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At least 847 records · Page 47

Rapid Prediction of Installed Jet Noise from RANS

A new method of computing jet noise, called mSrc, was developed on the general principles of acoustic analogies. In the method, the problem of translating turbulent flow energy into acoustic energy at a far-field observer is broken into two parts, a calculation of acoustic source strengths and then their propagation. The acoustic sources are related to turbulent quantities in the jet plume in a robust manner. The propagation, which is more properly computed using a Green's function accounting for nonuniform speed of sound and solid surfaces, is instead modeled using commonly observed features of jet acoustic far field directivity, and by diffraction barrier theory for surfaces. The mSrc method does not require gradients of the predicted flow field, allowing it to make use of robust unstructured RANS CFD methods, including embedded boundary codes. Such codes do not require specification of surface meshes, and auto-refine their grid to resolve flow gradients, putting resolution where it is required without a priori user input. The ability of mSrc to use such radically unstructured flow input results in an efficient method of estimating noise from jet flows from complex nozzles installed on aircraft. Many validation cases are presented to demonstrate the accuracy and range of applicability of the mSrc method for representative jet noise applications.

Bridges, James↗

Rapid Prediction of Installed Jet Noise From RANS

A new method of computing jet noise, called mSrc, was developed on the general principles of acoustic analogies. In the method, the problem of translating turbulent flow energy into acoustic energy at a far-field observer is broken into two parts, a calculation of acoustic source strengths and then their propagation. The acoustic sources are related to turbulent quantities in the jet plume in a robust manner. The propagation, which is more properly computed using a Green's function accounting for nonuniform speed of sound and solid surfaces, is instead modeled using commonly observed features of jet acoustic far field directivity, and by diffraction barrier theory for surfaces. The mSrc method does not require gradients of the predicted flow field, allowing it to make use of robust unstructured RANS CFD methods, including embedded boundary codes. Such codes do not require specification of surface meshes, and auto-refine their grid to resolve flow gradients, putting resolution where it is required without a priori user input. The ability of mSrc to use such radically unstructured flow input results in an efficient method of estimating noise from jet flows from complex nozzles installed on aircraft. Many validation cases are presented to demonstrate the accuracy and range of applicability of the mSrc method for representative jet noise applications.

Bridges, James↗

Daily Rainfall Estimate by Emissivity Temporal Variation from 10 Satellites

Rainfall retrieval algorithms for passive microwave radiometers often exploit the brightness temperature depression due to ice scattering at high-frequency channels (≥85 GHz) over land. This study presents an alternate method to estimate the daily rainfall amount using the emissivity temporal variation (i.e., Δe) under rain-free conditions at low-frequency channels (19, 24, and 37 GHz). Emissivity is derived from 10 passive microwave radiometers, including the Global Precipitation Measurement (GPM) Microwave Imager (GMI), the Advanced Microwave Scanning Radiometer 2 (AMSR2), three Special Sensor Microwave Imager/Sounders (SSMIS), the Advanced Technology Microwave Sounder (ATMS), and four Advanced Microwave Sounding Units-A (AMSU-A). Four different satellite combination schemes are used to derive the Δe for daily rainfall estimates. They are all 10 satellites, 5 imagers, 6 satellites with very different equator crossing times, and GMI only. Results show that Δe from all 10 satellites has the best performance with a correlation of 0.60 and RMSE of 6.52 mm, compared with the Integrated Multisatellite Retrievals for GPM (IMERG) Final run product. The 6-satellites scheme has comparable performance with the all-10-satellites scheme. The 5-imagers scheme performs noticeably worse with a correlation of 0.49 and RMSE of 7.28 mm, while the GMI-only scheme performs the worst with a correlation of 0.25 and RMSE of 11.36 mm. The inferior performance from the 5-imagers and GMI-only schemes can be explained by the much longer revisit time, which cannot accurately capture the emissivity temporal variation.

Yalei You↗

Estimating surface temperature in forced convection nucleate boiling - A simplified method

A simplified expression to estimate surface temperatures in forced convection boiling was developed using a liquid nitrogen data base. Using the principal of corresponding states and the Kutateladze relation for maximum pool boiling heat flux, the expression was normalized for use with other fluids. The expression was applied also to neon and water. For the neon data base, the agreement was acceptable with the exclusion of one set suspected to be in the transition boiling regime. For the water data base at reduced pressure greater than 0.05 the agreement is generally good. At lower reduced pressures, the water data scatter and the calculated temperature becomes a function of flow rate.

