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The topics discussed include the following: verification of cloud prediction from the PSU/NCAR mesoscale model; results form MAPS/NGM verification comparisons and MAPS observation sensitivity tests to ACARS and profiler data; systematic errors and mesoscale verification for a mesoscale model; and the COMPARE Project and the CME.
Vortex flows on a twin-tail and a single-tail modular transonic vortex interaction (MTVI) model, representative of a generic fighter configuration, are computationally simulated in this study using the Three-dimensional Euler/Navier-Stokes Aerodynamic Method (TEAM). The primary objective is to provide an assessment of viscous effects on benign (10 deg angle of attack) and burst (35 deg angle of attack) vortex flow solutions. This study was conducted in support of a NASA project aimed at assessing the viability of using Euler technology to predict aerodynamic characteristics of aircraft configurations at moderate-to-high angles of attack in a preliminary design environment. The TEAM code solves the Euler and Reynolds-average Navier-Stokes equations on patched multiblock structured grids. Its algorithm is based on a cell-centered finite-volume formulation with multistage time-stepping scheme. Viscous effects are assessed by comparing the computed inviscid and viscous solutions with each other and experimental data. Also, results of Euler solution sensitivity to grid density and numerical dissipation are presented for the twin-tail model. The results show that proper accounting of viscous effects is necessary for detailed design and optimization but Euler solutions can provide meaningful guidelines for preliminary design of flight vehicles which exhibit vortex flows in parts of their flight envelope.
This study is a direct result of an on-going project to model the reliability of a large real-time control avionics system. In previous modeling efforts with this system, hardware reliability models were applied in modeling the reliability behavior of this system. In an attempt to enhance the performance of the adapted reliability models, certain software attributes were introduced in these models to control for differences between programs and also sequential executions of the same program. As the basic nature of the software attributes that affect software reliability become better understood in the modeling process, this information begins to have important implications on the software development process. A significant problem arises when raw attribute measures are to be used in statistical models as predictors, for example, of measures of software quality. This is because many of the metrics are highly correlated. Consider the two attributes: lines of code, LOC, and number of program statements, Stmts. In this case, it is quite obvious that a program with a high value of LOC probably will also have a relatively high value of Stmts. In the case of low level languages, such as assembly language programs, there might be a one-to-one relationship between the statement count and the lines of code. When there is a complete absence of linear relationship among the metrics, they are said to be orthogonal or uncorrelated. Usually the lack of orthogonality is not serious enough to affect a statistical analysis. However, for the purposes of some statistical analysis such as multiple regression, the software metrics are so strongly interrelated that the regression results may be ambiguous and possibly even misleading. Typically, it is difficult to estimate the unique effects of individual software metrics in the regression equation. The estimated values of the coefficients are very sensitive to slight changes in the data and to the addition or deletion of variables in the regression equation. Since most of the existing metrics have common elements and are linear combinations of these common elements, it seems reasonable to investigate the structure of the underlying common factors or components that make up the raw metrics. The technique we have chosen to use to explore this structure is a procedure called principal components analysis. Principal components analysis is a decomposition technique that may be used to detect and analyze collinearity in software metrics. When confronted with a large number of metrics measuring a single construct, it may be desirable to represent the set by some smaller number of variables that convey all, or most, of the information in the original set. Principal components are linear transformations of a set of random variables that summarize the information contained in the variables. The transformations are chosen so that the first component accounts for the maximal amount of variation of the measures of any possible linear transform; the second component accounts for the maximal amount of residual variation; and so on. The principal components are constructed so that they represent transformed scores on dimensions that are orthogonal. Through the use of principal components analysis, it is possible to have a set of highly related software attributes mapped into a small number of uncorrelated attribute domains. This definitively solves the problem of multi-collinearity in subsequent regression analysis. There are many software metrics in the literature, but principal component analysis reveals that there are few distinct sources of variation, i.e. dimensions, in this set of metrics. It would appear perfectly reasonable to characterize the measurable attributes of a program with a simple function of a small number of orthogonal metrics each of which represents a distinct software attribute domain.
