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This bimonthly contractor progress report covers the operation, maintenance and data management of the Airborne Oceanographic Lidar and the Airborne Topographic Mapper. Monthly activities included: mission planning, sensor operation and calibration, data processing, data analysis, network development and maintenance and instrument maintenance engineering and fabrication.
A finite volume based network analysis procedure has been applied to model unsteady flow without and with heat transfer. Liquid has been modeled as compressible fluid where the compressibility factor is computed from the equation of state for a real fluid. The modeling approach recognizes that the pressure oscillation is linked with the variation of the compressibility factor; therefore, the speed of sound does not explicitly appear in the governing equations. The numerical results of chilldown process also suggest that the flow and heat transfer are strongly coupled. This is evident by observing that the mass flow rate during 90-second chilldown process increases by factor of ten.
Geographic information systems (GIS) deal with collecting, modeling, man- aging, analyzing, and integrating spatial (locational) and non-spatial (attribute) data required for geographic applications. Examples of spatial data are digital maps, administrative boundaries, road networks, and those of non-spatial data are census counts, land elevations and soil characteristics. GIS shares common areas with a number of other disciplines such as computer- aided design, computer cartography, database management, and remote sensing. None of these disciplines however, can by themselves fully meet the requirements of a GIS application. Examples of such requirements include: the ability to use locational data to produce high quality plots, perform complex operations such as network analysis, enable spatial searching and overlay operations, support spatial analysis and modeling, and provide data management functions such as efficient storage, retrieval, and modification of large datasets; independence, integrity, and security of data; and concurrent access to multiple users. It is on the data management issues that we devote our discussions in this monograph. Traditionally, database management technology have been developed for business applications. Such applications require, among other things, capturing the data requirements of high-level business functions and developing machine- level implementations; supporting multiple views of data and yet providing integration that would minimize redundancy and maintain data integrity and security; providing a high-level language for data definition and manipulation; allowing concurrent access to multiple users; and processing user transactions in an efficient manner. The demands on database management systems have been for speed, reliability, efficiency, cost effectiveness, and user-friendliness. Significant progress have been made in all of these areas over the last two decades to the point that many generalized database platforms are now available for developing data intensive applications that run in real-time. While continuous improvement is still being made at a very fast-paced and competitive rate, new application areas such as computer aided design, image processing, VLSI design, and GIS have been identified by many as the next generation of database applications. These new application areas pose serious challenges to the currently available database technology. At the core of these challenges is the nature of data that is manipulated. In traditional database applications, the database objects do not have any spatial dimension, and as such, can be thought of as point data in a multi-dimensional space. For example, each instance of an entity EMPLOYEE will have a unique value corresponding to every attribute such as employee id, employee name, employee address and so on. Thus, every Employee instance can be thought of as a point in a multi-dimensional space where each dimension is represented by an attribute. Furthermore, all operations on such data are one-dimensional. Thus, users may retrieve all entities satisfying one or more constraints. Examples of such constraints include employees with addresses in a certain area code, or salaries within a certain range. Even though constraints can be specified on multiple attributes (dimensions), the search for such data is essentially orthogonal across these dimensions.
There are numerous engineering design problems where the use of wires to transfer power and communicate data thru the walls of a structure is prohibitive or significantly difficult that it may require a complex design. Using physical feedthroughs in such systems may make them susceptible to leakage of chemicals or gasses, loss of pressure or vacuum, as well as difficulties in providing adequate thermal or electrical insulation. Moreover, feeding wires thru a wall of a structure reduces the strength of the structure and makes the structure prone to cracking due to fatigue that can result from cyclic loading and stress concentrations. One area that has already been identified to require a wireless alternative to electrical feedthroughs is the container of the Mars Sample Return Mission, which will need wireless sensors to sense a pressure leak and to avoid potential contamination. The idea of using elastic or acoustic waves to transfer power was suggested recently by [Y. Hu, et al., July 2003]. This system allows for the avoidance of cabling or wiring. The technology is applicable to the transfer of power for actuation, sensing and other tasks inside any sealed container or vacuum/pressure vessel. An alternative approach to the modeling presented previously [Sherrit et a., 2005] used network analysis to solve the same problem in a clear and expandable manner. Experimental tests on three different designs of these devices were performed. The three designs used different methods of coupling the piezoelectric element to the wall. In the first test the piezoelectric material was bolted using a backing structure. In the second test the piezoelectric was clamped after the application of grease and finally the piezoelectric element was attached using a conductive epoxy. The mechanical clamp with grease produced the highest measured efficiency of 53% however this design was the least practical from a fabrication viewpoint. The power transfer efficiency of conductive epoxy joint was 40% and the stress bolts (12%).
