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At least 19 records

Receiver-Based Auralization of Broadband Aircraft Flyover Noise Using the NASA Auralization Framework

The NASA Auralization Framework (NAF) consists of a set of dynamic link libraries (DLLs) to facilitate auralization of aircraft noise. Advanced capabilities for synthesis, propagation, and external interfaces are provided by the NAF Advanced Plugin Libraries (APL); a separate set of DLLs that are made accessible through the NAF’s plugin architecture. In the typical time domain use case, the sound is first synthesized at the source location based on a source noise definition, and is then propagated in the time domain to a receiver on or near the ground. Alternatively, it may be desirable to synthesize the sound at the receiver, after it has been propagated in the frequency domain, e.g., when the source definition is inaccessible or when alternative propagation methods are needed. Receiver-based auralization requires three new developments in the NAF APL: a component plugin to interpolate the propagated noise spectra as a function of time for input to sound synthesis, and a path finder and path traversal plugin to calculate the effects of the differential propagation path length between the direct and ground reflected rays. This paper describes those developments and demonstrates their use in the auralization of broadband flyover noise.

Aumann, Aric R.↗

Auralization Architectures for NASA?s Next Generation Aircraft Noise Prediction Program

Aircraft community noise is a significant concern due to continued growth in air traffic, increasingly stringent environmental goals, and operational limitations imposed by airport authorities. The assessment of human response to noise from future aircraft can only be afforded through laboratory testing using simulated flyover noise. Recent work by the authors demonstrated the ability to auralize predicted flyover noise for a state-of-the-art reference aircraft and a future hybrid wing body aircraft concept. This auralization used source noise predictions from NASA's Aircraft NOise Prediction Program (ANOPP) as input. The results from this process demonstrated that auralization based upon system noise predictions is consistent with, and complementary to, system noise predictions alone. To further develop and validate the auralization process, improvements to the interfaces between the synthesis capability and the system noise tools are required. This paper describes the key elements required for accurate noise synthesis and introduces auralization architectures for use with the next-generation ANOPP (ANOPP2). The architectures are built around a new auralization library and its associated Application Programming Interface (API) that utilize ANOPP2 APIs to access data required for auralization. The architectures are designed to make the process of auralizing flyover noise a common element of system noise prediction.

Rizzi, Stephen A.↗

Auralization of Unsteady Rotor Noise Using a Solution to the Ffowcs Williams-Hawkings Equation

Auralization of unsteady helicopter flight operations is needed to better understand the impact of maneuvers on community noise. Previous source sound synthesis methods were based on interpolated data, which may lead to artifacts in generating sound for helicopter maneuvers where sound pressure directivity may change rapidly. In this paper, the source sound is synthesized at every time sample using a solution to the Ffowcs Williams-Hawkings Equation along the path of the emission angle between the source and a ground observer. The synthesized sound is then propagated to the ground observer for auralization. Since no interpolation is performed, maneuvers with rapidly changing sound pressure directivities may be more accurately synthesized and auralized. The framework for accomplishing this synthesis and auralization is described, which couples the Fundamental Rotorcraft Acoustic Modeling from Experiments, the second-generation Aircraft NOise Prediction Program, and the NASA Auralization Framework. Synthesis of a hovering rotor is presented to compare with previous synthesis methods. Two examples with a periodic signals are then presented to demonstrate synthesis and auralization of unsteady rotor noise.

Krishnamurthy, Siddhartha↗

Human operator dynamics for aural compensatory tracking

The human operator's ability to control using aural information only and using combined aural and visual displays was investigated for a simple tracking task. Tracking error was presented to the test subjects using one- and two-ear displays. For both displays the pitch of the tone represented the magnitude of the tracking error. The operator's aural control characteristics were modeled as a describing function plus a remnant. The effects on the measured describing function and remnant of different system dynamics, changes in the frequency content of the input and different displays were determined during the study. The describing function and remnant data indicate that humans can control as well with aural cues as with visual cues for the task considered. However, the reduction in operator time delays, expected because of the generally faster human response to aural stimuli, was not evident in the results. It was also determined that the operators could control equally well with either the one- or two-ear display.