NASA personnel at Kennedy Space Center's Material Science Laboratory have developed new environmentally sound precision cleaning and verification techniques for systems and components found at the center. This technology is required to replace existing methods traditionally employing CFC-113. The new patent-pending technique of precision cleaning verification is for large components of cryogenic fluid systems. These are stainless steel, sand cast valve bodies with internal surface areas ranging from 0.2 to 0.9 m(exp 2). Extrapolation of this technique to components of even larger sizes (by orders of magnitude) is planned. Currently, the verification process is completely manual. In the new technique, a high velocity, low volume water stream impacts the part to be verified. This process is referred to as Breathing Air/Water Impingement and forms the basis for the Impingement Verification System (IVS). The system is unique in that a gas stream is used to accelerate the water droplets to high speeds. Water is injected into the gas stream in a small, continuous amount. The air/water mixture is then passed through a converging-diverging nozzle where the gas is accelerated to supersonic velocities. These droplets impart sufficient energy to the precision cleaned surface to place non-volatile residue (NVR) contaminants into suspension in the water. The sample water is collected and its NVR level is determined by total organic carbon (TOC) analysis at 880 C. The TOC, in ppm carbon, is used to establish the NVR level. A correlation between the present gravimetric CFC-113 NVR and the IVS NVR is found from experimental sensitivity factors measured for various contaminants. The sensitivity has the units of ppm of carbon per mg-ft(exp 2) of contaminant. In this paper, the equipment is described and data are presented showing the development of the sensitivity factors from a test set including four NVR's impinged from witness plates of 0.05 to 0.75 m(exp 2).
This first interim assessment of the subsonic assessment (SASS) project attempts to summarize concisely the status of our knowledge concerning the impacts of present and future subsonic aircraft fleets. It also highlights the major areas of scientific uncertainty, through review of existing data bases and model-based sensitivity studies. In view of the need for substantial improvements in both model formulations and experimental databases, this interim assessment cannot provide confident numerical predictions of aviation impacts. However, a number of quantitative estimates are presented, which provide some guidance to policy makers.
The feasibility of using satellite data for climate research over the Greenland ice sheet is discussed. In particular, we demonstrate the usefulness of Advanced Very High Resolution Radiometer (AVHRR) Local Area Coverage (LAC) and Global Area Coverage (GAC) data for narrow-band albedo retrieval. Our study supports the use of lower resolution AVHRR (GAC) data for process studies over most of the Greenland ice sheet. Based on LAC data time series analysis, we can resolve relative albedo changes on the order of 2-5%. In addition, we examine Scanning Multichannel Microwave Radiometer (SMMR) and Special Sensor Microwave Imager (SSM/I) passive microwave data for snow typing and other signals of climatological significance. Based on relationships between in situ measurements and horizontally polarized 19 and 37 GHz observations, wet snow regions are identified. The wet snow regions increase in aerial percentage from 9% of the total ice surface in June to a maximum of 26% in August 1990. Furthermore, the relationship between brightness temperatures and accumulation rates in the northeastern part of Greenland is described. We found a consistent increase in accumulation rate for the northeastern part of the ice sheet from 1981 to 1986.
Optical pressure measurements have been made on a NACA 0012 airfoil coated with Pressure Sensitive Paint (PSP) at very low flow speeds (less than 50 m/s). Angle of attack was limited to 5 deg. for most measurements. Effects of temperature gradients and mis-registration errors on PSP response have been established and minimized. By reducing measurement error caused by these effects. PSP sensitivity has been enhanced. Acceptable aerodynamic data at flow speeds down to 20 m/s have been obtained and valid pressure paint response was observed down to 10 m/s. Measurement errors (in terms of pressure and pressure coefficient) using PSP with pressure taps as a reference are provided for the range of flow speeds from 50 m/s to 10 m/s.