The contributions of precipitation and soil moisture observations to the skill of soil moisture estimates from a land data assimilation system are assessed. Relative to baseline estimates from the Modern Era Retrospective-analysis for Research and Applications (MERRA), the study investigates soil moisture skill derived from (i) model forcing corrections based on large-scale, gauge- and satellite-based precipitation observations and (ii) assimilation of surface soil moisture retrievals from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E). Soil moisture skill is measured against in situ observations in the continental United States at 44 single-profile sites within the Soil Climate Analysis Network (SCAN) for which skillful AMSR-E retrievals are available and at four CalVal watersheds with high-quality distributed sensor networks that measure soil moisture at the scale of land model and satellite estimates. The average skill (in terms of the anomaly time series correlation coefficient R) of AMSR-E retrievals is R=0.39 versus SCAN and R=0.53 versus CalVal measurements. The skill of MERRA surface and root-zone soil moisture is R=0.42 and R=0.46, respectively, versus SCAN measurements, and MERRA surface moisture skill is R=0.56 versus CalVal measurements. Adding information from either precipitation observations or soil moisture retrievals increases surface soil moisture skill levels by IDDeltaR=0.06-0.08, and root zone soil moisture skill levels by DeltaR=0.05-0.07. Adding information from both sources increases surface soil moisture skill levels by DeltaR=0.13, and root zone soil moisture skill by DeltaR=0.11, demonstrating that precipitation corrections and assimilation of satellite soil moisture retrievals contribute similar and largely independent amounts of information.
This internship focused on the development of additional capabilities for the General Fluid Systems Simulation Program (GFSSP). GFSSP is a thermo-fluid code used to evaluate system performance by a finite volume-based network analysis method. The program was developed primarily to analyze the complex internal flow of propulsion systems and is capable of solving many problems related to thermodynamics and fluid mechanics. GFSSP is integrated with thermodynamic programs that provide fluid properties for sub-cooled, superheated, and saturation states. For fluids that are not included in the thermodynamic property program, look-up property tables can be provided. The look-up property tables of the current release version can only handle sub-cooled and superheated states. The primary purpose of the internship was to extend the look-up tables to handle saturated states. This involves a) generation of a property table using REFPROP, a thermodynamic property program that is widely used, and b) modifications of the Fortran source code to read in an additional property table containing saturation data for both saturated liquid and saturated vapor states. Also, a method was implemented to calculate the thermodynamic properties of user-fluids within the saturation region, given values of pressure and enthalpy. These additions required new code to be written, and older code had to be adjusted to accommodate the new capabilities. Ultimately, the changes will lead to the incorporation of this new capability in future versions of GFSSP. This paper describes the development and validation of the new capability.
The Gravity Recovery and Climate Experiment (GRACE) twin satellites observe time variations in Earth's gravity field which yield valuable information about changes in terrestrial water storage (TWS). GRACE is characterized by low spatial (greater than 150,000 square kilometers) and temporal (greater than 10 day) resolution but has the unique ability to sense water stored at all levels (including groundwater) systematically and continuously. The GRACE Data Assimilation System (GRACE-DAS), based on the Catchment Land Surface Model (CLSM) enhances the value of the GRACE water storage data by enabling spatial and temporal downscaling and vertical decomposition into moisture 39 components (i.e. groundwater, soil moisture, snow), which individually are more useful for scientific applications. In this study, GRACE-DAS was applied to North America and GRACE-based drought indicators were developed as part of a larger effort that investigates the possibility of more comprehensive and objective identification of drought conditions by integrating spatially, temporally and vertically disaggregated GRACE data into the U.S. and North American Drought Monitors. Previously, the Drought Monitors lacked objective information on deep soil moisture and groundwater conditions, which are useful indicators of drought. Extensive datasets of groundwater storage from USGS monitoring wells and soil moisture from the Soil Climate Analysis Network (SCAN) were used to assess improvements in the hydrological modeling skill resulting from the assimilation of GRACE TWS data. The results point toward modest, but statistically significant, improvements in the hydrological modeling skill across major parts of the United States, highlighting the potential value of GRACE assimilated water storage field for improving drought detection.
