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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 1,423 records · Page 79
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Parametric Cost Modeling of a Mid-Lift-to-Drag Ratio Vehicle for Human Mars Entry, Descent, and Landing

Cost is one of the biggest obstacles to sending humans to Mars. However, spacecraft costs are typically estimated after the preliminary vehicle and mission concepts have been designed. By automating the cost estimation process, the effect of any change in vehicle or mission design on the mission cost can be determined more rapidly. This paper uses the tool SAPE-C (Systems Analysis for Planetary Entry, Descent, and Landing-Cost). SAPE-C integrates the cost modeling software SEER-H (System Estimation and Evaluation of Resources-Hardware) with a number of systems analysis tools and enables large design tradespace exploration early in the design process. SAPE-C is used to analyze several tradespaces of the Mid-Lift-to-Drag ratio rigid entry vehicle for human Mars missions. Key findings include quantifying the impacts of payload, vehicle length, and main engine specific impulse on the overall cost of the vehicle, and how the payload per lander and number of landers affects the cost of a campaign to Mars.

Paul D Friz↗
NASA NTRSDate not supplied

The problem of partial gravity on the Moon and Mars

Astronauts who spend many weeks or months in space in zero g suffer serious health problems including muscle atrophy, cardiovascular deconditioning, bone calcium loss, impaired vision, and immune system change. The debilitating effects of weightlessness were first demonstrated on the early Skylab, Salyut, and Mir missions, but it was too optimistically hoped that in-flight exercise and resistance training could prevent these problems. Similar problems are anticipated in the partial gravity of the Moon and Mars. Partial gravity exposure below 0.4 g seems too low to maintain musculoskeletal and cardiopulmonary conditioning in the long term. Some studies show a strong correlation between heart rate, oxygen consumption, net metabolic rate and simulated gravity from 0 to 1 g. Exposure to Moon and Mars gravities probably will cause less severe physiological deconditioning than experienced in 0 g, but the benefit of partial gravity seems likely to be roughly proportional to the 1/6 or 1/3 gravity experienced. As in 0 g, exercise countermeasures seem necessary but insufficient to preserve all physiological systems to a 1 g standard.

Harry W Jones↗
NASA NTRS2017

Dawn at Ceres: The First Exploration of the First Dwarf Planet

Dawn has conducted an extensive exploration of dwarf planet Ceres, the largest object between the Sun and Pluto that had not previously been visited by a spacecraft. Following its arrival at Ceres in March 2015, Dawn acquired all the planned data from four circular polar orbits ranging in altitude from 13,600 km to 385 km. After the successful conclusion of its primary mission in June 2016, Dawn's mission was extended, and new investigations, not previously considered, were conducted from three new orbits. The mission has provided a uniquely detailed view of the first dwarf planet discovered. The overall strategy for exploring Ceres was based strongly on the extremely successful 16 months of Vesta operations. Nevertheless, the loss of two of the spacecraft's four reaction wheels before arrival at Ceres necessitated some important changes in order to conserve hydrazine. These changes were so effective that Dawn has been able to operate beyond the expected end of life, acquiring significantly more data than planned. This paper describes Ceres operations in the primary and extended missions as well as some of the major findings there.

Rayman, Marc D.↗
NASA NTRS2019

Estuarine Dissolved Organic Carbon Flux From Space: With Application to Chesapeake and Delaware Bays

This study uses a neural network model trained with in situ data, combined with satellite data and hydrodynamic model products, to compute the daily estuarine export of dissolved organic carbon (DOC) at the mouths of Chesapeake Bay (CB) and Delaware Bay (DB) from 2007 to 2011. Both bays show large flux variability with highest fluxes in spring and lowest in fall as well as interannual flux variability (0.18 and 0.27 Tg C/year in 2008 and 2010 for CB; 0.04 and 0.09 Tg C/year in 2008 and 2011 for DB). Based on previous estimates of total organic carbon (TOCexp) exported by all Mid-Atlantic Bight estuaries (1.2 Tg C/year), the DOC export (CB + DB) of 0.3 Tg C/year estimated here corresponds to 25% of the TOCexp. Spatial and temporal covariations of velocity and DOC concentration provide contributions to the flux, with larger spatial influence. Differences in the discharge of fresh water into the bays (74 billion cu. m/year for CB and 21 billion cu. m/year for DB) and their geomorphologies are major drivers of the differences in DOC fluxes for these two systems. Terrestrial DOC inputs are similar to the export of DOC at the bay mouths at annual and longer time scales but diverge significantly at shorter time scales (days to months). Future efforts will expand to the Mid-Atlantic Bight and Gulf of Maine, and its major rivers and estuaries, in combination with coupled terrestrial-estuarine-ocean biogeochemical models that include effects of climate change, such as warming and CO2 increase.

