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NASA NTRS1989

A cloud model simulation of space shuttle exhaust clouds in different atmospheric conditions

A three-dimensional cloud model was used to characterize the dominant influence of the environment on the Space Shuttle exhaust cloud. The model was modified to accept the actual heat and moisture from rocket exhausts and deluge water as initial conditions. An upper-air sounding determined the ambient atmosphere in which the cloud could grow. The model was validated by comparing simulated clouds with observed clouds from four actual Shuttle launches. The model successfully produced clouds with dimensions, rise, decay, liquid water contents and vertical motion fields very similar to observed clouds whose dimensions were calculated from 16 mm film frames. Once validated, the model was used in a number of different atmospheric conditions ranging from very unstable to very stable. In moist, unstable atmospheres simulated clouds rose to about 3.5 km in the first 4 to 8 minutes then decayed. Liquid water contents ranged from 0.3 to 1.0 g kg-1 mixing ratios and vertical motions were from 2 to 10 ms-1. An inversion served both to reduce entrainment (and erosion) at the top and to prevent continued cloud rise. Even in the most unstable atmospheres, the ground cloud did not rise beyond 4 km and in stable atmospheres with strong low level inversions the cloud could be trapped below 500 m. Wind shear strongly affected the appearance of both the ground cloud and vertical column cloud. The ambient low-level atmospheric moisture governed the amount of cloud water in model clouds. Some dry atmospheres produced little or no cloud water. One case of a simulated TITAN rocket explosion is also discussed.

Chen, C.↗

NASA NTRS1973

A comparison of the noise characteristics of full scale and model helicopter rotors

Full scale and model rotor noise results are compared in terms of the spectral content, the directivity patterns, and the dependence on tip speed and rotor thrust/pitch angle. Each of the three main noise sources (rotational, low-frequency broadband, and high-frequency broadband) are reviewed separately, and account is taken of the measurement angle relative to the rotor disc. Blade 'scaling' effects are discussed together with the agreement between existing theoretical and semiempirical prediction methods. It is shown that in general good agreement is obtained between the full scale and model rotors for the spectral content and the velocity and thrust dependencies, while the 'scaling' factors associated with the low-frequency broadband noise do not seem to be appropriate.

Leverton, J. W.↗

NASA NTRS1975

Wall mounted heat exchanger characterization

Analytical models are presented for describing the heat and mass transfer and the energy distribution in the contents of a cryogenic propellant tank, under varying gravity levels. These models are used to analytically evaluate the effectiveness of a wall heat exchanger as a means of controlling the pressure in the tank during flight and during fill operations. Pressure and temperature histories are presented for tanks varying in size from 4 to 22.5 feet in diameter and gravity levels from 0-1. Results from the subscale test program, utilizing both non-cryogenic and cryogenic fluid, designed to evaluate a tank wall heat exchanger are described and compared with the analytical models. Both the model and test results indicate that a passive tank wall heat exchanger can effectively control tank pressure. However, the weight of such a system is considerably higher than that of an active mixer system.

Bullard, B. R.↗

NASA NTRS2013

High Water Contents in the Siberian Cratonic Mantle: An FTIR Study of Udachnaya Peridotite Xenoliths

