Employing digital pole-shifting filters to improve low-frequency response of sonic boom measurements

2019 ◽  
Vol 146 (4) ◽  
pp. 2754-2754
Author(s):  
Reese D. Rasband ◽  
Kent L. Gee ◽  
Thomas B. Gabrielson ◽  
Alexandra Loubeau
Keyword(s):  
Frequency Response ◽  
Low Frequency ◽  
Sonic Boom

scholarly journals Understanding the low frequency response of carbon nanotube thermoacoustic projectors

2021 ◽  
Vol 498 ◽  
pp. 115940
Author(s):  
Prashant Kumar ◽  
Rammohan Sriramdas ◽  
Ali E. Aliev ◽  
John B. Blottman ◽  
Nathanael K. Mayo ◽  
...  
Keyword(s):  
Carbon Nanotube ◽  
Frequency Response ◽  
Low Frequency

Collective oscillations in bubble clouds

2011 ◽  
Vol 680 ◽  
pp. 114-149 ◽  
Author(s):  
ZORANA ZERAVCIC ◽  
DETLEF LOHSE ◽  
WIM VAN SAARLOOS
Keyword(s):  
Frequency Response ◽  
Acoustic Field ◽  
Low Frequency ◽  
Acoustic Energy ◽  
Viscous Damping ◽  
Edge States ◽  
Bubble Cloud ◽  
Collective Oscillations ◽  
The Individual ◽  
Bubble Oscillations

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.

Low-Frequency Response of Superionic Conductors

1975 ◽  
Vol 34 (16) ◽  
pp. 1059-1060 ◽  
Author(s):  
P. N. Sen ◽  
B. A. Huberman
Keyword(s):  
Frequency Response ◽  
Low Frequency ◽  
Superionic Conductors

Correspondence between Site Amplification and Topographical, Geological Parameters: Collation of Data from Swiss and Japanese Stations, and Neural Networks-Based Prediction of Local Response

2021 ◽  
Author(s):  
Paolo Bergamo ◽  
Conny Hammer ◽  
Donat Fäh
Keyword(s):  
Frequency Response ◽  
Large Scale ◽  
Site Response ◽  
Low Frequency ◽  
Field Measurements ◽  
Site Amplification ◽  
Local Response ◽  
Regional Variability ◽  
Individual Site ◽  
Digital Elevation

ABSTRACT We address the relation between seismic local amplification and topographical and geological indicators describing the site morphology. We focus on parameters that can be derived from layers of diffuse information (e.g., digital elevation models, geological maps) and do not require in situ surveys; we term these parameters as “indirect” proxies, as opposed to “direct” indicators (e.g., f0, VS30) derived from field measurements. We first compiled an extensive database of indirect parameters covering 142 and 637 instrumented sites in Switzerland and Japan, respectively; we collected topographical indicators at various spatial extents and focused on shared features in the geological descriptions of the two countries. We paired this proxy database with a companion dataset of site amplification factors at 10 frequencies within 0.5–20 Hz, empirically measured at the same Swiss and Japanese stations. We then assessed the robustness of the correlation between individual site-condition indicators and local response by means of statistical analyses; we also compared the proxy-site amplification relations at Swiss versus Japanese sites. Finally, we tested the prediction of site amplification by feeding ensembles of indirect parameters to a neural network (NN) structure. The main results are: (1) indirect indicators show higher correlation with site amplification in the low-frequency range (0.5–3.33 Hz); (2) topographical parameters primarily relate to local response not because of topographical amplification effects but because topographical features correspond to the properties of the subsurface, hence to stratigraphic amplification; (3) large-scale topographical indicators relate to low-frequency response, smaller-scale to higher-frequency response; (4) site amplification versus indirect proxy relations show a more marked regional variability when compared with direct indicators; and (5) the NN-based prediction of site response is the best achieved in the 1.67–5 Hz band, with both geological and topographical proxies provided as input; topographical indicators alone perform better than geological parameters.

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