Balmer-line-blanketed model atmospheres for A- type stars.
Grid computations of model atmospheres for A-type stars, considering effects of Balmer-line blanketing
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Grid computations of model atmospheres for A-type stars, considering effects of Balmer-line blanketing
Experiments are reported on the noise generated by model V-gutter and semicylindrical target-type reversers with circular nozzles. Nozzles were 5.24 and 7.78 cm in diameter. Nozzle pressure ratio ranged from 1.25 to 1.72. The spacing between reversers and nozzle, as well as the reverser orientation, was also varied. More noise was generated with reversers than with the nozzle alone. The measured maximum overall sound pressure level varied with the sixth power of the nozzle exit velocity. Noise levels were more uniform in regard to directivity with reversers than with the nozzle alone. It is concluded that thrust reversers, can be a significant noise problem, especially for STOL aircraft using thrust reversers during approach.
Thermal scale modeling techniques for Voyager- type spacecraft
This paper presents a framework for calibrating computational models using data from several and possibly dissimilar validation experiments. The offset between model predictions and observations, which might be caused by measurement noise, model-form uncertainty, and numerical error, drives the process by which uncertainty in the models parameters is characterized. The resulting description of uncertainty along with the computational model constitute a predictor model. Two types of predictor models are studied: Interval Predictor Models (IPMs) and Random Predictor Models (RPMs). IPMs use sets to characterize uncertainty, whereas RPMs use random vectors. The propagation of a set through a model makes the response an interval valued function of the state, whereas the propagation of a random vector yields a random process. Optimization-based strategies for calculating both types of predictor models are proposed. Whereas the formulations used to calculate IPMs target solutions leading to the interval value function of minimal spread containing all observations, those for RPMs seek to maximize the models' ability to reproduce the distribution of observations. Regarding RPMs, we choose a structure for the random vector (i.e., the assignment of probability to points in the parameter space) solely dependent on the prediction error. As such, the probabilistic description of uncertainty is not a subjective assignment of belief, nor is it expected to asymptotically converge to a fixed value, but instead it casts the model's ability to reproduce the experimental data. This framework enables evaluating the spread and distribution of the predicted response of target applications depending on the same parameters beyond the validation domain.
We computed optical and infrared light curves of the pulsating class of delayed detonation models for Type Ia supernovae (SNe Ia). It is demonstrated that observations of the IR light curves can be used to identify subluminous SNe Ia by testing whether secondary maxima occur in the IR. Our pulsating delayed detonation models are in agreement with current observations both for subluminous and normal bright SN Ia, namely SN1991bg, SN1992bo, and SN1992bc. Observations of molecular bands provide a test to distinguish whether strongly subluminous supernovae are a consequence of the pulsating mechanism occurring in a high-mass white dwarf (WD) or, alternatively, are formed by the helium detonation in a low-mass WD as was suggested by Woosley. In the latter case, no carbon is left after the explosion of low-mass WDs whereas a log of C/O-rich material is present in pulsating delayed detonation models.
It is clear that polar cap convection during times of northward Interplanetary Magnetic Field (IMF) is more structured and of lower mean speed than at times of southward IMF. This, coupled with the fact that the polar cap is smaller, means that empirical models are more difficult to construct with certainty. It is also clear that sunward flow deep in the polar cap is often observed, but its connection with the rest of the flow pattern is controversial. At present, empirical models are of three types: 'statistical' models wherein data from different days but with similar IMF conditions are averaged together; 'pattern recognition' models, which are built up by examining individually hundreds of passes to derive a 'typical' pattern which embodies features frequently observed; and 'assimilative' models, which use data of different types and from as many locations as possible, but all taken at the same time, in order to derive a snapshot (or series of snapshots) of the entire pattern. Each type of model has its own difficulties. Statistical models, by their very nature, smooth out flow features (e.g. the convection reversal, and the locus of sunward flow deep in the polar cap) which are not found at precisely the same invariant latitudes and magnetic local times on different days. Pattern recognition models are better at reproducing small-scale features, but the large-scale pattern can be a matter of interpretation. Assimilative models (such as AMIE) hold out the best hope for creating instantaneous, global convection patterns; however, the analysis technique tends to be most irregular (and least reliable) in the regions which are not well covered by in situ data. It appears that, at least at times, a four cell model with sunward flow at the highest and lowest latitudes, and antisunward flow in between, is consistent with the observations. At other times, the observations may be consistent with a two-cell convection pattern, but which includes significant meanders within the polar cap.