Simultaneous radar (13.8 Ghz) and lightning (Lightning Imaging Sensor) observations from the NASA TRMM (Tropical Rainfall Measuring Mission) spacecraft afford a new opportunity to examine differences in tropical continental and oceanic convection on a global basis, The 250 meter vertical resolution of the radar data and the approximately 17 dBZ sensitivity are well suited to providing vertical profiles of radar reflectivity over the entire tropical belt. The reflectivity profile has been shown in numerous local ground-based studies to be a good indicator of both updraft velocity and electrical activity. The radar and lightning observations for multiple satellite orbits have been integrated to produce global CAPPI's for various altitudes. At 7 km altitude, where mixed phase microphysics is known to be active, the mean reflectivity in continental convection is 10-15 dB greater than the value in oceanic convection. These results provide a sound physical basis for the order-of-magnitude contrast in lightning counts between continental and oceanic convection. These observations still beg the question, however, about the contrast in updraft velocity in these distinct convective regimes.
Cirrus clouds are a particularly uncertain component of general circulation model (GCM simulations of long-term climate change for a variety of reasons: (1) They encompass a wide range of optical thicknesses and altitudes, from thin tropopause cirrus to thick anvil cirrus that descend to the freezing level, and thus can exert both positive and negative forcing and feedback on the climate; (2) The dynamical processes that create them are poorly resolved in climate GCMs and different in the tropics and midlatitudes; (3) Predictions of their formation and microphysical properties depend on the accuracy of dynamical transports of small concentrations of water vapor to and within the upper troposphere; (4) The relative humidity conditions at which they form depends on the nature and concentration of nucleating particles and is poorly understood; (5) They are more difficult to observe than other cloud types, and hence their parameterization is more loosely constrained by available data. We will illustrate the potential sensitivity of the perturbed climate to uncertainties in cirrus cloud formulation. We will also examine the processes that form cirrus in climate models and discuss the accuracy with which climate GCMs represent these processes. We will also discuss ways in which GCM grid-scale parameterizations might be derived from cloud-scale observations. Finally, we will emphasize the types of global observations needed to constrain parameterizations of cirrus in climate GCMs.
The purpose of this presentation is to discuss the COTS performance, clock upset / single event transient, device configuration upset, antifuse hardening, heavy ion SEU, total dose, proton sensitivities, latchup, and additional information and data.
In a recent paper ({\it Nature, 405,} 775, 2000) we concluded that 25 to 30\% of the ocean's tidal energy dissipation, or about 1 terawatt, occurs in the deep ocean, with the remaining 2.6 TW in shallow seas. The physical mechanism for deep-ocean dissipation is apparently scattering of the surface tide into internal modes; Munk and Wunsch have suggested that this mechanism may provide half the power needed for mixing the deep-ocean. This paper builds further evidence for $1\pm 0.2$ TW of deep-ocean dissipation. The evidence is extracted from tidal elevations deduced from seven years of Topex/Poseidon satellite altimeter data. The dissipation rate Is formed as a balance between the rate of working by tidal forces and the energy flux divergence. While dynamical assumptions are required to compute fluxes, area integrals of the energy balance are, owing to the tight satellite constraints, remarkably insensitive to these assumptions. A large suite of tidal solutions based on a wide range of dynamical assumptions, on perturbations to bathymetric models, and on simulated elevation data are used to assess this sensitivity. These and Monte Carlo error fields from a generalized inverse model are used to establish error uncertainties.
The Advanced Life Support (ALS) Metric is the predominant tool for predicting the cost of ALS systems. Metric goals for the ALS Program are daunting, requiring a threefold increase in the ALS Metric by 2010. Confounding the problem, the rate new ALS technologies reach the maturity required for consideration in the ALS Metric and the rate at which new configurations are developed is slow, limiting the search space and potentially giving the perspective of a ALS technology, the ALS Metric may remain elusive. This paper is a sequel to a paper published in the proceedings of the 2003 ICES conference entitled, "Managing to the metric: an approach to optimizing life support costs." The conclusions of that paper state that the largest contributors to the ALS Metric should be targeted by ALS researchers and management for maximum metric reductions. Certainly, these areas potentially offer large potential benefits to future ALS missions; however, the ALS Metric is not the only decision-making tool available to the community. To facilitate decision-making within the ALS community a combination of metrics should be utilized, such as the Equivalent System Mass (ESM)-based ALS metric, but also those available through techniques such as life cycle costing and faithful consideration of the sensitivity of the assumed models and data. Often a lack of data is cited as the reason why these techniques are not considered for utilization. An existing database development effort within the ALS community, known as OPIS, may provide the opportunity to collect the necessary information to enable the proposed systems analyses. A review of these additional analysis techniques is provided, focusing on the data necessary to enable these. The discussion is concluded by proposing how the data may be utilized by analysts in the future.