Estuarine DOC export↗
NASA NTRSDate not supplied

The Partial Gravity of the Moon and Mars Appears Insufficient to Maintain Human Health

Astronauts who spend many weeks or months in space in microgravity suffer serious health problems including muscle atrophy, cardiovascular deconditioning, bone calcium loss, impaired vision, and immune system changes. The debilitating effects of weightlessness were first demonstrated on the early Skylab, Salyut, and Mir missions, but it was then hoped that countermeasures including in-flight exercise and resistance training could reduce most of these problems. Similar effects are anticipated in the partial gravity of the Moon and Mars. Direct evidence of the long-term effects of partial gravity on humans is not yet available, but indirect evidence suggests that partial gravity exposure below 0.4 g will be insufficient to maintain musculoskeletal and cardiopulmonary conditioning over the long term. Some studies show a strong correlation between heart rate, oxygen consumption, net metabolic rate, and simulated gravity from 0 to 1 g. Exposure to moon and Mars gravities will probably cause less severe physiological deconditioning than microgravity, but the benefit of partial gravity seems likely to be roughly proportional to the level of gravity experienced. As in microgravity, exercise countermeasures seem useful but insufficient to preserve all physiological systems as they would be in Earth gravity.

Harry W. Jones↗
NASA NTRSDate not supplied

GeoNEX-ML: A Machine Learning System for Geostationary Satellite Imagery

Improved capabilities of earth monitoring satellites are enabling a wide range of studies on the environmental effects of climate change, often leveraging the recent advancements in machine learning. At the same time, the new capabilities, including higher spatial resolution and temporal frequency, are expanding the amount of data generated at exponential rates. Further, a large majority of archived datasets generated by scientific processing is never used. This motivates the development of an efficient machine learning system for end-to-end processing of multi-level satellite datasets, from level 1 top of atmosphere observations to user friendly environmental variables of interest. Using current generation geostationary satellites GOES-16/17 (NOAA/NASA), Himawari-8/9 (JAXA), and GK-2A (Korea), we present an interchangeable set of machine models to perform spectral adjustment, physical model emulation, LEO-GEO emulation, and optical flow in a high performance computing environment. We use these tools to generate consistent virtual observations across sensors, perform atmospheric correction and cloud detection, and estimate land surface temperature and atmospheric winds. This approach aims to improve the robustness of remotely sensed data processing by learning from diverse sets of observations while enabling near real-time and on-demand capabilities.

Geostationary satellites↗
NASA NTRSDate not supplied

GeoNEX-ML: A Machine Learning System for Earth Observations

Improved capabilities of earth monitoring satellites are enabling a wide range of studies on the environmental effects of climate change, often leveraging the recent advancements in machine learning. At the same time, the new capabilities, including higher spatial resolution and temporal frequency, are expanding the amount of data generated at exponential rates. At the NASA Earth eXchange (NEX), we build deep learning methods to learn from cross sensor satellite-based Earth observations for generating new datasets with efficient processing techniques. Using current generation geostationary satellites on NEX, we present an interchangeable set of machine models to perform spectral adjustment, physical model emulation, LEO-GEO emulation, and optical flow. These tools are used to generate consistent virtual observations across sensors, perform atmospheric correction and cloud detection, and estimate land surface temperature and atmospheric winds. This approach aims to improve the robustness of remotely sensed data processing by learning from diverse sets of observations while enabling near real-time and on-demand capabilities.

Geostationary↗
NASA NTRSDate not supplied

Deep Learning System for Efficient Processing of Geostationary Satellite Imagery

Improved capabilities of Earth monitoring satellites are enabling a wide range of studies on the environmental effects of climate change, often leveraging the recent advancements in machine learning. At the same time, the new capabilities, including higher spatial resolution and temporal frequency, are expanding the amount of data generated at exponential rates. Further, a large majority of archived datasets generated by scientific processing is never used. This motivates the development of an efficient machine learning system for end-to-end processing of multi-level satellite datasets, from level 1 top of atmosphere observations to user friendly environmental variables of interest. Using current generation geostationary satellites GOES-16/17 (NOAA/NASA), and Himawari-8/9 (JAXA), we present an interchangeable set of machine models to perform spectral adjustment among sensors, physical model emulation, LEO-GEO emulation, and optical flow in a high performance computing environment. We use these tools on the NASA Earth eXchange (NEX) to generate consistent virtual observations across sensors, perform atmospheric correction and cloud detection, and estimate surface reflectance, surface temperature and atmospheric winds. This approach aims to improve the robustness of remotely sensed data processing by learning from diverse sets of observations while enabling near real-time and on-demand capabilities.

