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Trapped Melt in IIIAB Irons: Solid/Liquid Elemental Partitioning During the Fractionation of the IIIAB Magma
Group IIIAB, the largest iron-meteorite group, shows compositional trends (including a three-order-of-magnitude It concentration range) indicating that it formed by fractional crystallization of a metallic magma. Because about 200 irons are available, and all degrees of crystallization are well represented, IIIAB offers an excellent set of samples for the study of crystallization at all depths of the asteroidal core. On log-log Ir-Au, and Ir-As diagrams IIIAB forms a broad band; the breadth represents real meteorite-to-meteorite variations, far outside experimental or sampling uncertainties. A successful model must explain the width of this band; I suggest that it mainly resulted from the trapping of parental magma within the crystallizing solid. Because S is essentially insoluble in metal, the abundance of FeS is a measure of the fraction of trapped liquid. The trapped-melt model is supported by the observation that irons having higher S contents plot closer to the inferred composition of the magmatic parental liquid. The lowest S values are found in the irons occupying the left envelope of the IIIAB Ir-Au or Ir-As compositional fields, thus it is this set of irons that should be interpreted as the solid products of a fractionating magma. This simplifies the modeling of the crystallization process and allows inferences regarding the distribution ratios for other elements in the evolved IIIAB system. The large (multiton) Cape York irons show wide variations in their trapped-melt fractions; their compositions seem best understood in terms of a low initial S content of the IIIAB magma, about 20 mg/g. The inferred initial IIIAB distribution coefficient for Ir, 4.6, is much higher than published values based on laboratory studies of low-S systems; I suggest that low-S (and low-P) partition-ratio measurements tend to err in the direction of unity. In IIIAB distribution coefficients for Au, As, and Ni were still < 1 when the most evolved IIIAB irons formed, another indication of a low initial S content.
Copper accumulation and the effect of chelation treatment on cerebral amyloid angiopathy compared to parenchymal amyloid plaques
Abstract not provided
The cosmological bootstrap: inflationary correlators from symmetries and singularities
Scattering amplitudes at weak coupling are highly constrained by Lorentz invariance, locality and unitarity, and depend on model details only through coupling constants and the particle content of the theory. For example, four-particle amplitudes are analytic for contact interactions and have simple poles with appropriately positive residues for tree-level exchange. In this paper, we develop an understanding of inflationary correlators which parallels that of flat-space scattering amplitudes. Specifically, we study slow-roll inflation with weak couplings to extra massive particles, for which all correlation functions are controlled by an approximate conformal symmetry on the boundary of the spacetime. After systematically classifying all possible contact terms in de Sitter space, we derive an analytic expression for the four-point function of conformally coupled scalars mediated by the tree-level exchange of massive scalars. Conformal symmetry implies that the correlator satisfies a pair of differential equations with respect to spatial momenta, encoding bulk time evolution in purely boundary terms. The absence of unphysical singularities (and the correct normalization of physical ones) completely fixes this correlator. Moreover, a “spin-raising” operator relates it to the correlators associated with the exchange of particles with spin, while “weight-shifting” operators map it to the four-point function of massless scalars. We explain how these de Sitter four-point functions can be perturbed to obtain inflationary three-point functions. Using our formalism, we reproduce many classic results in the literature, such as the three-point function of slow-roll inflation, and provide a complete classification of all inflationary three- and four-point functions arising from weakly broken conformal symmetry. Remarkably, the inflationary bispectrum associated with the exchange of particles with arbitrary spin is completely characterized by the soft limit of the simplest scalar-exchange four-point function of conformally coupled scalars and a series of contact terms. Finally, we demonstrate that the inflationary correlators contain flat-space scattering amplitudes via a suitable analytic continuation of the external momenta, which can also be directly connected with the signals for particle production seen in the squeezed limit.
