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At least 1,369 records · Page 76

Thermal Properties of A Solar Coronal Cavity Observed with the X-Ray Telescope on Hinode

Coronal cavities are voids in coronal emission often observed above high latitude filament channels. Sometimes, these cavities have areas of bright X-ray emission in their centers. In this study, we use data from the X-ray Telescope (XRT) on the Hinode satellite to examine the thermal emission properties of a cavity observed during July 2008 that contains bright X-ray emission in its center. Using ratios of XRT filters, we find evidence for elevated temperatures in the cavity center. The area of elevated temperature evolves from a ring-shaped structure at the beginning of the observation, to an elongated structure two days later, finally appearing as a compact round source four days after the initial observation. We use a morphological model to fit the cavity emission, and find that a uniform structure running through the cavity does not fit the observations well. Instead, the observations are reproduced by modeling several short cylindrical cavity "cores" with different parameters on different days. These changing core parameters may be due to some observed activity heating different parts of the cavity core at different times. We find that core temperatures of 1.75 MK, 1.7 MK and 2.0 MK (for July 19, July 21 and July 23, respectively) in the model lead to structures that are consistent with the data, and that line-of-sight effects serve to lower the effective temperature derived from the filter ratio.

Reeves, Katherine K.↗

Comparison of Likelihood Methods for Generalized Linear Mixed Models with Application to Quiet Supersonic Flights 2018 Data

Repeated measurement will be a feature of the survey data collected during the Quesst missionX-59 community response tests (CRT). Since each participant will report his or her categorical level of annoyance in response to multiple events, the responses from any single individual may be correlated with one another. Several models within the class of generalized linear mixed models (GLMM) are pertinent to the analysis of correlated categorical outcomes; the random intercept logistic regression model is one example. Both Bayesian and frequentist methods for fitting these models are available, with frequentist methods relying on some form of approximation (of either an integral or the integrand) that appears in the marginal likelihood function. Given several anticipated similarities of the X-59 CRT data to data collected during a past risk reduction, Quiet Supersonic Flights 2018 (QSF18), this short note is intended to create awareness. It documents an instance in which a reported population average dose-response relationship derived from QSF18 single event data was distorted by the integral approximation applied in likelihood-based methods. We review some of the available literature on the topic, compare the outputs of several different computational approaches implemented in available statistical software, and present simple corrective actions that may be useful during the Quesst mission.

dose-response model↗

Probing Galaxy assembly bias in BOSS galaxies using void probabilities

ABSTRACT We measure the void probability function (VPF) of galaxies in the Baryon Oscillation Spectroscopic Survey (BOSS). The VPF provides complementary information to standard two-point statistics in that it is sensitive to galaxy bias in the most extreme underdensities in the cosmic web. Thus, the VPF is ideal for testing whether halo occupation of galaxies depends on large-scale density, an effect known as galaxy assembly bias. We find that standard HOD model – one parameterized by halo mass only – fit only to the two-point function, accurately predicts the VPF. Additionally, for HOD models where density dependence is explicitly incorporated, the best-fitting models fit to the combination of the correlation function and the VPF have zero density dependence. Thus, galaxy assembly bias is not a strong source of systematic uncertainty when modelling the clustering of massive galaxies.