Vinje, E. W.↗

Prediction-Based Auralization of a Multirotor Urban Air Mobility Vehicle

Recent advances in auralization methods applicable to rotary wing vehicles have made it possible to undertake a prediction-based auralization of a representative multirotor urban air mobility vehicle. These advances include a new capability for synthesizing loading and thickness noise directly from the prediction method and a new capability for predicting and synthesizing modulating broadband self noise within a unified system noise prediction-auralization framework. These capabilities are demonstrated for a six passenger quadrotor reference vehicle design using collective-pitch control. Propagation of the source noise to a ground observer completes the auralization process. The demonstrated capability serves as the basis for future work directed at perception-influenced design of low noise urban air mobility vehicles.

Auralization↗

Friction stir processing on a strontium modified, thin-wall, vacuum-assisted high-pressure die-cast Aural-5 alloy to improve tensile and fatigue performance

Here, this study explores the application of friction stir processing (FSP) to enhance the material properties of Sr-modified Aural-5 alloy, with a focus on improved tensile and fatigue properties. Aural-5 is a well-known vacuum-assisted high-pressure die-cast (HPDC) Al-Si7-Mg alloy used in the automotive industry to reduce vehicle weight, enhance fuel efficiency, and lower carbon emissions. This alloy modifies its material chemistry with Sr for fine fibrous networks of eutectic silicon and manganese (Mn) to reduce die soldering. It has significantly less iron (Fe) content resulting in the elimination of detrimental needle-shaped Fe-bearing ß-phase intermetallic and improving ductility. The initial microstructure of as-received HPDC Aural-5 exhibits shrinkage porosity in the middle section, a dendritic microstructure with fibrous Al-Si eutectic colonies, a shear-band structure beneath the die-wall, large dendritic externally solidified crystals (ESCs), needle-shaped Mg 2 Si phase and significant second-phase particulates. Some of those microstructural features, such as porosity, ESCs, needle-shaped Mg 2 Si phase, and large second-phase particles, serve as initiation sites for cracks under mechanical loading, resulting in adverse effects on tensile properties, particularly ductility. FSP effectively transforms the microstructure into a wrought configuration with uniform particle distribution by eliminating porosity and disintegrating dendrites, eutectic colonies, ESCs, second-phase particles, and shear-band structures. FSP-driven microstructure modification enhances yield strength and tensile ductility by ~30% and ~35%, respectively. The fatigue life of the material in a bending mode configuration (stress ratio R = 0.1) after FSP exhibits enhancements ranging from 2.0 to 3.9 times that of the original HPDC Aural-5 alloy, depending on the applied stress level.

36 MATERIALS SCIENCE↗

A noise study of the A-6 airplane and techniques for reducing its aural detection distance

A study was undertaken to determine the noise reduction potential of the A-6 airplane in order to reduce its aural detection distance. Static and flyby noise measurements were taken to document the basic airplane signature. The low-frequency noise which is generally most critical for aural detection was found to be broad-band in nature from this airplane, and its source is the turbojet engine exhaust. High-frequency compressor noise, which is characteristic of turbojet powerplants, and which is prominent at close range for this airplane, has no measurable effect on aural detection distance. The use of fluted-engine exhaust nozzles to change the far-field noise spectra is suggested as a possible means for reducing the aural detection distances. Detection distances associated with eight-lobe and four-lobe nozzles are estimated for a 1,000-foot altitude and grassy terrain to decrease from 4 miles to about 3 miles, and from 3 miles to about 2 miles for a 300-foot altitude and grassy terrain.

Hilton, D. A.↗

Auralization of NASA N+2 Aircraft Concepts from System Noise Predictions

Auralization of aircraft flyover noise provides an auditory experience that complements integrated metrics obtained from system noise predictions. Recent efforts have focused on auralization methods development, specifically the process by which source noise information obtained from semi-empirical models, computational aeroacoustic analyses, and wind tunnel and flight test data, are used for simulated flyover noise at a receiver on the ground. The primary focus of this work, however, is to develop full vehicle auralizations in order to explore the distinguishing features of NASA's N+2 aircraft vis-à-vis current fleet reference vehicles for single-aisle and large twin-aisle classes. Some features can be seen in metric time histories associated with aircraft noise certification, e.g., tone-corrected perceived noise level used in the calculation of effective perceived noise level. Other features can be observed in sound quality metrics, e.g., loudness, sharpness, roughness, fluctuation strength and tone-to-noise ratio. A psychoacoustic annoyance model is employed to establish the relationship between sound quality metrics and noise certification metrics. Finally, the auralizations will serve as the basis for a separate psychoacoustic study aimed at assessing how well aircraft noise certification metrics predict human annoyance for these advanced vehicle concepts.

