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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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Light on Mars shows potential for providing the energy means necessary for enhanced In-Situ Resource Utilization (ISRU). Through photocatalysis, the energy barrier required to convert CO2 is lowered and CH4 production is favorable.
Here, we describe the expansion and enhancement of a large (surface area ≥100 sq. km) lake and reservoir database (1.GREALD). These efforts have also resulted in the spin-off of two additional databases, one containing lakes and reservoirs (2.GREALD, area 10–99 sq. km), and the other containing ephemeral lakes (3.GELD, area ≥100 sq. km). These databases are unique sources for projects that utilize satellite radar altimeter data to monitor surface water levels. While 1.GREALD aims to be a complete catalog, 2.GREALD focuses on reservoirs in response to applied sciences programs that monitor water and energy resources. The creation of 3.GELD has climate change objectives as well as water resources and ecosystem conservation applications. The recording of information pertaining to the potential overpasses (waterbody crossings) of the current and archive satellite altimeters is a primary objective as is the need to highlight any form of controlled water level variation. The permanent water databases now contain 6282 entries, half experience some form of anthropogenic influence and ∼430 have been identified as potential climatically sensitive terminal lakes. The revised integral surface area distribution is a power law with exponent −1.016. Statistics reveal that with altimetric repeat visit times of 10-day to monthly, at least 80% of the permanent water bodies (≥10 sq. km) have been overflown at some period since the 1990s. Current information on water use and reservoir formation date show that the primary use of the reservoir class is hydroelectric power, and that China, Brazil, India, Turkey, and Vietnam dominate the dam building in recent decades.
In the near-future, new space telescopes like JWST, LUVOIR, and HabEx will begin attempting to explore the properties of atmospheres of potentially habitable planets. This will require a significant amount of time and resources for even a single planet, which makes it essential to prioritize observations by those most-likely to have detectable life. Here we present a statistical method to estimate the probabilities that specific exoplanets have been continuously in the habitable zone of their host stars for more than 2 billion years, the approximate time it took life on Earth to significantly increase the oxygen content of the atmosphere. We introduce the use of statistics of an ensemble of 3D planetary general circulation models to estimate these probabilities, replacing prior 1D model estimates.
In the near-future, new space telescopes like JWST, LUVOIR, and HabEx will begin attempting to explore the properties of atmospheres of potentially habitable planets. This will require a significant amount of time and resources for even a single planet, which makes it essential to prioritize observations by those most-likely to have detectable life. Here we present a statistical method to estimate the probabilities that specific exoplanets have been continuously in the habitable zone of their host stars for more than 2 billion years, the approximate time it took life on Earth to significantly increase the oxygen content of the atmosphere. We introduce the use of statistics of an ensemble of 3D planetary general circulation models to estimate these probabilities, replacing prior 1D model estimates.
Spaceflight operations pose unique challenges to crew health, safety, and resource management. As space agencies and private companies continue to push the boundaries of human exploration, it is essential to understand the risks associated with Extravehicular Activities (EVAs) and develop strategies to mitigate them. The tempo at which EVAs are conducted – the total number and frequency of these activities – can have a profound impact on medical risks, resource consumption, and overall mission success. Probabilistic risk assessment (PRA) provides a powerful framework for evaluating complex systems and identifying potential hazards. Our work employs the Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) [1] to simulate mission events, occurrence and treatment of medical conditions, and track the utilization of resources. Coupled with the Evidence Library [2], a medical evidence base for exploration-class missions developed by the Exploration Medical Capability within NASA’s Human Research Program, we can estimate these risks with increased fidelity and optimize medical kit contents to meet specific mission requirements. This presentation provides a detailed examination of how EVA tempo influences medical risk estimates for a lunar surface design reference mission. A comprehensive analysis is conducted to assess the additional mass and volume burden imposed on medical kits required to maintain adequate levels of risk mitigation. Furthermore, we estimate the distribution of the number of successful EVAs completed based on the level of task impairment imposed by medical events and flight rules related to specific medical events, such as decompression sickness.