Water is believed to be a key factor controlling the long-term stability of cratonic lithosphere, but mechanisms responsible for the water content distribution in the mantle remain poorly constrained. Water contents were obtained by FTIR in olivine, pyroxene and garnet for 20 well-characterized peridotite xenoliths from the Udachnaya kimberlite (central Siberian craton) and equilibrated at 2-7 GPa. Water contents in minerals do not appear to be related to interaction with the host kimberlite. Diffusion modeling indicates that the core of olivines preserved their original water contents. The Udachnaya peridotites show a broad range of water contents in olivine (6.5 +/- 1.1 to 323 +- 65 ppm H2O (2 sigma)), and garnet (0 - 23 +/- 6 ppm H2O). The water contents of olivine and garnet are positively correlated with modal clinopyroxene, garnet and FeO in olivine. Water-rich garnets are also rich in middle rare earth elements. This is interpreted as the result of interaction between residual peridotites and water rich-melts, consistent with modal and cryptic metasomatism evidenced in the Siberian cratonic mantle. The most water-rich Udachnaya minerals contain 2 to 3 times more water than those from the Kaapvaal craton, the only craton with an intact mantle root for which water data is available. The highest water contents in olivine and orthopyroxene in this study (>= 300 ppm) are found at the bottom of the lithosphere (> 6.5 GPa). This is in contrast with the Kaapvaal craton where the olivines of peridotites equilibrated at > 6.4 GPa have < 1 ppm H2O. The latter "dry" olivine may make the base of the Kaapvaal cratonic root strong and thus protects it from erosion by the convective mantle The calculated viscosity for water-rich Udachnaya peridotites at > 6 GPa is lower or similar (8.4× 10(exp 16) to 8.0× 10(exp 18) Pa./s) to that of the asthenosphere (<= 3.7x10(exp 18) Pa./s ). Such lithologies would not be able to resist delamination by the convecting asthenosphere. However, seismology studies as well as the high equilibration pressures of our samples indicate that the Udachnaya cratonic lithosphere is 220-250 km thick. Consequently, the water-rich peridotites are likely not representative of the overall Siberian cratonic lithosphere. Their composition is linked to spatially limited melt metasomatism in mantle regions above asthenospheric upwellings responsible for the kimberlite magmatism prior to their ascent and eruption.

Doucet, Luc S.↗

DOE OSTI2020

Savannah River Site High Level Radioactive Waste Tank Sample Thermolytic Hydrogen Generation - 20346

To support the revision of flammability calculations and controls for the Savannah River Site (SRS) High-Level Radioactive Waste (HLW) tanks, Savannah River National Laboratory (SRNL) conducted laboratory measurements with the goal of quantifying hydrogen produced in HLW by non-radiolytic chemical reactions (i.e., thermolysis). Testing was performed with HLW tank supernate samples that represented a cross section of types of waste at SRS, including tanks that store typical evaporator concentrate, dissolved salt-cake, dilute recycle stream from the Defense Waste Processing Facility (DWPF), evaporator concentrate of the recycle stream from DWPF, fresh waste from the canyon separations facility, and waste that has been through salt processing cesium removal. Non-radioactive simulant testing included the classes of organic compounds historically introduced into the HLW tanks that were recently shown to be the most active toward the thermolytic generation of hydrogen. The reaction rate equations developed for thermolytic hydrogen generation from each class of organic compound showed direct proportionality to organic compound concentration and hydroxide concentration. Simulant testing identified that highly concentrated waste with high hydroxide concentration had the highest rates of thermolytic hydrogen generation. Using the radioactive tank sample thermolysis measurements at high temperatures and the mechanistic salt dependence of the simulant models, a global model was developed for tank waste thermolytic hydrogen generation as a function of total organic carbon content of SRS HLW. Through the primary functionality of the global model (organic carbon and free hydroxide concentration), the relative reactivity of the organic carbon in the waste was quantified. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRS1997

