Assessment of the contribution of surface roughness to airframe noise

The generation of sound by turbulent boundary-layer flow at low Mach number over a rough wall is investigated by applying a theoretical model that describes the scattering of the turbulence near field into sound by roughness elements. Attention is focused on the numerical method to approximately quantify the absolute level of far-field radiated roughness noise. Models for the source statistics are obtained by scaling smooth-wall data by the increased skin friction velocity and boundary-layer thickness for a rough surface. Numerical integration is performed to determine the roughness noise, and it reproduces the spectral characteristics of the available empirical formula and experimental data. Experiments are conducted to measure the radiated sound from two rough plates in an open jet The measured noise spectra of the rough plates are above that of a smooth plate in 1-2.5 kHz frequency and exhibit reasonable agreement with the predicted level. Estimates of the roughness noise for a Boeing 757 sized aircraft wing with idealized levels of surface roughness show that hi the high-frequency region the sound radiated from surface roughness may exceed that from the trailing edge, and higher overall sound pressure levels are observed for the roughness noise. The trailing edge noise is also enhanced by surface roughness somewhat A parametric study indicates that roughness height and roughness density significantly affect the roughness noise with roughness height having the dominant effect The roughness noise directivity varies with different levels of surface roughness. Copyright © 2007 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

Effects of surface roughness on airframe noise

The generation of sound by turbulent boundary layer flow at low Mach number over a rough wall is investigated by applying the theoretical model which describes the scattering of the turbulence near field into sound by roughness elements. Attention is focused on the numerical method to approximately quantify the absolute level of the roughness noise radiated to far field. Empirical models for the source statistics are obtained by scaling smooth-wall data through increased skin friction velocity and boundary layer thickness for the rough surface. Numerical integration is performed to determine the roughness noise, and it reproduces the spectral characteristics of the available empirical formula and experimental data. Experiments are conducted to measure the radiated sound from two rough plates in an open jet by four 1/2'' free-field condenser microphones. The measured noise spectra of the rough plates are above that of a smooth plate in 1-2.5 kHz frequency and exhibits encouraging agreement with the predicted spectra. Also, a phased microphone array is utilized to localize the sound source, and it confirms that the rough plates generate higher source strengthes in this frequency range. A parametric study illustrates that the roughness height and roughness density significantly affect the far-field radiated roughness noise with the roughness height having the dominant effect. The estimates of the roughness noise for a Boeing 757 sized aircraft wing show that in high frequency region the sound radiated from surface roughness may exceed that from the trailing edge, and higher overall sound pressure levels for the roughness noise are also observed.

Unified circuit model for the polysilicon TFT

This paper proposes two methods to improve the modelling of thin film transistors (TFTs). The first involves integrating Poissons equation numerically, given a density of trap states and other relevant material parameters including a constant mobility. Theresult is conductance as a numerical function of gate voltage. The second method recognizes that the data for areal conductance found by numerical integration, may easily be found by measurement without making assumptions about the density of trap states.

Bayesian approach to signal modelling

In this paper, an introduction to Bayesian methods in signal processing will be given. The paper starts by considering the important issues of model selection and parameter estimation and derives analytic expressions for the model probabilities of two simple models. The idea of marginal estimation of certain model parameter is then introduced and expressions are derived for the marginal probability densities for frequencies in white Gaussian noise and a Bayesian approach to general changepoint analysis is given. Numerical integration methods are introduced based on Markov chain Monte Carlo techniques and the Gibbs sampler in particular.

The Bayesian Approach to Signal Modelling

In this paper, an introduction to Bayesian methods in signal processing will be given. The paper starts by considering the important issues of model selection and parameter estimation and derives analytic expressions for the model probabilities of two simple models. The idea of marginal estimation of certain model parameter is then introduced and expressions are derived for the marginal probabilitiy densities for frequencies in white Gaussian noise and a Bayesian approach to general changepoint analysis is given. Numerical integration methods are introduced based on Markov chain Monte Carlo techniques and the Gibbs sampler in particular.

The post-processed Galerkin method applied to non-linear shell vibrations

We present the results of a computational study of the post-processed Galerkin methods put forward by Garcia-Archilla et al. applied to the non-linear von Karman equations governing the dynamic response of a thin cylindrical panel periodically forced by a transverse point load. We spatially discretize the shell using finite differences to produce a large system of ordinary differential equations (ODEs). By analogy with spectral non-linear Galerkin methods we split this large system into a 'slowly' contracting subsystem and a 'quickly' contracting subsystem. We then compare the accuracy and efficiency of (i) ignoring the dynamics of the 'quick' system (analogous to a traditional spectral Galerkin truncation and sometimes referred to as 'subspace dynamics' in the finite element community when applied to numerical eigenvectors), (ii) slaving the dynamics of the quick system to the slow system during numerical integration (analogous to a non-linear Galerkin method), and (iii) ignoring the influence of the dynamics of the quick system on the evolution of the slow system until we require some output, when we 'lift' the variables from the slow system to the quick using the same slaving rule as in (ii). This corresponds to the post-processing of Garcia-Archilla et al. We find that method (iii) produces essentially the same accuracy as method (ii) but requires only the computational power of method (i) and is thus more efficient than either. In contrast with spectral methods, this type of finite-difference technique can be applied to irregularly shaped domains. We feel that post-processing of this form is a valuable method that can be implemented in computational schemes for a wide variety of partial differential equations (PDEs) of practical importance.

The Modeling of Ion Implantation in a Three-Layer Structure Using the Method of Dose Matching

The method of modeling ion implantation in a multilayer target using moments of a statistical distribution and numerical integration for dose calculation in each target layer is applied to the modelling of As⁺ in poly-Si/SiO2/Si. Good agreement with experiment is obtained. Copyright © 1985 by The Institute of Electrical and Electronics Engineers, Inc.

Active learning of model evidence using Bayesian quadrature

Numerical integration is a key component of many problems in scientific computing, statistical modelling, and machine learning. Bayesian Quadrature is a modelbased method for numerical integration which, relative to standard Monte Carlo methods, offers increased sample efficiency and a more robust estimate of the uncertainty in the estimated integral. We propose a novel Bayesian Quadrature approach for numerical integration when the integrand is non-negative, such as the case of computing the marginal likelihood, predictive distribution, or normalising constant of a probabilistic model. Our approach approximately marginalises the quadrature model's hyperparameters in closed form, and introduces an active learning scheme to optimally select function evaluations, as opposed to using Monte Carlo samples. We demonstrate our method on both a number of synthetic benchmarks and a real scientific problem from astronomy.

p-MOS controlled lateral thyristor: A MOS controllable thyristor suitable for integration

A novel CMOS compatible lateral thyristor is proposed in this paper. Its thyristor conduction is fully controlled by a p-MOS gate. Loss of MOS control due to parasitic latch-up has been eliminated and triggering of the main thyristor at lower forward current achieved. The device operation has been verified by 2-D numerical simulations and experimental fabrication.