Hendricks, R. C.↗

Subsonic aircraft: Evolution and the matching of size to performance

Methods for estimating the approximate size, weight, and power of aircraft intended to meet specified performance requirements are presented for both jet-powered and propeller-driven aircraft. The methods are simple and require only the use of a pocket computer for rapid application to specific sizing problems. Application of the methods is illustrated by means of sizing studies of a series of jet-powered and propeller-driven aircraft with varying design constraints. Some aspects of the technical evolution of the airplane from 1918 to the present are also briefly discussed.

Loftin, L. K., Jr.↗

A scheme for computing surface fluxes from mean flow observations

A computational method for estimating turbulent surface stress, sensible heat flux, and humidity flux from mean velocity, temperature, and humidity at a single height in the atmospheric surface layer is presented. The method is based on coupling a Monin-Obukhov similarity profile to a 'force-restore' formulation for the evolution of surface soil temperature to yield the local values of shear stress, heat flux, and surface temperature. In addition to reference-level mean flow properties, the parameters required to implement this method are the thermal heat capacity of the soil, the surface aerodynamic roughness, latitude, solar declination, surface albedo, surface emissivity, and atmospheric transmissivity. Sample calculations are presented for (1) constant atmospheric forcing at the reference level, and (2) variable atmospheric forcing corresponding to Kahle's (1977) measurements of windspeed, air temperature, and radiometer soil surface temperature under dry vegetatively sparse conditions in the Mohave Desert, Calif.

Hoffert, M. I.↗

Mass estimating techniques for earth-to-orbit transports with various configuration factors and technologies applied

A study of an array of advanced earth-to-orbit space transportation systems with a focus on mass properties and technology requirements is presented. Methods of estimating weights of these vehicles differ from those used for commercial and military aircraft; the new techniques emphasizing winged horizontal and vertical takeoff advanced systems are described utilizing the space shuttle subsystem data base for the weight estimating equations. The weight equations require information on mission profile, the structural materials, the thermal protection system, and the ascent propulsion system, allowing for the type of construction and various propellant tank shapes. The overall system weights are calculated using this information and incorporated into the Systems Engineering Mass Properties Computer Program.

Klich, P. J.↗

Applications of estimation theory to numerical weather prediction

Numerical weather prediction (NWP) is an initial value problem for a system of nonlinear partial differential equations in which the initial values are known only incompletely and inaccurately. Data at initial time can be supplemented, however, by observations of the system distributed over a time interval preceding it. Estimation theory was successful in approaching such problems for models governed by systems of ordinary differential equations and of linear PDEs. Estimation-theoretic methods for NWP are developed. A model exhibiting many features of large scale atmospheric flow important in NWP is the one governed by the shallow fluid equations. The estimation problem for a linearized formulation of these equations is studied. A finite difference version of the equations is used as a forecast model to simulate the numerical models used in NWP.

Cohn, S.↗

Laser Doppler anemometer measurements using nonorthogonal velocity components - Error estimates

Laser Doppler anemometers (LDAs) that are arranged to measure nonorthogonal velocity components (from which orthogonal components are computed through transformation equations) are more susceptible to calibration and sampling errors than are systems with uncoupled channels. In this paper uncertainty methods and estimation theory are used to evaluate, respectively, the systematic and statistical errors that are present when such devices are applied to the measurement of mean velocities in turbulent flows. Statistical errors are estimated for two-channel LDA data that are either correlated or uncorrelated. For uncorrelated data the directional uncertainty of the measured velocity vector is considered for applications where mean streamline patterns are desired.

Orloff, K. L.↗

Domain identification in impedance computed tomography by spline collocation method

A method for estimating an unknown domain in elliptic boundary value problems is considered. The problem is formulated as an inverse problem of integral equations of the second kind. A computational method is developed using a splice collocation scheme. The results can be applied to the inverse problem of impedance computed tomography (ICT) for image reconstruction.