Walsh, Kilian↗

Derivation of surface properties from Magellan altimetry data

The fit of the Hagfors model to the Magellan altimetry data provides a means to characterize the surface properties of Venus. However, the derived surface properties are only meaningful if the model provides a good representation of the data. The Hagfors model provides a good representation of the data. The Hagfors model is generally a realistic fit to surface scattering properties of a nadir-directed antenna such as the Magellan altimeter; however, some regions of the surface of Venus are poorly described by the existing model, according to the goodness of fit parameter provided on the ARCDR CD-ROMs. Poorly characterized regions need to be identified and fit to new models in order to derive more accurate surface properties for use in inferring the geological processes that affect the surface in those regions. We have compared the goodness of fit of the Hagfors model to the distribution of features across the planet, and preliminary results show a correlation between steep topographic slopes and poor fits to the standard model, as has been noticed by others. In this paper, we investigate possible relations between many classes of features and the ability of the Hagfors model to fit the observed echo profiles. In the regions that are not well characterized by existing models, we calculate new models that compensate for topographic relief in order to derive improved estimates of surface properties. Areas investigated to date span from longitude 315 through 45, at all latitudes covered by Magellan. A survey of those areas yields preliminary results that suggest that topographically high regions are well suited to the current implementation of the Hagfors model. Striking examples of such large-scale good fits are Alpha Regio, the northern edges of Lada Terra, and the southern edge of Ishtar Terra. Other features that are typically well fit are the rims of coronae such as Heng-O and the peaks of volcanos such as Gula Mons. Surprisingly, topographically low regions, such as the ubiquitous plains areas, are modeled poorly in comparison. However, this generalization has has exceptions: Lakshmi Planum is an elevated region that is not well fit compared to the rest of neighboring Ishtar, while the southern parts of topographically low Guinevere Planitia are characterized quite well by the Hagfors model. Features that are candidates for improved models are impact craters, coronae, ridges of significant scale, complex ridged terrains, moderate-sized mountains, and sharp terrain boundaries. These features are chosen because the goodness of fit is likely to be most affected either by departures from normal incidence angles or by sharp changes in terrain type within a single footprint. Most large features that are elevated with respect to their surroundings will suffer from steep slope effects, and smaller coronae and impact craters will probably suffer due to rapid changes in their appearance within a single footprint (10-20 km).

Lovell, Amy J.↗

A Peculiar Class of Debris Disks from Herschel/DUNES: A Steep Fall Off in the Far Infrared

Context. The existence of debris disks around old main sequence stars is usually explained by continuous replenishment of small dust grains through collisions from a reservoir of larger objects. Aims. We present photometric data of debris disks around HIP 103389 (HD199260), HIP 100350 (HN Peg, HD206860), and HIP 114948 (HD 219482), obtained in the context of our Herschel Open TIme Key Program DUNES (DUst around NEarby Stars). Methods. We used Herschel/PACS to detect the thermal emission of the three debris disks with a 30 sigma sensitivity of a few mJy at l00 micron and 160 micron. In addition, we obtained Herschel/PACS photometric data at 70 micron for HIP 103389. These observations are complemented by a large variety of optical to far-infrared photometric data. Two different approaches are applied to reduce the Herschel data to investigate the impact of data reduction on the photometry. We fit analytical models to the available spectral energy distribution (SED) data using the fitting method of simulated therma1 annealing as well as a classical grid search method. Results. The SEDs of the three disks potentially exhibit an unusually steep decrease at wavelengths >= 70 micron. We investigate the significance of the peculiar shape of these SEDs and the impact on models of the disks provided it is real. Using grain compositions that have been applied successfully for modeling of many other debris disks, our modeling reveals that such a steep decrease of the SEDs in the long wavelength regime is inconsistent with a power-law exponent of the grain size distribution -3.5 expected from a standard equilibrium collisional cascade. In contrast, a steep grain size distribution or, alternatively an upper grain size in the range of few tens of micrometers are implied. This suggests that a very distinct range of grain sizes would dominate the thermal. emission of such disks. However, we demonstrate that the understanding of the data of faint sources obtained with Herschel is still incomplete and that the significance of our results depends on the version of the data reduction pipeline used. Conclusions. A new mechanism to produce the dust in the presented debris disks, deviations from the conditions required for a standard equilibrium collisional cascade (grain size exponent of -3.5), and/or significantly different dust properties would be necessary to explain the potentially steep SED shape of the three debris disks presented.