Model Intercomparison of Indirect Aerosol Effects
Modeled differences in predicted effects are increasingly used to help quantify the uncertainty of these effects. Here, we examine modeled differences in the aerosol indirect effect in a series of experiments that help to quantify how and why model-predicted aerosol indirect forcing varies between models. The experiments start with an experiment in which aerosol concentrations, the parameterization of droplet concentrations and the autoconversion scheme are all specified and end with an experiment that examines the predicted aerosol indirect forcing when only aerosol sources are specified. Although there are large differences in the predicted liquid water path among the models, the predicted aerosol first indirect effect for the first experiment is rather similar, about -0.6 W/sq m to -0.7 W/sq m. Changes to the autoconversion scheme can lead to large changes in the liquid water path of the models and to the response of the liquid water path to changes in aerosols. Adding an autoconversion scheme that depends on the droplet concentration caused a larger (negative) change in net outgoing shortwave radiation compared to the 1st indirect effect, and the increase varied from only 22% to more than a factor of three. The change in net shortwave forcing in the models due to varying the autoconversion scheme depends on the liquid water content of the clouds as well as their predicted droplet concentrations, and both increases and decreases in the net shortwave forcing can occur when autoconversion schemes are changed. The parameterization of cloud fraction within models is not sensitive to the aerosol concentration, and, therefore, the response of the modeled cloud fraction within the present models appears to be smaller than that which would be associated with model "noise". The prediction of aerosol concentrations, given a fixed set of sources, leads to some of the largest differences in the predicted aerosol indirect radiative forcing among the models, with values of cloud forcing ranging from -0.3 W/sq m to -1.4W/sq m. Thus, this aspect of modeling requires significant improvement in order to improve the prediction of aerosol indirect effects.
(GO)2-SIM: a GCM-Oriented Ground-Observation Forward-Simulator Framework for Objective Evaluation of Cloud and Precipitation Phase
General circulation model (GCM) evaluation using ground-based observations is complicated by inconsistencies in hydrometeor and phase definitions. Here we describe (GO)2-SIM, a forward simulator designed for objective hydrometeor-phase evaluation, and assess its performance over the North Slope of Alaska using a 1-year GCM simulation. For uncertainty assessment, 18 empirical relationships are used to convert model grid-average hydrometeor (liquid and ice, cloud, and precipitation) water contents to zenith polarimetric micropulse lidar and Ka-band Doppler radar measurements, producing an ensemble of 576 forward-simulation realizations. Sensor limitations are represented in forward space to objectively remove from consideration model grid cells with undetectable hydrometeor mixing ratios, some of which may correspond to numerical noise.Phase classification in forward space is complicated by the inability of sensors to measure ice and liquid signals distinctly. However, signatures exist in lidar–radar space such that thresholds on observables can be objectively estimated and related to hydrometeor phase. The proposed phase-classification technique leads to misclassification in fewer than 8% of hydrometeor-containing grid cells. Such misclassifications arise because, while the radar is capable of detecting mixed-phase conditions, it can mistake water- for ice-dominated layers. However, applying the same classification algorithm to forward-simulated and observed fields should generate hydrometeor-phase statistics with similar uncertainty. Alternatively, choosing to disregard how sensors define hydrometeor phase leads to frequency of occurrence discrepancies of up to 40%. So, while hydrometeor-phase maps determined in forward space are very different from model "reality" they capture the information sensors can provide and thereby enable objective model evaluation.
Cryogenic Pressure Control Modeling for Ellipsoidal Space Tanks in Reduced Gravity
A computational fluid dynamics (CFD) model is developed to simulate pressure control of an ellipsoidal-shaped liquid hydrogen tank under external heating in low gravity. Pressure control is provided by an axial jet thermodynamic vent system (TVS) centered within the vessel that injects cooler liquid into the tank, mixing the contents and reducing tank pressure. The two-phase cryogenic tank model considers liquid hydrogen in its own vapor with liquid density varying with temperature only and a fully compressible ullage. The axisymmetric model is developed using a custom version of the commercially available FLOW-3D software and simulates low gravity extrapolations of engineering checkout tests performed at Marshall Space Flight Center in 1999 in support of the Solar Thermal Upper Stage Technology Demonstrator (STUSTD) program. Model results illustrate that stable low gravity liquid-gas interfaces are maintained during all phases of the pressure control cycle. Steady and relatively smooth ullage pressurization rates are predicted. This work advances current low gravity CFD modeling capabilities for cryogenic pressure control and aids the development of a low cost CFD-based design process for space hardware.
