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Autocorrelation function plots were used to determine if peak distortion data taken from a subscale inlet model can be used to predict peak distortion levels for a full scale flight test vehicle.
Stability audit plots were used to determine if peak distortion data taken from a subscale inlet model can be used to predict peak distortion levels for a full scale flight test vehicle.
Papers and viewgraphs from the conference are presented. Discussion topics include the IEEE Mass Storage System Reference Model, data archiving standards, high-performance storage devices, magnetic and magneto-optic storage systems, magnetic and optical recording technologies, high-performance helical scan recording systems, and low end helical scan tape drives. Additional discussion topics addressed the evolution of the identifiable unit for processing (file, granule, data set, or some similar object) as data ingestion rates increase dramatically, and the present state of the art in mass storage technology.
This Letter presents the result of a 3+1 sterile neutrino search using 10.7 yr of IceCube data. We analyze atmospheric muon neutrinos that traverse the Earth with energies ranging from 0.5 to 100 TeV, incorporating significant improvements in modeling neutrino flux and detector response compared to earlier studies. Notably, for the first time, we categorize data into starting and throughgoing events, distinguishing neutrino interactions with vertices inside or outside the instrumented volume, to improve energy resolution. The best-fit point for a 3+1 model is found to be at sin^{2}(2θ_{24})=0.16 and Δm_{41}^{2}=3.5 eV^{2}, which agrees with previous iterations of this Letter. The result is consistent with the null hypothesis of no sterile neutrinos with a p value of 3.1%.
The topside segment of the International Reference Ionosphere (IRI) electron density model (and also of the Bent model) is based on the limited amount of topside data available at the time (40,OOO Alouette 1 profiles). Being established from such a small database it is therefore not surprising that the models have well-known shortcomings, for example, at high solar activities. Meanwhile a large data base of close to 200,000 topside profiles from Alouette 1,2, and ISIS I, 2 has become available online. A program of automated scaling and inversion of a large volume of digitized ionograms adds continuously to this data pool. We have used the currently available ISIs/Alouette topside profiles to evaluate the IRI topside model and to investigate ways of improving the model. The IRI model performs generally well at middle latitudes and shows discrepancies at low and high latitudes and these discrepancies are largest during high solar activity. In the upper topside IRI consistently overestimates the measurements. Based on averages of the data-model ratios we have established correction factors for the IRI model. These factors vary with altitude, modified dip latitude, and local time.
Previous echocardiographic techniques for quantifying valvular regurgitation are limited by factors including uncertainties for orifice location and a hemispheric convergence assumption that often results in over- and underestimation of flow rate and regurgitant orifice area. Using computational fluid dynamics simulations, these factors were eliminated, allowing a more accurate assessment of regurgitation. A model was developed to allow automated quantification of regurgitant orifice diameter based on the centerline velocity data available from color M-mode echocardiography. The model, validated using in vitro unsteady flow data, demonstrated improved accuracy for orifice diameter (y=0.95x + 0.38, r=0.96) and volume (y=1.18x - 4.72, r=0.93).
We have compared theoretical H I models to a large area H I channel map centered at 1 = 205 deg, b = 0 deg. One set of models were calculated in which uncorrelated clouds of H I populate space. Of the cloud models considered, which include 'Spitzer'-type clouds and the McKee-Ostriker formulation, none were able to reproduce the data successfully. In another class of models, holes and holes with shells permeate an otherwise continuous H I medium. Although not entirely successful, these models contain characteristics that are similar to the data. In order to reproduce the amplitude of the H I variations on angular scales of over 1 deg, the volume occupied by ionized gas must be less than 30 percent, in sharp conflict with the models that suggest that the volume of the ISM is dominated by hot gas.
Qualification and certification of safety critical parts is a hurdle to the adoption of metallic additively manufactured components for aerospace vehicle applications. Challenges include variability in part properties due to inconsistent defect distribution and microstructure. Understanding of the process through finite element modeling (FEM), and process control through in-situ monitoring, may result in significant improvements; however, solutions useful to manufacturers will require large volumes of data and automated data utilization. Toward this end, a generalizable automated FEM calibration paradigm is developed. This paradigm leverages existing and novel tools from machine learning and uncertainty quantification to enable the automatic calibration of FEMs without requiring prior knowledge of the model performance across input parameter space, including meshing and solver settings, which can require time consuming manual model probing or cause noisy and inconsistent predictions. The result is a probabilistic distribution of calibrated and validated FEM input parameters targeting measured data.
