Wavepacket noise source model for microphone array data analysis of hot supersonic jets

2013 ◽  
Vol 134 (5) ◽  
pp. 4127-4127 ◽  
Author(s):  
Philip Morris ◽  
Robert Dougherty ◽  
Chris Nelson ◽  
Alan Cain ◽  
Kenneth Brentner
Keyword(s):  
Data Analysis ◽  
Noise Source ◽  
Microphone Array ◽  
Source Model ◽  
Supersonic Jets ◽  
Array Data

scholarly journals Anisotropic source modelling for turbulent jet noise prediction

2019 ◽  
Vol 377 (2159) ◽  
pp. 20190075 ◽  
Author(s):  
Xihai Xu ◽  
Xiaodong Li
Keyword(s):  
Large Scale ◽  
Noise Source ◽  
Jet Noise ◽  
Source Model ◽  
Noise Prediction ◽  
Reynolds Stresses ◽  
Acoustic Analogy ◽  
Mean Flow ◽  
Navier Stokes Equation ◽  
Anisotropic Source

An anisotropic component of the jet noise source model for the Reynolds-averaged Navier–Stokes equation-based jet noise prediction method is proposed. The modelling is based on Goldstein's generalized acoustic analogy, and both the fine-scale and large-scale turbulent noise sources are considered. To model the anisotropic characteristics of jet noise source, the Reynolds stress tensor is used in place of the turbulent kinetic energy. The Launder–Reece–Rodi model (LRR), combined with Menter's ω -equation for the length scale, with modified coefficients developed by the present authors, is used to calculate the mean flow velocities and Reynolds stresses accurately. Comparison between predicted results and acoustic data has been carried out to verify the accuracy of the new anisotropic source model. This article is part of the theme issue ‘Frontiers of aeroacoustics research: theory, computation and experiment’.

Implementation of array analysis on seismic signals from volcanoes in Iceland recorded on the small-aperture HEKSISZ array

2020 ◽  
Author(s):  
Tim Sonneman ◽  
Kristín Vogfjörd ◽  
Christopher Bean ◽  
Benedikt Halldórsson ◽  
Johannes Schweitzer
Keyword(s):  
Data Analysis ◽  
Real Time ◽  
Seismic Array ◽  
Raspberry Pi ◽  
Seismic Zone ◽  
Seismic Signals ◽  
Secondary Phases ◽  
Small Aperture ◽  
Array Data ◽  
Single Station

<p>We present preliminary results and progress updates of ongoing work at the Icelandic Meteorological Office carried out within the EUROVOLC work package on Volcano pre-eruptive unrest detection schemes. Our main goal is improved understanding of volcanic systems and fracture zones in South Iceland. This requires enhanced detection and mapping capabilities of seismic events from volcanoes in the Eastern Volcanic Zone (EVZ) and faults in the South Iceland Seismic Zone (SISZ), including continuous real-time analysis of seismic signals associated with magma movement in volcanoes and activity on faults in South Iceland. The chosen measures to achieve these tasks are the deployment of a seismic array at the intersection between the EVZ and the SISZ, the implementation of appropriate real-time array data processing and the investigation of spatiotemporal seismic source characteristics such as tracking of magma movements and intrusions from deep to shallow levels in the crust to image the volcanoes’ plumbing systems, shallow caldera seismicity, and earthquake rupture propagation and microseismicity on nearby tectonic faults. Through funding from an Icelandic infrastructure grant and cooperation between IMO and DIAS, the HEKSISZ small-aperture seismic array is being installed about 6 km south of Hekla. The array, which will consist of 12 stations (7 broadband seismometers and at least 5 additional Raspberry PI seismometers and some co-located accelerometers), builds upon experience gained from temporary array operations in the FUTUREVOLC project and will be the first permanent seismic array in Iceland. The array is surrounded by four different volcanic systems and a prominent fracture zone, providing an abundance of seismicity for analysis. The detection of volcanic and local earthquake events depends on signal coherency and the algorithms used. The signal coherency is mainly affected by array geometry and the site and noise conditions. To analyze the wavefield we will use algorithms such as beamforming, signal-to-noise triggers, FK analysis, and cross-correlation on both vertical and horizontal channels. The implementation is through free open-source software, based mainly on Python obspy and further extensions. While the array is still in the process of coming online, we use data from its existing central permanent network station, MJO to analyze signals from the volcanoes and faults in preparation for the future array data analysis. Relevant single-station observations are first arrival polarization and search for existence and timing of secondary phases, such as surface and Moho reflections from different distances and depths. These observed peculiarities will guide the focus of the array data analysis, specifically as one of the main interests is the depth determination of magma movements and intrusions below Hekla. The volcanic region may have strong lateral crustal heterogeneities, so if significant azimuthal deviations are estimated from the single-station analysis, correction parameters for the array will need to be constrained as well. To further test how a future array might perform in this location, we invert synthetic sources at various depths and distances and also use observed source arrays to search for additional phases from possible conversions and reflections and measure their phase velocities.</p>

Open-source software for the application of microphone array methods

2017 ◽  
Vol 48 (3-4) ◽  
pp. 44-51 ◽  
Author(s):  
Gert Herold ◽  
Ennes Sarradj
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Time Domain ◽  
Microphone Array ◽  
Synthetic Data ◽  
Object Oriented ◽  
Source Characterization ◽  
Acoustic Source ◽  
Array Data ◽  
Flexible Programming

The open-source Python library Acoular is aimed at the processing of microphone array data. It features a number of algorithms for acoustic source characterization in time domain and frequency domain. The modular, object-oriented architecture allows for flexible programming and a multitude of applications. This includes the processing of measured array data, the mapping of sources, the filtering of subcomponent noise, and the generation of synthetic data for test purposes. Several examples illustrating its versatility are given, as well as one example for implementing a new algorithm into the package.

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