The purging operations for cryogenic main propulsion systems of upper stage are usually carried out for the following cases: 1) Purging of the Fill/Drain line after completion of propellant loading. This operation allows the removal of residual propellant mass; and 2) Purging of the Feed/Drain line if the mission is scrubbed. The lines would be purged by connections to a ground high-pressure gas storage source. The flowrate of purge gas should be regulated such that the pressure in the line will not exceed the required maximum allowable value. Exceeding the maximum allowable pressure may lead to structural damage in the line. To gain confidence in analytical models of the purge process, a test series was conducted. The test article, a 20-cm incline line, was filled with liquid hydrogen and then purged with gaseous helium (GHe). The influences of GHe flowrates and initial temperatures were evaluated. The Generalized Fluid System Simulation Program, an in-house general-purpose computer program for flow network analysis, was utilized to model and simulate the testing. The test procedures, modeling descriptions, and the results will be presented in the final paper.
The purging operations for cryogenic main propulsion systems of upper stage are usually carried out for the following cases: 1) Purging of the Fill/Drain line after completion of propellant loading. This operation allows the removal of residual propellant mass; and 2) Purging of the Feed/Drain line if the mission is scrubbed. The lines would be purged by connections to a ground high-pressure gas storage source. The flow-rate of purge gas should be regulated such that the pressure in the line will not exceed the required maximum allowable value. Exceeding the maximum allowable pressure may lead to structural damage in the line. To gain confidence in analytical models of the purge process, a test series was conducted. The test article, a 20-cm incline line, was filled with liquid hydrogen and then purged with gaseous helium (GHe). The influences of GHe flow-rates and initial temperatures were evaluated. The Generalized Fluid System Simulation Program, an in-house general-purpose computer program for flow network analysis, was utilized to model and simulate the testing. The test procedures, modeling descriptions, and the results will be presented in the final paper.
This paper describes the experience of the authors in using the Generalized Fluid System Simulation Program (GFSSP) in teaching Design of Thermal Systems class at University of Alabama in Huntsville. GFSSP is a finite volume based thermo-fluid system network analysis code, developed at NASA/Marshall Space Flight Center, and is extensively used in NASA, Department of Defense, and aerospace industries for propulsion system design, analysis, and performance evaluation. The educational version of GFSSP is freely available to all US higher education institutions. The main purpose of the paper is to illustrate the utilization of this user-friendly code for the thermal systems design and fluid engineering courses and to encourage the instructors to utilize the code for the class assignments as well as senior design projects.
Among the potential reasons for enhanced warming rates in many high elevation regions is the nonlinear relationship between surface downward longwave radiation (DLR) and specific humidity (q). In this study we use ground-based observations at two neighboring high elevation sites in Southwestern Colorado that have different local topography and are 1.3 kilometers apart horizontally and 348 meters vertically. We examine the spatial consistency of the sensitivities (partial derivatives) of DLR with respect to changes in q, and the sensitivities are obtained from the Jacobian matrix of a neural network analysis. Although the relationship between DLR and q is the same at both sites, the sensitivities are higher when q is smaller, which occurs more frequently at the higher elevation site. There is a distinct hourly distribution in the sensitivities at both sites especially for high sensitivity cases, although the range is greater at the lower elevation site. The hourly distribution of the sensitivities relates to that of q. Under clear skies during daytime, q is similar between the two sites, however under cloudy skies or at night, it is not. This means that the DLR-q sensitivities are similar at the two sites during daytime but not at night, and care must be exercised when using data from one site to infer the impact of water vapor feedbacks at another site, particularly at night. Our analysis suggests that care should be exercised when using the lapse rate adjustment to infill high frequency data in a complex topographical region, particularly when one of the stations is subject to cold air pooling as found here.
This paper presents a numerical model of pressurization of a cryogenic propellant tank for the Integrated Vehicle Fluid (IVF) system using the Generalized Fluid System Simulation Program (GFSSP). The IVF propulsion system, being developed by United Launch Alliance, uses boiloff propellants to drive thrusters for the reaction control system as well as to run internal combustion engines to develop power and drive compressors to pressurize propellant tanks. NASA Marshall Space Flight Center (MSFC) has been running tests to verify the functioning of the IVF system using a flight tank. GFSSP, a finite volume based flow network analysis software developed at MSFC, has been used to develop an integrated model of the tank and the pressurization system. This paper presents an iterative algorithm for converging the interface boundary conditions between different component models of a large system model. The model results have been compared with test data.
