Underwater Acoustic Modeling and Simulation

Etter, Paul C.

doi:10.1201/9781315166346

Cited by 152 publications

(80 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Cavitation and bubble cloud dynamics are of importance in various fields of engineering, including therapeutic ultrasound [1,2,3], hydraulic machineries [4,5], and underwater acoustics [6]: cavitation bubbles formed in a human body during a passage of the tensile part of ultrasound pulses can scatter and absorb subsequent pulses to lower the efficiency of ultrasound therapies and violent collapse of bubbles can cause cavitation damage on the surface of various materials, from ship propellers to kidney stones. Bubbles that naturally exist in the ocean can attenuate and modulate acoustic signals to decrement the performance of underwater acoustic systems.…”

Section: Introductionmentioning

confidence: 99%

Eulerian–Lagrangian method for simulation of cloud cavitation

Maeda

Colonius

2018

Journal of Computational Physics

View full text Add to dashboard Cite

We present a coupled Eulerian-Lagrangian method to simulate cloud cavitation in a compressible liquid. The method is designed to capture the strong, volumetric oscillations of each bubble and the bubble-scattered acoustics. The dynamics of the bubbly mixture is formulated using volume-averaged equations of motion. The continuous phase is discretized on an Eulerian grid and integrated using a high-order, finite-volume weighted essentially non-oscillatory (WENO) scheme, while the gas phase is modeled as spherical, Lagrangian point-bubbles at the sub-grid scale, each of whose radial evolution is tracked by solving the Keller-Miksis equation. The volume of bubbles is mapped onto the Eulerian grid as the void fraction by using a regularization (smearing) kernel. In the most general case, where the bubble distribution is arbitrary, three-dimensional Cartesian grids are used for spatial discretization. In order to reduce the computational cost for problems possessing translational or rotational homogeneities, we spatially average the governing equations along the direction of symmetry and discretize the continuous phase on two-dimensional or axi-symmetric grids, respectively. We specify a regularization kernel that maps the three-dimensional distribution of bubbles onto the field of an averaged two-dimensional or axi-symmetric void fraction. A closure is developed to model the pressure fluctuations at the sub-grid scale as synthetic noise. For the examples considered here, modeling the sub-grid pressure fluctuations as white noise agrees a priori with computed distributions from three-dimensional simulations, and suffices, a posteriori, to accurately reproduce the statistics of the bubble dynamics. The numerical method and its verification are described by considering test cases of the dynamics of a single bubble and cloud cavitaiton induced by ultrasound fields. (Kazuki Maeda) arXiv:1712.00670v2 [physics.flu-dyn] 5 Dec 2017 such methods are the most suitable for simulations at the scales of bubbles for a short period of time, typically single events of the collapse of bubbles [17,18,19,20].For more complex bubbly mixtures with relatively low void fractions of O(10 −2 ), methods that solve volume-or ensemble-averaged equations of motion have been used to compute the propagation of acoustic and shock waves [21,22,23,24,25,26,27,28,29,30] and the dynamics of bubble clouds [31,32,33,34,35,36,37]. In the classical averaging approaches, the bubbly-mixture is treated as a continuous media. The volume of dispersed bubbles is converted to a continuous void fraction field in a control volume that contains a sufficiently large number of bubbles. The length-scale of the control volume (averaging length-scale) is larger than the characteristic inter-bubble distance. The dynamics of the gas-phase are closed by considering the averaged change in the volumetric oscillations of bubbles in response to pressure fluctuations in the mixture. Bubbles are typically modeled as spherical cavities, of which dynamics are described by ordinary...

show abstract

Section: Introductionmentioning

confidence: 99%

Eulerian–Lagrangian method for simulation of cloud cavitation

Maeda

Colonius

2018

Journal of Computational Physics

View full text Add to dashboard Cite

show abstract

“…(9.6.2)]. In order to illustrate the versatility and heavytail behaviour of M ν (µ, σ 2 ), the PDF, given in (25), is aptly illustrated with respect to ν ∈ R + and σ 2 ∈ R + for a certain µ ∈ R in Fig. 1 on the next page.…”

Section: A Mcleish Distributionmentioning

confidence: 99%

“…The special cases of M ν (µ, σ 2 ) consist of Dirac, Laplacian and Gaussian distributions. In more details, as ν → 0, (25)…”

Section: A Mcleish Distributionmentioning

confidence: 99%

“…With the aid of utilizing K n (x) G 2,0 0,2 x 2 /2 n/2,−n/2 [ (25), E W n is then expressed for k ∈ N as follows where denotes the empty coefficient set. Immediately afterwards, in (33), changing the variable x 2 → y and employing [151, Eqs.…”

Section: A Mcleish Distributionmentioning

confidence: 99%

“…Proof. Let us define a new random variable, W (X − µ)/σ, where W ∼ M ν (0, 1), whose PDF is given, using (25), by…”

Section: Definition 1 (Mcleish's Quantile) the Mcleish's Q-function mentioning

confidence: 99%

See 2 more Smart Citations

McLeish Distribution: Performance of Digital Communications Over Additive White McLeish Noise (AWMN) Channels

Yılmaz

2020

IEEE Access

View full text Add to dashboard Cite

The objective of this article is to statistically characterize and describe a more general additive noise distribution, termed as McLeish distribution, whose random nature can model different impulsive noise environments often encountered in practice and provides a robust alternative to Gaussian distribution. Accordingly, we develop circularly and elliptically symmetric multivariate McLeish distribution and introduce additive white McLeish noise (AWMN) channels. In particular, we propose novel analytical and closed-form expressions for the symbol error rate (SER) performance of coherent / non-coherent signaling using various digital modulation techniques over AWMN channels. In that context, we illustrate some novel expressions by some selected numerical examples and verify them by some well chosen computer-based simulations.

show abstract