The ways that the finite size of the source region and directivity of the emitted radiation modify the observed characteristics of type III radio bursts as they propagate through the interplanetary medium are investigated. A simple model that simulates the radio source region is developed to provide insight into the spatial behavior of the parameters that characterize radio bursts. The model is used to demonstrate that observed radio azimuths are systematically displaced from the geometric centroid of the exciter electron beam in such a way as to cause trajectories of the radio bursts to track back to the observer at low frequencies, rather than to follow expected Archimedean spiral-like paths. The source region model is used to investigate the spatial behavior of the peak intensities of radio bursts, and it is found that the model can qualitatively account for both the frequency dependence and the east-west asymmetry of the observed peak flux densities.
A static test of a large-scale, over-the-wing (OTW) powered-lift model was performed. The OTW propulsion system had been modified to incorporate a simple target-type thrust reverser as well as the normal rectangular OTW exhaust nozzle. Tests were performed in both the reverse thrust and approach configurations. The thrust reverser noise created by jet turbulence mixing and the OTW approach noise were both low frequency and broadband. When scaled to a 45,400-kg (100,000-lb) aircraft, the thrust reverser and approach configurations produced peak 152-m (500-ft) sideline perceived noise levels of 110 and 105 PNdB, respectively. The aerodynamic performance of the model showed that 50% or greater reverser effectiveness can be achieved without experiencing ingestion of exhaust gas or ground debris into the engine inlets.
An evaluation was made of Thompson-Type models which use trend terms (as a surrogate for technology), meteorological variables based on monthly average temperature, and total precipitation to forecast and estimate corn yields in Iowa, Illinois, and Indiana. Pooled and unpooled Thompson-type models were compared. Neither was found to be consistently superior to the other. Yield reliability indicators show that the models are of limited use for large area yield estimation. The models are objective and consistent with scientific knowledge. Timely yield forecasts and estimates can be made during the growing season by using normals or long range weather forecasts. The models are not costly to operate and are easy to use and understand. The model standard errors of prediction do not provide a useful current measure of modeled yield reliability.
It is essential to evaluate and refine aerosol classification methods applied to passive satellite remote sensing. We have developed an aerosol classification algorithm (called Specified Clustering and Mahalanobis Classification, SCMC) that assigns an aerosol type to multi-parameter retrievals by spaceborne, airborne or ground-based passive remote sensing instruments [1]. The aerosol types identified by our scheme are pure dust, polluted dust, urban-industrialdeveloped economy, urban-industrialdeveloping economy, dark biomass smoke, light biomass smoke and pure marine. We apply the SCMC method to inversions from the ground-based AErosol RObotic NETwork (AERONET [2]) and retrievals from the space-borne Polarization and Directionality of Earths Reflectances instrument (POLDER, [3]). The POLDER retrievals that we use differ from the standard POLDER retrievals [4] as they make full use of multi-angle, multispectral polarimetric data [5]. We analyze agreement in the aerosol types inferred from both AERONET and POLDER and evaluate GEOS-Chem [6] simulations over the globe. Finally, we use in-situ observations from the SEAC4RS airborne field experiment to bridge the gap between remote sensing-inferred qualitative SCMC aerosol types and their corresponding quantitative chemical speciation. We apply the SCMC method to airborne in-situ observations from the NASA Langley Aerosol Research Group Experiment (LARGE, [7]) and the Differential Aerosol Sizing and Hygroscopicity Spectrometer Probe (DASH-SP, [8]) instruments; we then relate each coarsely defined SCMC type to a sum of percentage of individual aerosol species, using in-situ observations from the Particle Analysis by Laser Mass Spectrometry (PALMS, [9]), the Soluble Acidic Gases and Aerosol (SAGA, [10]), and the High - Resolution Time - of - Flight Aerosol Mass Spectrometer (HR ToF AMS, [11]).
This paper describes 'type 1' servo techniques applied to explicit model following systems. The advantages of using these are: (1) steady-state decoupling is provided for the controlled state variables as determined by a reference input (the pilot command for example), (2) overall stability of the decoupled adaptive scheme is guaranteed, (3) a zero steady-state error for those state variables incorporating integral feedback, to a constant input reference, and (4) improved transient performance. Example calculations have been performed using a mathematical model for a typical fighter aircraft and a desired performance reference model based on satisfactory aircraft flying qualities. Numerical results are given.