Ertel, S.↗

Ion mass dependence of irradiation-induced damage accumulation in KTaO 3

Damage production and amorphization resulting from the interaction of medium-energy (from 40 to 480 keV) noble-gas ions (from He to Kr) with potassium tantalate (KTaO 3 ) are determined using ion channeling measurements. A disorder accumulation model has been fit to the maximum damage concentration versus ion fluence to extract the cross sections for direct-impact and defect-stimulated amorphization, and the results indicate that defect-stimulated amorphization is the dominant mechanism. These cross sections exhibit a strong dependence on the calculated cross sections for displacing lattice atoms, indicating a dominant contribution of nuclear interactions to the defect production and amorphization processes under the irradiation conditions used in this study. Furthermore, these experimental findings, along with the model fits, suggest that the difference in recoil spectra between He and the other heavier ions may be the main driving force for the decreased damage efficiency observed for He ions, which results in a reduced rate of damage accumulation.

36 MATERIALS SCIENCE↗

Analysis of Snow Bidirectional Reflectance from ARCTAS Spring-2008 Campaign

The spring 2008 Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) experiment was one of major intensive field campaigns of the International Polar Year aimed at detailed characterization of atmospheric physical and chemical processes in the Arctic region. A part of this campaign was a unique snow bidirectional reflectance experiment on the NASA P-3B aircraft conducted on 7 and 15 April by the Cloud Absorption Radiometer (CAR) jointly with airborne Ames Airborne Tracking Sunphotometer (AATS) and ground-based Aerosol Robotic Network (AERONET) sunphotometers. The CAR data were atmospherically corrected to derive snow bidirectional reflectance at high 1 degree angular resolution in view zenith and azimuthal angles along with surface albedo. The derived albedo was generally in good agreement with ground albedo measurements collected on 15 April. The CAR snow bidirectional reflectance factor (BRF) was used to study the accuracy of analytical Ross-Thick Li-Sparse (RTLS), Modified Rahman-Pinty-Verstraete (MRPV) and Asymptotic Analytical Radiative Transfer (AART) BRF models. Except for the glint region (azimuthal angles phi less than 40 degrees), the best fit MRPV and RTLS models fit snow BRF to within 0.05. The plane-parallel radiative transfer (PPRT) solution was also analyzed with the models of spheres, spheroids, randomly oriented fractal crystals, and with a synthetic phase function. The latter merged the model of spheroids for the forward scattering angles with the fractal model in the backscattering direction. The PPRT solution with synthetic phase function provided the best fit to measured BRF in the full range of angles. Regardless of the snow grain shape, the PPRT model significantly over-/underestimated snow BRF in the glint/backscattering regions, respectively, which agrees with other studies. To improve agreement with experiment, we introduced a model of macroscopic snow surface roughness by averaging the PPRT solution over the slope distribution function and by adding a simple model of shadows. With macroscopic roughness described by two parameters, the AART model achieved an accuracy of about plus or minus 0.05 with a possible bias of plus or minus 0.03 in the spectral range 0.4-2.2 micrometers. This high accuracy holds at view zenith angles below 55-60 degrees covering the practically important range for remote sensing applications, and includes both glint and backscattering directions.

Lyapustin, A.↗

Formal Experimentation and Analysis of Handheld RFID Readers as a Tool for Nuclear Material Accounting

Commercial, off-the-shelf Radio Frequency Identification (RFID) systems have been successfully deployed for inventory tracking in numerous industries, but their viability in the tracking of complex environments containing nuclear material is less understood. Of primary interest in this setting is a RFID tracking system for nuclear material accounting which can reliably identify as many tags as possible with high accuracy, while also reducing an operator’s exposure to radiation. In this work, we develop a formal statistical approach to identify relevant handheld RFID reader settings which optimize tagging performance. To achieve this goal, we design a full factorial split-plot experiment for a static shelf configuration scene with 50 randomly placed nuclear material containers affixed with RFID tags. We use Bayesian inference to fit a second-order response surface model which expresses the probability of a successful match for each container as a function of the experimental factors. Such effects are allowed to vary across individual containers and the containers’ population in its entirety to estimate overall effects. Uncertainties of estimates and predictions are quantified via their corresponding posterior distributions. Following extensive model checking and validation, the fitted model is used to identify experimental factors which maximize matching probabilities at both the container-level and for the full shelf configuration scene. We also analyze sensitivity of performance to relevant factors.