Cloud droplet size distribution is one of the most fundamental subjects in cloud physics. Understanding of spatial distribution and small-scale fluctuations of cloud droplets is essential for both cloud physics and atmospheric radiation. For cloud physics, it relates to the coalescence growth of raindrops while for radiation, it has a strong impact on a cloud's radiative properties. Most of the existing cloud radiation and precipitation formation models assume that the mean number of drops with a given radius varies proportionally to volume. The analysis of microphysical data on liquid water drop sizes shows that, for sufficiently small volumes, the number is proportional to the drop size dependent power of the volume. For abundant small drops present, the exponent is 1 as assumed in the conventional approach. However, for rarer large drops, the exponents fall below unity. At small scales, therefore, the mean number of large drops decreases with volume at a slower rate than the conventional approach assumes, suggesting more large drops at these scales than conventional models account for; their impact is consequently underestimated. Size dependent models of spatial distribution of cloud drops that simulate the observed power laws show strong drop clustering, the more so the larger the drops. The degree of clustering is determined by the observed exponents. The strong clustering of large drops arises naturally from the observed power-law statistics. Current theories of photon-cloud interaction and warm rain formation will need radical revision in order to produce these statistics; their underlying equations are unable to yield the observed power law.
Icarus is a three-dimensional, unstructured, finite-volume material response solver developed at NASA Ames Research Center and has been recently used to analyze the material response of the Dragonfly capsule for a variety of problems. Since the Dragonfly capsule will be instrumented in a similar manner to the Mars 2020 and MSL capsules, it is important to assess how our current simulation tools can aid in understanding Dragonfly Entry Aerosciences Measurements (DrEAM). In this presentation, the Ares multi-physics tool that couples Icarus to the flow and radiation physics of the aerothermal environment will be used to better understand how modeling sensitivities might affect environment reconstructions and our understanding of measured data. Ares couples Icarus to the US3D flow solver and NERO, a reduced-order, finite-volume radiation transport solver and uses a customized time-scale management to enable coupled simulations for a large portion of the Dragonfly trajectory. As a result of the coupling, a more accurate and consistent assessment of modelling sensitivities to aerothermal reconstruction can be assessed. For example, the radiometer is sensitive to the quantity of CN in the flow, which is a byproduct of the free-stream methane but also the pyrolysis gas products injected into the boundary layer due to ablation of the heatshield. Ares will be used to conduct a simulation of the full Dragonfly capsule for 50 seconds of the trajectory. The different aerothermal and material response models will be discussed and the key results will be presented in terms of the simulated thermocouple and radiometer measurements on the backshell.
User models defined as any explicit process or procedure used to transform information extracted from remotely sensed data into a form useful as a resource management information input are discussed. The role of the user models as information, technological, and operations interfaces between the TERSSE and the resource managers is emphasized. It is recommended that guidelines and management strategies be developed for a systems approach to user model development.
Data on selected payload equipments (components) which were collected for the purpose of exercising the systems cost performance model are reported in an appendix. The reader should be aware that, although most of the data are accurate, approximations based on engineering judgment and experience are used wherever actual data were unavailable. The approximations are justified by the objective of the study which was to develop a cost/performance model. Assuming that the model is successfully developed and is accepted for use by a body of users, the data base should be expanded and the approximations replaced by actual data.