The 9500 Node Test System is a representative section of distribution power system model developed as a part of the GridAPPS-D™ project, an effort funded by DOE as a part of the Grid Modernization Lab Consortium (GMLC) program. The test system was developed to fulfill a growing need to represent the rapidly evolving state of electric distribution systems by combining elements of legacy infrastructure systems, modern feeder topologies, and an anticipated future with smart grid technologies. It also provides a network model capable of supporting the simulation of operational scenarios such as the ones in a utility distribution control center. This test system allows the evaluation of the performance of advanced power applications in real-time, such as one that simulates the operations of an Advanced Distribution Management Systems (ADMS), Distributed Energy Resource Management Systems (DERMS), etc. in a Distribution control center. This model is an extension of the widely used IEEE 8500 Node Test Feeder and is currently being validated to become an IEEE test case to help increase adoption and widespread usage among both academia and industry. It is a full-size model representative of a section of a utility’s distribution system with multiple feeders fed from different substations. The model includes multiple distribution circuits, a sub-transmission system, multiple substations, behind the meter customer rooftop photovoltaics (PV), and multiple utility-scale distributed energy resources. To enable accurate simulations of operational scenarios, the 9500 Node Test System is designed to support procedure-based operations, with the ability to realistically demonstrate switching operations, feeder reconfiguration, adjustment of volt-var control equipment, dispatch of distributed generation, and response to planned and unplanned outages. The 9500 Node Test System includes three radial distribution feeders with 12.3 MVA of average load, consisting of both medium voltage and low voltage equipment each supplied by a different distribution substation. The three distribution feeders are connected to each other through Normally-Open switches which can be closed when needed to simulate restoration scenarios due to a fault. One feeder represents today’s grid with low penetration of customer-side renewables. The second represents a potential future grid with microgrids and 100% renewable penetration. The third has no customer generation resources, a district steam plant, and a utility-scale solar farm. The three diverse circuits were created to allow the simulation of both today’s situation as well as potential future scenarios. All three feeders have customers connected by low-voltage secondary triplex lines. This test system meets all requirements outlined in the report for creating a simulation environment that would enable discussion between key technical stakeholders as well as having the potential to accelerate operational application development and their subsequent testing and integration. The new model is a possible representation of what we believe the distribution grid may look like in the future: a high penetration of renewables, reconfigurable radial and mesh topology, numerous DERs, islanded microgrids, and significantly increased data and measurement density. The system supports both the solution of existing and newer algorithms but also enables the evaluation of applications in a realistic operational environment defined by task oriented procedural steps that represent the interaction between the control center operator and field personnel.
The International Offshore Geologic Carbon Storage Story Map provides an overview of offshore geologic carbon storage (GCS) project maturity on a global scale. This story map reviews the history of CO2 storage in offshore sedimentary basins, basic geologic requirements, project timelines and lessons learned through defined project stage progression, areas previously studied for potential future domestic offshore GCS development, and regulatory considerations within different global regions. This resource is a distillation of information collected and recorded in the Offshore Geologic Carbon Storage Inventory Version 1.0 (https://edx.netl.doe.gov/dataset/offshore-gcs-data-inventory).
Robotic surface missions to the Moon should be capable of measuring mineral as well as chemical abundances in regolith samples. Although much is already known about the lunar regolith, our data are far from comprehensive. Most of the regolith samples returned to Earth for analysis had lost the upper surface, or it was intermixed with deeper regolith. This upper surface is the part of the regolith most recently exposed to the solar wind; as such it will be important to resource assessment. In addition, it may be far easier to mine and process the uppermost few centimeters of regolith over a broad area than to engage in deep excavation of a smaller area. The most direct means of analyzing the regolith surface will be by studies in situ. In addition, the analysis of the impact-origin regolith surfaces, the Fe-rich glasses of mare pyroclastic deposits, are of resource interest, but are inadequately known; none of the extensive surface-exposed pyroclastic deposits of the Moon have been systematically sampled, although we know something about such deposits from the Apollo 17 site. Because of the potential importance of pyroclastic deposits, methods to quantify glass as well as mineral abundances will be important to resource evaluation. Combined x ray diffraction (XRD) and x ray fluorescence (XRF) analysis will address many resource characterization problems on the Moon. XRF methods are valuable for obtaining full major-element abundances with high precision. Such data, collected in parallel with quantitative mineralogy, permit unambiguous determination of both mineral and chemical abundances where concentrations are high enough to be of resource grade. Collection of both XRD and XRF data from a single sample provides simultaneous chemical and mineralogic information. These data can be used to correlate quantitative chemistry and mineralogy as a set of simultaneous linear equations, the solution of which can lead to full characterization of the sample. The use of Rietveld methods for XRD data analysis can provide a powerful tool for quantitative mineralogy and for obtaining crystallographic data on complex minerals.