25 ENERGY STORAGE↗

Exploring the spatial variability of soil properties in an Alfisol Catena

Detailed digital soil maps showing the spatial heterogeneity of soil properties consistent with the landscape are required for site-specific management of plant nutrients, land use planning and process-based environmental modeling. We characterized the short-scale spatial heterogeneity of soil properties in an Alfisol catena in a tropical landscape of Sri Lanka. The impact of different land-uses (paddy, vegetable and un-cultivated) was examined to assess the impact of anthropogenic activities on the variability of soil properties at the catenary level. Conditioned Latin hypercube sampling was used to collect 58 geo-referenced topsoil samples (0–30 cm) from the study area. Soil samples were analyzed for pH, electrical conductivity (EC), organic carbon (OC), cation exchange capacity (CEC) and texture. The spatial correlation between soil properties was analyzed by computing crossvariograms and subsequent fitting of theoretical model. Spatial distribution maps were developed using ordinary kriging. The range of soil properties, pH: 4.3–7.9; EC: 0.01–0.18 dS m –1 ; OC: 0.1–1.37%; CEC: 0.44– 11.51 cmol (+) kg –1 ; clay: 1.5–25% and sand: 59.1–84.4% and their coefficient of variations indicated a large variability in the study area. Electrical conductivity and pH showed a strong spatial correlation which was reflected by the cross-variogram close to the hull of the perfect correlation. Moreover, cross-variograms calculated for EC and Clay, CEC and OC, CEC and clay and CEC and pH indicated weak positive spatial correlation between these properties. Relative nugget effect (RNE) calculated from variograms showed strongly structured spatial variability for pH, EC and sand content (RNE < 25%) while CEC, organic carbon and clay content showed moderately structured spatial variability (25% < RNE < 75%). Spatial dependencies for examined soil properties ranged from 48 to 984 m. The mixed effects model fitting followed by Tukey's post-hoc test showed significant effect of land use on the spatial variability of EC. Our study revealed a structured variability of topsoil properties in the selected tropical Alfisol catena. Except for EC, observed variability was not modified by the land uses. Investigated soil properties showed distinct spatial structures at different scales and magnitudes of strength. Our results will be useful for digital soil mapping, site specific management of soil properties, developing appropriate land use plans and quantifying anthropogenic impacts on the soil system.

Geo-statistical analysis↗

Evaluating penalized logistic regression models to predict Heat-Related Electric grid stress days

Understanding the conditions associated with stress on the electricity grid is important in the development of contingency plans for maintaining reliability during periods when the grid is stressed. In this paper, heat-related grid stress and the relationship with weather conditions is examined using data from the eastern United States. Penalized logistic regression models were developed and applied to predict stress on the electric grid using weather data. The inclusion of other weather variables, such as precipitation, in addition to temperature improved model performance. Several candidate models and datasets were examined. A penalized logistic regression model fit at the operation-zone level was found to provide predictive value and interpretability. Additionally, the importance of different weather variables observed at different time scales were examined. Maximum temperature and precipitation were identified as important across all zones while the importance of other weather variables was zone specific. The methods presented in this work are extensible to other regions and can be used to aid in planning and development of the electrical grid.