Thomas Vandal↗
NASA NTRSDate not supplied

Atmospheric winds with deep optical flow

Improved capabilities of earth monitoring satellites are enabling a wide range of studies on the environmental effects of climate change, often leveraging the recent advancements in machine learning. At the same time, the new capabilities, including higher spatial resolution and temporal frequency, are expanding the amount of data generated at exponential rates. At the NASA Earth eXchange (NEX), we build deep learning methods to learn from cross sensor satellite-based Earth observations for generating new datasets with efficient processing techniques. Using current generation geostationary satellites on NEX, we present an interchangeable set of machine models to perform spectral adjustment, physical model emulation, LEO-GEO emulation, and optical flow. These tools are used to generate consistent virtual observations across sensors, perform atmospheric correction and cloud detection, and estimate land surface temperature and atmospheric winds. This approach aims to improve the robustness of remotely sensed data processing by learning from diverse sets of observations while enabling near real-time and on-demand capabilities.

Atmospheric winds↗
NASA NTRSDate not supplied

Modeling Autogenous Pressurization and Draining of a Cryogenic Storage Tank in Normal Gravity

A two-phase CFD model for autogenous pressurization and draining of a cryogenic storage tank is presented using both the Sharp Interface and Volume-Of-Fluid (VOF) approaches for capturing the front and the associated interfacial heat, mass and momentum transfer between the liquid and vapor regions. Both models are validated against data provided by the Cryogenic Propellant Storage and Transfer (CPST) Engineering Development Unit (EDU) experiment. The results of the autogenous pressurization are presented first, focusing on the phase change and turbulence effects on the tank pressure and temperature predictions. Both the Sharp Interface (SI-CFD) and VOF (VOF-CFD) multiphase models predict tank pressure during pressurization within 3% of the measured values. The sensitivity of key physical and numerical parameters of the problem are tested using the Sharp Interface model. Effects of the accommodation coefficient (AC), and the computational grid structure are studied. The second part of this paper is devoted to validating the SI-VOF model, with an enhanced capability of moving the liquid-vapor interface, against the draining segment of the EDU experiment. The VOF-CFD model was also used to simulate tank draining and its results were compared with the results of the Sharp Interface model with the moving interface. Both models predict tank pressure during draining within 3.5% of the measured values. Pressure decrease rate is underpredicted by both models during the first 100 seconds of draining but matches the experimental rate for the rest of the simulation.

Computational Fluid Dynamics↗
NASA NTRSDate not supplied

Modeling Autogenous Pressurization and Draining of a Cryogenic Storage Tank in Normal Gravity

A two-phase CFD model for autogenous pressurization and draining of a cryogenic storage tank is presented using both the Sharp Interface and Volume-Of-Fluid (VOF) approaches for capturing the front and the associated interfacial heat, mass and momentum transfer between the liquid and vapor regions. Both models are validated against data provided by the Cryogenic Propellant Storage and Transfer (CPST) Engineering Development Unit (EDU) experiment1. The results of the autogenous pressurization are presented first, focusing on the phase change and turbulence effects on the tank pressure and temperature predictions. Both the Sharp Interface (SI-CFD) and VOF (VOF-CFD) multiphase models predict tank pressure during pressurization within 3% of the measured values. The sensitivity of key physical and numerical parameters of the problem are tested using the Sharp Interface model. Effects of the accommodation coefficient (AC), and the computational grid structure are studied. The second part of this paper is devoted to validating the SI-VOF model, with an enhanced capability of moving the liquid-vapor interface, against the draining segment of the EDU experiment. The VOF-CFD model was also used to simulate tank draining and its results were compared with the results of the Sharp Interface model with the moving interface. Both models predict tank pressure during draining within 3.5% of the measured values. Pressure decrease rate is underpredicted by both models during the first 100 seconds of draining but matches the experimental rate for the rest of the simulation.