Hyperosmotic solutions cause markedly enhanced spontaneous quantal release of neurotransmitter from many nerve terminals. The mechanism of this enhancement is unknown. We have investigated this phenomenon at the frog neuromuscular junction with the aim of determining the degree to which it resembles the modulation of release by stretch, which has been shown to be mediated by mechanical tension on integrins.The hypertonicity enhancement, like the stretch effect, does not require Ca2+ influx or release from internal stores, although internal release may contribute to the effect. The hypertonicity effect is sharply reduced (but not eliminated) by peptides containing the RGD sequence, which compete with native ligands for integrin bonds.There is co-variance in the magnitude of the stretch and osmotic effects; that is, individual terminals exhibiting a large stretch effect also show strong enhancement by hypertonicity, and vice versa. The stretch and osmotic enhancements also can partially occlude each other.There remain some clear-cut differences between osmotic and stretch forms of modulation: the larger range of enhancement by hypertonic solutions, the relative lack of effect of osmolarity on evoked release, and the reported higher temperature sensitivity of osmotic enhancement. Nevertheless, our data strongly implicate integrins in a significant fraction of the osmotic enhancement, possibly acting via the same mechanism as stretch modulation.
The current work incorporates the Transportation Systems Analysis Model (TSAM) to predict the future demand for airline travel. TSAM is a multi-mode, national model that predicts the demand for all long distance travel at a county level based upon population and demographics. The model conducts a mode choice analysis to compute the demand for commercial airline travel based upon the traveler s purpose of the trip, value of time, cost and time of the trip,. The county demand for airline travel is then aggregated (or distributed) to the airport level, and the enplanement demand at commercial airports is modeled. With the growth in flight demand, and utilizing current airline flight schedules, the Fratar algorithm is used to develop future flight schedules in the NAS. The projected flights can then be flown through air transportation simulators to quantify the ability of the NAS to meet future demand. A major strength of the TSAM analysis is that scenario planning can be conducted to quantify capacity requirements at individual airports, based upon different future scenarios. Different demographic scenarios can be analyzed to model the demand sensitivity to them. Also, it is fairly well know, but not well modeled at the airport level, that the demand for travel is highly dependent on the cost of travel, or the fare yield of the airline industry. The FAA projects the fare yield (in constant year dollars) to keep decreasing into the future. The magnitude and/or direction of these projections can be suspect in light of the general lack of airline profits and the large rises in airline fuel cost. Also, changes in travel time and convenience have an influence on the demand for air travel, especially for business travel. Future planners cannot easily conduct sensitivity studies of future demand with the FAA TAF data, nor with the Boeing or Airbus projections. In TSAM many factors can be parameterized and various demand sensitivities can be predicted for future travel. These resulting demand scenarios can be incorporated into future flight schedules, therefore providing a quantifiable demand for flights in the NAS for a range of futures. In addition, new future airline business scenarios are investigated that illustrate when direct flights can replace connecting flights and larger aircraft can be substituted, only when justified by demand.