Bramer, L. M.↗

WE-H-BRA-07: Mechanistic Modelling of the Relative Biological Effectiveness of Heavy Charged Particles

Purpose Uncertainty in the Relative Biological Effectiveness (RBE) of heavy charged particles compared to photons remains one of the major uncertainties in particle therapy. As RBEs depend strongly on clinical variables such as tissue type, dose, and radiation quality, more accurate individualised models are needed to fully optimise treatments. MethodsWe have developed a model of DNA damage and repair following X-ray irradiation in a number of settings, incorporating mechanistic descriptions of DNA repair pathways, geometric effects on DNA repair, cell cycle effects and cell death. Our model has previously been shown to accurately predict a range of biological endpoints including chromosome aberrations, mutations, and cell death. This model was combined with nanodosimetric models of individual ion tracks to calculate the additional probability of lethal damage forming within a single track. These lethal damage probabilities can be used to predict survival and RBE for cells irradiated with ions of different Linear Energy Transfer (LET). ResultsBy combining the X-ray response model with nanodosimetry information, predictions of RBE can be made without cell-line specific fitting. The model’s RBE predictions were found to agree well with empirical proton RBE models (Mean absolute difference between models of 1.9% and 1.8% for cells with α/β ratios of 9 and 1.4, respectively, for LETs between 0 and 15 keV/µm). The model also accurately recovers the impact of high-LET carbon ion exposures, showing both the reduced efficacy of ions at extremely high LET, as well as the impact of defects in non-homologous end joining on RBE values in Chinese Hamster Ovary cells.ConclusionOur model is predicts RBE without the inclusion of empirical LET fitting parameters for a range of experimental conditions. This approach has the potential to deliver improved personalisation of particle therapy, with future developments allowing for the calculation of individualised RBEs. SJM is supported by a Marie Curie International Outgoing Fellowship from the European Commission’s FP7 program (EC FP7 MC-IOF-623630)

60 APPLIED LIFE SCIENCES↗

Luminescence methodology to determine grain-boundary, grain-interior, and surface recombination in thin-film solar cells

We determine the grain-boundary (GB) recombination velocity, S GB , and grain-interior (GI) lifetime, t GI , parameters in superstrate CdS/CdTe thin-film solar cell technology by combining cathodoluminescence (CL) spectrum imaging and time-resolved photoluminescence (TRPL) measurements. We consider critical device formation stages, including after CdTe deposition, CdCl 2 treatment, and Cu diffusion. CL image analysis methods extract GB and GI intensities and grain size for hundreds of grains per sample. Concurrently, a three-dimensional CL model is developed to simulate the GI intensity as a function of t GI , S GB , grain size, and the surface recombination velocity, S surf . TRPL measurements provide an estimate of S surf for the CL model. A fit of GI intensity vs. grain size data with the CL model gives a self-consistent and representative set of S GB and t GI values for the samples: S GB (t GI ) = 2.6 x 10 6 cm/s (68-250 ps), S GB (t GI ) = 4.1 x 10 5 cm/s (1.5-3.3 ns), and S GB (t GI ) = 5.5 x 10 5 cm/s (1.0-3.8 ns) for as-deposited, CdCl 2 -treated, and CdCl 2- A nd Cu-treated samples, respectively. Thus, we find that the CdCl2 treatment both helps to passivate GBs and significantly increase the GI lifetime. Subsequent Cu diffusion increases GB recombination slightly and has nuanced effects on the GI lifetime. Finally, as a partial check on the S GB and t GI values, they are input to a Sentaurus device model, and the simulated performance is compared to the measured performance. The methodology developed here can be applied broadly to CdTe and CdSeTe thin-film technology and to other thin-film solar cell materials including Cu(In 1-x Ga x )Se 2 , Cu 2 ZnSnS 4 , and perovskites.

14 SOLAR ENERGY↗

CHARA/MIRC observations of two M supergiants in Perseus OB1: Temperature, bayesian modeling, and compressed sensing imaging

Two red supergiants (RSGs) of the Per OB1 association, RS Per and T Per, have been observed in the H band using the Michigan Infra-Red Combiner (MIRC) instrument at the CHARA array. The data show clear evidence of a departure from circular symmetry. We present here new techniques specially developed to analyze such cases, based on state-of-the-art statistical frameworks. The stellar surfaces are first modeled as limb-darkened disks based on SATLAS models that fit both MIRC interferometric data and publicly available spectrophotometric data. Bayesian model selection is then used to determine the most probable number of spots. The effective surface temperatures are also determined and give further support to the recently derived hotter temperature scales of RSGs. The stellar surfaces are reconstructed by our model-independent imaging code SQUEEZE, making use of its novel regularizer based on Compressed Sensing theory. We find excellent agreement between the model-selection results and the reconstructions. Our results provide evidence for the presence of near-infrared spots representing about 3%-5% of the stellar flux.