A framework to infer volume water fraction, soluble fraction and dry size distributions of fine mode aerosol from multi-angle, multi-spectral polarimetry retrievals of column-averaged ambient aerosol properties is presented. The method is applied to observations of the Research Scanning Polarimeter (RSP) obtained during two NASA aircraft campaigns, namely the Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) and the Cloud, Aerosol, and Monsoon Processes-Philippines Experiment (CAMP2Ex). All aerosol retrievals are statistically evaluated using in situ data. Volume water fraction is inferred from the retrieved ambient real part of the refractive index, assuming a dry refractive index of 1.54 and by applying a volume mixing rule to obtain the effective ambient refractive index. The uncertainties in inferred volume water fraction resulting from this simplified model are discussed and estimated to be lower than 0.2 and decreasing with increasing volume water fraction. The daily mean retrieved volume water fractions correlate well with the in situ values with a mean absolute difference of 0.09. Polarimeter-retrieved ambient effective radius for daily data is shown to increase as a function of volume water fraction as expected. Furthermore, the effective variance of the size distributions also increases with increasing effective radius, which we show is consistent with an external mixture of soluble and insoluble aerosol. The relative variations of effective radius and variance over an observation period are then used to estimate the soluble fraction of the aerosol. Daily results of soluble fraction correlate well with in situ observed sulfate mass fraction with a correlation coefficient of 0.79. Subsequently, inferred water and soluble fractions are used to derive dry fine-mode size distributions from their ambient counterparts. While dry effective radii obtained in situ and from RSP show similar ranges, in situ values are generally substantially smaller during the ACTIVATE deployments, which may be due to biases in RSP retrievals or in the in situ observations, or both. Both RSP and in situ observations indicate the dominance of aerosol with low hygroscopicity during the ACTIVATE and CAMP2Ex campaigns. Furthermore, RSP indicates a high degree of external mixing of particles with low and high hygroscopicity. These retrievals of fine mode water volume fraction and soluble fraction may be used for the evaluation of water uptake in atmospheric models. Furthermore, the framework allows to estimate the variation in the concentration of fine-mode aerosol larger than a specific dry radius limit, which can be used as a proxy for the variation in cloud condensation nucleus concentrations. This framework may be applied to multi-angle, multi-spectral satellite data expected to be available in the near future.
This paper compares four continental ice sheet radar altimeter retracking algorithms using airborne radar and laser altimeter data taken over the Greenland ice sheet in 1991. The refurbished Advanced Application Flight Experiment (AAFE) airborne radar altimeter has a large range window and stores the entire return waveform during flight. Once the return waveforms are retracked, or post-processed to obtain the most accurate altitude measurement possible, they are compared with the high-precision Airborne Oceanographic Lidar (AOL) altimeter measurements. The AAFE waveforms show evidence of varying degrees of both surface and volume scattering from different regions of the Greenland ice sheet. The AOL laser altimeter, however, obtains a return only from the surface of the ice sheet. Retracking altimeter waveforms with a surface scattering model results in a good correlation with the laser measurements in the wet and dry-snow zones, but in the percolation region of the ice sheet, the deviation between the two data sets is large due to the effects of subsurface and volume scattering. The Martin et al model results in a lower bias than the surface scattering model, but still shows an increase in the noise level in the percolation zone. Using an Offset Center of Gravity algorithm to retrack altimeter waveforms results in measurements that are only slightly affected by subsurface and volume scattering and, despite a higher bias, this algorithm works well in all regions of the ice sheet. A cubic spline provides retracked altitudes that agree with AOL measurements over all regions of Greenland. This method is not sensitive to changes in the scattering mechanisms of the ice sheet and it has the lowest noise level and bias of all the retracking methods presented.
Segmented polyureas (PUa) are industrially important class of polymers widely used in coatings, sealant, and adhesive applications. Here, in this paper, we report synthesis, characterization, and modeling of Isophorone Diisocyanate-Diethyl-Toluene-Diamine-Polyether amine (IPDI-DETDA-PO PUa) with varied hard segment contents of 20, 30, and 40 weight percent. For each of the three materials, we study its structure and phase behavior using FTIR, DSC, and TEM, and clearly show the presence of microphase separation between the hard and soft nanodomains. We then measure the linear viscoelastic response of the PUa-s using DMA (frequency sweeps at multiple temperatures). The DMA data are shown to obey the time-temperature superposition. Finally, we develop a new micromechanical model describing the DMA results; the model describes a phase-separated PUa as two “Fractional-order Maxwell gels” branches, connected in parallel, with the first FMG branch representing the “percolated hard phase" and the second one modeling the “filled soft phase". In agreement with the earlier thermodynamic theories, the volume-fraction of the percolated hard phase is related to the hard segment weight-fraction (HSWF), defined as the combined mass of IPDI and DETDA normalized to the total mass of the polymer. The data and model are found to be in a good qualitative and quantitative agreement.