Cryogenic Pressure Control Modeling for Ellipsoidal Space Tanks
A computational fluid dynamics (CFD) model is developed to simulate pressure control of an ellipsoidal-shaped liquid hydrogen tank under external heating in normal gravity. Pressure control is provided by an axial jet thermodynamic vent system (TVS) centered within the vessel that injects cooler liquid into the tank, mixing the contents and reducing tank pressure. The two-phase cryogenic tank model considers liquid hydrogen in its own vapor with liquid density varying with temperature only and a fully compressible ullage. The axisymmetric model is developed using a custom version of the commercially available FLOW-31) software. Quantitative model validation is ,provided by engineering checkout tests performed at Marshall Space Flight Center in 1999 in support of the Solar Thermal Upper Stage_ Technology Demonstrator (STUSTD) program. The engineering checkout tests provide cryogenic tank self-pressurization test data at various heat leaks and tank fill levels. The predicted self-pressurization rates, ullage and liquid temperatures at discrete locations within the STUSTD tank are in good agreement with test data. The work presented here advances current CFD modeling capabilities for cryogenic pressure control and helps develop a low cost CFD-based design process for space hardware.
Modeling and parameterization of horizontally inhomogeneous cloud radiative properties
One of the fundamental difficulties in modeling cloud fields is the large variability of cloud optical properties (liquid water content, reflectance, emissivity). The stratocumulus and cirrus clouds, under special consideration for FIRE, exhibit spatial variability on scales of 1 km or less. While it is impractical to model individual cloud elements, the research direction is to model a statistical ensembles of cloud elements with mean-cloud properties specified. The major areas of this investigation are: (1) analysis of cloud field properties; (2) intercomparison of cloud radiative model results with satellite observations; (3) radiative parameterization of cloud fields; and (4) development of improved cloud classification algorithms.
Ice-Accretion Test Results for Three Large-Scale Swept-Wing Models in the NASA Icing Research Tunnel
Icing simulation tools and computational fluid dynamics codes are reaching levels of maturity such that they are being proposed by manufacturers for use in certification of aircraft for flight in icing conditions with increasingly less reliance on natural-icing flight testing and icing-wind-tunnel testing. Sufficient high-quality data to evaluate the performance of these tools is not currently available. The objective of this work was to generate a database of ice-accretion geometry that can be used for development and validation of icing simulation tools as well as for aerodynamic testing. Three large-scale swept wing models were built and tested at the NASA Glenn Icing Research Tunnel (IRT). The models represented the Inboard (20 percent semispan), Midspan (64 percent semispan) and Outboard stations (83 percent semispan) of a wing based upon a 65 percent scale version of the Common Research Model (CRM). The IRT models utilized a hybrid design that maintained the full-scale leading-edge geometry with a truncated afterbody and flap. The models were instrumented with surface pressure taps in order to acquire sufficient aerodynamic data to verify the hybrid model design capability to simulate the full-scale wing section. A series of ice-accretion tests were conducted over a range of total temperatures from -23.8 to -1.4 C with all other conditions held constant. The results showed the changing ice-accretion morphology from rime ice at the colder temperatures to highly 3-D scallop ice in the range of -11.2 to -6.3 C. Warmer temperatures generated highly 3-D ice accretion with glaze ice characteristics. The results indicated that the general scallop ice morphology was similar for all three models. Icing results were documented for limited parametric variations in angle of attack, drop size and cloud liquid-water content (LWC). The effect of velocity on ice accretion was documented for the Midspan and Outboard models for a limited number of test cases. The data suggest that there are morphological characteristics of glaze and scallop ice accretion on these swept-wing models that are dependent upon the velocity. This work has resulted in a large database of ice-accretion geometry on large-scale, swept-wing models.
