Langley Research Center

About: Langley Research Center is a facility organization based out in Hampton, Virginia, United States. It is known for research contribution in the topics: Mach number & Wind tunnel. The organization has 15945 authors who have published 37602 publications receiving 821623 citations. The organization is also known as: NASA Langley & NASA Langley Research Center.

Effect of Directional Array Size on the Measurement of Airframe Noise Components

Abstract: A study was conducted to examine the effects of overall size of directional (or phased) arrays on the measurement of aeroacoustic components An airframe model was mounted in the potential core of an open-jet windtunnel, with the directional arrays located outside the flow in an anechoic environment Two array systems were used; one with a solid measurement angle that encompasses 316 degrees of source directivity and a smaller one that encompasses 72 degrees The arrays, and sub-arrays of various sizes, measured noise from a calibrator source and flap edge model setups In these cases, noise was emitted from relatively small, but finite size source regions, with intense levels compared to other sources Although the larger arrays revealed much more source region detail, the measured source levels were substantially reduced due to finer resolution compared to that of the smaller arrays To better understand the measurements quantitatively, an analytical model was used to define the basic relationships between array to source region sizes and measured output level Also, the effect of noise scattering by shear layer turbulence was examined using the present data and those of previous studies Taken together, the two effects were sufficient to explain spectral level differences between arrays of different sizes An important result of this study is that total (integrated) noise source levels are retrievable and the levels are independent of the array size as long as certain experimental and processing criteria are met The criteria for both open and closed tunnels are discussed The success of special purpose diagonal-removal processing in obtaining integrated results is apparently dependent in part on source distribution Also discussed is the fact that extended sources are subject to substantial measurement error, especially for large arrays

An observationally based energy balance for the Earth since 1950

Abstract: [1] We examine the Earth's energy balance since 1950, identifying results that can be obtained without using global climate models. Important terms that can be constrained using only measurements and radiative transfer models are ocean heat content, radiative forcing by long-lived trace gases, and radiative forcing from volcanic eruptions. We explicitly consider the emission of energy by a warming Earth by using correlations between surface temperature and satellite radiant flux data and show that this term is already quite significant. About 20% of the integrated positive forcing by greenhouse gases and solar radiation since 1950 has been radiated to space. Only about 10% of the positive forcing (about 1/3 of the net forcing) has gone into heating the Earth, almost all into the oceans. About 20% of the positive forcing has been balanced by volcanic aerosols, and the remaining 50% is mainly attributable to tropospheric aerosols. After accounting for the measured terms, the residual forcing between 1970 and 2000 due to direct and indirect forcing by aerosols as well as semidirect forcing from greenhouse gases and any unknown mechanism can be estimated as −1.1 ± 0.4 W m−2 (1σ). This is consistent with the Intergovernmental Panel on Climate Change's best estimates but rules out very large negative forcings from aerosol indirect effects. Further, the data imply an increase from the 1950s to the 1980s followed by constant or slightly declining aerosol forcing into the 1990s, consistent with estimates of trends in global sulfate emissions. An apparent increase in residual forcing in the late 1990s is discussed.

An analysis of lidar observations of polar stratospheric clouds

Abstract: Lidar observations by Browell et al. (1990) are interpreted using single scattering calculations for nonspherical particles and aerosol microphysical calculations. Many of the lidar observations are consistent with particles containing 10 ppbv of condensed nitric acid vapor and an equivalent mass of water. The lidar observations of these Type 1 clouds identify two subtypes, whose properties are deduced. Type 1b particles are spherical, or nearly spherical, and typically have radii near 0.5 micron; Type 1a particles are not spherical, and have a spherical volume equivalent radius exceeding 1.0 micron. Several factors may cause variations in the size of the particles. The most significant factors are the cooling rate and the degree to which the air parcels cool below the condensation point. Specific examples in which cooling rate and cooling point may have led to variations in particle size are found in the Browell et al. (1990) data set. Condensation of 1 ppmm of water or less is quantitatively sufficient to account for the magnitude of the lidar backscatter observed from water ice clouds. The ice particles are not spherical in shape. The sizes of particles in water ice clouds cannot be determined because they are much larger than the wavelength of the lidar.

Upwind relaxation algorithms for the Navier-Stokes equations

Abstract: The development of upwind relaxation algorithms for obtaining efficient steady-state solutions to the compressible Navier-Stokes equations is described. The method is second-order accurate spatially and naturally disipative, using third-order flux splitting of the pressure and convective terms and second-order central differencing for shear and heat flux terms. A line Gauss-Seidel relaxation approach, shown to be unconditionally stable for model convection and diffusion equations, is used. The algorithm is demonstrated for several flows using the thin-layer form of the equations, including the problem of shock-induced separation over a flat plate.

An eigensystem realization algorithm using data correlations (ERA/DC) for modal parameter identification

Abstract: A modification to the Eigensystem Realization Algorithm (ERA) for modal parameter identification is presented in this paper. The ERA minimum order realization approach using singular value decomposition is combined with the philosophy of the Correlation Fit method in state space form such that response data correlations rather than actual response values are used for modal parameter identification. This new method, the ERA using data correlations (ERA/DC), reduces bias errors due to noise corruption significantly without the need for model overspecification. This method is tested using simulated five-degree-of-freedom system responses corrupted by measurement noise. It is found for this case that, when model overspecification is permitted and a minimum order solution obtained via singular value truncation, the results from the two methods are of similar quality.