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NASA NTRS2011

Auroral Current and Electrodynamics Structure (ACES) Observations of Ionospheric Feedback in the Alfven Resonator

In 2009, the Auroral Current and Electrodynamics Structure (ACES) High and Low sounding rockets were launched from the Poker Flat Rocket Range (PFRR) in Alaska, with the science objective of gathering in-situ data to quantify current closure in a discrete auroral arc. As ACES High crossed through the return current of an arc (that was monitored using an all sky camera from the ground at Fort Yukon), its instruments recorded clear Alfv nic signatures both poleward and equatorward of the return current region, but not within the main region of the return current itself. These data provide an excellent opportunity to study ionospheric feedback and how it interacts with the Alfv n resonator. We compare the observations with predictions and new results from a model of ionospheric feedback in the ionospheric Alfv n resonator (IAR) and report the significance and impact of these new data for the Magnetosphere-Ionosphere Coupling in the Alfv n Resonator (MICA) rocket mission to launch from PFRR this winter. MICA s primary science objectives specifically focus on better understanding the small-scale structure that the model predicts should exist within the return current region.

Cohen, Ian J.↗

NASA NTRS1998

A Conceptual Approach to Assimilating Remote Sensing Data to Improve Soil Moisture Profile Estimates in a Surface Flux/Hydrology Model: Overview - Part 1

Knowledge of the amount of water in the soil is of great importance to many earth science disciplines. Soil moisture is a key variable in controlling the exchange of water and energy between the land surface and the atmosphere. Thus, soil moisture information is valuable in a wide range of applications including weather and climate, runoff potential and flood control, early warning of droughts, irrigation, crop yield forecasting, soil erosion, reservoir management, geotechnical engineering, and water quality. Despite the importance of soil moisture information, widespread and continuous measurements of soil moisture are not possible today. Although many earth surface conditions can be measured from satellites, we still cannot adequately measure soil moisture from space. Research in soil moisture remote sensing began in the mid 1970s shortly after the surge in satellite development. Recent advances in remote sensing have shown that soil moisture can be measured, at least qualitatively, by several methods. Quantitative measurements of moisture in the soil surface layer have been most successful using both passive and active microwave remote sensing, although complications arise from surface roughness and vegetation type and density. Early attempts to measure soil moisture from space-borne microwave instruments were hindered by what is now considered sub-optimal wavelengths (shorter than 5 cm) and the coarse spatial resolution of the measurements. L-band frequencies between 1 and 3 GHz (10-30 cm) have been deemed optimal for detection of soil moisture in the upper few centimeters of soil. The Electronically Steered Thinned Array Radiometer (ESTAR), an aircraft-based instrument operating a 1,4 GHz, has shown great promise for soil moisture determination. Initiatives are underway to develop a similar instrument for space. Existing space-borne synthetic aperture radars (SARS) operating at C- and L-band have also shown some potential to detect surface wetness. The advantage of radar is its much higher resolution than passive microwave systems, but it is currently hampered by surface roughness effects and the lack of a good algorithm based on a single frequency and single polarization. In addition, its repeat frequency is generally low (about 40 days). In the meantime, two new radiometers offer some hope for remote sensing of soil moisture from space. The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), launched in November 1997, possesses a 10.65 GHz channel and the Advanced Microwave Scanning Radiometer (AMSR) on both the ADEOS-11 and Earth Observing System AM-1 platforms to be launched in 1999 possesses a 6.9 GHz channel. Aside from issues about interference from vegetation, the coarse resolution of these data will provide considerable challenges pertaining to their application. The resolution of TMI is about 45 km and that of AMSR is about 70 km. These resolutions are grossly inconsistent with the scale of soil moisture processes and the spatial variability of factors that control soil moisture. Scale disparities such as these are forcing us to rethink how we assimilate data of various scales in hydrologic models. Of particular interest is how to assimilate soil moisture data by reconciling the scale disparity between what we can expect from present and future remote sensing measurements of soil moisture and modeling soil moisture processes. It is because of this disparity between the resolution of space-based sensors and the scale of data needed for capturing the spatial variability of soil moisture and related properties that remote sensing of soil moisture has not met with more widespread success. Within a single footprint of current sensors at the wavelengths optimal for this application, in most cases there is enormous heterogeneity in soil moisture created by differences in landcover, soils and topography, as well as variability in antecedent precipitation. It is difficult to interpret the meaning of 'mean' soil moisture under such conditions and even more difficult to apply such a value. Because of the non-linear relationships between near-surface soil moisture and other variables of interest, such as surface energy fluxes and runoff, mean soil moisture has little applicability at such large scales. It is for these reasons that the use of remote sensing in conjunction with a hydrologic model appears to be of benefit in capturing the complete spatial and temporal structure of soil moisture. This paper is Part I of a four-part series describing a method for intermittently assimilating remotely-sensed soil moisture information to improve performance of a distributed land surface hydrology model. The method, summarized in section II, involves the following components, each of which is detailed in the indicated section of the paper or subsequent papers in this series: Forward radiative transfer model methods (section II and Part IV); Use of a Kalman filter to assimilate remotely-sensed soil moisture estimates with the model profile (section II and Part IV); Application of a soil hydrology model to capture the continuous evolution of the soil moisture profile within and below the root zone (section III); Statistical aggregation techniques (section IV and Part II); Disaggregation techniques using a neural network approach (section IV and Part III); and Maximum likelihood and Bayesian algorithms for inversely solving for the soil moisture profile in the upper few cm (Part IV).