Ice-Accretion Test Results for Three Large-Scale Swept-Wing Models in the NASA Icing Research Tunnel
Icing simulation tools and computational fluid dynamics codes are reaching levels of maturity such that they are being proposed by manufacturers for use in certification of aircraft for flight in icing conditions with increasingly less reliance on natural-icing flight testing and icing-wind-tunnel testing. Sufficient high-quality data to evaluate the performance of these tools is not currently available. The objective of this work was to generate a database of ice-accretion geometry that can be used for development and validation of icing simulation tools as well as for aerodynamic testing. Three large-scale swept wing models were built and tested at the NASA Glenn Icing Research Tunnel (IRT). The models represented the Inboard (20% semispan), Midspan (64% semispan) and Outboard stations (83% semispan) of a wing based upon a 65% scale version of the Common Research Model (CRM). The IRT models utilized a hybrid design that maintained the full-scale leading-edge geometry with a truncated afterbody and flap. The models were instrumented with surface pressure taps in order to acquire sufficient aerodynamic data to verify the hybrid model design capability to simulate the full-scale wing section. A series of ice-accretion tests were conducted over a range of total temperatures from -23.8 deg C to -1.4 deg C with all other conditions held constant. The results showed the changing ice-accretion morphology from rime ice at the colder temperatures to highly 3-D scallop ice in the range of -11.2 deg C to -6.3 deg C. Warmer temperatures generated highly 3-D ice accretion with glaze ice characteristics. The results indicated that the general scallop ice morphology was similar for all three models. Icing results were documented for limited parametric variations in angle of attack, drop size and cloud liquid-water content (LWC). The effect of velocity on ice accretion was documented for the Midspan and Outboard models for a limited number of test cases. The data suggest that there are morphological characteristics of glaze and scallop ice accretion on these swept-wing models that are dependent upon the velocity. This work has resulted in a large database of ice-accretion geometry on large-scale, swept-wing models.
Interpretable machine learning-guided design of Fe-based soft magnetic alloys
Here, we present a machine learning (ML) guided approach to predict saturation magnetization (𝑀 S ) and coercivity (𝐻 C ) in Fe-rich soft magnetic alloys, particularly Fe-Si-B systems. ML models trained on experimental data reveal that increasing Si and B content reduces 𝑀 S from 1.81 T (DFT ≈ 2.04 T) to ≈1.54 T (DFT ≈ 1.56T) in Fe-Si-B, which is attributed to decreased magnetic density and structural modifications. Experimental validation of ML predicted magnetic saturation on Fe-1Si-1B (2.09 T), Fe-5Si-5B (2.01 T), and Fe-10Si-10B (1.54 T) alloy compositions further supports our findings. These trends are consistent with density functional theory predictions, which link increased electronic disorder and band broadening to lower 𝑀 S values. Experimental validation on selected alloys confirms the predictive accuracy of the ML model, with good agreement across compositions. Beyond predictive accuracy, detailed uncertainty quantification and model interpretability including through feature importance and partial dependence analysis reveal that 𝑀 S is governed by a nonlinear interplay between Fe content and early transition metal ratios, while 𝐻 C is more sensitive to processing conditions such as ribbon thickness and thermal treatment windows. The ML framework was further applied to Fe-Si-B/Cr/Cu/Zr/Nb alloys in a pseudoquaternary compositional space, which shows comparable magnetic properties to NANOMET (Fe 84.8 Si 0.5 B 9.4 Cu 0.8 P 3.5 C 1 ), FINEMET (Fe 73.5 Si 13.5 B 9 Cu 1 Nb 3 ), NANOPERM (Fe 88 Zr 7 B 4 Cu 1 ), and HITPERM (Fe 44 Co 44 Zr 7 B 4 Cu 1 . Our findings demonstrate the potential of the ML framework for accelerated search of high-performance soft magnetic materials.