Kojima, Fumio↗

Quantifying parameters for Bayesian prior assumptions when estimating the probability of failure of software

Software reliability has become increasingly important, especially in life-critical situations. The ability to measure the results of testing and to quantify software reliability is needed. If this is accomplished, a certain minimum amount of reliability for a piece of software can be specified, and testing and/or other analysis may be done until that minimum number has been attained. There are many models for estimating software reliability. The accuracy of these models has been challenged and many revisions for the models and recalibration techniques have been devised. Of particular interest is the method of estimating the probability of failure of software when no failures have yet occurred in its current version as described by Miller. This model uses black box testing with formulae based on Bayesian estimation. The focus is on three interrelated issues: estimating the probability of failure when testing has revealed no errors; modifying this estimation when the input use distribution does not match the test distribution; and combining the results from random testing with other relevant information to obtain a possibly more accurate estimate of the probability of failure. Obtaining relevant information about the software and combining the results for a better estimate for the Miller model are discussed.

Long, Jacquelyn E.↗

Cirrus Clouds Optical, Microphysical and Radiative Properties Observed During Crystal-Face Experiment: I. A Radar-Lidar Retrieval System

A method of retrieving cloud microphysical properties using combined observations from both cloud radar and lidar is introduced. This retrieval makes use of an improvement to the traditional optimal estimation retrieval method, whereby a series of corrections are applied to the state vector during the search for an iterative solution. This allows faster convergence to a solution and is less processor intensive. The method is first applied to a synthetic cloud t o demonstrate its validity, and it is shown that the retrieval reliably reproduces vertical profiles of ice water content. The retrieval method is then applied to radar and lidar observations from the CRYSTAL-FACE experiment, and vertical profiles of ice crystal diameter, number concentration, and ice water content are retrieved for a cirrus cloud layers observed one day of that experiment. The validity of the relationship between visible extinction coefficient and radar reflectivity was examined. While synthetic tests showed such a functional relationship, the measured data only partially supported such a conclusion. This is due to errors in the forward model (as explained above) as well as errors in the data sets, including possible mismatch between lidar and radar profiles or errors in the optical depth. Empirical relationships between number concentrations and mean particle diameter were also examined. The results indicate that a distinct and robust relationship exists between these retrieved quantities and it is argued that such a relationship is more than an artifact of the retrieval process offering insight into the nature of the microphysical processes taking place in cirrus.

Mitrescu, C.↗

Regional Distribution of Forest Height and Biomass from Multisensor Data Fusion

Elevation data acquired from radar interferometry at C-band from SRTM are used in data fusion techniques to estimate regional scale forest height and aboveground live biomass (AGLB) over the state of Maine. Two fusion techniques have been developed to perform post-processing and parameter estimations from four data sets: 1 arc sec National Elevation Data (NED), SRTM derived elevation (30 m), Landsat Enhanced Thematic Mapper (ETM) bands (30 m), derived vegetation index (VI) and NLCD2001 land cover map. The first fusion algorithm corrects for missing or erroneous NED data using an iterative interpolation approach and produces distribution of scattering phase centers from SRTM-NED in three dominant forest types of evergreen conifers, deciduous, and mixed stands. The second fusion technique integrates the USDA Forest Service, Forest Inventory and Analysis (FIA) ground-based plot data to develop an algorithm to transform the scattering phase centers into mean forest height and aboveground biomass. Height estimates over evergreen (R2 = 0.86, P < 0.001; RMSE = 1.1 m) and mixed forests (R2 = 0.93, P < 0.001, RMSE = 0.8 m) produced the best results. Estimates over deciduous forests were less accurate because of the winter acquisition of SRTM data and loss of scattering phase center from tree ]surface interaction. We used two methods to estimate AGLB; algorithms based on direct estimation from the scattering phase center produced higher precision (R2 = 0.79, RMSE = 25 Mg/ha) than those estimated from forest height (R2 = 0.25, RMSE = 66 Mg/ha). We discuss sources of uncertainty and implications of the results in the context of mapping regional and continental scale forest biomass distribution.