Retrieval of Soil Moisture and Roughness from the Polarimetric Radar Response

The main objective of this investigation was the characterization of soil moisture using imaging radars. In order to accomplish this task, a number of intermediate steps had to be undertaken. In this proposal, the theoretical, numerical, and experimental aspects of electromagnetic scattering from natural surfaces was considered with emphasis on remote sensing of soil moisture. In the general case, the microwave backscatter from natural surfaces is mainly influenced by three major factors: (1) the roughness statistics of the soil surface, (2) soil moisture content, and (3) soil surface cover. First the scattering problem from bare-soil surfaces was considered and a hybrid model that relates the radar backscattering coefficient to soil moisture and surface roughness was developed. This model is based on extensive experimental measurements of the radar polarimetric backscatter response of bare soil surfaces at microwave frequencies over a wide range of moisture conditions and roughness scales in conjunction with existing theoretical surface scattering models in limiting cases (small perturbation, physical optics, and geometrical optics models). Also a simple inversion algorithm capable of providing accurate estimates of soil moisture content and surface rms height from single-frequency multi-polarization radar observations was developed. The accuracy of the model and its inversion algorithm is demonstrated using independent data sets. Next the hybrid model for bare-soil surfaces is made fully polarimetric by incorporating the parameters of the co- and cross-polarized phase difference into the model. Experimental data in conjunction with numerical simulations are used to relate the soil moisture content and surface roughness to the phase difference statistics. For this purpose, a novel numerical scattering simulation for inhomogeneous dielectric random surfaces was developed. Finally the scattering problem of short vegetation cover above a rough soil surface was considered. A general scattering model for grass-blades of arbitrary cross section was developed and incorporated in a first order random media model. The vegetation model and the bare-soil model are combined and the accuracy of the combined model is evaluated against experimental observations from a wheat field over the entire growing season. A complete set of ground-truth data and polarimetric backscatter data were collected. Also an inversion algorithm for estimating soil moisture and surface roughness from multi-polarized multi-frequency observations of vegetation-covered ground is developed.

Sarabandi, Kamal↗

DOE OSTI2021

On the use of air temperature and precipitation as surrogate predictors in soil respiration modelling

Soil respiration (R S ), the soil-to-atmosphere CO 2 flux that is a major component of the global carbon cycle, is strongly influenced by local soil temperature (T soil ) and water content (SWC). Regional to global-scale R S modelling thus requires this information at local scales, but few high-quality, wall-to-wall (global) T soil and SWC data exist. As a result, such modelling efforts commonly use air temperature (T air ) and monthly precipitation (P m ) as surrogate predictors, but their site-scale accuracy and potential bias are unknown. In this report we used monthly data from 880 sites across a wide variety of different environmental conditions (i.e., climate, ecosystem type, elevation, vegetation leaf habit and drainage conditions) to determine the suitability of T air as a surrogate for T soil , and data from 507 sites to examine the suitability of P m as a surrogate for SWC. Site-specific linear and second-order exponential non-linear models were compared using model evaluation metrics (i.e., slope, p-value of slope, root mean square error [RMSE], index of agreement and model efficiency). We found that T soil and T air are highly correlated and explain similar R S variability. In contrast, P m is not a good surrogate for SWC, even though P m explains a similar amount of R S variability to SWC. The wide variability in the site-specific relationships between R S and SWC means that no single relationship can be used for large-scale modelling. The results from this study support the use of T air in continental-to-global scale R S models, and highlight the urgent need for continental-to-global scale SWC datasets for the modelling and evaluation of future soil carbon dynamics under global climate change.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2023

An Evaluation of A Physics-Based Firn Model and A Semi-Empirical Firn Model Across the Greenland Ice Sheet (1980–2020)

The Greenland Ice Sheet's (GrIS) firn layer buffers the ice sheet's contribution to sea level rise by storing meltwater in its pore space. However, available pore space and meltwater retention capability is lost due to ablation of the firn layer and refreezing of meltwater as near-surface ice slabs in the firn. Understanding how firn properties respond to climate is important for constraining the GrIS's future contribution to sea level rise in a warming climate. Observations of firn density provide detailed information about firn properties, but they are spatially and temporally limited. Here we use two firn models, the physics-based SNOWPACK model and the Community Firn Model configured with a semi-empirical densification equation (CFM-GSFC), to quantify firn properties across the GrIS from 1980 through 2020. We use an identical forcing (Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) atmospheric reanalysis) for SNOWPACK and the CFM-GSFC in order to isolate firn model differences. To evaluate the models, we compare simulated firn properties, including firn air content (FAC), to measurements from the Surface Mass Balance and Snow on Sea Ice Working Group (SUMup) dataset of snow and firn density. Both models perform well (mean absolute percentage errors of 14 % in SNOWPACK and 16 % in the CFM-GSFC), though their performance is hindered by the spatial resolution of the atmospheric forcing. In the ice-sheet-wide simulations, the 1980–1995 average spatially integrated FAC (i.e., air volume in the firn) for the upper 100 m is 34 645 km 3 from SNOWPACK and 28 581 km 3 from the CFM-GSFC. The discrepancy in the magnitude of the modeled FAC stems from differences in densification with depth and variations in the sensitivity of the models to atmospheric forcing. In more recent years (2005–2020), both models simulate substantial depletion of pore space. During this period, the spatially integrated FAC across the entire GrIS decreases by 3.2 % (−66.6 km 3 yr −1 ) in SNOWPACK and 1.5 % (−17.4 km 3 yr −1 ) in the CFM-GSFC. These differing magnitudes demonstrate how model differences propagate throughout the FAC record. Over the full modeled record (1980–2020), SNOWPACK simulates a loss of pore space equivalent to 3 mm of sea level rise buffering, while the CFM-GSFC simulates a loss of 1 mm. The greatest depletion in FAC is along the margins and especially along the western margin where observations and models show the formation of near-surface, low-permeability ice slabs that may inhibit meltwater storage.