Rizzi, Stephen A.↗

A Recording-Based Method for Auralization of Rotorcraft Flyover Noise

Rotorcraft noise is an active field of study as the sound produced by these vehicles is often found to be annoying. A means to auralize rotorcraft flyover noise is sought to help understand the factors leading to annoyance. Previous work by the authors focused on auralization of rotorcraft fly-in noise, in which a simplification was made that enabled the source noise synthesis to be based on a single emission angle. Here, the goal is to auralize a complete flyover event, so the source noise synthesis must be capable of traversing a range of emission angles. The synthesis uses a source noise definition process that yields periodic and aperiodic (modulation) components at a set of discrete emission angles. In this work, only the periodic components are used for the source noise synthesis for the flyover; the inclusion of modulation components is the subject of ongoing research. Propagation of the synthesized source noise to a ground observer is performed using the NASA Auralization Framework. The method is demonstrated using ground recordings from a flight test of the AS350 helicopter for the source noise definition.

Pera, Nicholas M.↗

Helicopter main-rotor speed effects: A comparison of predicted ranges of detection from the aural detection program ICHIN and the electronic detection program ARCAS

NASA LaRC personnel have conducted a strudy of the predicted acoustic detection ranges associated with reduced helicopter main rotor speeds. This was accomplished by providing identical input information to both the aural detection program ICHIN 6, (I Can Hear It Now, version 6) and the electronic acoustic detection program ARCAS (Assessment of Rotorcraft Detection by Acoustics Sensing). In this study, it was concluded that reducing the main rotor speed of the helicopter by 27 percent reduced both the predicted aural and electronic detection ranges by approximately 50 percent. Additionally, ARCAS was observed to function better with narrowband spectral input than with one-third octave band spectral inputs and the predicted electronic range of acoustic detection is greater than the predicted aural detection range.

Mueller, Arnold W.↗

Improvement of the predicted aural detection code ICHIN (I Can Hear It Now)

Acoustic tests were conducted to study the far-field sound pressure levels and aural detection ranges associated with a Sikorsky S-76A helicopter in straight and level flight at various advancing blade tip Mach numbers. The flight altitude was nominally 150 meters above ground level. This paper compares the normalized predicted aural detection distances, based on the measured far-field sound pressure levels, to the normalized measured aural detection distances obtained from sound jury response measurements obtained during the same test. Both unmodified and modified versions of the prediction code ICHIN-6 (I Can Hear It Now) were used to produce the results for this study.

Mueller, Arnold W.↗

Auralization of Hybrid Wing Body Aircraft Flyover Noise from System Noise Predictions

System noise assessments of a state-of-the-art reference aircraft (similar to a Boeing 777-200ER with GE90-like turbofan engines) and several hybrid wing body (HWB) aircraft configurations were recently performed using NASA engine and aircraft system analysis tools. The HWB aircraft were sized to an equivalent mission as the reference aircraft and assessments were performed using measurements of airframe shielding from a series of propulsion airframe aeroacoustic experiments. The focus of this work is to auralize flyover noise from the reference aircraft and the best HWB configuration using source noise predictions and shielding data based largely on the earlier assessments. For each aircraft, three flyover conditions are auralized. These correspond to approach, sideline, and cutback operating states, but flown in straight and level flight trajectories. The auralizations are performed using synthesis and simulation tools developed at NASA. Audio and visual presentations are provided to allow the reader to experience the flyover from the perspective of a listener in the simulated environment.

