The measurement of acoustic dispersion in loosely consolidated, saturated sediments using a water‐filled impedance tube

2002 ◽  
Vol 112 (5) ◽  
pp. 2363-2363
Author(s):  
Preston S. Wilson ◽  
Eun‐Joo Park ◽  
Ronald A. Roy ◽  
William M. Carey
Keyword(s):  
Impedance Tube ◽  
Acoustic Dispersion

Dispersion relations, rays and ray splitting in magnetohelioseismology

2005 ◽  
Vol 364 (1839) ◽  
pp. 333-349 ◽  
Author(s):  
P.S Cally
Keyword(s):  
Strong Field ◽  
Mode Conversion ◽  
Low Frequency ◽  
Active Regions ◽  
Dispersion Relations ◽  
Near Surface ◽  
Wave Splitting ◽  
Proper Account ◽  
Acoustic Dispersion ◽  
Field Inclination

Local helioseismology seeks to probe the near surface regions of the Sun, and in particular of active regions. These are distinguished by their strong magnetic fields, yet current local techniques do not take proper account of this. Here, we first derive appropriate gravito-magneto-acoustic dispersion relations, and then use these to examine how acoustic rays entering regions of strong field split into fast and slow components, and the subsequent fates of each. Specifically, two types of transmission point, where wave energy can transfer from the fast to slow branch (or vice versa) are identified; one close to the equipartition level where the sound and Alfvén speeds coincide, and one higher up near the acoustic cutoff turning point. This second type only exists for rays of low frequency or low l though. In accord with recent studies of fast-to-slow mode conversion from the perspective of p-modes, magnetic field inclination is found to have significant consequences for wave splitting.

scholarly journals Kinetic and Activation Characteristics of Structure Rearrangement Processes in Ethoxylated Isononylphenol Derivates

2020 ◽  
pp. 17-30
Author(s):  
S. V. Mysik
Keyword(s):  
Activation Parameters ◽  
Liquid Structure ◽  
Relaxation Theory ◽  
Structure Rearrangement ◽  
Collective Processes ◽  
Region Data ◽  
Calculation Results ◽  
Acoustic Spectra ◽  
Rearrangement Processes ◽  
Acoustic Dispersion

The paper presents the calculation results of the kinetic and activation characteristics of fast and ultrafast structure rearrangement processes in liquid hydroxyethylated derivates of isononylphenol (ОНФn). Parameters were calculated using the relaxation theory of acoustic spectroscopy of liquids based on the analysis of the acoustic spectra of speed and sound absorption of the hydroxyethylated derivates of isononylphenol. The paper shows that two simple regions of acoustic dispersion can describe the acoustic spectra in the frequency range from 12 MHz to 2 GHz and the temperature range from 253 K to 323 K. The dispersion region data in the hydroxyethylated derivates of isononylphenol correspond to the interconnected reactions of OH ... O bonding and breaking in chain associates and spatially branched network structures. It is noted that the change in the spatial structure of liquid hydroxyethylated derivates of isononylphenol can be considered as a set of the large number of independent (for non-collective processes) and interconnected (for collective processes) local rearrangements of the liquid structure as a result of the thermal motion of molecules. The proposed molecular mechanism of acoustic relaxation and the kinetic model of fast and ultrafast structure rearrangement processes of the hydroxyethylated derivates of isononylphenol made it possible to explain the main experimental results and to calculate the kinetic and activation characteristics of the structure rearrangement processes of the hydroxyethylated derivates of isononylphenol. This model and the kinetic and activation parameters of the hydroxyethylated derivates of isononylphenol can find application in development of various technologies for using nonionic surfactants.

ON DESIGNING IMPEDANCE TUBE WITH GRAZING FLOW

Akustika ◽  
2021 ◽  
pp. 80
Author(s):  
Vadim Palchikovskiy ◽  
Igor Khramtsov ◽  
Aleksander Kuznetsov ◽  
Victor Pavlogradskiy
Keyword(s):  
Numerical Simulation ◽  
Analytical Solutions ◽  
Gas Dynamics ◽  
Aircraft Engine ◽  
Design Parameters ◽  
Experimental Setup ◽  
Structural Units ◽  
Impedance Tube ◽  
Acoustic Liners ◽  
Grazing Flow

The article considers the general issues arising in designing the experimental setup “Impedance tube with grazing flow”, the main structural units of the setup, and their purpose. It is given the basic requirements to be provided by the setup when testing samples of acoustic liners used in an aircraft engine. The choosing of the design parameters of the setup is based on the analysis of the known analytical solutions of the acoustics and gas dynamics, and on the numerical simulation of the grazing flow in the impedance tube.

Explainable machine learning: A case study on impedance tube measurements

2021 ◽  
Vol 263 (3) ◽  
pp. 3223-3234
Author(s):  
Merten Stender ◽  
Mathies Wedler ◽  
Norbert Hoffmann ◽  
Christian Adams
Keyword(s):  
Machine Learning ◽  
Absorption Coefficient ◽  
Specimen Thickness ◽  
Learning Models ◽  
Impedance Tube ◽  
Frequency Regime ◽  
Hidden Patterns ◽  
Model Diagnosis ◽  
Machine Learning Models

Machine learning (ML) techniques allow for finding hidden patterns and signatures in data. Currently, these methods are gaining increased interest in engineering in general and in vibroacoustics in particular. Although ML methods are successfully applied, it is hardly understood how these black box-type methods make their decisions. Explainable machine learning aims at overcoming this issue by deepening the understanding of the decision-making process through perturbation-based model diagnosis. This paper introduces machine learning methods and reviews recent techniques for explainability and interpretability. These methods are exemplified on sound absorption coefficient spectra of one sound absorbing foam material measured in an impedance tube. Variances of the absorption coefficient measurements as a function of the specimen thickness and the operator are modeled by univariate and multivariate machine learning models. In order to identify the driving patterns, i.e. how and in which frequency regime the measurements are affected by the setup specifications, Shapley additive explanations are derived for the ML models. It is demonstrated how explaining machine learning models can be used to discover and express complicated relations in experimental data, thereby paving the way to novel knowledge discovery strategies in evidence-based modeling.

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