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Deviations in periods of output from tracking filter measured. Signal-analyzing system measures degree to which sinusoid contaminated by noise. Noise-contamination detector indirectly measures noise in noisy sinusoidal signal by measuring average value of fractional absolute dither in period of signal. Developed to measure noise in outputs of vibration sensors used to test bearings and other components of rotating machinery.
Aircraft engine noise research and development depends on the ability to study and predict the noise created by each engine component in isolation. The presence of a downstream pylon for a model fan test, however, may result in noise contamination through pylon interactions with the free stream and model exhaust airflows. Additionally, there is the problem of separating the fan and jet noise components generated by the model fan. A methodology was therefore developed to improve the data quality for the 9 15 Low Speed Wind Tunnel (LSWT) at the NASA Glenn Research Center that identifies three noise sources: fan noise, jet noise, and rig noise. The jet noise and rig noise were then measured by mounting a scale model of the 9 15 LSWT model fan installation in a jet rig to simulate everything except the rotating machinery and in duct components of fan noise. The data showed that the spectra measured in the LSWT has a strong rig noise component at frequencies as high as 3 kHz depending on the fan and airflow fan exit velocity. The jet noise was determined to be significantly lower than the rig noise (i.e., noise generated by flow interaction with the downstream support pylon). A mathematical model for the rig noise was then developed using a multi-dimensional least squares fit to the rig noise data. This allows the rig noise to be subtracted or removed, depending on the amplitude of the rig noise relative to the fan noise, at any given frequency, observer angle, or nozzle pressure ratio. The impact of isolating the fan noise with this method on spectra, overall power level (OAPWL), and Effective Perceived Noise Level (EPNL) is studied.
Trailing edge (TE) noise measurements for a NACA 63-215 airfoil model are presented, providing benchmark experimental data for a cambered airfoil. The effects of flow Mach number and angle of attack of the airfoil model with different TE bluntnesses are shown. Far-field noise spectra and directivity are obtained using a directional microphone array. Standard and diagonal removal beamforming techniques are evaluated employing tailored weighting functions for quantitatively accounting for the distributed line character of TE noise. Diagonal removal processing is used for the primary database as it successfully removes noise contaminates. Some TE noise predictions are reported to help interpret the data, with respect to flow speed, angle of attack, and TE bluntness on spectral shape and peak levels. Important findings include the validation of a TE noise directivity function for different airfoil angles of attack and the demonstration of the importance of the directivity function s convective amplification terms.
Trailing edge (TE) noise measurements for a NACA 63-215 airfoil model are presented, providing benchmark experimental data for a cambered airfoil. The effects of flow Mach number and angle of attack of the airfoil model with different TE bluntnesses are shown. Far-field noise spectra and directivity are obtained using a directional microphone array. Standard and diagonal removal beamforming techniques are evaluated employing tailored weighting functions for quantitatively accounting for the distributed line character of TE noise. Diagonal removal processing is used for the primary database as it successfully removes noise contaminates. Some TE noise predictions are reported to help interpret the data, with respect to flow speed, angle of attack, and TE bluntness on spectral shape and peak levels. Important findings include the validation of a TE noise directivity function for different airfoil angles of attack and the demonstration of the importance of the directivity function s convective amplification terms.
The Surface Contour Radar (SCR) is a 36-GHz computer-controlled airborne radar which generates a false-color-coded elevation map of the sea surface below the aircraft in real time, and can routinely produce ocean directional wave spectra with post-flight data processing which have much higher angular resolution than pitch-and-roll buoys. The SCR range measurements are not error-free and the resulting errors in the elevations corrupt the directional wave spectrum. This paper presents a technique for eliminating that contamination.
Closed loop system with reference to different gyro stabilization loops present in modern space vehicle inertial measuring unit, using Prony exponential series curve fitting method as analytical tool
Estimates of the total uncertainty for empirically determined loudness levels are documented when GRS (Ground Recording System) noise monitors are used to record X 59 sonic boom waveforms. The total uncertainty is characterized by combining nine different sources of uncertainty that may affect the apparent gain of the measurement chain. These uncertainty estimates are presented as expected measurement error relative to the true loudness level, and separate error estimates are provided for eight different noise metrics in which NASA has interest. The behavior of the Perceived Level (PL) metric is studied within the report body, while the total uncertainties for the seven other noise metrics are summarized in appendices for brevity. The effects of four sources of uncertainty are estimated simply from information found on hardware specification sheets provided by the manufacturer. However, mock acoustic recordings are created to estimate the effects of other sources because those effects are expected to induce spectral coloration, so they may vary with noise metric type and sound level. These sources are not well modeled by simple gain adjustments. Importantly, measurement error is computable when processing mock recordings since the true levels are knowable, which is not the case when processing data recorded in the field. Specifically, the true levels are knowable because the components of the mock recordings are separable – e.g., loudness levels of booms can be computed with or without superimposed background noise. Mock acoustic recordings also have the benefit of allowing analysis of sonic booms from vehicles that are not yet flying, like the X-59, since the mock recordings are created by combining vehicle-specific predicted waveforms with other audio sources. The estimates of total measurement error are documented as a function of the signal-to-noise ratio (SNR) of the loudness level, where the corrected SNR is computed while accounting for the effects of the method that is used to correct for background noise contamination when computing the noise metric values. The corrected SNR calculations used here can be applied to both mock recordings and in-field measurements, so the uncertainty of in-field recordings can be found using pre-computed lookup tables that identify the relationship between metric type, corrected SNR, and the expected measurement error.
