Acoustic energy generation of “air-jet” instruments: Energy transfer between jet oscillation and acoustic field

2016 ◽  
Vol 140 (4) ◽  
pp. 3253-3253
Author(s):  
Kin'ya Takahashi ◽  
Sho Iwagami ◽  
Taizo Kobayashi ◽  
Toshiya Takami
2016 ◽  
Vol 85 (4) ◽  
pp. 044402 ◽  
Author(s):  
Kin’ya Takahashi ◽  
Sho Iwagami ◽  
Taizo Kobayashi ◽  
Toshiya Takami

2021 ◽  
Vol 149 (4) ◽  
pp. A68-A68
Author(s):  
Ryoya Tabata ◽  
Hiroko Midorikawa ◽  
Taizo Kobayashi ◽  
Ki’nya Takahashi

2011 ◽  
Vol 680 ◽  
pp. 114-149 ◽  
Author(s):  
ZORANA ZERAVCIC ◽  
DETLEF LOHSE ◽  
WIM VAN SAARLOOS

In this paper the collective oscillations of a bubble cloud in an acoustic field are theoretically analysed with concepts and techniques of condensed matter physics. More specifically, we will calculate the eigenmodes and their excitabilities, eigenfrequencies, densities of states, responses, absorption and participation ratios to better understand the collective dynamics of coupled bubbles and address the question of possible localization of acoustic energy in the bubble cloud. The radial oscillations of the individual bubbles in the acoustic field are described by coupled linearized Rayleigh–Plesset equations. We explore the effects of viscous damping, distance between bubbles, polydispersity, geometric disorder, size of the bubbles and size of the cloud. For large enough clusters, the collective response is often very different from that of a typical mode, as the frequency response of each mode is sufficiently wide that many modes are excited when the cloud is driven by ultrasound. The reason is the strong effect of viscosity on the collective mode response, which is surprising, as viscous damping effects are small for single-bubble oscillations in water. Localization of acoustic energy is only found in the case of substantial bubble size polydispersity or geometric disorder. The lack of localization for a weak disorder is traced back to the long-range 1/r interaction potential between the individual bubbles. The results of the present paper are connected to recent experimental observations of collective bubble oscillations in a two-dimensional bubble cloud, where pronounced edge states and a pronounced low-frequency response had been observed, both consistent with the present theoretical findings. Finally, an outlook to future possible experiments is given.


2021 ◽  
Vol 05 ◽  
Author(s):  
Ammar Mohammed ◽  
Changki Mo ◽  
John Miller ◽  
David Lowry ◽  
Jassim Alhamid

Background: Acoustic power transfer is a method for wireless energy transfer to implanted medical devices that permits a greater range of separation between transmitter and receiver than is possible with inductive power transfer. In some cases, short-distance ultrasonic power transfer may be employed; consequently, their operation may be complicated by the near-field aspects of piezoelectric acoustic energy transfer. Methods: A piezoelectric energy transfer system consisting of two lead zirconate titanate (PZT) transducers was analyzed in this work using a combination of experimental measurements and computer simulations. Results: Simulations using the COMSOL Software package showed good agreement with a measured output voltage as a function of the distance between and alignment of the transmitter and receiver with water as a medium. We also simulated how operating frequency affects power transfer efficiency at various distances between the transmitter and receiver and found reasonable agreement with experiments. We report model predictions for power transfer efficiency as a function of the thickness and diameter of the transmitter and receiver. Conclusion: The results show that with proper choice of parameters, piezoelectric systems can provide high power transfer efficiency in the near-field region.


2017 ◽  
Vol 199 ◽  
pp. 1356-1361 ◽  
Author(s):  
G. Lacerra ◽  
F. Massi ◽  
E. Chatelet ◽  
E. Moulin

2016 ◽  
Vol 23 (3) ◽  
pp. 333-343 ◽  
Author(s):  
Maciej Szczodrak ◽  
Adam Kurowski ◽  
Józef Kotus ◽  
Andrzej Czyżewski ◽  
Bożena Kostek

AbstractA system setup for measurements of acoustic field, together with the results of 3D visualisations of acoustic energy flow are presented in the paper. Spatial sampling of the field is performed by a Cartesian robot. Automatization of the measurement process is achieved with the use of a specialized control system. The method is based on measuring the sound pressure (scalar) and particle velocity(vector) quantities. The aim of the system is to collect data with a high precision and repeatability. The system is employed for measurements of acoustic energy flow in the proximity of an artificial head in an anechoic chamber. In the measurement setup an algorithm for generation of the probe movement path is included. The algorithm finds the optimum path of the robot movement, taking into account a given 3D object shape present in the measurement space. The results are presented for two cases, first without any obstacle and the other - with an artificial head in the sound field.


2021 ◽  
Vol 119 (14) ◽  
pp. 144101
Author(s):  
Ahmed Sallam ◽  
Vamsi C. Meesala ◽  
Muhammad R. Hajj ◽  
Shima Shahab

Author(s):  
Aimin Wang ◽  
Nickolas Vlahopoulos ◽  
Jason Zhu ◽  
Mike Qian

An Energy Boundary Element Analysis (EBEA) formulation is presented for calculating sound radiation from a source with arbitrary shape at high frequency. The basic integral equation for the EBEA is derived including a half-space boundary condition. The time and frequency averaged acoustic energy density and acoustic intensity constitutes the primary variables of the new formulation, and the corresponding Green’s functions are derived. The governing equations for the EBEA are established and the numerical formulae for the coefficients of the system matrix, the acoustic energy density, and the acoustic intensity are derived using a Gaussian quadrature. The EBEA formulation and the corresponding numerical implementation are validated by comparing EBEA results to test data for the acoustic field around a vehicle that originates from an airborne noise source. Good correlation is demonstrated between numerical predictions and test data.


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