79 ASTRONOMY AND ASTROPHYSICS↗

Revenue-Maximizing Shared Parking and Electric Vehicle Charging Management in Multi-Unit Dwellings

In urban areas, searching for parking and electric vehicle (EV) charging can result in cruising, congestion, and environmental externalities. Recognizing the business opportunity of offering private parking and charging infrastructure access within multi-unit dwellings (MUDs) during daytime, we model a shared parking and EV charging management system. We maximize the revenue of MUD charging hubs in mixed land use, catering to public demand. Our approach accounts for the objectives of the two stakeholders involved: a demand model is fitted on the choices of EV charging users, and the supply model optimizes the allocation of parking and charging requests in an MUD parking lot. A binary integer linear programming model for the allocation of parking and charging spaces with a rolling horizon is integrated with matching rules that handle both parking and charging requests. In our numerical experiments in a neighborhood of Chicago, Illinois, we estimate the performance of the MUD parking and charging system with metrics that include revenue, number of matchings, and utilization rates. At any given time, MUDs with lower prices attract more charging requests, particularly those of longer duration, resulting in higher revenue and greater charging utilization. Dynamic pricing facilitates a more equitable distribution of requests; as MUD parking lots reach capacity and their fees increase, other MUDs become more competitive, attracting additional requests. Comparing our method against first-come-first-served and optimal-solution benchmarks, we demonstrate our model’s effectiveness in dynamically managing mixed parking and charging demand in MUD charging hubs.

electric vehicle, multi-unit dwelling, charging in↗

Semi-Analytical Hierarchical Bayesian Inference of Nonlinear Model Structure in Stochastic Dynamics: Applied to Compartmental Models of Infectious Diseases

A Bayesian computational framework for parsimonious inference in stochastic nonlinear dynamical systems is presented. This framework enables the concurrent estimation of system states, time-varying parameters, time-invariant parameters, and the optimal sparsity structure of the model parameters. Because differential equation-based models are often simplified mechanistic or phenomenological representations, robust inference from noisy measurement data requires explicit treatment of model error and uncertainty. Model error and time-varying parameters can be represented as random processes, enabling inference while making minimal assumptions about the underlying sources of discrepancy and variability. Adopting stochastic differential equation representations affords the model significant flexibility, but can also render it susceptible to overfitting during statistical inversion, where the inferred model may track noise rather than the underlying signal. To alleviate the effects of overfitting and to enable the discovery of the optimal sparse representation of the time-invariant parameters, a Bayesian sparse learning algorithm is embedded within the framework. This sparse learning framework adopts an approximate hierarchical Bayesian setting defined by a series of semi-analytical expressions. The model structure inference framework is validated using a stochastic compartmental model for tracking and forecasting active cases of an infectious disease. Compartmental models describe population-level infectious disease dynamics through interactions among population fractions grouped by disease state. Mathematically, such models consist of a system of coupled ordinary differential equations. This example adopts an expressive compartmental model that includes multiple possible interactions between disease states, motivated by early uncertainty surrounding COVID-19 reinfection dynamics and their implications for long-term epidemic forecasting. The sparse learning exercise permits the inference of a priori unknown epidemiological dynamics from simulated public health data, discovering the nested compartmental model that optimizes the trade-off between average data-fit and model complexity. It is shown that inducing sparsity among the model parameters eliminates redundant interactions between compartments, equivalently revealing the optimal coupling structure between differential equations.

