• The Journal of the Acoustical Society of Korea
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• v.42 no.4
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• pp.313-319
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• 2023

In this paper, a time series machine learning model, Long Short Term Memory (LSTM), is applied into the bubble flow noise data and the underwater projectile launch noise data to predict missing values of time-series underwater noise data. The former is mixed with bubble noise, flow noise, and fluid-induced interaction noise measured in a pipe and can be classified into three types. The latter is the noise generated when an underwater projectile is ejected from a launch tube and has a characteristic of instantaenous noise. For such types of noise, a data-driven model can be more useful than an analytical model. We constructed an LSTM model with given data and evaluated the model's performance based on the number of hidden units, the number of input sequences, and the decimation factor of signal. It is shown that the optimal LSTM model works well for new data of the same type.

• The Journal of the Acoustical Society of Korea
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• v.41 no.3
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• pp.255-267
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• 2022

In this paper, noise characteristics of bubbles created by an orifice-type bubble generator are studied. In order to understand the overall bubble noise characteristics, the bubble noise spectra proposed by Strasberg and Blake, respectively, are examined, and an air injection experiment was performed in the large cavitation tunnel of KRISO to measure the bubble noise. The experiments were performed under a quiescent condition and flow conditions using 5 types of air bubble generator. From the measurement results, the characteristics of the bubble noise spectrum according to the experimental conditions are observed, and the effect of each parameter on bubble noise is analyzed by regression analysis. Finally, empirical models based on the regression analysis for bubble noise are presented, and it is confirmed that the estimated bubble noise is in good agreement with the measured results.

• The Journal of the Acoustical Society of Korea
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• v.39 no.2
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• pp.77-86
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• 2020

In this study, the Eulerian/Lagrangian one-way coupling method is proposed to predict flow noise due to Blade-Tip Vortex Cavitation (BTVC). The proposed method consists of four sequential steps: flow field simulation using Computational Fluid Dynamics (CFD) techniques, reconstruction of wing-tip vortex using vortex model, generation of BTVC using bubble dynamics model and acoustic wave prediction using the acoustic analogy. Because the CFD prediction of tip vortex structure generally suffers from severe under-prediction of its strength along the steamwise direction due to the intrinsic numerical damping of CFD schemes and excessive turbulence intensity, the wing-tip vortex along the freestream direction is regenerated by using the vortex modeling. Then, the bubble dynamics model based on the Rayleigh-Plesset equation was employed to simulate the generation and variation of BTVC. Finally, the flow noise due to BTVC is predicted by modeling each of spherical bubbles as a monople source whose strength is proportional to the rate of time-variation of bubble volume. The validity of the proposed numerical methods is confirmed by comparing the predicted results with the measured data.

• The Journal of the Acoustical Society of Korea
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• v.40 no.4
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• pp.320-329
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• 2021

In this paper, an empirical model of air bubble size to be applied to an air masker for reduction of underwater radiation noise is presented. The proposed model improves the divergence problem under the low-speed flow condition of the existing model derived using Rayleigh's jet instability model and simple continuity condition by introducing a jet flow velocity of air. The jet flow velocity of air is estimated using the bubble size where the liquid is quiescent. In a medium without flow, the size of the bubble is estimated by an empirical method where bubble formation regime is divided into a laminar-flow range, a transition range, and a turbulent-flow range based on the Reynolds number of the injected air. The proposed bubble size model is confirmed to be in good agreement with the Computational Fluid Dynamics (CFD) analysis result and the experimental results of the existing literature. Using the acoustic inversion method, the air bubble population is estimated from the insertion loss measured during the air injection experiment of the air- masker model in a large cavitation tunnel. The results of the experiments and the bubble size model are compared in the paper.

• Proceedings of the KSME Conference
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• 2001.11b
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• pp.300-305
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• 2001

The unsteady flow structure and the related noise generation, which are caused by the separation of a two-dimensional, incompressible, laminar boundary-layer on the flat plate under the influence of local adverse pressure gradient, are numerically examined. The characteristic lines of the wall pressure are examined to understand the unsteady behavior of vortex shedding near the reattachment point of the separation bubble. Also, the generation and propagation of the vortex-induced noise in the separated boundary-layer are calculated by the method of computational aero-acoustics (CAA), and the effects of Reynolds number, Mach number and the strength of the adverse pressure gradient on the unsteady flow and noise characteristics are examined.