A spectral-based correction adapted from ISO 11204 [1] is investigated here to mitigate the effects of ambient noise contamination of sonic booms recorded by noise monitors during field tests. The algorithm from sections 5.4.2 and 7 of ISO 11204 is customized using six nonstandard correction grades in addition to implementing the two standardized grades described in ISO 11204. The six nonstandard grades allow for more aggressive correction of the levels of the sonic boom spectrum when they are proximate to the ambient spectrum. Of the eight correction grades, the most aggressive custom correction grade, termed “Custom F” here, performed best under the conditions that were studied. Consequently, “Custom F” is recommended for use when processing in-field recordings of X-59 sonic booms. To evaluate the eight correction grades, mock X-59 acoustic recordings were generated using predicted ground-level X-59 sonic booms. These ground-level waveforms were created by Doebler [2] by propagating nearfield CFD solutions of the X-59 C612A configuration to the ground using PCBoom [3] (please see Acknowledgements and Refs. [4, 5, 2, 6]). To generate the mock acoustic recordings from those ground-level waveforms, they were further modified using turbulence filters developed during the NASA SonicBAT effort [7], post boom noise audio clips from the NASA SonicBAT tests [7], and ambient noise from the NASA QSF18 test [8, 9]. These mock acoustic recordings enabled evaluation of the ambient noise mitigation methods since the proper loudness levels of the X-59 waveforms in absence of ambient noise are known. Specifically, these known levels provide a benchmark against which the corrected loudness levels are compared, where the corrected loudness levels are computed when ambient noise is present within the waveforms. Importantly, similar analyses using in-field recordings are not possible since the proper loudness levels of the sonic boom waveforms in absence of ambient noise are unknowable when analyzing in-field recordings. Consequently, if additional analyses of ambient noise mitigation methods are needed, then use of mock recordings like those used here is recommended.
The 9- by 15-Foot Low Speed Wind Tunnel is an acoustic testing facility with a long history of aircraft propulsion noise research. Due to interest in renovating the facility to support future testing of advanced quiet engine designs, a study was conducted to document the background noise level in the facility and investigate the sources of contaminating noise. The anechoic quality of the facility was also investigated using an interrupted noise method. The present report discusses these aspects of the noise environment in this facility.
The use of a directional (or phased) array of microphones for the measurement of trailing edge (TE) noise is described and tested. The capabilities of this method arc evaluated via measurements of TE noise from a NACA 63-215 airfoil model and from a cylindrical rod. This TE noise measurement approach is compared to one that is based on thc cross spectral analysis of output signals from a pair of microphones placed on opposite sides of an airframe model (COP method). Advantages and limitations of both methods arc examined. It is shown that the microphone array can accurately measures TE noise and captures its two-dimensional characteristic over a large frequency range for any TE configuration as long as noise contamination from extraneous sources is within bounds. The COP method is shown to also accurately measure TE noise but over a more limited frequency range that narrows for increased TE thickness. Finally, the applicability and generality of an airfoil self-noise prediction method was evaluated via comparison to the experimental data obtained using the COP and array measurement methods. The predicted and experimental results are shown to agree over large frequency ranges.
The use of a directional array of microphones for the measurement of trailing edge (TE) noise is described. The capabilities of this method are evaluated via measurements of TE noise from a NACA 63-215 airfoil model and from a cylindrical rod. This TE noise measurement approach is compared to one that is based on the cross spectral analysis of output signals from a pair of microphones (COP method). Advantages and limitations of both methods are examined. It is shown that the microphone array can accurately measures TE noise and captures its two-dimensional characteristic over a large frequency range for any TE configuration as long as noise contamination from extraneous sources is within bounds. The COP method is shown to also accurately measure TE noise but over a more limited frequency range that narrows for increased TE thickness. Finally, the applicability and generality of an airfoil self-noise prediction method was evaluated via comparison to the experimental data obtained using the COP and array measurement methods. The predicted and experimental results are shown to agree over large frequency ranges.
informing measurement planning and analysis of quiet supersonic aircraft community testing. The report is divided into three main sections: (1) investigations on the impacts of contaminating noise on sonic boom community noise metrics; (2) investigations into sonic boom variability, including turbulence effects on metrics of interest and development of a data-driven boom variability analysis framework; (3) other studies that support developing improved methodologies for community testing and analysis.