For more than six years, the Soil Moisture and Ocean Salinity (SMOS) mission has provided multi angular and full-polarization brightness temperature (TB) measurements at L-band. Geophysical products such as soil moisture (SM) and vegetation optical depth at nadir (tau(sub nad)) are retrieved by an operational algorithm using TB observations at different angles of incidence and polarizations. However, the quality of the retrievals depends on several surface effects, such as vegetation, soil roughness and texture, etc. In the microwave forward emission model used in the retrievals (L-band Microwave Emission Model, L-MEB),soil roughness is modeled with a semi-empirical equation using four main parameters (Q(sub r), H(sub r), N(sub rp), with p = H or V polarizations). At present, these parameters are calibrated with data provided by airborne studies and in situ measurements made at a local scale that is not necessarily representative of the large SMOS footprints (43 km on average) at global scale. In this study, we evaluate the impact of the calibrated values of N(sub rp) and H(sub r) on the SM and tau(sub nad) retrievals based on SMOS TB measurements (SMOS Level 3 product) over the Soil Climate Analysis Network (SCAN) network located in North America over five years (2011-2015). In this study, Qr was set equal to zero and we assumed that N(sub rH)= N(sub rV). The retrievals were performed by varying N(sub rp) from −1 to 2 by steps of 1 and H(sub r) from 0 to 0.6 by steps of 0.1. At satellite scale, the results show that combining vegetation and roughness effects in a single parameter provides the best results in terms of soil moisture retrievals, as evaluated against the in situ SM data. Even though our retrieval approach was very simplified, as we did not account for pixel heterogeneity, the accuracy we obtained in the SM retrievals was almost systematically better than those of the Level 3 product. Improved results were also obtained in terms of optical depth retrievals. These new results may have key consequences in terms of calibration of roughness effects within the algorithms of the SMOS (ESA) and the SMAP (NASA) space missions.
This paper presents a numerical model of the pressurization loop of the Integrated Vehicle Fluids (IVF) system using the Generalized Fluid System Simulation Program (GFSSP). The IVF propulsion system, being developed by United Launch Alliance to reduce system weight and enhance reliability, uses boiloff propellants to drive thrusters for the reaction control system as well as to run internal combustion engines to develop power and drive compressors to pressurize propellant tanks. NASA Marshall Space Flight Center (MSFC) conducted tests to verify the functioning of the IVF system using a flight-like tank. GFSSP, a finite volume based flow network analysis software developed at MSFC, has been used to support the test program. This paper presents the simulation of three different test series, comparison of numerical prediction and test data and a novel method of presenting data in a dimensionless form. The paper also presents a methodology of implementing a compressor map in a system level code.
The Soil Moisture Active Passive (SMAP) mission is dedicated toward global soil moisture mapping. Typically, an L-band microwave radiometer has spatial resolution on the order of 36-40 km, which is too coarse for many specific hydro-meteorological and agricultural applications. With the failure of the SMAP active radar within three months of becoming operational, an intermediate (9-km) and finer (3-km) scale soil moisture product solely from the SMAP mission is no longer possible. Therefore, the focus of this study is a disaggregation of the 36-km resolution SMAP passive-only surface soil moisture (SSM) using the Soil Evaporative Efficiency (SEE) approach to spatial scales of 3-km and 9-km. The SEE was computed using thermal-infrared (TIR) estimation of surface evaporation over Continental U.S. (CONUS). The disaggregation results were compared with the 3 months of SMAP-Active (SMAP-A) and Active/Passive (AP) products, while comparisons with SMAP-Enhanced (SMAP-E), SMAP-Passive (SMAP-P), as well as with more than 180 Soil Climate Analysis Network (SCAN) stations across CONUS were performed for a 19 month period. At the 9-km spatial scale, the TIR-Downscaled data correlated strongly with the SMAP-E SSM both spatially (r = 0.90) and temporally (r = 0.87). In comparison with SCAN observations, overall correlations of 0.49 and 0.47; bias of −0.022 and −0.019 and unbiased RMSD of 0.105 and 0.100 were found for SMAP-E and TIR-Downscaled SSM across the Continental U.S., respectively. At 3-km scale, TIR-Downscaled and SMAP-A had a mean temporal correlation of only 0.27. In terms of gain statistics, the highest percentage of SCAN sites with positive gains (>55%) was observed with the TIR-Downscaled SSM at 9-km. Overall, the TIR-based downscaled SSM showed strong correspondence with SMAP-E; compared to SCAN, and overall both SMAP-E and TIR-Downscaled performed similarly, however, gain statistics show that TIR-Downscaled SSM slightly outperformed SMAP-E.