Computational Fluid Dynamics↗
NASA NTRSDate not supplied

LASSIE: Legged Autonomous Surface Science In Analogue Environments

Roving planetary surface exploration missions operate using a pre-programmed plan that can limit the capability to effectively detect unexpected changes in terrain properties, adjust locomotion or sampling strategies, and autonomously identify scientifically valuable observations and adjust exploration strategies. In particular, the inability to measure and react to unexpected changes in regolith properties can negatively impact mission operations. The Mars Exploration Rover Spirit and InSight lander both experienced challenges related to understanding the geotechnical properties of regolith. The advancements in legged robotic platforms hold potential to address these challenges through greater sensitivity to changing surface properties. The Legged Autonomous Surface Science In Analogue Environments (LASSIE) project explores how legged roving platforms can use the leg motors to measure geotechnical properties of crusted and icy surface regolith and utilize those measurements to autonomously update the science operations plans. These tests will be performed in martian and lunar analog environments and also within lab settings.

K. R. Fisher↗
NASA NTRS2018

Hydraulic Diversity of Forests Regulates Ecosystem Resilience During Drought

Plants influence the atmosphere through fluxes of carbon, water and energy, and can intensify drought through land–atmosphere feedback effects. The diversity of plant functional traits in forests, especially physiological traits related to water (hydraulic) transport, may have a critical role in land–atmosphere feedback, particularly during drought. Here we combine 352 site-years of eddy covariance measurements from 40 forest sites, remote-sensing observations of plant water content and plant functional-trait data to test whether the diversity in plant traits affects the response of the ecosystem to drought. We find evidence that higher hydraulic diversity buffers variation in ecosystem flux during dry periods across temperate and boreal forests. Hydraulic traits were the predominant significant predictors of cross-site patterns in drought response. By contrast, standard leaf and wood traits, such as specific leaf area and wood density, had little explanatory power. Our results demonstrate that diversity in the hydraulic traits of trees mediates ecosystem resilience to drought and is likely to have an important role in future ecosystem–atmosphere feedback effects in a changing climate.

Climate and Earth system modelling↗
NASA NTRSDate not supplied

Thermophysical Property Measurements of Indium Iodide Crystals

InI single crystals are a promising room temperature detector material for X-rays and γ-rays. To improve crystal growth of the material by simulations, knowledge of thermophysical properties is essential, and since InI is orthorhombic, the anisotropy has to be taken into account. The temperature dependence of several thermophysical properties have been measured for InI, including the anisotropic thermal expansion, specific heat, and the thermal diffusivity in the b direction. The anisotropic thermal expansion coefficients, determined by X-ray diffraction, were α11=1.03·10-5 K-1, α22= 3.77·10-5 K-1, and α33 = 6.26·10-5 K-1. The specific heat, measured by DSC, was 0.226 J g-1 K-1 at 335K, with a temperature dependence of 9.582·10-5 J g-1 K-2. In the course of the X-ray diffraction and DSC measurements, it was shown that supposed phase changes, reported in older literature, are actually not phase changes but oxidation effects. The thermal diffusivity in the b direction, measured by the Xenon Flash method, was 0.288·10-6 m2s-1 at RT, decreasing to 0.253·10-6 m2 s-1 at 450K. In addition, the volume increase upon melting and the thermal expansion of the melt have been determined.

Martin Volz↗
NASA NTRS2024

GRACE and GRACE Follow-On Gravity Observations of Intermediate-Depth Earthquakes Contrasted with Those of Shallow Events

Earthquakes involve mass redistribution within the solid Earth and the ocean, and as a result, perturb the Earth's gravitational field. For most of the shallow (<60 km) earthquakes with Mw > 8.0, the GRACE satellite gravity measurements suggest considerable volumetric disturbance of rocks. At a spatial scale of hundreds of km, the effect of volumetric change exceeds gravity change by vertical deformation; for example, negative gravity anomalies associated with volumetric expansion are characteristic patterns after shallow thrust events. In this study, however, we report contrasting observations of gravity change from two intermediate-depth (100–150 km) earthquakes of 2016 & 2017 Mw 8.0 (two combined) Papua New Guinea thrust faulting events and 2019 Mw 8.0 Peru normal faulting and highlight the importance of compressibility in earthquake deformation. The combined 2016/17 thrust events resulted in a positive gravity anomaly of 5–6 microGal around the epicenter, while the 2019 normal faulting produced a negative gravity anomaly of 3–4 microGal. Our modeling found that these gravity changes are manifestation of vertical deformation with limited volumetric change, distinct from gravity changes after the shallow earthquakes. The stronger resistance of rocks to volume change at intermediate-depth results in largely incompressible deformation and thus in a gravity change dominated by vertical deformation. In addition, malleable rocks under high pressure and temperature at depth facilitated substantial afterslip and/or fast viscoelastic relaxation causing additional vertical deformation and gravity change equivalent to the coseismic change. For the Papua New Guinea events, this means that postseismic relaxation enhanced coseismic uplift and relative sea level decrease.