Comparisons of Mixed-Phase Icing Cloud Simulations with Experiments Conducted at the NASA Propulsion Systems Laboratory
This paper builds on previous work that compares numerical simulations of mixed-phase icing clouds with experimental data. The model couples the thermal interaction between ice particles and water droplets of the icing cloud with the flowing air of an icing wind tunnel for simulation of NASA Glenn Research Centers (GRC) Propulsion Systems Laboratory (PSL). Measurements were taken during the Fundamentals of Ice Crystal Icing Physics Tests at the PSL tunnel in March 2016. The tests simulated ice-crystal and mixed-phase icing that relate to ice accretions within turbofan engines. Experimentally measured air temperature, humidity, total water content, liquid and ice water content, as well as cloud particle size, are compared with model predictions. The model showed good trend agreement with experimentally measured values, but often over-predicted aero-thermodynamic changes. This discrepancy is likely attributed to radial variations that this one-dimensional model does not address. One of the key findings of this work is that greater aero-thermodynamic changes occur when humidity conditions are low. In addition a range of mixed-phase clouds can be achieved by varying only the tunnel humidity conditions, but the range of humidities to generate a mixed-phase cloud becomes smaller when clouds are composed of smaller particles. In general, the model predicted melt fraction well, in particular with clouds composed of larger particle sizes.
Modeling Spatial Distribution of Snow Water Equivalent by Combining Meteorological and Satellite Data with Lidar Maps
Abstract An accurate characterization of the water content of snowpack, or snow water equivalent (SWE), is necessary to quantify water availability and constrain hydrologic and land surface models. Recently, airborne observations (e.g., lidar) have emerged as a promising method to accurately quantify SWE at high resolutions (scales of ∼100 m and finer). However, the frequency of these observations is very low, typically once or twice per season in the Rocky Mountains of Colorado. Here, we present a machine learning framework that is based on random forests to model temporally sparse lidar-derived SWE, enabling estimation of SWE at unmapped time points. We approximated the physical processes governing snow accumulation and melt as well as snow characteristics by obtaining 15 different variables from gridded estimates of precipitation, temperature, surface reflectance, elevation, and canopy. Results showed that, in the Rocky Mountains of Colorado, our framework is capable of modeling SWE with a higher accuracy when compared with estimates generated by the Snow Data Assimilation System (SNODAS). The mean value of the coefficient of determination R 2 using our approach was 0.57, and the root-mean-square error (RMSE) was 13 cm, which was a significant improvement over SNODAS (mean R 2 = 0.13; RMSE = 20 cm). We explored the relative importance of the input variables and observed that, at the spatial resolution of 800 m, meteorological variables are more important drivers of predictive accuracy than surface variables that characterize the properties of snow on the ground. This research provides a framework to expand the applicability of lidar-derived SWE to unmapped time points. Significance Statement Snowpack is the main source of freshwater for close to 2 billion people globally and needs to be estimated accurately. Mountainous snowpack is highly variable and is challenging to quantify. Recently, lidar technology has been employed to observe snow in great detail, but it is costly and can only be used sparingly. To counter that, we use machine learning to estimate snowpack when lidar data are not available. We approximate the processes that govern snowpack by incorporating meteorological and satellite data. We found that variables associated with precipitation and temperature have more predictive power than variables that characterize snowpack properties. Our work helps to improve snowpack estimation, which is critical for sustainable management of water resources.
Expression of a bacterial 3-dehydroshikimate dehydratase (QsuB) reduces lignin and improves biomass saccharification efficiency in switchgrass (Panicum virgatum L.)
