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A noise source identification procedure based on a boundary element implementation of the Helmholtz integral equation is presented. This technique makes it possible to identify noise sources in highly reactive fields and to include complex sources and the effects of environmental interaction in the analysis.
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An intelligent air-noise recognition system is described that uses pattern recognition techniques to distinguish noise signatures of five different types of acoustic sources, including jet planes, propeller planes, a helicopter, train, and wind turbine. Information for classification is calculated using the power spectral density and autocorrelation taken from the output of a single microphone. Using this system, as many as 90 percent of test recordings were correctly identified, indicating that the linear discriminant functions developed can be used for aerospace source identification.
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A pattern recognition system was developed that successfully recognizes simulated spectra of five different types of transportation noise sources. The system generates hyperplanes during a training stage to separate the classes and correctly classify unknown patterns in classification mode. A feature selector in the system reduces a large number of features to a smaller optimal set, maximizing performance and minimizing computation.
A pattern recognition system has been developed to classify five different aerospace acoustic sources. In this paper the performance of two new classifiers, an associative memory classifier and a neural network classifier, is compared to the performance of a previously designed system. Sources are classified using features calculated from the time and frequency domain. Each classifier undergoes a training period where it learns to classify sources correctly based on a set of known sources. After training the classifier is tested with unknown sources. Results show that over 96 percent of sources were identified correctly with the new associative memory classifier. The neural network classifier identified over 81 percent of the sources correctly.
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High-fidelity, time-dependent simulations of a Boeing-designed, transonic, truss-braced-wing aircraft in cruise (clean) and landing configurations are leveraged to generate synthetic microphone-phased-array data for airframe noise prediction and assessment. These data sets are used to compute source localization (beamform) maps to determine the location and strength of primary and secondary airframe noise sources associated with this unique configuration. The synthetic phased-array implementation mimics the setup of a flight test. As this study is ongoing, preliminary integrated far-field spectra for the cruise configuration obtained at multiple spatial resolutions revealed significant tonal content that lacked convergence with increased resolution. The origin of several of these tones and their unusual convergence behavior was traced to the larger-than-normal trailing-edge thickness of the “as-tested” cruise model being simulated. Reducing the trailing-edge thickness to more realistic values eliminated most of the tones at low to moderate frequencies and improved spectrum convergence significantly. Applying lessons learned from the cruise simulations, several modifications to the geometry of the landing configuration were made and are described in this work. Results from permeable and solid Ffowcs-Williams and Hawkings surfaces at two different spatial resolutions (coarse and medium) are used to illustrate the major noise sources and determine convergence of the CLEAN integrated noise levels for the entire aircraft as well as major subcomponents. We demonstrate that the low-frequency content of the far-field spectrum is dominated by noise generated from the main landing gear, while the medium- and high-frequency content is dominated by the wing-leading-edge Krueger flaps. Since analysis of the acoustic maps for the landing configuration revealed several clusters of multiple sources along the wing leading edge, “high resolution” processing of the array data was used to distinguish more accurately the locations of sources.
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Laser Doppler velocimeters (LDVs) are used as velocity microphones to measure sound pressure level in the range from 90 to 130 dB, spectral components, and two-point correlation functions for acoustic-noise source identification. Close agreement between LDV and microphone data is observed. Directional sensitivity and the ability to measure remotely make LDVs useful tools for acoustic measurement where placement of any physical probe is difficult or undesirable, as in the diagnosis of jet noise.
Laser Doppler velocimeters (LDVs) were used as velocity microphones to measure sound pressure level in the range of 90-130 db, spectral components, and two-point cross correlation functions for acoustic noise source identification. Close agreement between LDV and microphone data is observed. It was concluded that directional sensitivity and the ability to measure remotely make LDVs useful tools for acoustic measurement where placement of any physical probe is difficult or undesirable, as in the diagnosis of jet aircraft noise.
An acoustic source localization scheme applicable to noncompact moving sources is developed and applied to the blade-vortex interaction (BVI) noise data of a 40-percent scale BO-105 model rotor. A generalized rotor wake code is employed to predict possible VBI locations on the rotor disk and is found quite useful in interpreting the acoustic localization results. The highly varying directivity patterns of different BVI impulses generated at the same test condition are explained by both the localization results and predicted tip vortex trajectories. The effects of rotor tip-path-plane angle and advance ratio on the BVI source positions is studied. Decreasing tip-path-plane angle (at constant advance ratio) moves the general interaction region upwind on the rotor disk, significantly changing the interaction geometry. Increasing advance ratio (at constant tip-path-plane angle) shifts the general source region downwind on the rotor disk with the increased convection of the vortices until about 60 deg azimuth, where the BVI sources appear to become acoustically less effective. The region of strongest BVI sources lies between 60 and 70 deg azimuth and 80 and 90 percent radius for the moderate range of advance ratios studied.