Space Toxicology is a specialized discipline for spaceflight, space habitation and occupation of celestial bodies including planets, moons and asteroids [1]. Astronaut explorers face unique challenges to their health while working and living with limited resources for rescue and medical care during space operation. At its core the practice of space toxicology to identify, assess and predict potential chemical contaminants and limit the astronaut s exposure to these environmental factors in order to protect crew health. Space toxicologists are also charged with setting safe exposure limits that will protect the astronaut against a multitude of chemical exposures, in a physiologically altered state. In order to maintain sustained occupation in space, toxicological risks are gauged and managed within the context of isolation, continual exposures, reuse of air and water, limited rescue options, and the necessary use of highly toxic compounds required for propulsion. As the space program move towards human presence and exploration other celestial bodies in situ toxicological risks, such as inhalation of unusual and/or reactive mineral dusts must also be analyzed and controlled. Placing humans for long-term presence in space creates several problems and challenges to the long-term health of the crew, such as bone-loss and immunological challenges and has spurred research into acute, chronic and episodic exposure of the pulmonary system to mineral dusts [2]. NASA has demonstrated that lunar soil contains several types of reactive dusts, including an extremely fine respirable component. In order to protect astronaut health, NASA is now investigating the toxicity of this unique class of dusts. Understanding how these reactive components behave "biochemically" in a moisture-rich pulmonary environment will aid in determining how toxic these particles are to humans. The data obtained from toxicological examination of lunar dusts will determine the human risk criteria for lunar dust exposure and produce a lunar health standard.
Securing decarbonized economies for energy and commodities will require abundant and widely available green H 2 . Ubiquitous wastewaters and nontraditional water sources could potentially feed water electrolyzers to produce this green hydrogen without competing with drinking water sources. Herein, we show that the energy and costs of treating nontraditional water sources such as municipal wastewater, industrial and resource extraction wastewater, and seawater are negligible with respect to those for water electrolysis. We also illustrate that the potential hydrogen energy that could be mined from these sources is vast. Based on these findings, we evaluate the implications of small-scale, distributed water electrolysis using disperse nontraditional water sources. Techno-economic analysis and life cycle analysis reveal that the significant contribution of H 2 transportation to costs and CO 2 emissions results in an optimal levelized cost of hydrogen at small- to moderate-scale water electrolyzer size. The implications of utilizing nontraditional water sources and decentralized or stranded renewable energy for distributed water electrolysis are highlighted for several hydrogen energy storage and chemical feedstock applications. Finally, we discuss challenges and opportunities for mining H 2 from nontraditional water sources to achieve resilient and sustainable economies for water and energy.
Active forest management is applied in many parts of the western United States to reduce wildfire severity, mitigate vulnerability to drought and bark beetle mortality, and more recently, to increase snow retention and late-season streamflow. A rapidly warming climate accelerates the need for these restorative treatments, but the treatment priority among forest patches varies considerably. We simulated four treatment scenarios across the 3,450 km 2 Wenatchee River basin in eastern Washington, United States. We used a decision support tool (DST) to assess trade-offs and synergies within and among treatments on wildfire risk and smoke emissions, water yield and snow retention, biomass production, and economic return. Treatment scenarios emphasized prescribed burning ( BurnOnly ), biomass production ( MaxBiomass ), gap-based thinning to optimize water yield ( IdealWater ), and a principle-based restoration scenario ( RA1 ). Fire hazard, smoke emissions, and biomass production metrics were evaluated across scenarios using the Forest Vegetation Simulator, and water yields were modeled using the Distributed Hydrology Soil Vegetation Model. Simulations were summarized to both patch- (10 1 –10 2 ha) and subwatershed- (10 3 –10 4 ha) scales, and treatment effects were evaluated against an untreated baseline landscape. We used logic models to rank effect sizes by scenario across metrics along a continuum between −1 (no or weak effect) to +1 (large effect). All treatments produced benefits across one or more ecosystem services and led to synergistic benefits to water yield and wildfire hazard reduction. Tradeoffs among resource benefits were clear in wilderness where reliance on prescribed burning without mechanical treatment increased costs and eliminated the potential for biomass recovery. The BurnOnly scenario improved fire risk metrics and streamflow, but effect sizes were lower compared to other treatments. IdealWater showed the strongest benefits overall, demonstrating the ability to capture multiple resource benefits through spatially explicit thinning. Our study provides a framework for integrating strategic and tactical models that evaluate tradeoffs and synergies gained through varied management approaches. We demonstrate the utility of decision support modeling to enhance management synergies across large landscapes.
