities with Extremely High Energy Costs to install a 65kW wind turbine. The measured average power density and wind speed at Sand Point measured at 20m (66ft), are 424 W/m2 and 6.7 m/s (14.9 mph) respectively. Two 500kW Vestas turbines were installed and when fully integrated in 2012 are expected to provide a cost effective and clean source of electricity, reduce overall diesel fuel consumption estimated at 130,000 gallons/year and decrease air emissions associated with the consumption of diesel fuel. St. George Island has a Class 7 wind resource, which is superior for wind power development. The current strategy, led by Alaska Energy Authority, is to upgrade the St. George electrical distribution system and power plant. Avian studies in Nikolski and Sand Point have allowed for proper wind turbine siting without killing birds, especially endangered species and bald eagles. APIA continues coordinating and looking for funding opportunities for regional renewable energy projects. An important goal for APIA has been, and will continue to be, to involve community members with renewable energy projects and energy conservation efforts.

17 WIND ENERGY↗
NASA NTRS2016

Risk of Performance Decrement and Crew Illness Due to an Inadequate Food System

NASA is preparing for long duration manned missions beyond low-Earth orbit that will be challenged in several ways, including long-term exposure to the space environment, impacts to crew physiological and psychological health, limited resources, and no resupply. The food system is one of the most significant daily factors that can be altered to improve human health, and performance during space exploration. Therefore, the paramount importance of determining the methods, technologies, and requirements to provide a safe, nutritious, and acceptable food system that promotes crew health and performance cannot be underestimated. The processed and prepackaged food system is the main source of nutrition to the crew, therefore significant losses in nutrition, either through degradation of nutrients during processing and storage or inadequate food intake due to low acceptability, variety, or usability, may significantly compromise the crew's health and performance. Shelf life studies indicate that key nutrients and quality factors in many space foods degrade to concerning levels within three years, suggesting that food system will not meet the nutrition and acceptability requirements of a long duration mission beyond low-Earth orbit. Likewise, mass and volume evaluations indicate that the current food system is a significant resource burden. Alternative provisioning strategies, such as inclusion of bioregenerative foods, are challenged with resource requirements, and food safety and scarcity concerns. Ensuring provisioning of an adequate food system relies not only upon determining technologies, and requirements for nutrition, quality, and safety, but upon establishing a food system that will support nutritional adequacy, even with individual crew preference and self-selection. In short, the space food system is challenged to maintain safety, nutrition, and acceptability for all phases of an exploration mission within resource constraints. This document presents the evidence for the Risk of Performance Decrement and Crew Illness Due to an Inadequate Food System and the gaps in relation to exploration, as identified by the NASA Human Research Program (HRP). The research reviewed here indicates strategies to establish methods, technologies, and requirements that increase food stability, support adequate nutrition, quality, and variety, enable supplementation with grow-pick-and-eat salad crops, ensure safety, and reduce resource use. Obtaining the evidence to establish an adequate food system is essential, as the resources allocated to the food system may be defined based on the data relating nutritional stability and food quality requirements to crew performance and health.

Douglas, Grace L.↗
DOE OSTI2024

Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are identifying hidden geothermal resources in the USA and designing profitable enhanced geothermal systems (EGS). Many non-obvious processes and parameters could characterize geothermal resources and could control the ultimate energy potential of geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize geothermal resources, but this data is sparse and multi-scale. This has hindered attempts to leverage the datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) give promise to overcome these issues. Modern ML methods and tools can (1) analyze large datasets, (2) assimilate model ensembles that include a multitude of inputs and outputs, (3) process sparse datasets, (4) perform transfer learning between sites with different data quality, (5) extract hidden geothermal signatures from field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. In this work, we implement ML-based geothermal exploration and an enhanced geothermal systems (EGS) design tool to achieve the above goals. Our exploration tool is GeoThermalCloud (GTC) EGS design tool is GeoDT-ML. GTC (github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. It enables the identification of critical measurements needed to identify geothermal resource signatures. GeoDT-ML (github.com/SmartTensors/GeoThermalCloud.jl/tree/master/) adds coupling to GeoDT (https://github.com/GeoDesignTool/GeoDT.git) for stochastic EGS design optimization and performance prediction. GeoDT-ML leverages recent advances in deep learning and high-performance computing. Contributors to this effort include LANL, PNNL, Google, Stanford, and Julia Computing.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Assessment of oil and gas fields in California as potential CO 2 storage sites

California's total annual greenhouse gas (GHG) emissions (425.3 MtCO 2 e) in 2018 were about 6.4% of the US total (6,677 MtCO 2 e) and around 1% of global emissions. About 39% of 2018 GHG emissions in California were from the industrial and electrical sectors. Many of these emissions were from large stationary point sources and were suitable for carbon capture retrofit with subsequent storage of the captured carbon dioxide (CO 2 ) in geological formations. Previous studies of California found suitable geology and CO 2 storage resource. This study refines and furthers prior work using a three-stage screening process of oil fields, gas fields, and underground natural gas storage (UGS) sites by combining criteria from previous studies while excluding sites that pose technical risk or are located in regions with surface restrictions including sensitive habitats and dense populations. In the first stage, 129 CO 2 storage sites in California were identified using qualification criteria based upon formation properties including geological conditions and pore pressure. The second stage identified sensitive sites by applying conservative screens including seismic activity, faulting, population density, restricted lands, and sensitive habitats. During the third stage, 61 CO 2 potential storage sites were identified by subtraction of stage 2 areas from stage 1. The potential storage volume in the third stage ranged from 1.0 to 2.0 GtCO 2 . Finally, we applied a scoring system with seven parameters to rank the 61 potential sites based on subsurface technical criteria. The scored sites are classified as high priority, medium priority, and sites for future study. Prospective CO 2 storage sites with high and moderate priority were selected and linked to CO 2 sources. There are 14 prospective sites (above 20 MtCO 2 storage resource per site) with a total storage resource of 1024 MtCO 2 distributed in Northern and Southern California. Of these sites, there are 9 potential CO 2 -EOR sites and 1 depleted oil field with a total estimated CO 2 storage volume of ~800 MtCO 2 in the Southern San Joaquin and Ventura Basin. These 10 prospective sites with a storage resource greater than 20 MtCO 2 could potentially deliver more than 20 years of storage with an average injection rate of 40 MtCO 2 /year. The remaining 4 highly prospective sites are in Northern California. Additionally, study results suggest that saline formations should be re-evaluated in concert with storage in oil, gas, and natural gas storage reservoirs.

58 GEOSCIENCES↗