Radio Astronomy Explorer (RAE) I observations of thunderstorms over regions of low man-made noise levels are analyzed to assess the satellite's capability for noise source differentiation. The investigation of storms over Australia indicates that RAE can resolve noise generation due to thunderstorms from the general noise background over areas of low man-made noise activity. Noise temperatures observed by RAE over stormy regions are on the average 10DB higher than noise temperatures over the same regions in the absence of thunderstorms. In order to determine the extent of noise contamination due to distant transmitters comprehensive three dimensional computer ray tracings were generated. The results indicate that generally, distant transmitters contribute negligibly to the total noise power, being 30DB or more below contributions arriving from an area immediately below the satellite.
The design requirements for two new Compact Jet Engine Simulator (CJES) units for upcoming wind tunnel testing lead to the distinct possibility of rig noise contamination. The acoustic and aerodynamic properties of several flow conditioner devices are investigated over a range of operating conditions relevant to the CJES units to mitigate the risk of rig noise. An impinging jet broadband noise source is placed in the upstream plenum of the test facility permitting measurements of not only flow conditioner self-noise, but also noise attenuation characteristics. Several perforated plate and honeycomb samples of high porosity show minimal self-noise but also minimal attenuation capability. Conversely, low porosity perforated plate and sintered wire mesh conditioners exhibit noticeable attenuation but also unacceptable self-noise. One fine wire mesh sample (DP450661) shows minimal selfnoise and reasonable attenuation, particularly when combined in series with a 15.6 percent open area (POA) perforated plate upstream. This configuration is the preferred flow conditioner system for the CJES, providing up to 20 dB of broadband attenuation capability with minimal self-noise.
A potentially important source of structure-borne interior noise transmission in advanced turboprop aircraft is the impingement of the propeller wake/vortex on downstream aerodynamic surfaces. The expected levels of propeller wake/vortex-induced structure-borne noise transmission are not known nor can they be determined with present-day technology. A test apparatus has been designed, built and calibrated for the purposes of studying propeller-induced, structure-borne noise transmission in prototypical aircraft structures. The principal approach to the test apparatus design was to provide a physical means of separating the airborne and structure-borne noise components so that the structure-borne noise transmission response could be studied directly without airborne noise contamination. This was accomplished by housing the receiving fuselage structure in an acoustic shield fitted with a wing-to-fuselage acoustic seal. Initial evaluation of the wing-to-fuselage acoustic seal indicates adequate airborne noise isolation to allow direct study of structure-borne noise transmission.
An experimental campaign was performed to assess acoustic modifications to the NASA Langley Research Center 14- by 22-Foot Subsonic Tunnel. Recent acoustic test campaigns in this facility have emphasized the reduction of aerodynamic noise due to flow scrubbing across acoustically treated floor baskets. However, the measures implemented to address this noise contaminant have resulted in the installation of floor basket coverings of high acoustic reflectivity. Therefore, a controlled test campaign was performed to identify a suitable acoustic basket configuration in terms of both acoustic absorptivity and reduced flow scrubbing noise. This test campaign was conducted in two phases: the first in an anechoic chamber facility on a single acoustic basket panel to identify a notional configuration of acceptable absorptivity, and the second in a complete floor basket installation checkout test in the NASA Langley 14- by 22-Foot Subsonic Tunnel. Results show that the newly implemented floor treatment exhibits suitable performance in both desired categories of acoustic absorptivity and reduced flow scrubbing noise. This campaign has also validated the effectiveness and usefulness of the anechoic chamber test configuration as a means of assessing acoustic reflections at oblique angles of incidence.
Outdoor static and 40 x 80 FT wind tunnel tests of the J79-15 engine/nacelle system with the conic nozzle and 32-chute exhaust suppressor were conducted to acquire the data necessary to evaluate the simulated in-flight signature of an engine-size 32-chute exhaust nozzle suppressor using the 40 x 80 ft wind tunnel and to study possible engine core noise contamination of the jet signature. The tests are described and and a sampling of the data acquired is presented. Included are aero performance summaries, as-measured and composite 1/3 OBSPL spectra for the 70 ft sideline high and low mics from the outdoor static tests, sideline traverse spectra and internal noise measurements from both the outdoor static and the 40 x 80 ft wind tunnel tests.
The ability of feed-forward neural network architectures to learn continuous valued mappings in the presence of noise was demonstrated in relation to parameter identification and real-time adaptive control applications. An error function was introduced to help optimize parameter values such as number of training iterations, observation time, sampling rate, and scaling of the control signal. The learning performance depended essentially on the degree of embodiment of the control law in the training data set and on the degree of uniformity of the probability distribution function of the data that are presented to the net during sequence. When a control law was corrupted by noise, the fluctuations of the training data biased the probability distribution function of the training data sequence. Only if the noise contamination is minimized and the degree of embodiment of the control law is maximized, can a neural net develop a good representation of the mapping and be used as a neurocontroller. A multilayer net was trained with back-error-propagation to control a cart-pole system for linear and nonlinear control laws in the presence of data processing noise and measurement noise. The neurocontroller exhibited noise-filtering properties and was found to operate more smoothly than the teacher in the presence of measurement noise.