Frank Mobley

Measurement of Near-Field and Far-Field Noise From Full Scale High Performance Jet Engines

Accurate measurement of the noise fields emitted by a full scale high performance jet engine and jet plume (with supersonic jet flow) requires detailed planning and careful execution. The apparent acoustic source can be very large, more than 50 feet long and 20 feet high and wide. The jet plume contains many noise generating sources, the main two being shock (broad band and shock cells) and turbulent mixing. This paper is an initial description of a detailed method to accurately measure and describe the near-field noise while simultaneously measuring the far-field noise. For a large high performance jet engine, the acoustic far-field may not be formed until more than 1000 ft away from the plume. The paper also describes proposed methods to measure the non-linear propagation of the noise from the near-field to the far-field. The proposed methodology described with vetting will be considered as an US military standard (MILSTD) with possible later consideration as American standard measurement technique to describe noise fields for personnel noise exposure and for measuring the performance of jet engine noise reduction technologies.Copyright

Aeroacoustic research complex for aircraft source noise characterization

Aircraft noise has been traditionally measured with either a few ground-based microphones or a linear groundplane array of microphones. These techniques capture one-dimensional and/or two-dimensional characteristics of aircraft flight noise. The US Air Force Research Laboratory has started the construction of a 3-dimensional measurement facility at White Sands Missile Range in New Mexico. This facility, the Aeroacoustic Research Complex (ARC), will allow aircraft to fly through the array, collecting fully 3D acoustic data. ARC is initially being developed in two phases The first phase includes two 91.4 m tall towers separated by 244 m and will focus on noise from rotary wing and UAV aircraft. The second phase will add two 366 m tall towers separated by 610 m and will focus on large and high performance fixed wing aircraft. This facility will allow more accurate characterization of in-flight noise directivity by providing synchronized 3-dimensional magnitude & spectral acoustical signatures from 50+ microphones. ARC responds to a critical need for validation of existing predictive acoustic models. Such models are used for aircraft design, survivability, nonlinear acoustic propagation research and assessing noise exposure to residents living adjacent to airfields.

Acoustic measurements of the noise generated by the Silver Fox Unmanned Aerial System

Acoustic measurements of the noise generated by the Silver Fox Unmanned Aerial System (UAS) were accomplished on a test fixture at Owens-Corning. These measurements, made in one-percent throttle increments, revealed a region over which the noise power curve was linear. Source noise directivity patterns were constructed for each throttle increment using a spherical harmonic series expansion and compared to directivity patterns constructed using a proposed linear interpolation methodology. Moreover, these predictions from the two source construction methods were compared to validation measurements and demonstrate that the interpolation method is viable for spherical harmonic source representations.

Auditory acuity for aircraft in real-world ambient environments

Although many psychoacoustic studies have been conducted to examine the detection of masked target sounds, the vast majority of these studies have been conducted in carefully controlled laboratory listening environments, and their results may not apply to the detection of real-world sounds in the presence of naturalistic ambient sound fields. Those studies that have examined the detection of realistic naturally-occurring sounds have been conducted in uncontrolled listening environments (i.e., outdoor listening tests) where the experimenters were unable to precisely control, or even measure, the specific characteristics of the target and masker at the time of the detection judgment. This study represents an attempt to bridge the gap between unrealistic laboratory listening studies and uncontrolled outdoor listening studies through the use of pseudorandomly-presented real world recordings of target and masking sounds. Subjects were asked to detect helicopter signals in the context of an ongoing ambient recording in a two interval detection task. The results show that the signal-to-noise ratio required to detect an aircraft sound varies across different types of ambient environments (i.e., rural, suburban, or urban).

Modeling listening performance with decision forests

Human audibility measurements of rotary-wing aircraft in a series of real auditory ambient environments is compared with output from a random decision forest machine learning algorithm. A model of human audition in different acoustic environments is built on an error function, but the coefficients of the error function must be determined with detailed human subject data. A random decision forest is constructed from a large set of existing human detection data to test the algorithm’s ability to generate human surrogate data. The forest algorithm is shown to reproduce predictions of the human response for the test ambient data in all cases with R2 of 0.9 or greater. It produces results at a rate of 6 to 20 minutes per prediction, depending upon the number of calculations, which is significantly faster than collecting results using human testing.

Simplification of real-world terrain for outdoor acoustic propagation modeling

Approaches to outdoor sound propagation include models of attenuation for a set of standardized barriers and terrains. These various approaches take care to represent the physical nature of each barrier type. However selection of the appropriate barrier model for a real-world terrain segment is rarely presented. Complex logic is required to determine the appropriate barrier model to apply in the instances where the selection is addressed. This paper presents the Critical Point Analysis Terrain Simplification (CPATS), an algorithm for reducing the complexity of real-world terrain profiles. CPATS implements a finite differencing schema to determine critical points within the terrain segment. The results of this model are compared to a maximum deviation algorithm (MaxDev) of reducing terrain complexity. A statistical comparison between CPATS and MaxDev terrains is completed showing the superior capabilities of the CPATS model. The effect of the different simplified terrains is examined using the outdoor sound propagation code NORD2000.

De-Dopplerization of acoustic signatures

Shifting energy within and between adjacent fractional octave bands removes Doppler shifting in overflight measurements of acoustic signatures without complex resampling techniques and stationary requirements. Analyzing acoustic measurements with Fourier transforms to obtain spectral information requires complex resampling to overcome stationary signal requirements. A simple shifting of band energy obtained from fractional octave digital filters generates a de-Dopplerized spectrum without complex algorithms. A numerical equation is developed based on the fractional octave center frequency to define the amount of energy shifting required. This equation is applied to a numerical simulation and an overflight measurement to remove the Doppler effect in spectral data. The de-Dopplerization through application of energy shifting accurately removes vehicle motion effects from acoustic measurements without complex resampling.

Assessment of a computational model for auditory perception for rotary‐wing aircraft using human responses.

Traditional auditory perceptual models for detection of complex signals against complex ambient soundscapes are based on the human audibility threshold imposed upon computed representations of auditory critical band filters. Such models attempt to locate a positive signal‐to‐noise ratio in any critical band and then apply classic signal detection theory to derive detectability measures (d′) and probability of detection values for the event. One limitation to these models is the limited experimental validation against human sound jury performance, especially using very low‐frequency target signals such as helicopters. This study compares computational auditory detection model predictions against a corresponding large sample of human sound jury data points obtained in the laboratory. Helicopter and ambient soundscape signals were obtained from high‐sensitivity recordings in the field. Playback in the laboratory was achieved under high‐fidelity headphones calibrated to accommodate helicopter primary rotor fr...