Here, this paper studies Tikhonov regularization (ridge regression) parameter selection for problems in vibrations and acoustics. The selection method is based on a popular Bayesian method, but it incorporates measurements of sensor noise. The regularization parameter is closely related to the ratio of system input energy to noise energy, so noise measurements inform the inference procedure and improve parameter identification. In cases where standard Bayesian regularization identifies zero as the optimal regularization parameter, noise measurements guarantee a unique nonzero optimum. Sufficient theoretical criteria are developed for this guarantee. The method is verified in even-determined and under-determined configurations in an acoustic source localization simulation and a vibration load identification experiment. It is shown to yield significant improvements over existing empirical Bayesian regularization. Improvements are larger in the even-determined case and smaller in the under-determined case, wherein the inverse solution is less sensitive to the regularization parameter.
The increased interest in electric motors for propulsion systems has driven interest in quantifying the contribution of electric motor noise to the overall sound levels and possible human annoyance of the propulsion system. This work presents acoustic measurements of electric motors used for small quadcopters to quantify the sound produced by a number of outrunning motors with different types of controllers. Results are presented for loaded and unloaded motors as installed and uninstalled configurations. Motor resonance frequencies were measured and computed. Current probe measurements showed that the supply current from the controllers contained significant harmonic content for the conventional and sinewave controllers. Acoustic results showed motor noise is typically radiated at frequencies near the mode 2 vibration frequency at roughly 5000 Hz. Electric motor noise was evident in the spectra produced by many of the motor-controller combinations for motors loaded with propellers with levels often greater than those for the motor alone due to increase in the stator magnetic flux density with increased current. An installed configuration produced increases in acoustic radiation over that of the uninstalled motor in a frequency range near the mode 1 vibration frequency near 1200 Hz. A companion paper (Part I - Acoustic Measurements), focuses on source identification using a phased array and directivity characteristics for a baseline configuration.
A new physics-based parameter identification method for rotor harmonic noise sources is developed using an acoustic inverse simulation technique. This new method allows for the identification of individual rotor harmonic noise sources and allows them to be characterized in terms of their individual non-dimensional governing parameters. This new method is applied to both wind tunnel measurements and ground noise measurements of two-bladed rotors. The method is shown to match the parametric trends of main rotor Blade-Vortex Interaction (BVI) noise, allowing accurate estimates of BVI noise to be made for operating conditions based on a small number of measurements taken at different operating conditions.
Several methods for the prediction of jet noise are described. All but one of the noise prediction schemes are based on Lighthill's or Lilley's acoustic analogy, whereas the other is the jet noise generation model recently proposed by Tam and Auriault. In all of the approaches, some assumptions must be made concerning the statistical properties of the turbulent sources. In each case the characteristic scales of the turbulence are obtained from a solution of the Reynolds-averaged Navier-Stokes equation using a kappa-sigma turbulence model. It is shown that, for the same level of empiricism, Tam and Auriault's model yields better agreement with experimental noise measurements than the acoustic analogy. It is then shown that this result is not because of some fundamental flaw in the acoustic analogy approach, but instead is associated with the assumptions made in the approximation of the turbulent source statistics. If consistent assumptions are made, both the acoustic analogy and Tam and Auriault's model yield identical noise predictions. In conclusion, a proposal is presented for an acoustic analogy that provides a clearer identification of the equivalent source mechanisms, as is a discussion of noise prediction issues that remain to be resolved.
This paper describes several methods for the prediction of jet noise. All but one of the noise prediction schemes are based on Lighthill's or Lilley's acoustic analogy while the other is the jet noise generation model recently proposed by Tam and Auriault. In all the approaches some assumptions must be made concerning the statistical properties of the turbulent sources. In each case the characteristic scales of the turbulence are obtained from a solution of the Reynolds-averaged Navier Stokes equation using a k - epsilon turbulence model. It is shown that, for the same level of empiricism, Tam and Auriault's model yields better agreement with experimental noise measurements than the acoustic analogy. It is then shown that this result is not because of some fundamental flaw in the acoustic analogy approach: but, is associated with the assumptions made in the approximation of the turbulent source statistics. If consistent assumptions are made, both the acoustic analogy and Tam and Auriault's model yield identical noise predictions. The paper concludes with a proposal for an acoustic analogy that provides a clearer identification of the equivalent source mechanisms and a discussion of noise prediction issues that remain to be resolved.