Inverse methods in aeroacoustic three-dimensional volumetric noise source localization and quantification

Abstract Inverse three-dimensional heat conduction problems devoted to heating source localization are ill posed. Identification can be performed using an iterative regularization method based on the conjugate gradient algorithm. Such a method is usually implemented off-line, taking into account observations (temperature measurements, for example). However, in a practical context, if the source has to be located as fast as possible (e.g., for diagnosis), the observation horizon has to be reduced. To this end, several configurations are detailed and effects of noisy observations are investigated.

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A Three-Dimensional Inverse Method for Turbomachinery: Part I—Theory

Journal of Turbomachinery ◽

10.1115/1.2927666 ◽

1990 ◽

Vol 112 (3) ◽

pp. 346-354 ◽

Cited By ~ 43

Author(s):

J. E. Borges

Keyword(s):

Velocity Field ◽

Inverse Method ◽

Three Dimensional ◽

Tangential Velocity ◽

Inverse Methods ◽

Meridional Section ◽

Low Speed ◽

Present Procedure ◽

Blade Row ◽

The Mean

There are surprisingly few inverse methods described in the literature that are truly three dimensional. Here, one such method is presented. This technique uses as input a prescribed distribution of the mean swirl, i.e., radius times mean tangential velocity, given throughout the meridional section of the machine. In the present implementation the flow is considered inviscid and incompressible and is assumed irrotational at the inlet to the blade row. In order to evaluate the velocity field inside the turbomachine, the blades (supposed infinitely thin) are replaced by sheets of vorticity, whose strength is related to the specified mean swirl. Some advice on the choice of a suitable mean swirl distribution is given. In order to assess the usefulness of the present procedure, it was decided to apply it to the design of an impeller for a low-speed radial-inflow turbine. The results of the tests are described in the second part of this paper.

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Acoustic beamforming for noise source localization – Reviews, methodology and applications

Mechanical Systems and Signal Processing ◽

10.1016/j.ymssp.2018.09.019 ◽

2019 ◽

Vol 120 ◽

pp. 422-448 ◽

Cited By ~ 34

Author(s):

Paolo Chiariotti ◽

Milena Martarelli ◽

Paolo Castellini

Keyword(s):

Source Localization ◽

Noise Source

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Inverse methods to gradient etch three-dimensional features with prescribed topographies using abrasive jet micro-machining: Part I – Modeling

Precision Engineering ◽

10.1016/j.precisioneng.2016.03.004 ◽

2016 ◽

Vol 45 ◽

pp. 272-284 ◽

Cited By ~ 12

Author(s):

M.R. Sookhak Lari ◽

M. Papini

Keyword(s):

Three Dimensional ◽

Inverse Methods ◽

Micro Machining

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Three-dimensional multi-source localization of underwater objects using convolutional neural networks for artificial lateral lines

Journal of The Royal Society Interface ◽

10.1098/rsif.2019.0616 ◽

2020 ◽

Vol 17 (162) ◽

pp. 20190616 ◽

Cited By ~ 2

Author(s):

Ben J. Wolf ◽

Jos van de Wolfshaar ◽

Sietse M. van Netten

Keyword(s):

Neural Network ◽

Source Localization ◽

Fluid Velocity ◽

Three Dimensional ◽

Sensory System ◽

Three Dimensions ◽

Hydrodynamic Theory ◽

Multiple Sources ◽

Combined System ◽

Lateral Lines

This research focuses on the signal processing required for a sensory system that can simultaneously localize multiple moving underwater objects in a three-dimensional (3D) volume by simulating the hydrodynamic flow caused by these objects. We propose a method for localization in a simulated setting based on an established hydrodynamic theory founded in fish lateral line organ research. Fish neurally concatenate the information of multiple sensors to localize sources. Similarly, we use the sampled fluid velocity via two parallel lateral lines to perform source localization in three dimensions in two steps. Using a convolutional neural network, we first estimate a two-dimensional image of the probability of a present source. Then we determine the position of each source, via an automated iterative 3D-aware algorithm. We study various neural network architectural designs and different ways of presenting the input to the neural network; multi-level amplified inputs and merged convolutional streams are shown to improve the imaging performance. Results show that the combined system can exhibit adequate 3D localization of multiple sources.

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