Crosson, William L.↗

NASA NTRS2013

Visualization and Analysis of Multi-scale Land Surface Products via Giovanni Portals

Large volumes of MODIS land data products at multiple spatial resolutions have been integrated into the Giovanni online analysis system to support studies on land cover and land use changes,focused on the Northern Eurasia and Monsoon Asia regions through the LCLUC program. Giovanni (Goddard Interactive Online Visualization ANd aNalysis Infrastructure) is a Web-based application developed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), providing a simple and intuitive way to visualize, analyze, and access Earth science remotely-sensed and modeled data.Customized Giovanni Web portals (Giovanni-NEESPI andGiovanni-MAIRS) have been created to integrate land, atmospheric,cryospheric, and societal products, enabling researchers to do quick exploration and basic analyses of land surface changes, and their relationships to climate, at global and regional scales. This presentation shows a sample Giovanni portal page, lists selected data products in the system, and illustrates potential analyses with imagesand time-series at global and regional scales, focusing on climatology and anomaly analysis. More information is available at the GES DISCMAIRS data support project portal: http:disc.sci.gsfc.nasa.govmairs.

Shen, Suhung↗

NASA NTRSDate not supplied

Examination of L-Band Brightness Temperature Forecasts in the SMAP Level-4 Soil Moisture Analysis

The NASA Soil Moisture Active Passive (SMAP) mission [4] has been providing L-band (1.4 GHz) passive microwave brightness temperature (Tb) observations since April 2015. By assimilating the Tb observations into the NASA Catchment land surface model [5] using a spatially distributed ensemble Kalman filter, the NASA Global Modeling and Assimilation Office generates the SMAP Level-4 Soil Moisture (L4_SM) product, which provides global, 3-hourly, 9-km resolution estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture with ~2.5-day latency for use in research and applications [6]. The L4_SM product also includes estimates of soil temperature, land surface fluxes, and assimilation diagnostics such as the model forecast and observed Tb values [7]. The output from the L4_SM system is routinely monitored by the L4_SM team. Such monitoring provides valuable information; instances of unusually large Tb observation-minus-forecast (O-F) residuals can indicate events during which soil moisture conditions are poorly described in the land modeling system [7,8]. For example, repeated occurrences of very large Tb O-F values in central Australia were traced back to deficiencies in the gauge-based precipitation product used in the land modeling system through L4_SM Version 5; this discovery prompted the use, beginning in Version 6, of satellite- and gauge-based precipitation observations outside of North America [9]. Recently, a statistical analysis of the Tb O-F residuals revealed systematic errors in the “tau-omega” L-band radiative transfer model that converts the land-model simulated soil moisture and temperature into the forecast Tb prior to the L4_SM analysis. The L4_SM analysis is built on the ensemble Kalman filter and assumes unbiased forecast errors. The presence of systematic Tb forecast errors could thus adversely impact the quality of the analyzed soil moisture. In this paper, we examine the Tb O-F residuals of the latest Version 7 L4_SM data (Science Version ID Vv7030 and Vv7032) [10,11,12]. In the L4_SM Version 7 algorithm, key parameters of the L-band radiative transfer model, including soil roughness, scattering albedo, and a (seasonally varying) climatology of vegetation opacity, are obtained from the SMAP Level-2 Radiometer retrieval product (Version 5) [1]. Additionally, we also use ground measurements of surface soil moisture and soil temperature from the SMAP core validation sites as in situ reference of soil conditions [2,3]. As part of the L4_SM system calibration, the seasonally-varying bias between the model forecast Tb and the observed values is removed prior to the assimilation of the SMAP Tb observations [7]. That is, the L4_SM system is designed to only correct errors in synoptic-scale and interannual variations from the long-term mean seasonal cycle while maintaining the model’s (potentially erroneous) climatology. The Tb O-F residuals examined here are thus computed using SMAP Tb observations after they are rescaled to the mean seasonal cycle of Tb from the modeling system without SMAP data assimilation. Consequently, the long-term average of the Tb O-F residuals has a global mean of only 0.13 K and locally small values, ranging from -1 to 3 K. Despite the small time-average values of the Tb O-F residuals, the model forecast Tb was nevertheless found to exhibit undesirable systematic errors. At some locations, the time-average Tb O-F values strongly depend on surface soil moisture (SM). At the Yanco SMAP core validation site, for example, the Tb O-F residuals typically range from 5 to 15 K under dry soil moisture conditions (SM < 0.15 m3 m-3) yet are predominantly negative under wet soil moisture conditions (SM > 0.25 m3 m-3), with values ranging from 0 to -40 K. This results in soil moisture analysis increments that persistently make the soil drier under dry SM conditions and persistently make the soil wetter under wet SM conditions, suggesting an error in the dynamic range of the simulated Tb, soil moisture or soil temperature. In this paper, we describe the higher-order systematic Tb forecast errors in more detail, examine their impact on the L4_SM product quality, and explore potential avenues to further improve the L4_SM algorithm.