Rizzi, Stephen A.↗

The NASA Auralization Framework and Plugin Architecture

NASA has a long history of investigating human response to aircraft flyover noise and in recent years has developed a capability to fully auralize the noise of aircraft during their design. This capability is particularly useful for unconventional designs with noise signatures significantly different from the current fleet. To that end, a flexible software architecture has been developed to facilitate rapid integration of new simulation techniques for noise source synthesis and propagation, and to foster collaboration amongst researchers through a common releasable code base. The NASA Auralization Framework (NAF) is a skeletal framework written in C++ with basic functionalities and a plugin architecture that allows users to mix and match NAF capabilities with their own methods through the development and use of dynamically linked libraries. This paper presents the NAF software architecture and discusses several advanced auralization techniques that have been implemented as plugins to the framework.

Aumann, Aric R.↗

Auralization of Air Vehicle Noise for Community Noise Assessment

This paper serves as an introduction to air vehicle noise auralization and documents the current state-of-the-art. Auralization of flyover noise considers the source, path, and receiver as part of a time marching simulation. Two approaches are offered; a time domain approach performs synthesis followed by propagation, while a frequency domain approach performs propagation followed by synthesis. Source noise description methods are offered for isolated and installed propulsion system and airframe noise sources for a wide range of air vehicles. Methods for synthesis of broadband, discrete tones, steady and unsteady periodic, and a periodic sources are presented, and propagation methods and receiver considerations are discussed. Auralizations applied to vehicles ranging from large transport aircraft to small unmanned aerial systems demonstrate current capabilities.

Rizzi, Stephen A.↗

Auralization of a Supersonic Business Jet Using Advanced Takeoff Procedures

Recent NASA studies of a supersonic business jet airplane indicated that advanced takeoff procedures could be used to reduce noise at the lateral sideline location to a level at which Chapter 4 noise certification requirements could be met. The studies were conducted with the NASA Aircraft Noise Prediction Program, using an analytical model of the airframe and its engines. The advanced procedure consists of a higher-speed climbout and a programmed thrust lapse in which the engine thrust is automatically and gradually reduced immediately after the runway obstacle is cleared. In this paper, the authors utilize the results of the most recent study as the basis of an auralization of the predicted noise. Modifications to the NASA Auralization Framework necessary for that process are described. The auralizations are used to demonstrate differences between standard and advanced takeoff pro ond those that may be observed through comparison of integrated noise metrics.

Rizzi, Stephen A.↗

A Comparison of Aircraft Flyover Auralizations by the Aircraft Noise Simulation Working Group

The Aircraft Noise Simulation Working Group (ANSWr), comprised of NASA, DLR, and ONERA, recently completed an analysis campaign to compare aircraft noise simulation tools, establish guidelines for noise prediction, and launch activities to assess uncertainties associated with the simulation. The campaign included the analyses of two DLR conceptual aircraft, a reference tube-and-wing aircraft, and a low noise aircraft with engines mounted above the fuselage-wing-junction. While the total predicted noise for each of the concepts compared favorably among analyses at the peak level, significant differences were noted at the component level. This paper aims to further that effort by auralizing aircraft flyover noise associated with those predictions. Comparisons are made among the NASA, DLR/Empa, and ONERA generated sounds to determine how differences between the system noise prediction and auralization methods result in changes to the auralized sound.

Stephen A Rizzi↗

A Synthesis Plugin for Auralization of Rotor Self Noise

This paper describes the development and architecture of a software plugin for the NASA Auralization Framework that synthesizes rotor modulating broadband self noise. A conceptual overview of the synthesis process is reviewed. The architecture description includes use of the plugin within a broader framework to auralize rotorcraft flyovers. Use of the plugin is demonstrated for a six-passenger quadrotor urban air mobility reference vehicle design using collective-pitch control.

Self Noise↗

A Synthesis Plugin for Auralization of Rotor Self Noise

This paper describes the development and architecture of a software plugin for the NASA Auralization Framework that synthesizes rotor modulating broadband self noise. A conceptual overview of the synthesis process is reviewed. The architecture description includes use of the plugin within a broader framework to auralize rotorcraft flyovers. Use of the plugin is demonstrated for a six-passenger quadrotor urban air mobility reference vehicle design using collective-pitch control.

Auralization↗