Shin-Chan Han↗
NASA NTRSDate not supplied

Utilizing Strain Rate Jump Testing to Predict Flow Formability of Al Alloys Sensitive to Portevin-Le Chatelier Instabilities

Predicting the flow formability of Al alloys is challenging due to the incremental deformation, complex loading path, and wide range of strain rates characteristic of the process. State-of-the-art research efforts have primarily focused on formability metrics derived from ambient temperature, constant strain rate uniaxial tensile testing. However, Al alloys in formable tempers tend to exhibit serrated Portevin-Le Chatelier (PLC) flow. The appearance of PLC instabilities implies a negative strain rate sensitivity, loss of tensile area reduction capacity, and decreased formability. Elevated temperature and interrupted strain rate tensile tests of relevant Al alloys were conducted to investigate the effects of rapid changes in strain rate (analogous to a forming increment by a passing roller) on common formability metrics. Emphasis was placed on the character/extent of PLC instabilities using digital image correlation strain measurements. The outcome of this work highlights the possibility of predicting flow formability using strain rate jump testing.

flow forming↗
NASA NTRSDate not supplied

A Neural Network Parametrization of Volumetric Cloud Fraction Profiles Using Satellite Observations and MERRA-2 Reanalysis Meteorological Data

Clouds play a crucial role in regulating the hydrologic cycle and Earth's radiative energy budget, yet they are often poorly represented in global climate models (GCMs). This study applies deep machine learning techniques to develop a physical parameterization of volumetric cloud fraction (VCF), the fraction of a 3-D grid volume occupied by clouds using satellite lidar-radar measurements. The neural network (NN) captures the complicated relationships between observed VCF profiles and collocated meteorological variables from MERRA-2 reanalysis data. Our results show that the NN model, particularly a sequence-to-sequence long short-term memory (LSTM) network with a sixfactor loss function, effectively learns the underlying cloud physical processes. The NN model outperforms MERRA-2 reanalysis in representing low-level clouds in tropical and subtropical regions and low- and middle-level clouds over midlatitude storm-track regions, and also improves VCF histograms. These improvements are reflected in the vertical distributions of zonally, meridionally, and globally averaged VCFs, geographic distributions of low-, middle-, and high-level clouds, and seasonal variations in monthly-mean VCF. Furthermore, the NN predictions effectively capture the El Niño-Southern Oscillation (ENSO) effects and other interannual variations. The NN parameterization is further evaluated through a sensitivity analysis, in which a single predictor is perturbed at a time. This reveals that relative humidity (RH) is the dominant factor influencing variations in globally averaged VCF at low and middle altitudes, followed by temperature. At higher altitudes, temperature becomes the primary driver of VCF through its effect on RH. Changes in wind components had minimal impact on globally averaged VCF.

Shan Zeng↗
NASA NTRS1981

Influence of nonequilibrium radiation and shape change on aerothermal environment of a Jovian entry body

The influence of nonequilibrium radiative energy transfer and the effect of probe configuration changes on the flow phenomena around a Jovian entry body are investigated. The radiating shock layer flow is assumed to be axisymmetric, viscous, laminar and in chemical equilibrium. The radiative transfer equations are derived under nonequilibrium conditions which include multilevel energy transitions. The equilibrium radiative transfer analysis is performed with an existing nongray radiation model which accounts for molecular band, atomic line, and continuum transitions. The nonequilibrium results are obtained with and without ablation injection in the shock layer. The nonequilibrium results are found to be greatly influenced by the temperature distribution in the shock layer. In the absence of ablative products, the convective and radiative heating to the entry body are reduced under nonequilibrium conditions. The influence of nonequilibrium is found to be greater at higher entry altitudes. With coupled ablation and carbon phenolic injection, 16 chemical species are used in the ablation layer for radiation absorption. Equilibrium and nonequilibrium results are compared under peak heating conditions.

Tiwari, S. N.↗