Rolf Reichle↗

NASA NTRS2010

Preface: International Reference Ionosphere - Progress in Ionospheric Modelling

The international reference ionosphere (lRI) is the internationally recommended empirical model for the specification of ionospheric parameters supported by the Committee on Space Research (COSPAR) and the International Union of Radio Science (URSI) and recognized by the International Standardization Organization (ISO). IRI is being continually improved by a team of international experts as new data become available and better models are being developed. This issue chronicles the latest phase of model updates as reported during two IRI-related meetings. The first was a special session during the Scientific Assembly of the Committee of Space Research (COSPAR) in Montreal, Canada in July 2008 and the second was an IRI Task Force Activity at the US Air Force Academy in Colorado Springs in May 2009. This work led to several improvements and additions of the model which will be included in the next version, IRI-201O. The issue is divided into three sections focusing on the improvements made in the topside ionosphere, the F-peak, and the lower ionosphere, respectively. This issue would not have been possible without the reviewing efforts of many individuals. Each paper was reviewed by two referees. We thankfully acknowledge the contribution to this issue made by the following reviewers: Jacob Adeniyi, David Altadill, Eduardo Araujo, Feza Arikan, Dieter Bilitza, Jilijana Cander, Bela Fejer, Tamara Gulyaeva, Manuel Hermindez-Pajares, Ivan Kutiev, John MacDougal, Leo McNamara, Bruno Nava, Olivier Obrou, Elijah Oyeyemi, Vadym Paznukhov, Bodo Reinisch, John Retterer, Phil Richards, Gary Sales, J.H. Sastri, Ludger Scherliess, Iwona Stanislavska, Stamir Stankov, Shin-Yi Su, Manlian Zhang, Y ongliang Zhang, and Irina Zakharenkova. We are grateful to Peggy Ann Shea for her final review and guidance as the editor-in-chief for special issues of Advances in Space Research. We thank the authors for their timely submission and their quick response to the reviewer comments and humbly apologize for any delays in the editing process.

Bilitza Dieter↗

NASA NTRSDate not supplied

Physical Oceanography in the Solar System and Beyond

A key controller of a planet’s rotational evolution, and hence habitability, is tidal dissipation, which on Earth occurs primarily in the oceans. As the discovery of habitable exoplanets is a primary objective of exoplanet research, it is imperative that we understand how “exo-oceans” behave. Despite this importance, little research has investigated the physical oceanography of worlds other than Earth. This oversight has occurred even though the Earth science community has studied tidal flows in Earth’s oceans for over a century and developed sophisticated models that exquisitely match satellite altimetry data, e.g. Here, we present a) models of tidal effects on exoplanets to motivate the problem, b) the application of a physical oceanography model to a putative ancient Venus ocean, and c) the application of that model to an ensemble of “alternative Earths” with a range of continental configurations and seafloor properties. We find that oceanic tidal dissipation can span 5 orders of magnitude, revealing that simulating exo-oceans with Earth science tools will provide fundamental insight into exoplanet evolution and habitability