The current work incorporates the Transportation Systems Analysis Model (TSAM) to predict the future demand for airline travel. TSAM is a multi-mode, national model that predicts the demand for all long distance travel at a county level based upon population and demographics. The model conducts a mode choice analysis to compute the demand for commercial airline travel based upon the traveler s purpose of the trip, value of time, cost and time of the trip,. The county demand for airline travel is then aggregated (or distributed) to the airport level, and the enplanement demand at commercial airports is modeled. With the growth in flight demand, and utilizing current airline flight schedules, the Fratar algorithm is used to develop future flight schedules in the NAS. The projected flights can then be flown through air transportation simulators to quantify the ability of the NAS to meet future demand. A major strength of the TSAM analysis is that scenario planning can be conducted to quantify capacity requirements at individual airports, based upon different future scenarios. Different demographic scenarios can be analyzed to model the demand sensitivity to them. Also, it is fairly well know, but not well modeled at the airport level, that the demand for travel is highly dependent on the cost of travel, or the fare yield of the airline industry. The FAA projects the fare yield (in constant year dollars) to keep decreasing into the future. The magnitude and/or direction of these projections can be suspect in light of the general lack of airline profits and the large rises in airline fuel cost. Also, changes in travel time and convenience have an influence on the demand for air travel, especially for business travel. Future planners cannot easily conduct sensitivity studies of future demand with the FAA TAF data, nor with the Boeing or Airbus projections. In TSAM many factors can be parameterized and various demand sensitivities can be predicted for future travel. These resulting demand scenarios can be incorporated into future flight schedules, therefore providing a quantifiable demand for flights in the NAS for a range of futures. In addition, new future airline business scenarios are investigated that illustrate when direct flights can replace connecting flights and larger aircraft can be substituted, only when justified by demand.
Zeeman spectra of the v3 fundamental (at approximately 1620 c/m) of nitrogen dioxide (14N02) have been recorded with magnetic fields of approximately 500 Gauss using a FTS (at the Kitt Peak National Observatory), as well as a tunable diode laser. At low magnetic fields the Zeeman effect is small compared to the spin-rotation interaction, and the Zeeman tuning rates are expected to be linear with magnetic field. Measured tuning rates of the Q-branch (KA = 2 - 9, N = 2 - 6) sigma transitions are compared to those expected for 2Sigma transitions in low magnetic fields, 2mu ogB/(2N+l). These measurements are required in the data analysis of an ultra-sensitive (pptv) in situ detector based on magnetic-rotation spectroscopy.
A great deal of work has been devoted to the accumulation of accurate quantities describing atomic processes for use in analysis of astrophysical spectra. But in many situations of interest the interpretation of a quantity which is observed, such as a line flux, depends on the results of a modeling- or spectrum synthesis code. The results of such a code depends in turn on many atomic rates or cross sections, and the sensitivity of the observable quantity on the various rates and cross sections may be non-linear and if so cannot easily be derived analytically. In such cases the most practical approach to understanding the sensitivity of observables to atomic cross sections is to perform numerical experiments, by calculating models with various rates perturbed by random (but known) factors. In addition, it is useful to compare the results of such experiments with some sample observations, in order to focus attention on the rates which are of the greatest relevance to real observations. In this paper I will present some attempts to carry out this program, focussing on two sample datasets taken with the Chandra HETG. I will discuss the sensitivity of synthetic spectra to atomic data affecting ionization balance, temperature, and line opacity or emissivity, and discuss the implications for the ultimate goal of inferring astrophysical parameters.
Ocean color measured from satellites provides daily, global estimates of marine inherent optical properties (IOPs). Semi-analytical algorithms (SAAs) provide one mechanism for inverting the color of the water observed by the satellite into IOPs. While numerous SAAs exist, most are similarly constructed and few are appropriately parameterized for all water masses for all seasons. To initiate community-wide discussion of these limitations, NASA organized two workshops that deconstructed SAAs to identify similarities and uniqueness and to progress toward consensus on a unified SAA. This effort resulted in the development of the generalized IOP (GIOP) model software that allows for the construction of different SAAs at runtime by selection from an assortment of model parameterizations. As such, GIOP permits isolation and evaluation of specific modeling assumptions, construction of SAAs, development of regionally tuned SAAs, and execution of ensemble inversion modeling. Working groups associated with the workshops proposed a preliminary default configuration for GIOP (GIOP-DC), with alternative model parameterizations and features defined for subsequent evaluation. In this paper, we: (1) describe the theoretical basis of GIOP; (2) present GIOP-DC and verify its comparable performance to other popular SAAs using both in situ and synthetic data sets; and, (3) quantify the sensitivities of their output to their parameterization. We use the latter to develop a hierarchical sensitivity of SAAs to various model parameterizations, to identify components of SAAs that merit focus in future research, and to provide material for discussion on algorithm uncertainties and future ensemble applications.