Yu, Yifan↗

Structural model optimization using statistical evaluation

The results of research in applying statistical methods to the problem of structural dynamic system identification are presented. The study is in three parts: a review of previous approaches by other researchers, a development of various linear estimators which might find application, and the design and development of a computer program which uses a Bayesian estimator. The method is tried on two models and is successful where the predicted stiffness matrix is a proper model, e.g., a bending beam is represented by a bending model. Difficulties are encountered when the model concept varies. There is also evidence that nonlinearity must be handled properly to speed the convergence.

Collins, J. D.↗

Application of microelectronic technology to general aviation flight control

This paper presents several different methods that can be used to determine the aircraft orientation (attitude) without direct measurement. The methods combine state estimation techniques with measurements from solid state pressure sensors, accelerometers, and magnetometers to determine the aircraft state. The paper demonstrates how the estimation algorithms were validated and compared using flight test data, and it presents results of performance sensitivity analyses of sensor error, modeling inaccuracies, and wind disturbance effects on the attitude estimation errors. If implemented, the methods presented could make it possible to eliminate directional and vertical gyros and to change from many isolated sensors to an integrated, small, reliable sensing package for determining the aircraft state.

Sorensen, J. A.↗

A simple approximate method for obtaining spanwise lift distributions over swept wings

It is shown how Schrenk's empirical method of estimating the lift distribution over straight wings can be adapted to swept wings by replacing the elliptical distribution by a new "ideal" distribution which varies with sweep.The application of the method is discussed in detail and several comparisons are made to show the agreement of the proposed method with more rigorous ones. It is shown how first-order compressibility corrections applicable to subcritical speeds may be included in this method.

Diederich, Franklin W↗

A Faraday rotation search for magnetic fields in quasar damped Ly alpha absorption systems

We present the results of a Faraday rotation survey of 61 radio-bright QSOs conducted at the National Radio Astronomy Observatory (NRAO) Very Large Array (VLA). The Galactic contribution to the Faraday rotation is estimated and subtracted to determine the extragalactic rotation measure (RRM) for each source. Eleven of these QSOs are known to exhibit damped Ly alpha absorption. The rate of incidence of significant Faraday rotation of these 11 sources is compared to the remaining 50 and is found to be higher at the 99.8% confidence level. However, as this is based upon only two detections of Faraday rotation in the damped Ly alpha sample, the result is only tentative. If the two detections in the damped Ly alpha sample are dug to the absorbing systems, then the inferred rotation measure induced by these systems is roughly 250 rad/sq m. The two detections were for the two lowest redshift absorbers in the sample. We find that a rotation measure of 250 rad/sq m would have gone undetected for any other absorber in the damped Ly alpha sample due to the 1/(1 + 2) squared dilution of the observed RRM with redshift. Thus the data are consistent with, but do not prove, the hypothesis that Faraday rotation is a generic property of damped Ly alpha absorbers. We do not confirm the suggestion that the amplitude of RRMs increases with redshift. Rather, the data are consistent with no redshift evolution. We find that the uncertainty in the estimation of the Galactic rotation measure (GRM) is a more serious problem than previously realized for extra-galactic Faraday rotation studies of QSO absorbers. A careful analysis of current methods for estimating GRM indicate that it can be determined to an accuracy of about 15 - 20 rad/sq m. Previous studies underestimated this uncertainty by more than a factor of 2. Due to this uncertainty, rotation measures such as we suspect are associated with damped Ly alpha absorption systems can only be detected at redshifts less than z approximately equal 1.

Oren, Abraham L.↗

Estimation With Range Dependent Sensor Model

This paper focuses on the improvement of target tracking accuracy taking into account the sensor’s reading degradation as the relative range increases. The approach is based on the assumption that range measurement error depends on the actual range. Specifically, we model the measurement error as a proportional to the actual range term plus a zero-mean, Gaussian distributed, and uncorrelated process. We present two approaches to tracking a target with constant velocity model. The first approach uses Extended Kalman Filter (EKF) framework to estimate target's states and the unknown proportionality coefficient with linearization on the observation model on the predicted states at each time step. The second one uses a measurement conversion method to estimate the target's scaled by the unknown constant term states, which is shown to be unbiased. This conversion results in linear state and observation models, hence the standard Kalman filter can be applied. The actual target state is computed by application of back scaling with the estimate of unknown scale factor. We evaluate the approaches in desktop simulations.

Target tracking↗