Physical Oceanography↗

NASA NTRS2020

The Atacama Cosmology Telescope: A CMB lensing mass map over 2100 square degrees of sky and its cross-correlation with BOSS-CMASS galaxies

We construct cosmic microwave background lensing mass maps using data from the 2014 and 2015 seasons of observations with the Atacama Cosmology Telescope (ACT). These maps cover 2100 square degrees of sky and overlap with a wide variety of optical surveys. The maps are signal dominated on large scales and have fidelity such that their correlation with the cosmic infrared background is clearly visible by eye. We also create lensing maps with thermal Sunyaev-Zel’dovich contamination removed using a novel cleaning procedure that only slightly degrades the lensing signal-to-noise ratio. The cross-spectrum between the cleaned lensing map and the BOSS CMASS galaxy sample is detected at 10-σ significance, with an amplitude of A = 1.02±0.10 relative to the Planck best-fit LCDM cosmological model with fiducial linear galaxy bias. Our measurement lays the foundation for lensing cross-correlation science with current ACT data and beyond.

gravitational lensing↗

NASA NTRSDate not supplied

Science Workflows using Kamodo

Kamodo is a powerful python software package based on data functionalization. Once a given data set is functionalized, a large variety of capabilities are easily accessible in Kamodo, including unit conversions, custom analysis via function composition, interactive publication quality visualizations, and LaTeX encoding. The entirety of capabilities available in Kamodo are easily applied to both simulated and observed data across the multiple domains of Heliophysics and even in other disciplines. This work includes a variety of science workflows using Kamodo in combination with other resources, including with other python software packages, that expand the utility of Kamodo even further. These workflows include model-data comparisons, ensemble modeling examples, satellite mission planning examples, and other applications, all of which are freely available on CCMC’s Kamodo Github page for the community to adapt to their own uses (https://github.com/nasa/Kamodo). We invite the community to use these workflows and to contribute their own to share.

software↗

NASA NTRSDate not supplied

Science Workflows using Kamodo

python↗

NASA NTRS2002

Great (Flame) Balls of Fire! Structure of Flame Balls at Low Lewis-number-2 (SOFBALL-2)

Everyone knows that an automobile engine wastes fuel and energy when it runs with a fuel-rich mixture. 'Lean' burning, mixing in more air and less fuel, is better for the environment. But lean mixtures also lead to engine misfiring and rough operation. No one knows the ultimate limits for lean operation, for 'weak' combustion that is friendly to the environment while still moving us around. This is where the accidental verification of a decades-old prediction may have strong implications for designing and running low-emissions engines in the 21st century. In 1944, Soviet physicist Yakov Zeldovich predicted that stationary, spherical flames are possible under limited conditions in lean fuel-air mixtures. Dr. Paul Ronney of the University of Southern California accidentally discovered such 'flame balls' in experiments with lean hydrogen-air mixtures in 1984 during drop-tower experiments that provided just 2.2 seconds of near weightlessness. Experiments aboard NASA's low-g aircraft confirmed the results, but a thorough investigation was hampered by the aircraft's bumpy ride. And stable flame balls can only exist in microgravity. The potential for investigating combustion at the limits of flammability, and the implications for spacecraft fire safety, led to the Structure of Flame Balls at Low Lewis-number (SOFBALL) experiment flown twice aboard the Space Shuttle on the Microgravity Sciences Laboratory-1 (MSL-1) in 1997. Success there led to the planned reflight on STS-107. Flame balls are the weakest fires yet produced in space or on Earth. Typically each flame ball produced only 1 watt of thermal power. By comparison, a birthday candle produces 50 watts. The Lewis-number measures the rate of diffusion of fuel into the flame ball relative to the rate of diffusion of heat away from the flame ball. Lewis-number mixtures conduct heat poorly. Hydrogen and methane are the only fuels that provide low enough Lewis-numbers to produce stable flame balls, and even then only for very weak, barely flammable mixtures. Nevertheless, under these conditions flame balls give scientists the opportunity to test models in one of the simplest combustion experiments possible. SOFBALL-2 science objectives include: Improving our understanding of the flame ball phenomenon; Determining the conditions under which flame balls exist; Testing predictions of flame ball lifetimes; Acquiring more precise data for critical model comparison.

