An experimental program was undertaken to evaluate the feasibility of using piezoelectric patches for damage detection in composite materials. The fact that damage development can cause shifts in the natural frequencies of a structural component suggested an impulse-frequency response approach, in which free vibration was initiated by a single external mechanical pulse and was sensed by piezoelectric patches. Patches in both surface-bonded and embedded configurations were tried. The investigation was conducted on unidirectional glass fiber/epoxy laminated plates containing controlled levels of damage, present as interply delaminations. The data obtained from the piezoelectric patches showed the expected decrease in frequency of the natural vibrational modes with increase in damage. The attractiveness of this method lies in its convenience and its potential for use in the field.
This paper proposes a two-dimensional underwater sound propagation model using the Discontinuous Galerkin Finite Element Method (DG-FEM) to investigate the influence of current on sound propagation. The acoustic field is calculated by the convected wave equation with the current speed parameter. Based on the current speed data from an assimilation model, a two-dimensional coupled acoustic propagation model in the Fram Strait water area is established to observe the variability in propagation loss under different seasonal velocities in the real ocean environment. The transmission loss and signal time structure are examined. The results obtained in different source frequencies are also compared. It appears that the current velocity, time and range variation all have an effect on underwater sound propagation.
It is important to distinguish the dominant mechanism of seabed acoustic scattering for the quantitative inversion of seabed parameters. An identification scheme is proposed based on Bayesian inversion with the relative entropy used to estimate dominant acoustic backscatter mechanism. DiffeRential Evolution Adaptive Metropolis is used to obtain samples from posterior probability density in Bayesian inversion. Three mechanisms for seabed scattering are considered: scattering from a rough water-seabed interface, scattering from volume heterogeneities, and mixed scattering from both interface roughness and volume heterogeneities. Roughness scattering and volume scattering are modelled based on Fluid Theories using Small-Slope Approximation and Small-Perturbation Fluid Approximation, respectively. The identification scheme is applied to three simulated observation data sets. The results indicate that the scheme is promising and appears capable of distinguishing sediment volume from interface roughness scattering and can correctly identify the dominant acoustic backscatter mechanism.
Warm current has a strong impact on the melting of sea ice, so clarifying the current features plays a very important role in the Arctic sea ice coverage forecasting study field. Currently, Arctic acoustic tomography is the only feasible method for the large-range current measurement under the Arctic sea ice. Furthermore, affected by the high latitudes Coriolis force, small-scale variability greatly affects the accuracy of Arctic acoustic tomography. However, small-scale variability could not be measured by empirical parameters and resolved by Regularized Least Squares (RLS) in the inverse problem of Arctic acoustic tomography. In this paper, the convolutional neural network (CNN) is proposed to enhance the prediction accuracy in the Arctic, and especially, Gaussian noise is added to reflect the disturbance of the Arctic environment. First, we use the finite element method to build the background ocean model. Then, the deep learning CNN method constructs the non-linear mapping relationship between the acoustic data and the corresponding flow velocity. Finally, the simulation result shows that the deep learning convolutional neural network method being applied to Arctic acoustic tomography could achieve 45.87% accurate improvement than the common RLS method in the current inversion.
The Arctic region is undergoing drastic environmental change. In recent studies (Nuttall, 2018;Richter-Menge et al., 2018), this area is warming at more than twice the global average due to the Arctic amplification effect. Most human activities in the Arctic Ocean require knowledge of the ocean environment. According to the research of Timmermans and Marshall (2020), complex ocean currents and drifting ice floes at high latitudes are major environmental features of the Arctic Ocean. Wang et al. have already researched ocean current-dependent acoustic propagation modeling (Wang et al., 2021). However, the ice floes-dependent acoustic propagation modeling has not been solved. Applying the drifting ice floes to the acoustic propagation modeling will forward the Arctic Ocean oil exploration, fishery survey, mineral extraction, shipping planning and tourism (Worcester et al., 2020).Arctic ice floes have a general structure and a microstructure (Thomas & Dieckmann, 2010). The general structure contains the physical parameters describing the form of ice floes, such as ice concentration and thickness. The microstructure contains physical parameters describing the interior of ice floes like temperature, salinity, and density. Scientists have done much work on the general structure. Some scholars proposed the earliest sea ice model for acoustic and described the sea ice as semi-elliptical protrusions on a randomly distributed free or rigid base surface (Burke & Twersky, 1966;Diachok, 1976). To verify the Burke model, Yang measured sonic reflectivity below 1 kHz and found the sea ice could be seen as a smooth homogeneous medium (Yang & Votaw, 1981). In 1992, based on the Biot theory, Rajan also suggested that sea ice could be treated as a brine-saturated porous
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