scholarly journals Influence of a constriction in the near field of the vocal folds: Physical modeling and experimental validation

2008 ◽  
Vol 124 (5) ◽  
pp. 3296-3308 ◽  
Author(s):  
Lucie Bailly ◽  
Xavier Pelorson ◽  
Nathalie Henrich ◽  
Nicolas Ruty
Keyword(s):  
Physical Modeling ◽  
Experimental Validation ◽  
Near Field ◽  
Vocal Folds

Coherent structures of the near field flow in a self-oscillating physical model of the vocal folds

2007 ◽  
Vol 121 (2) ◽  
pp. 1102-1118 ◽  
Author(s):  
Jürgen Neubauer ◽  
Zhaoyan Zhang ◽  
Reza Miraghaie ◽  
David A. Berry
Keyword(s):  
Physical Model ◽  
Coherent Structures ◽  
Near Field ◽  
Vocal Folds

scholarly journals Inverse Patch Transfer Functions Method as a Tool for Source Field Identification

2015 ◽  
Vol 137 (2) ◽  
Author(s):  
Dorian Vigoureux ◽  
Nicolas Totaro ◽  
Jonathan Lagneaux ◽  
Jean-Louis Guyader
Keyword(s):  
Background Noise ◽  
Experimental Validation ◽  
Transfer Functions ◽  
Near Field ◽  
Theoretical Background ◽  
Wall Pressure ◽  
Additional Measurement ◽  
Field Identification ◽  
Open Issue ◽  
Source Velocity

Many methods to detect, quantify, or reconstruct acoustic sources exist in the literature and are widely used in industry (near-field acoustic holography, inverse boundary element method, etc.). However, the source identification in a reverberant or nonanechoic environment on an irregularly shaped structure is still an open issue. In this context, the inverse patch transfer functions (iPTF) method first introduced by Aucejo et al. (2010, “Identification of Source Velocities on 3D Structures in Non-Anechoic Environments: Theoretical Background and Experimental Validation of the Inverse Patch Transfer Functions Method,” J. Sound Vib., 329(18), pp. 3691–3708) can be a suitable method. Indeed, the iPTF method has been developed to identify source velocity on complex geometries and in a nonanechoic environment. However, to obtain good results, the application of the method must follow rigorous criteria that were not fully investigated yet. In addition, as it was first defined, the iPTF method only provides source velocity while wall pressure or intensity should also give useful information to engineers. In the present article, a procedure to identify wall pressure and intensity of the source without any additional measurement is proposed. This procedure only needs simple numerical postprocessing. Using this new intensity identification, the influence of background noise, evanescent waves, and mesh discretization are illustrated on numerical examples. Finally, an experiment on a vibrating plate is shown to illustrate the iPTF procedure.

Thermo-physical modeling and experimental validation of core-shell nanoparticle fabrication of nickel-titanium (nitinol) alloy

2021 ◽  
Vol 138 ◽  
pp. 106880
Author(s):  
Vinod Parmar ◽  
Sonu Singh ◽  
Sunil Kumar ◽  
G. Vijaya Prakash ◽  
Dinesh Kalyanasundaram
Keyword(s):  
Physical Modeling ◽  
Experimental Validation ◽  
Nickel Titanium ◽  
Core Shell ◽  
Nanoparticle Fabrication ◽  
Nitinol Alloy

Analysis and Experimental Validation of Circularly Slotted Near-Field WPT Systems

2021 ◽  
Author(s):  
Kassen Dautov ◽  
Zhanel Kudaibergenova ◽  
Mohammad Hashmi ◽  
Galymzhan Nauryzbayev ◽  
Muhammad A. Chaudhary
Keyword(s):  
Experimental Validation ◽  
Near Field

Near-field-far-field transformation with helicoidal scan: An experimental validation

2008 ◽  
Author(s):  
F. DAgostino ◽  
F. Ferrara ◽  
C. Gennarelli ◽  
R. Guerriero ◽  
M. Migliozzi ◽  
...  
Keyword(s):  
Experimental Validation ◽  
Near Field ◽  
Far Field ◽  
Field Transformation

scholarly journals Intelligent Sensing Using Multiple Sensors for Material Characterization

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4766 ◽  
Author(s):  
Ali M. Albishi ◽  
Seyed H. Mirjahanmardi ◽  
Abdulbaset M. Ali ◽  
Vahid Nayyeri ◽  
Saud M. Wasly ◽  
...  
Keyword(s):  
Material Characterization ◽  
Experimental Validation ◽  
Near Field ◽  
Physical Parameters ◽  
Multiple Sensors ◽  
Frequency Responses ◽  
Sensing System ◽  
Fluid Concentration ◽  
The Neural Networks ◽  
Very High

This paper presents a concept of an intelligent sensing technique based on modulating the frequency responses of microwave near-field sensors to characterize material parameters. The concept is based on the assumption that the physical parameters being extracted such as fluid concentration are constant over the range of frequency of the sensor. The modulation of the frequency response is based on the interactions between the material under test and multiple sensors. The concept is based on observing the responses of the sensors over a frequency wideband as vectors of many dimensions. The dimensions are then considered as the features for a neural network. With small datasets, the neural networks can produce highly accurate and generalized models. The concept is demonstrated by designing a microwave sensing system based on a two-port microstrip line exciting three-identical planar resonators. For experimental validation, the sensor is used to detect the concentration of a fluid material composed of two pure fluids. Very high accuracy is achieved.

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