Ronney, Paul↗

NASA NTRS2022

Low-Level Jets and the Convergence of Mars Data Assimilation Algorithms

Data assimilation is an increasingly popular technique in Mars atmospheric science, but its effect on the mean states of the underlying atmosphere models has not been thoroughly examined. The robustness of results to the choice of model and assimilation algorithm also warrants further study. We investigate these issues using two Mars general circulation models (MGCMs), with particular emphasis on zonal wind and temperature fields. When temperature retrievals from the Mars Global Surveyor Thermal Emission Spectrometer (TES) are assimilated into the U.K.-Laboratoire de M ́et ́eorologie Dynamique (UK-LMD) MGCM to create the Mars Analysis Correction Data Assimilation (MACDA) reanalysis, low-level zonal jets in the winter northern hemisphere shift equatorward and weaken relative to a free-running control simulation from the same MGCM. The Ensemble Mars Atmosphere Reanalysis System (EMARS) reanalysis, which is also based on TES temperature retrievals, also shows jet weakening (but less if any shifting) relative to a control simulation performed with the underlying Geophysical Fluid Dynamics Laboratory (GFDL) MGCM. Examining higher levels of the atmosphere, monthly mean three-dimensional temperature and zonal wind fields are in generally better agreement between the two reanalyses than between the two control simulations. In conjunction with information about the MGCMs’ physical parametrizations, intercomparisons between the various reanalyses and control simulations suggest that overall the EMARS control run is plausibly less biased (relative to the true state of the Martian atmosphere) than the MACDA control run. Implications for future observational studies are discussed. Plain Language Summary An increasingly popular way to study Martian weather and climate is to combine atmospheric temperature observations with a computer model (specifically, a global climate model). The process of combining model and observations is called “data assimilation”, and the resulting merged data set is called a “reanalysis”. One advantage of reanalyses is that they include variables (such as wind) that are not directly observed. For scientific and practical applications we want these variables to be reasonably accurate— however, it is not clear how well data assimilation algorithms compute them. Our study investigates this issue using two Mars reanalyses and two model simulations that do not assimilate temperature data. We focus on slowly-varying atmospheric phenomena (timescales from 10 Mars days to a season). Assimilating temperature data into two different global climate models changes the strength and/or spatial pattern of east-west winds at low altitudes. Furthermore, monthly mean three-dimensional temperature and east-west wind fields agree better between reanalyses than between non-assimilating model simulations. This suggests that the data assimilation process is basically successful. One non-assimilating model simulation has less realistic representations of atmospheric physical processes than the other—we argue that this plausibly gives it larger biases relative to the true state of the atmosphere.

Todd A Mooring↗

NASA NTRS1995

SAVS: A Space and Atmospheric Visualization Science system

The research environment faced by space and atmospheric scientists in the 1990s is characterized by unprecedented volumes of new data, by ever-increasing repositories of unexploited mission files, and by the widespread use of empirical and large-scale computational models needed for the synthesis of understanding across data sets and discipline boundaries. The effective analysis and interpretation of such massive amounts of information have become the subjects of legitimate concern. With SAVS (a Space and Atmospheric Visualization Science System), we address these issues by creating a 'push-button' software environment that mimics the logical scientific processes in data acquisition, reduction, and analysis without requiring a detailed understanding of the methods, networks, and modules that link the tools and effectively execute the functions. SAVS provides (1) a customizable framework for accessing a powerful set of visualization tools based on the popular AVS visualization software with hooks to PV-Wave and access to Khoros modules, (2) a set of mathematical and statistical tools, (3) an extensible library of discipline-specific functions and models (e.g., MSIS, IRI, Feldstein Oval, IGRF, satellite tracking with CADRE-3, etc.), and (4) capabilities for local and remote data base access. The system treats scalar, vector, and image data, and runs on most common Unix workstations. We present a description of SAVS and its components, followed by several applications based on generic research interests in interplanetary and magnetospheric physics (IMP/ISTP), active experiments in space (CRRES), and mission planning focused on the Earth's thermospheric, ionospheric, and mesospheric domains (TIMED).

