Low‐frequency ocean surface noise sources

1985 ◽  
Vol 78 (S1) ◽  
pp. S1-S2 ◽  
Author(s):  
W. M. Carey ◽  
M. P. Bradley
Keyword(s):  
Low Frequency ◽  
Ocean Surface ◽  
Noise Sources ◽  
Surface Noise

Noise source location and scaling of subsonic upper-surface blowing

2020 ◽  
Vol 19 (3-5) ◽  
pp. 191-206
Author(s):  
Trae L Jennette ◽  
Krish K Ahuja
Keyword(s):  
Strouhal Number ◽  
Noise Source ◽  
Low Frequency ◽  
Directivity Pattern ◽  
Source Location ◽  
Dipole Source ◽  
Frequency Noise ◽  
Noise Sources ◽  
Trailing Edge ◽  
Trailing Edge Noise

This paper deals with the topic of upper surface blowing noise. Using a model-scale rectangular nozzle of an aspect ratio of 10 and a sharp trailing edge, detailed noise contours were acquired with and without a subsonic jet blowing over a flat surface to determine the noise source location as a function of frequency. Additionally, velocity scaling of the upper surface blowing noise was carried out. It was found that the upper surface blowing increases the noise significantly. This is a result of both the trailing edge noise and turbulence downstream of the trailing edge, referred to as wake noise in the paper. It was found that low-frequency noise with a peak Strouhal number of 0.02 originates from the trailing edge whereas the high-frequency noise with the peak in the vicinity of Strouhal number of 0.2 originates near the nozzle exit. Low frequency (low Strouhal number) follows a velocity scaling corresponding to a dipole source where as the high Strouhal numbers as quadrupole sources. The culmination of these two effects is a cardioid-shaped directivity pattern. On the shielded side, the most dominant noise sources were at the trailing edge and in the near wake. The trailing edge mounting geometry also created anomalous acoustic diffraction indicating that not only is the geometry of the edge itself important, but also all geometry near the trailing edge.

Low frequency noise sources in AlGaN/GaN HEMTs

1999 ◽  
Author(s):  
J. A. Garrido ◽  
F. Calle ◽  
E. Muñoz ◽  
I. Izpura ◽  
J. L. Sánchez-Rojas ◽  
...  
Keyword(s):  
Low Frequency ◽  
Frequency Noise ◽  
Low Frequency Noise ◽  
Noise Sources ◽  
Gan Hemts

scholarly journals Identification and Removal of Non-acoustic Noise in Towed Array Sonar Using F-Κ Transform for Enhanced Torpedo Detection

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Vibration Isolation ◽  
Acoustic Noise ◽  
Detection System ◽  
Mechanical Vibration ◽  
Low Frequency ◽  
Acoustic Energy ◽  
Noise Sources ◽  
Towed Array ◽  
Spatio Temporal ◽  
Towed Arrays

Low frequency passive towed array sonar is an essential component in a torpedo detection system for surface ships. Compact towed arrays are used for torpedo detection and they will be towed at higher towing speeds compared to conventional towed array sonars used for surveillance. Presence of non-acoustic noise in towed array sensors at higher towing speeds degrades torpedo detection capability at lower frequencies. High wavenumber mechanical vibrations are induced in the array by vortex shedding associated with hydrodynamic flow over the array body and cable scope. These vibrations are known to couple into the hydrophone array as nonacoustic noise sources and can impair acoustic detection performance, particularly in the forward end fire direction. Lengthy mechanical vibration isolation modules can isolate vibration induced noise in towed arrays, but this is not recommended in a towed array which is towed at high speeds as it will increase the drag and system complexity. An algorithm for decomposing acoustic and non-acoustic components of signals received at sensor level using well known frequency-wavenumber transform (F-K transform) is presented here. Frequency-wavenumber diagrams can be used for differentiating between acoustic and non-acoustic signals. An area of V shape is identified within the F-K spectrum where acoustic energy is confined. Energy outside this V will highlight non-acoustic energy. Enhanced simultaneous spatio-temporal and spatio-amplitude detection is possible with this algorithm. Performance of this algorithm is validated through simulation and experimental data.

scholarly journals INFRASOUND AND LOW FREQUENCY NOISE IMMISION FROM PIPELINES. iMPROVING THE ISO 1996-2:2017 PROPOSED METHODS. CASES STUDIES CONDUCTED AT LOS ANDES AND PERUVIAN RAINFOREST