Megan Thompson-Munson↗

NASA NTRS1979

Two-dimensional zonal mean flow model

The interactions of dynamics, photochemistry and radiation in the stratosphere can be described by the continuity, momentum, and energy equations. A more comprehensive model for stratospheric transport theory was developed for the purpose of aiding predictions of changes in the stratospheric ozone content as a consequence of natural and anthropogenic processes. This model is time dependent and the dependent variables are zonal means of the relevant meteorological quantities which are functions of latitude and height. The detailed formulation of a numerical model both in physics and mathematics is given. A set of fundamental dynamical equations, and the numerical method used in the integration are described.

Chen, H. C.↗

DOE OSTI2022

CFD-DEM Modeling of Autothermal Pyrolysis of Corn Stover with a Coupled Particle- and Reactor-Scale Framework

Autothermal operation of fast pyrolysis is an efficient process-intensification technique wherein exothermic oxidation reactions are used to overcome the heat-transfer bottleneck of conventional pyrolysis. The development of accurate, reliable modeling toolsets is imperative to generating a deeper understanding of biomass autothermal pyrolysis systems to support scale-up and industrial deployment. This modeling effort describes the development of single-particle and reactor models which incorporate detailed reaction schemes and simultaneous exothermic oxidation reactions. The particle-scale model was parameterized for corn stover feedstock with particle morphology, density, ash content, and biopolymer composition, all of which impact the emergent conversion characteristics during pyrolysis. Results were then used to parameterize a reactor-scale autothermal pyrolysis model, which was developed using a coarse-grained computational fluid dynamic-discrete element method. The simulation results compared well with experimental results, with the predicted bio-oil, light gas, and biochar yield within 3.0 wt% of the experimental yields. Further analyses were performed to test the influence of equivalence ratio, biomass injection position, and particle size distribution on autothermal pyrolysis. The analysis of the physio-chemical properties of the fluid and solid phase inside the reactor and at the reactor outlet help reveal important process interactions of autothermal pyrolysis.

BIOMASS FUELS,INORGANIC, ORGANIC, PHYSICAL, AND AN↗

NASA NTRS1994

Structural and stratigraphic features and ERS 1 synthetic aperture radar backscatter characteristics of ice growing on shallow lakes in NW Alaska, winter 1991-1992