Szuszczewicz, E. P.↗

NASA NTRSDate not supplied

CERES BiDirectional Scans (BDS) data in HDF (CER_BDS_Aqua-FM4_Edition1)

Each BiDirectional Scans (BDS) data product contains twenty-four hours of Level-1b data for each CERES scanner instrument mounted on each spacecraft. The BDS includes samples taken in normal and short Earth scan elevation profiles in both fixed and rotating azimuth scan modes (including space, internal calibration, and solar calibration views). The BDS contains Level-0 raw (unconverted) science and instrument data as well as the geolocated converted science and instrument data. The BDS contains additional data not found in the Level-0 input file, including converted satellite position and velocity data, celestial data, converted digital status data, and parameters used in the radiance count conversion equations. The following CERES BDS data sets are currently available: CER_BDS_TRMM-PFM_Edition1 CER_BDS_Terra-FM1_Edition1 CER_BDS_Terra-FM2_Edition1 CER_BDS_Terra-FM1_Edition2 CER_BDS_Terra-FM2_Edition2 CER_BDS_Aqua-FM3_Edition1 CER_BDS_Aqua-FM4_Edition1 CER_BDS_Aqua-FM3_Edition2 CER_BDS_Aqua-FM4_Edition2 CER_BDS_Aqua-FM3_Edition1-CV CER_BDS_Aqua-FM4_Edition1-CV CER_BDS_Terra-FM1_Edition1-CV CER_BDS_Terra-FM2_Edition1-CV. [Location=GLOBAL] [Temporal_Coverage: Start_Date=1997-12-27; Stop_Date=2005-04-02] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Temporal_Resolution=1 day; Temporal_Resolution_Range=Daily - < Weekly].

WN FILTERED RADIANCE UPWARDS↗

NASA NTRS2014

Earth Science Data Analysis in the Era of Big Data

Anyone with even a cursory interest in information technology cannot help but recognize that "Big Data" is one of the most fashionable catchphrases of late. From accurate voice and facial recognition, language translation, and airfare prediction and comparison, to monitoring the real-time spread of flu, Big Data techniques have been applied to many seemingly intractable problems with spectacular successes. They appear to be a rewarding way to approach many currently unsolved problems. Few fields of research can claim a longer history with problems involving voluminous data than Earth science. The problems we are facing today with our Earth's future are more complex and carry potentially graver consequences than the examples given above. How has our climate changed? Beside natural variations, what is causing these changes? What are the processes involved and through what mechanisms are these connected? How will they impact life as we know it? In attempts to answer these questions, we have resorted to observations and numerical simulations with ever-finer resolutions, which continue to feed the "data deluge." Plausibly, many Earth scientists are wondering: How will Big Data technologies benefit Earth science research? As an example from the global water cycle, one subdomain among many in Earth science, how would these technologies accelerate the analysis of decades of global precipitation to ascertain the changes in its characteristics, to validate these changes in predictive climate models, and to infer the implications of these changes to ecosystems, economies, and public health? Earth science researchers need a viable way to harness the power of Big Data technologies to analyze large volumes and varieties of data with velocity and veracity. Beyond providing speedy data analysis capabilities, Big Data technologies can also play a crucial, albeit indirect, role in boosting scientific productivity by facilitating effective collaboration within an analysis environment. To illustrate the effects of combining a Big Data technology with an effective means of collaboration, we relate the (fictitious) experience of an early-career Earth science researcher a few years beyond the present, interlaced and contrasted with reminiscences of its recent past (i.e., the present).

Kuo, K.-S.↗

NASA NTRS2001

Ranges of applicability for the continuum beam model in the mechanics of carbon nanotubes and nanorods

Limitations in the validity of the continuum beam model for carbon nanotubes (NTs) and nanorods are examined. Applicability of all assumptions used in the model is restricted by the two criteria for geometric parameters that characterize the structure of NTs. The key non-dimensional parameters that control the NT buckling behavior are derived via dimensional analysis of the nanomechanical problem. A mechanical law of geometric similitude for NT buckling is extended from continuum mechanics for different molecular structures. A model applicability map, where two classes of beam-like NTs are identified, is constructed for distinct ranges of non-dimensional parameters. Expressions for the critical buckling loads and strains are tailored for two classes of NTs and compared with the data provided by the molecular dynamics simulations. copyright 2001 Elsevier Science Ltd. All rights reserved.