Abstract Motivation High-throughput sequencing technologies have greatly facilitated microbiome research and have generated a large volume of microbiome data with the potential to answer key questions regarding microbiome assembly, structure and function. Cluster analysis aims to group features that behave similarly across treatments, and such grouping helps to highlight the functional relationships among features and may provide biological insights into microbiome networks. However, clustering microbiome data are challenging due to the sparsity and high dimensionality. Results We propose a model-based clustering method based on Poisson hurdle models for sparse microbiome count data. We describe an expectation–maximization algorithm and a modified version using simulated annealing to conduct the cluster analysis. Moreover, we provide algorithms for initialization and choosing the number of clusters. Simulation results demonstrate that our proposed methods provide better clustering results than alternative methods under a variety of settings. We also apply the proposed method to a sorghum rhizosphere microbiome dataset that results in interesting biological findings. Availability and implementation R package is freely available for download at https://cran.r-project.org/package=PHclust. Supplementary information Supplementary data are available at Bioinformatics online.
Property models are becoming more widely adopted by commercial Calphad databases, but they are not nearly as common in non-commercial or traditional academic Calphad databases. A primary driver is that user-friendly Calphad modeling tools that support property models are not widely available. Here we present new property modeling capabilities that have been implemented in ESPEI (the Extensible, Self-optimizing Phase Equilibrium Infrastructure). These capabilities include both generating property model parameters from data and improvements to the algorithmic selection of the most appropriate model from a series of candidates. Additionally, two illustrative examples are given that use ESPEI to fit different property models. First, we generate molar volume model parameters for Group IV, V, and VI refractory BCC alloys based on the model by Lu et al. (2005). Second, we demonstrate the extensibility of ESPEI’s property modeling capabilities by implementing a custom PyCalphad model for BCC elastic stiffness parameters to generate and compare parameters to the ones assessed by Marker et al. (2018) using the same data. Property models generated by ESPEI can be used in PyCalphad or further optimized with uncertainty quantification using ESPEI.
Surface-mass-balance (SMB) and firn-densification (FD) models are widely used in altimetry studies as a tool to separate atmospheric-driven from ice-dynamics-driven ice-sheet mass changes and to partition observed volume changes into ice-mass changes and firn-air-content changes. Until now, SMB models have been principally validated based on comparison with ice core and weather station data or comparison with widely separated flight radar-survey flight lines. Firn-densification models have been primarily validated based on their ability to match net densification over decades, as recorded in firn cores, and the short-term time-dependent component of densification has rarely been evaluated at all. The advent of systematic ice-sheet-wide repeated ice-surface-height measurements from ICESat-2 (the Ice Cloud, and land Elevation Satellite, 2) allows us to measure the net surface-height change of the Greenland ice sheet at quarterly resolution and compare the measured surface-height differences directly with those predicted by three FD–SMB models: MARv3.5.11 (Modèle Atmosphérique Régional version 3.5.11) and GSFCv1.1 and GSFCv1.2 (the Goddard Space Flight Center FD–SMB models version 1.1 and 1.2). By segregating the data by season and elevation, and based on the timing and magnitude of modelled processes in areas where we expect minimal ice-dynamics-driven height changes, we investigate the models' accuracy in predicting atmospherically driven height changes. We find that while all three models do well in predicting the large seasonal changes in the low-elevation parts of the ice sheet where melt rates are highest, two of the models (MARv3.5.11 and GSFCv1.1) systematically overpredict, by around a factor of 2, the magnitude of height changes in the high-elevation parts of the ice sheet, particularly those associated with melt events. This overprediction seems to be associated with the melt sensitivity of the models in the high-elevation part of the ice sheet. The third model, GSFCv1.2, which has an updated high-elevation melt parameterization, avoids this overprediction.