Akustika ◽  
2019 ◽  
Vol 32 ◽  
pp. 335-345
Author(s):  
Walter Montano
Keyword(s):  
Low Frequency ◽  
Sound Level ◽  
Frequency Noise ◽  
Gas Extraction ◽  
Howler Monkeys ◽  
Noise Sources ◽  
Industrial Facilities ◽  
Sound Levels ◽  
Continuous Noise ◽  
Amazonian Rainforest

The gas extraction wells are in Amazonian rainforest and by them there are their industrial facilities. The pipeline has about 800 km with four pumps stations and two compressor stations. The challenge of conducting sound measurements was important-there is no specialized literature-and other noise "sources" are howler monkeys, cicadidae chirping, woodpeckers, trees´foliage, etc. However the problem is simply because those fixed industrial facilities are the only ones. People live in isolated hamlet on the side of dirt roads, so they are exposed 24/7 to the continuous noise; at homes 4 km away from the plants the sound level is 60 dBC, but in the spectrum of ILFN tones could not be identified. This Paper presents the procedures that were developed to identify the ILFN tones, improving the methods proposed in ISO 1996-2, writing a software that "automatically eliminates" the sound levels that don´t belong to the industry,

Exposure to industrial noise: impacts on cognitive performance

2021 ◽  
Vol 263 (4) ◽  
pp. 2590-2600
Author(s):  
Luiz Henrique Mesa Casa Pereira ◽  
Björn Knöfel ◽  
Jan Troge ◽  
Welf-Guntram Drossel ◽  
Marcel Klein ◽  
...  
Keyword(s):  
Cognitive Performance ◽  
Noise Analysis ◽  
Low Frequency ◽  
Cumulative Effect ◽  
Noise Sources ◽  
Industrial Noise ◽  
Sound Levels ◽  
Office Environments ◽  
High Sound ◽  
High Frequency Content

Research on the relation between exposure to noise and cognitive performance inside industrial environments is not as broad as on office environments. For a better understanding of the specific industrial noise problems, participants performed arithmetic tests inside a hemi anechoic room while they were exposed to sounds of five typical industrial noise sources. The subjects also classified how annoying they perceived the noise signals. The effect of noise on the arithmetic test's performance was larger on accuracy than on velocity, which was verified using a Student t-test. Spectral-temporal characteristics - especially high frequency content and strong low frequency modulation - appear to relate better with lower performance on the test than high sound levels. Subjects that evaluated noise as more annoying performed worse in a final arithmetic test (under silence) after being exposed to the noises, indicating a possible cumulative effect of noise on performance. The findings provide a better insight in the cognitive behavior of people who are exposed to industrial noise. Hence, the study will proceed with the specific noise analysis of single industrial workplaces.

Airframe noise measurements on JAXA Jet Flying Test Bed “Hisho” using a phased microphone array

2017 ◽  
Vol 16 (4-5) ◽  
pp. 255-273 ◽  
Author(s):  
Takehisa Takaishi ◽  
Hiroki Ura ◽  
Kenichiro Nagai ◽  
Yuzuru Yokokawa ◽  
Mitsuhiro Murayama ◽  
...  
Keyword(s):  
Microphone Array ◽  
Low Frequency ◽  
Landing Gear ◽  
Frequency Resolution ◽  
Test Bed ◽  
Noise Sources ◽  
Low Frequencies ◽  
Airframe Noise ◽  
Noise Measurements ◽  
Wooden Platform

In 2015, the Japan Aerospace Exploration Agency launched the Flight demonstration of QUiet technology to Reduce nOise from High-lift configurations project to verify by flight demonstration the feasibility of practical noise-reducing aircraft modification concepts. In order to serve as a baseline for comparison before modification, airframe noise sources of the JAXA Jet Flying Test Bed “Hisho” were measured with a 30 m diameter array of 195 microphones mounted on a wooden platform built temporary beside the runway of Noto Satoyama Airport in Japan. A classical Delay and Sum in the time domain beamforming algorithm was adapted for the present study, with weight factors introduced to improve the low-frequency resolution and autocorrelations eliminated to suppress wind noise at high frequencies. In the landing configuration at idle thrust, the main landing gear, nose landing gear, and side edges of the six extended flap panels were found to be the dominant “Hisho” airframe noise sources. Deconvolution by the DAMAS and CLEAN-SC algorithms provided clearer positions of these sound sources at low frequencies. Integration of acoustical maps agreed well with the sound pressure level measured by a microphone placed at the center of the microphone array and gave detailed information about the contribution of each noise source.

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