Continuum beam model↗

NASA NTRS2018

Thermal Analysis of Propulsion Components for the Europa Clipper Mission

This presentation describes the thermal analysis and model development that occurred for selected components on the propulsion module subsystem of the Europa Clipper mission, which will fly to Jupiter's icy moon Europa and collect science data from orbit. An overview of a bipropellant system is given, as well as a description of a typical thermal propulsion design. A comparison is also provided, describing the unique Europa Clipper thermal design, which is atypical in many respects. The engine thermal model development is also discussed, including hot-firing tests with nozzle convection correlation, as well as thermal vacuum tests to measure and correlate the emissivity of critical nozzle surfaces. A description of engine firing, as well as valve soak back, is also provided, including temperature maps and results of engine cases. A summary is also provided, of lessons learned regarding thermal propulsion considerations.

propulsion module subsystem↗

NASA NTRS2020

A Systems Analysis Approach to Understanding the Physiological Adaptation to Spaceflight

This book is a summary of interdisciplinary research (physiology, space medicine, engineering, computer science, mathematics) that spans two decades (1972-1992). The research was an attempt to use systems analysis, mathematical modeling, computer simulations, and database systems to integrate the biomedical spaceflight data that was being collected during this period. The goal of the effort was to achieve a better understanding of the human physiological response to short-term and long-term space travel. The activity was primarily devoted to analyzing the biomedical results of Skylab (1973-74), a series of three space missions which is still ranked as the most comprehensive of all long-term biomedical space studies to date. This work was begun as a coordinated effort between the National Aeronautics and Space Administration (Johnson Space Center) and the General Electric Company’s Space Division (Houston, TX). It was the intent that this multidisciplined, integrative approach could reveal aspects of the then-new science of microgravity adaptation that were not obvious by adhering to the traditional methodology of examining each organ system in isolation. Some joint work with the Russians, including the Apollo-Soyez test project and a joint bedrest study was also supported during this period. In the 1980’s the systems analysis group’s effort was redirected to support the science management of human and animal experiments on the Space Shuttle. A few examples from this era are also included in the book. Parts of this work have been published elsewhere, presented at technical meetings, and documented in reports with limited distribution. These publications will be referenced throughout the text and the interested reader is advised to use these as resource material where additional details are desired. The intent of this book is not to reproduce these documents but rather to present a coherent view of the integrative analysis under one cover. This volume contains the first detailed publication (other than in internal reports) of an extensive metabolic balance analysis of Skylab data, the development and validation of the “Whole-Body Algorithm,” and simulation studies of diverse hypogravic environments. An analysis of cardiovascular deconditioning and a description of the calcium regulatory model are also new. A long period has passed between the completion of the main body of work represented in this book and its publication in this form. It was inevitable that new research efforts would lead to developments related to the spaceflight problems addressed and thereby make some of our biomedical conclusions obsolete. Although in some cases reference to more recent work have been included, for the most part this book should be considered an historical summary demonstrating the approach and utility of systems analysis and computer modeling in the NASA Life Sciences program at the time the studies were conducted.

Leonard, Joel I.↗

NASA NTRSDate not supplied

CERES Fast Longwave And SHortwave Radiative Flux (FLASHFlux): Research to Operation

The Clouds and Earth’s Radiant Energy System (CERES) project provides satellite-based observations of how Earth’s energy flows are varying in time and space and how clouds and aerosols are affecting the Earth’s radiation budget. Nominally, CERES data products require months of validation and calibration before releasing a climate quality data. The Fast Longwave and Shortwave Radiative Flux (FLASHFlux) data product was developed as to provide key data for applied science research involving the renewable energy and agricultural sectors within a week of observations. FLASHFlux achieves this by using simplified calibration, an operation meteorology product from Global Modeling and Assimilation Office (GMAO), and its own surface parameterizations model. The CERES FLASHFlux provides two data products: 1) an hourly Level 2 Single Scanner Footprint (SSF) data separately for Aqua and Terra observations, and 2) a daily Level 3 Time Interpolated and Spatially Averaged (TISA) 1ox 1ogridded data that combines Aqua and Terra observations. We inter-compared of FLASHFlux Version4A calibration to the climate quality CERES Edition4A data product over time. In addition, we also inter-compared the impact of the fluxes by using the near real-time GMAO GEOS FP-IT instead of GMAO G5-CERES meteorology data. We also compare both SSF overpass and TISA radiative flux products to a set of surface measurement sites that are globally distributed and assess agreement.

P. C. Sawaengphokhai↗