Changes in Earth Remote-Sensing Satellite (ERS) 1 C band synthetic aperture radar (SAR) backscatter intensity (sigma(exp 0)) from ice growing on shallow tundra lakes at three locations in NW Alaska are described. Ice core analysis shows that all lakes on the coast at Barrow the ice, whether floating or frozen to the bottom, includes an inclusion-free layer overlying a layer of ice with tubular bubbles oriented parallel to the direction of growth. The clear ice may also be overlain by a discontinuous layer of bubbly snow ice. Backscatter is low (-16 to -22 dB) at the time of initial ice formation, probably due to the specular nature of the upper and lower ice surfaces causing the radar pulse to be reflected away from the radar. As the ice thickens during the autumn, backscatter rises steadily. Once the ice freezes to the lake bottom, regardless of the presence of foward scattering tubular bubbles, low backscatter values of -17 to -18 dB are caused by absorption of the radar signal in the lake bed. For ice that remains afloat all winter the ice-water interface and the tubular bubbles combine, presumably via an incoherent double-bounce mechanism, to cause maximum backscatter values of the order of -6 to -7 dB. The sigma(exp 0) saturates at -6 to -7 dB before maximum ice thickness and tubular bubble content are attained. A simple ice growth model suggests that the layer of ice with tubular bubbles need be only a few centimeters thick midway through the growth season to cause maximum backscatter from floating ice. During the spring thaw a previously unreported backscatter reversal is observed on the floating and grounded portions of the coastal lakes but not on the lakes farther inland. This reversal may be related to the ice surface topography and wetness plus the effects of a longer, cooler melt period by the coast. Time series of backscatter variations from shallow tundra lakes are a record of (1) the development of tubular bubbles in the ice and, by association, changes in the gas content of the underlying water and (2) the freezing of ice to the bottoms of the lakes and therefore lake bathymetry and water availability. SAR is also able to detect the onset of lake ice growth in autumn and the initiation of the spring thaw and thus has potential for monitoring high-altitude lake ice growth and decay processes in relation to climate variability.

Jeffries, M. O.↗

NASA NTRS2009

Molecular Modeling for Calculation of Mechanical Properties of Epoxies with Moisture Ingress

Atomistic models of epoxy structures were built in order to assess the effect of crosslink degree, moisture content and temperature on the calculated properties of a typical representative generic epoxy. Each atomistic model had approximately 7000 atoms and was contained within a periodic boundary condition cell with edge lengths of about 4 nm. Four atomistic models were built with a range of crosslink degree and moisture content. Each of these structures was simulated at three temperatures: 300 K, 350 K, and 400 K. Elastic constants were calculated for these structures by monitoring the stress tensor as a function of applied strain deformations to the periodic boundary conditions. The mechanical properties showed reasonably consistent behavior with respect to these parameters. The moduli decreased with decreasing crosslink degree with increasing temperature. The moduli generally decreased with increasing moisture content, although this effect was not as consistent as that seen for temperature and crosslink degree.

Clancy, Thomas C.↗

NASA NTRS1988

Visibility of the lunar crescent

A model designed to predict whether or not the moon will be visible under any set of observing conditions is presented. It explicitly includes atmospheric clarity, which is calculated from the site's altitude, latitude, temperature, relative humidity, aerosol content, and time of the year. This mathematical model is combined with a lunar and solar ephemeris to yield a computer program which will predict the date of the first crescent sighting from any location. A list of 201 observations of lunar visibility was collected to test this model and other models. It is found that criteria involving the moonset lagtime and the moon's age have zones of uncertainty which typically cover the entire earth.

Schaefer, Bradley E.↗

NASA NTRS1998

High Salt Diets, Bone Strength and Mineral Content of Mature Femur After Skeletal Unloading

It is known that high salt diets increase urinary calcium (Ca) loss, but it is not known whether this effect weakens bone during space flight. The Bone Hormone Lab has studied the effect of high salt diets on Ca balance and whole body Ca in a space flight model (2,8). Neither the strength nor mineral content of the femurs from these studies has been evaluated. The purpose of this study was to determine the effect of high salt diets (HiNa) and skeletal unloading on femoral bone strength and bone mineral content (BMC) in mature rats.

Liang, Michael T. C.↗

NASA NTRS1994

Carbon isotope effects associated with aceticlastic methanogenesis

The carbon isotope effects associated with synthesis of methane from acetate have been determined for Methanosarcina barkeri 227 and for methanogenic archaea in sediments of Wintergreen Lake, Michigan. At 37 degrees C, the 13C isotope effect for the reaction acetate (methyl carbon) --> methane, as measured in replicate experiments with M. barkeri, was - 21.3% +/- 0.3%. The isotope effect at the carboxyl portion of acetate was essentially equal, indicating participation of both positions in the rate-determining step, as expected for reactions catalyzed by carbon monoxide dehydrogenase. A similar isotope effect, - 19.2% +/- 0.3% was found for this reaction in the natural community (temperature = 20 degrees C). Given these observations, it has been possible to model the flow of carbon to methane within lake sediment communities and to account for carbon isotope compositions of evolving methane. Extension of the model allows interpretation of seasonal fluctuations in 13C contents of methane in other systems.