Abstract The aquatic Lemnaceae family, commonly called duckweed, comprises some of the smallest and fastest growing angiosperms known on Earth. Their tiny size, rapid growth by clonal propagation, and facile uptake of labeled compounds from the media were attractive features that made them a well-known model for plant biology from 1950 to 1990. Interest in duckweed has steadily regained momentum over the past decade, driven in part by the growing need to identify alternative plants from traditional agricultural crops that can help tackle urgent societal challenges, such as climate change and rapid population expansion. Propelled by rapid advances in genomic technologies, recent studies with duckweed again highlight the potential of these small plants to enable discoveries in diverse fields from ecology to chronobiology. Building on established community resources, duckweed is reemerging as a platform to study plant processes at the systems level and to translate knowledge gained for field deployment to address some of society’s pressing needs. This review details the anatomy, development, physiology, and molecular characteristics of the Lemnaceae to introduce them to the broader plant research community. We highlight recent research enabled by Lemnaceae to demonstrate how these plants can be used for quantitative studies of complex processes and for revealing potentially novel strategies in plant defense and genome maintenance.
NASA is putting emphases on reusable cryogenic systems to enable returning astronauts to the lunar surface, and eventually on to Mars. Such systems will require replenishing cryogens on-orbit via a cryogenic tanker or propellant depot, and potentially on the lunar and Martian surfaces with the utilization of in-situ resources. Replenishing cryogens on-orbit requires a tank-to-tank transfer which presents challenges due to thermal environments and the absence of gravity. If the transfer process is initiated with the receiving tank empty and at atmospheric temperature, it then must be chilled to cryogenic temperature to enable filling. Likewise, the transfer lines and associated hardware must be prechilled to assure liquid is transferred. Since prechilling results in propellant loss, it is preferred to minimize this process in interest of saving propellant mass. To simplify the operations associated with propellant transfer and explore potential mass savings, NASA has been experimenting with a new methodology which uses a Vented Chill/No-Vent Fill (VC/NVF) approach, and enhances the process using a 3-D printed spray injector augmented with a Thermodynamic Vent System (TVS).
Geothermal and concentrating solar power (CSP) technologies typically use heat at different temperatures in their commercial deployments. This enables a technically viable hybridization of a solar topping cycle and a geothermal bottoming cycle at locations where both resources are available. In this article, an underperforming geothermal power system based at Burley, Idaho is used as a baseline to investigate the technical and economic potential of such a hybrid cycle. A direct thermal energy storage (TES) system is also integrated to overcome the intermittency of the solar resource. Design and off-design behavior of key components are modelled to simulate the annual performance of the hybrid system on an hourly basis. The sizing of the solar field and thermal storage is investigated and is seen to significantly impact the annual electricity generation and efficiency. Various cost scenarios for the solar field and thermal energy storage are investigated. An economic metric - levelized cost of electricity (LCOE) - is used to optimize the solar field sizing and TES capacity. The hybrid plant converts the additional solar heat input into additional work with an efficiency of 32.9%. Retrofitting a geothermal plant with solar and eight hours of energy storage can achieve an LCOE of 0.136 $/kWhe in current cost scenarios and 0.081 $/kWhe in a future cost reduction scenario. Although the hybrid system cannot directly compete with current PV systems without batteries, it has an LCOE 32% lower than that of a PV-battery system. This paper provides insights into the research and development of the future grid with a high renewable energy penetration and encourages further study of energy hybridization for improved efficiencies and economics.
The mystery and controversy surrounding the possibility of finding water/ice at the lunar poles of the Moon based on the interpretation of neutron spectrometer data from Lunar Prospector and radar data from Clementine raises questions that both Science and the Human Exploration proponents want answered. From the Science perspective, the determination of lunar volatiles and in particular the increased hydrogen concentration detected at the lunar poles was identified as an important objectives for lunar exploration and understanding the history of the Moon, Sun, and the solar system. From the Human Exploration perspective, the potential for large concentrations of accessible water opens up possibilities for utilizing in-situ resources, known as In-Situ Resource Utilization (ISRU), to implement a sustained and affordable human exploration program of the Moon and beyond through production of propellants, fuel cell reagents, and life support consumables for lunar surface operations and mobility, and Earth-Moon transportation. Both the Science and Human Exploration proponents agree that a mission to the lunar poles to obtain ground truth data is the only means to conclusively answer the questions of whether water/ice exists, how much, what form, and where did it come from. In 2005, NASA initiated the Regolith and Environment Science & Oxygen and Lunar Volatiles Extraction (RESOLVE) project, and is currently developing hardware under the NASA Exploration Technology Development Program (ETDP). The purpose of the project was to begin developing technologies and operations that would answer the fundamental science questions, such as What resources are available on the Moon, where are they, what form, and where did they come from? as well as critical engineering questions, such as How will we mine these resources, what chemical extraction processes are the most practical and efficient, and what are the engineering challenges to be faced in this environment? .