A simple yet effective scheme to estimate volumetric soil moisture (VSM) using infrared (IR) land surface emissivity retrieved from satellite IR spectral radiance measurements was recently developed, assuming other parameters impacting the radiative transfer (e.g., temperature, vegetation coverage, and surface roughness) are known for an acceptable time and space reference location. This scheme was applied to a decade of global IR emissivity data retrieved from MetOp-A Infrared Atmospheric Sounding Interferometer measurements. Global datasets of monthly-mean 0.25° spatial-grid VSM, estimated from IR emissivity data (denoted as IR-VSM), are assembled to compare with those routinely provided by satellite microwave (MW) multi-sensor measurements (obtained from the ESA CCI website). The IR-VSM dataset is used to demonstrate its spatial variations as well as seasonal-cycles and inter-annual variability which are spatially coherent and consistent with that from MW measurements. Herein we report on some initial evaluation of the IR-VSM absolute values using in-situ measurements available from the Soil Climate Analysis Network of the USDA website. From the 114 ground station locations, we infer a mean bias between monthly-mean in-situ VSM and satellite derived IR-VSM of 0.005 (m3/m3) over the past decade, despite the differences between physical measurements. The absolute accuracy of IR-VSM is also evaluated using an independent source of soil moisture measurements.
A soil moisture (SM) disaggregation algorithm based on thermal inertia (TI) theory was implemented to downscale the Soil Moisture Active Passive (SMAP) Enhanced product(SPL2SMPE) from 9 km to 1 km over the continental United States. The algorithm applies land surface temperature and normalized difference vegetation index from Moderate Resolution Imaging Spectroradiometer (MODIS) at higher spatial resolution to estimate relative soil wetness within a coarse SMAP grid -this MODIS-derived relative wetness is then used to produce the downscaled SMAP SM. Results from the algorithm were evaluated in terms of their spatiotemporal coverage and accuracy using in situ measurements from SMAP Core Validation Sites (CVS), the US Department of Agriculture Soil Climate Analysis Network (USDA-SCAN), and the National Oceanic and Atmospheric Administration Climate Reference Network(NOAA-CRN). Results were also compared with the baselineSPL2SMPE and the SMAP/Sentinel-1 (SPL2SMAPS) 1kmproduct. Overall, the unbiased root mean square error (ubRMSE)of the disaggregated SM at the CVS using the TI approach is approximately 0.04 m3=m3, which is the SMAP mission requirement for the baseline products. The TI approach out performs the SMAP/Sentinel SL2SMAPS 1km product by approximately0.02 m3=m3. Over the agriculture/crop areas from SCAN and CRN sparse network stations, the TI approach exhibits better ubRMSE compared to SPL2SMPE andSPL2SMAPS byabout0.01 and 0.02 m3=m3, indicating its advantage in these areas. However, a drawback of this approach is that there are data gaps due to cloud cover as optical sensors cannot have a clear view of the land surface.
An extensive number of studies have experimentally investigated how elements distribute between metal and silicate phases, to better constrain core-mantle chemical equilibrium. Here, we present a new database compiling all (to our knowledge) experimental data on liquid metal-silicate partitioning from 118 peer-reviewed publications. We applied various machine learning techniques to gain further insights into these partitioning equilibria and their dependencies. We performed a network analysis to investigate the relationship between experiments and partition coefficients, which enables visualizing gaps in the experimental dataset and biases related to varying experimental conditions and analytical setup. In addition, semi-empirical thermodynamic models are commonly used to extrapolate these chemical reactions to the wide range of pressure, temperature and compositional conditions of planetary differentiation. These models are based on linear regressions that assume continuous relationship between partition coefficients and experimental variables. Here, we considered random forest regressions, which are algorithms based on ensembles of decision trees and does not consider continuous effects of each variable. The application of this regression significantly improves the prediction of metal-silicate partitioning for several elements including Ni, Si and Cr. We will show how this new approach improves our understanding of elemental exchange between metal and silicate and their implications for the Earth’s core formation.