NASA Discipline Exobiology↗

DOE OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Pump House in East River Watershed, Colorado 2019-2024

This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors near Pump House at Mount Crested Butte in the East River Watershed. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format ER-X-Y, where ER refers to East River, X is the location identifier, and Y is the depth identifier at the same X (shallow Y=1). Specifically, ER-PHS, ER-LMC, ER-LMF, and ER-SMN are associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and ER-RBTn (upslope n=1) are sampling transects during the 2019 Rootball Campaign. The sample and location information can be found in metadata.csv. Sampling and Measurements Each sample falls into one of the three sampling methods – (1) intact cores, (2) repacked samples, or (3) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. Both intact cores and repacked samples were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores and in-situ soil sensors could be prioritized because these sampling methods are less destructive. While the repacked samples were packed to the target bulk density (estimated post-sampling, when sample volume was known), these samples had altered pore structures. Nevertheless, intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method also has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all files. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

EARTH SCIENCE > LAND SURFACE > SOILS↗

NASA NTRS1974

Mercury - Internal structure and thermal evolution

Mercury's thermal evolution and internal structure are modeled based on the planet's gross physical properties (which imply a high metallic iron content) and predictions for its chemistry made from the Lewis-Cameron model of condensation of the primitive solar nebula (which implies that Mercury may be composed only of those materials that condensed at temperatures near that of metallic iron condensation in the cooling nebula). Various heat sources, initial temperatures, and thermal conductivities are considered for a homogeneous model and a differentiated two-layer model. Density distributions are calculated from the mean density and estimates of the present-day temperature. The moment of inertia and the hydrostatic value of the second degree harmonic coefficient of Mercury's gravity field are found for the differentiated and undifferentiated models. These results should be useful for preliminary interpretation of the Mariner 10 measurements of Mercury's gravitational field.

Siegfried, R. W., II↗

NASA NTRSDate not supplied

Enhancements, Verification, and VMS Integration of VTOL Concept Vehicle Simulation Models

Advanced Rotorcraft Technology (ART) and the NASA Ames Aeromechanics branch have jointly developed FLIGHTLAB simulation models for Advanced Air Mobility (AAM) VTOL concept vehicles. The overarching purpose of the simulation model development is to establish a set of well defined reference vehicles for FLIGHTLAB users and the rotorcraft community. The ongoing research effort and enhancement of these AAM simulation models to fulfill the role of quality reference vehicles is this paper’s focus. The content of this paper expands on the established characteristics of these AAM models in three primary areas. First, enhancement of the lift+cruise and tiltwing models with elastic airframe properties is discussed. The process of setting up the elastic airframe model in FLIGHTLAB, as well as the impacts on flight characteristics are explained. The introduction of the elastic airframe modeling allows these models to be used in flight dynamics, loads, and vibration analysis of the configuration designs. Next, linear model generation from the enhanced simulation model is covered. Confirming the validity of the linearized models is of importance, as these linear models are utilized for flight control design and tuning for these experimental configurations. For the final focus, the progress towards implementation of these models into the NASA Ames Vertical Motion Simulator (VMS) is described. This task seeks to demonstrate the procedures of integrating a FLIGHTLAB flight simulation in the VMS environment, test fully integrated simulation with communication between flight dynamics, control, and propulsion models, and explore the essential aspects of simulation model integration in a full flight simulator environment. This includes I/O definition, initialization, trim, flying, etc. By expanding the capabilities of the AAM simulation models, they continue to develop as valuable and approachable modeling references.

VMS↗