97 MATHEMATICS AND COMPUTING↗

Explosion Source Models and the Scattering Origin of Regional Phases from SPE Phase 1 Coda Spectral Ratios

The Source Physics Experiment(SPE)explosions provide comprehensive ground truth information,and nearby recordings that are rarely available in typical monitoring scenarios. Here, we look for relationships with local and regional distance data to understand source and propagation effects in an effort to improve monitoring at a distance. We examine source spectral ratios, which can be measured to high precision by taking advantage of many SPE and independently operated stations, as well as the redundancy available from coda waves. Classical source models (Mueller and Murphy, 1971, MM71; Denny and Johnson, 1991, DJ91)predict the source ratios poorly; however, a hybrid model(MM71 with DJ91 cavity radii)performs better, and by varying cavity and elastic radii we obtain models that fit to within measurement precision. Based on this, the best prediction model for the SPE suite is the hybrid model with cavity radius decreased by 95%, and elastic radius decreased by 75%. This works best for mid-range scaled depths of burial (SPE-1, 2, 3, and 5). We do not guarantee transportability to other geologies,depth and scaled depth ranges. Finally, we observe a distinct spectral modulation at 6-9Hzthat is not predicted by classical models, and is most likely the result of interference of short period surface waves(Rg)with those produced by spallation of near surface layers.The same modulationis observed for compressional (P) and shear(S) waves at distance, indicating the origin of these waves as scattered Rg.The scattering process must be accounted for quantitatively when using distant signals to monitor treaty compliance over broad regions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

X-57 Ground Dynamics Modeling and Analysis

This paper presents simulation work to assess the ground handling behavior of the X-57 airplane, prior to its first flight under electric propulsion. A tire deformation model was fit to manufacturer ground testing data to develop a detailed simulation model of previously unmodeled rolling drag forces on the tires. A special modeling effort corrected for a difference between the runway surface in the manufacturer data (grass) and in intended X-57 surface operations (asphalt/concrete). Analysis based on variation in the test conditions of the manufacturer test data showed that uncertainty in the new estimated rolling drag model should lead to no more than 1.5-percent uncertainty in the takeoff acceleration. Another batch simulation series swept across ground operating conditions and airplane configurations, characterizing turning radii and roll angles experienced for different open-loop steering commands in these various situations. This simulation allowed the identification of ground handling characteristics even beyond the regime of normal operations. As part of this task, a new, modified variant of the Fibonacci search algorithm identified the correct steering inputs for maintaining a straight path as a function of the operating condition. Finally, this paper describes a fixed-base piloted simulator assessment. This assessment supplements the previous simulated ground dynamics data with pilot-in-the-loop data and pilot feedback and confirms the overall acceptability of the ground handling qualities of the X-57 airplane prior to first flight.

Loren J. Newton↗

Geopotential models in the Australian region

The ability of three high-order geopotential models (OSU81, GPM2 and OSU86E) to recover the gravity anomaly field (delta g) in the Australian region was tested. The region was divided into 2 x 2 deg blocks, and the mean and rms of the residual gravity (delta g measured - delta g modeled) was found to estimate the fit of the model to the point gravity data. The results showed that OSU81 and GPM2 performed similarly, recovering the delta g with a mean value of less than plus or minus 5 mGal in 63 and 70 percent of the blocks, respectively. However, both these models achieved a fit of worse that was plus or minus 13 mGal in 6 to 7 percent of cases. These were in areas either on or near the coast, or in the Central Australian region, inferring that for a precise geoid slope determination in these regions, a detailed analysis of delta g in region is needed. On the other hand, OSU86E produced a very good result, having a mean fit of less than plus or minus 5 mGal in 80 percent of the blocks, and worse than plus or minus 13 mGal in only 1 percent of cases. The rms values for this model were also improved over the other two models, indicating that for applications requiring highest precision, the preferred model is OSU86E.

Kearsley, A. H. W.↗