Development of a water-filled four-sensor impedance tube

2021 ◽  
Vol 150 (4) ◽  
pp. A186-A186
Author(s):  
Andrew R. McNeese ◽  
Preston S. Wilson ◽  
Michael R. Haberman ◽  
Kevin M. Lee
Keyword(s):  
Impedance Tube

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.

Automation of impedance tube measurements

1975 ◽  
Vol 58 (5) ◽  
pp. 1111-1111
Author(s):  
J. I. Dunlop
Keyword(s):  
Impedance Tube
Sign in / Sign up

Export Citation Format

Share Document