Climate change refers to significant and long-term alterations in the Earth’s climate patterns, typically resulting from human activities that increase greenhouse gas emissions. Addressing climate change is not merely an option but a necessity, demanding creative solutions and efforts from individuals, researchers, communities, and governments. Despite the capabilities of machine learning (ML) with data-driven solutions promising to combat climate change-related problems, they face challenges stemming from traditional computational methods and prolonged training times, impeding their practical utility. Recent strides in quantum computing have permeated diverse domains, spanning from manufacturing engineering and pharmaceutical discovery to the latest frontier of detecting climate anomalies. With the potential to substantially reduce time and computational complexity, quantum computing shows promise in addressing climate change impacts. Its distinctive features will enable the concurrent exploration of expansive solution spaces, making it well-suited for analyzing extensive climate datasets, simulating intricate climate models, optimizing resource allocation, and discerning patterns in climate data for mitigation and adaptation endeavors. This study explores the potential of using Quantum machine learning (QML) techniques on climate and weather data obtained from NASA Giovannis. We used two QML algorithms, the Quantum Support Vector Classifier (QSVC) and the Variational Quantum Classifier (VQC) models, using the IBM Qiskit ML 0.7.2 ecosystem. We used an actual 127-Qubit IBM Quantum Computer (IBM 127-qubit Eagle) in this study. The methodology and results sections describe the experiences gained from applying and evaluating quantum ML results on climate and weather data obtained from NASA satellites as a novel practical application of quantum computing.
Geothermal resources have delivered renewable electricity for more than 100 years, and renewable heat for far longer, but recent research and advancements have shown that geothermal is more than a 24/7 clean energy source. With the ability to also provide cooling and storage - plus the potential to access critical minerals, capture and sequester carbon, produce green hydrogen, and more - geothermal technologies and resources are emerging as key solutions to the climate crisis. In fiscal year 2024 (FY24), the National Renewable Energy Laboratory (NREL) broadened its research, development, demonstration, and deployment (RDD&D) portfolio and partnerships with exciting innovations such as next-generation geothermal technologies for power, geothermal energy networks for heating and cooling, subsurface thermal energy storage, well repurposing, and more. In support of the U.S. Department of Energy (DOE) Geothermal Technologies Office's (GTO) mission to increase geothermal energy deployment through research, development, and demonstration of innovative technologies that enhance exploration and production, NREL led impactful research in geothermal technologies and resources, market acceleration, and grid integration while also demonstrating leadership in the sector and increasing stakeholder engagement and outreach efforts.
Prediction of Worldwide Energy Resources (POWER) project provides irradiance dataset to support renewable energy, building energy efficiency and agricultural needs. These datasets are derived from Global Energy and Water Cycle Experiment Surface Radiation Budget (GEWEX SRB) and Clouds and the Earth’s Radiant Energy System (CERES SYN1Deg). A systematic bias has been reported between these two datasets for the years with overlapping observations. For obtaining a consistent climate data record spanning the entire time record of observations, it is crucial to understand and remove the bias in the irradiance dataset. Inconsistency in solar radiation data can lead to inaccurate conclusions about solar energy potential and obscure real trends in solar radiation patterns that would impact energy availability assessments. In this study, we adapt quantile mapping approach to remove the systematic bias and to improve reliability of shortwave and longwave irradiance data. We present a validation of the bias corrected data against ground truth. For each 1° latitude and 1° longitude grid box across the globe, we match the CDFs of the reference dataset (CERES SYN1Deg) to that of the SRB dataset, thereby, adjusting the irradiance values to match the empirical distribution of two different measurements. The performance of quantile mapping is evaluated by using the metrics such as Mean Absolute Deviation (MAD). The results indicate that the quantile mapping significantly improves the accuracy and reliability of solar irradiance dataset especially for the weather conditions associated with high cloud cover and extreme irradiance values. The initial range of MAD for the studied sites for daily data was 4 to 19 Wm-2. After correction these reduced to 3 to 7 Wm-2. The findings from this study have important implications for solar energy system design, agricultural planning, and climate modeling community. Reducing the inconsistency and biases in solar irradiance dataset can enable better planning and operation of solar energy systems, leading to increased efficiency and cost-effectiveness. Additionally, this work also contributes to the statistical post-processing techniques in the renewable energy domain and highlights the potential of historical and near-real-time NASA POWER dataset as a valuable resource for solar energy research and applications.