The Radiative-Convective Equilibrium Model Intercomparison Project (RCEMIP) is an intercomparison of multiple types of numerical models configured in radiative-convective56equilibrium (RCE). RCE is an idealization of the tropical atmosphere that has long been used to study basic questions in climate science. Here, we employ RCE to investigate the role that clouds and convective activity play in determining cloud feedbacks, climatecsensitivity, the state of convective aggregation, and the equilibrium climate. RCEMIP is unique amongst intercomparisons in its inclusion of a wide range of model types, including atmospheric general circulation models (GCMs), single column models (SCMs), cloud-resolving models (CRMs), large eddy simulations (LES), and global cloud-resolving models (GCRMs). The first results are presented from the RCEMIP ensemble of more than 30 models. While there are large differences across the RCEMIP ensemble in the representation of mean profiles of temperature, humidity, and cloudiness, in a majority of models anvil clouds rise, warm, and decrease in area coverage in response to an increase in sea surface temperature (SST). Nearly all models exhibit self-aggregation in large domains and agree that self-aggregation acts to dry and warm the troposphere, reduce high cloudiness, and increase cooling to space. The degree of self-aggregation exhibits no clear tendency with warming. There is a wide range of climate sensitivities, but models with parameterized convection tend to have lower climate sensitivities than models with explicit convection. In models with parameterized convection, aggregated simulations have lower climate sensitivities than un-aggregated simulations. Plain Language Summary This study investigates tropical clouds and climate using results from more than 30 different numerical models set up in a simplified framework. The dataset of model simulations is unique in that it includes a wide range of model types configured in a consistent manner. We address some of the biggest open questions in climate science, including how cloud properties change with warming and the role that the tendency of clouds to form clusters plays in determining the average climate and how climate changes. While there are large differences in how the different models simulate average temperature, humidity, and cloudiness, in a majority of models, the amount of high clouds decreases as climate warms. Nearly all models simulate a tendency for clouds to cluster together. There is agreement that when the clouds are clustered, the atmosphere is drier with fewer clouds overall. We don’t find a conclusive result for how cloud clustering changes as the climate warms.
Stellar atmosphere model constructed to test opacity effect due to water vapor on model atmosphere structures
This study compares the results from different parameter identification methods used to determine longitudinal aircraft characteristics from flight data. In general, these comparisons have found that the estimated short-period dynamics (natural frequency, damping, transfer functions) are only weakly affected by the type of identification method, however, the estimated aerodynamic coefficients may be strongly affected by the type of identification method. The estimated values for aerodynamic coefficients were found to depend upon the type of math model and type of test data used with each of the identification methods. The use of fairly complete math models and the use of long data lengths, combining both steady and nonsteady motion, are shown to provide aerodynamic coefficient values that compare favorably with the results from other testing methods such as steady-state flight and full-scale wind-tunnel experiments.
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The past contributions of NASA to adaptive control technology are reviewed. The review places emphasis on aircraft applications although spacecraft and launch vehicle control applications are included. Particular emphasis is given to the adaptive control system used in the X-15 research aircraft. Problem areas that limited the realizable performance of this adaptive system are discussed. Current technological capabilities are used to extrapolate the present-day potential for adaptive flight control. Specifically, the potential created by use of the modern high-speed digital computer in flight control is discussed. Present plans for research in digital adaptive control systems for the NASA F8-C digital fly-by-wire program are presented. These plans are currently envisioned to include research in at least two types of adaptive controls, the system identification/on-line design type, and the model reference type.
The data from the Apollo-14 and Apollo-16 Active Seismic Experiments were reanalyzed and show that a power-law velocity variation with depth is consistent with both the traveltimes and amplitudes of the first arrivals for source-to-geophone separations up to 32m. The data were improved by removing spurious glithches, flickering and stacking. While this improved the signal-to-noise ratios, it was not possible to measure the arrivals beyond 32m. The physical evidence that the shallow lunar regolith is made up of fine particles adds weight to the 1/6-power velocity model. The 1/6-power law predicts the traveltime t(x), varies with separation, x, as t(x) = t sub 0 (x/x sub 0) to the 5/6 power and, using a first-order theory, the amplitude, A(x), varies as A(x) = A sub 0 (x/x sub 0) to the (13-m)/12, M 1; the layer-velocity model predicts t(x) = t sub 0 (x/xsub 0) and A(x) = A sub 0 (x/x sub 0) to the 2nd power.
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