Abstract Background Lignin deposited in plant cell walls negatively affects biomass conversion into advanced bioproducts. There is therefore a strong interest in developing bioenergy crops with reduced lignin content or altered lignin structures. Another desired trait for bioenergy crops is the ability to accumulate novel bioproducts, which would enhance the development of economically sustainable biorefineries. As previously demonstrated in the model plant Arabidopsis, expression of a 3-dehydroshikimate dehydratase in plants offers the potential for decreasing lignin content and overproducing a value-added metabolic coproduct (i.e., protocatechuate) suitable for biological upgrading. Results The 3-dehydroshikimate dehydratase QsuB from Corynebacterium glutamicum was expressed in the bioenergy crop switchgrass ( Panicum virgatum L.) using the stem-specific promoter of an O-methyltransferase gene ( pShOMT ) from sugarcane. The activity of pShOMT was validated in switchgrass after observation in-situ of beta-glucuronidase (GUS) activity in stem nodes of plants carrying a pShOMT::GUS fusion construct. Under controlled growth conditions, engineered switchgrass lines containing a pShOMT::QsuB construct showed reductions of lignin content, improvements of biomass saccharification efficiency, and accumulated higher amount of protocatechuate compared to control plants. Attempts to generate transgenic switchgrass lines carrying the QsuB gene under the control of the constitutive promoter pZmUbi-1 were unsuccessful, suggesting possible toxicity issues associated with ectopic QsuB expression during the plant regeneration process. Conclusion This study validates the transfer of the QsuB engineering approach from a model plant to switchgrass. We have demonstrated altered expression of two important traits: lignin content and accumulation of a co-product. We found that the choice of promoter to drive QsuB expression should be carefully considered when deploying this strategy to other bioenergy crops. Field-testing of engineered QsuB switchgrass are in progress to assess the performance of the introduced traits and agronomic performances of the transgenic plants.
A numerical modeling study of a Montana thunderstorm. I - Model results versus observations involving nonelectrical aspects. II - Model results versus observations involving electrical aspects
Model results and the observed cloud behavior are examined in terms of nonelectrical and electrical aspects of a thunderstorm. The characteristics of the two-dimensional, time-dependent atmospheric electricity model used to simulate the cloud observations of July 19, 1981 in Miles City, Montana are described. The interactions of the dynamics and microphysics of the cloud with the charge separation mechanisms are analyzed. It is observed that the model accurately represents many of the observed characteristics of the cloud; however, the cloud base height, maximum liquid water content, and the time from first formation of precipitation until it reaches the ground are not accurately modeled. It is found that the model adequately represents the electrical field structure of the cloud and the electrical field strengths.
An infrared sky model based on the IRAS point source data
A detailed model for the infrared point source sky is presented that comprises geometrically and physically realistic representations of the galactic disk, bulge, spheroid, spiral arms, molecular ring, and absolute magnitudes. The model was guided by a parallel Monte Carlo simulation of the Galaxy. The content of the galactic source table constitutes an excellent match to the 12 micrometer luminosity function in the simulation, as well as the luminosity functions at V and K. Models are given for predicting the density of asteroids to be observed, and the diffuse background radiance of the Zodiacal cloud. The model can be used to predict the character of the point source sky expected for observations from future infrared space experiments.
Time-dependent water vapor desorption isotherm model of hardened cement paste
Highlights: • Short-term and long-term desorption isotherms of hardened cement paste are reviewed. • Water content at RH depends on both thermodynamic and microstructural equilibria. • An extended GAB equation is proposed to fit the cavitation drop during desorption. • A dynamic time-dependent sorption isotherm model is shown and used for simulations. • Numerical results capture anomalous drying in the same RH as seen experimentally. This study aims to enhance moisture transport modeling by elucidating the so-called anomalous water diffusion in cementitious materials. Water desorption isotherms are measured from samples at various drying stages using techniques with different durations to quantify the impact of the drying duration on the water sorption ability. A single water sorption isotherm does not solely give a relationship between the water content and relative humidity but also represents the state of microstructure. The continuous evolution of a desorption isotherm due to drying-induced microstructural rearrangement is demonstrated. For numerical modeling, the microstructural alteration can be explicitly considered through a dynamic desorption isotherm model, which governs the local thermodynamic equilibrium at the capillary meniscus. This approach is implemented into a multiphase transport code, whose ability to predict drying is validated using literature data. Finally, the effect of prolonged drying on the colloidal nature of the calcium-silicate-hydrate gel is discussed.