A numerical investigation is conducted to study the propulsive performance of the semi-active flapping foil of the wave glider, where the heaving smotion is fully prescribed, and the pitching motion is determined by the hydrodynamic force and torsion spring. A mesh for two-dimensional NACA0012 foil with the Reynolds number Re = 42000 is produced, and a dynamic mesh and sliding interface are used in the computation. The influences of reduced frequency, spring stiffness, and critical pitching amplitude on the hydrodynamic characteristics of semi-active flapping foil are systematically investigated. We find that there is a critical reduced frequency: When the reduced frequency is lower than the critical value, the propulsive performance of flapping foil can be improved exponentially, and when the reduced frequency is higher than the critical value, the semi-active flapping foil cannot provide an effective thrust. For a greater reduced frequency, there is an optimal spring stiffness value, which corresponds to the maximum value of the output power coefficient. For a lower reduced frequency, the mean value of the output power coefficient monotonically decreases as the spring stiffness increases. We also notice that the propulsive efficiency of flapping foil monotonically decreases as the spring stiffness increases. Finally, we find that the appropriate critical pitching amplitude can improve the propulsive performance of semi-active flapping foil, especially for greater heaving amplitudes.

A numerical investigation on the propulsion performance of the semi-active flapping foil of the wave glider with different mass ratio and damping coefficient is investigated. The commercial CFD software Fluent is used to solve the URANS equations around the flapping foil by the Finite Volume Method. A mesh of 2D NACA0012 foil with the Reynolds number Re = 42,000 is used in all simulations. We first analyze the effect of the mass ratio on the mean output power coefficient and propulsion efficiency and note that with the variation of the mass ratio, the propulsion efficiency decreases slightly. Besides, we find that the mass ratio has a noticeable influence on the mean output power coefficient, and the influence is determined by the reduced frequency. For high reduced frequency, with the increase of the mass ratio, the propulsion performance of the flapping foil decreases monotonously. For low reduced frequency, the mean output power increases slightly. For critically reduced frequency, the mean output power coefficient of the foil firstly increases and then decreases via the mass ratio increase. Then, we examine the influence of the damping coefficient on the propulsion performance of the flapping foil and find that the damping coefficient has a severe adverse effect on the output power and propulsion efficiency. We conclude that the influence of the damping coefficient should be considered first when we design the propulsion device of the semi-active flapping foil. Meanwhile, we should also consider the sea conditions to choose the mass ratio to optimize the flapping foil.

The navigation and localization of autonomous underwater vehicles (AUVs) in seawater are of the utmost importance for scientific research, petroleum engineering, search and rescue, and military missions concerning the special environment of seawater. However, there is still no general method for AUVs navigation and localization, especially in the featureless seabed. The reported approaches to solving AUVs navigation and localization problems employ an expensive inertial navigation system (INS), with cumulative errors and dead reckoning, and a high-cost long baseline (LBL) in a featureless subsea. In this study, a simultaneous localization and mapping (AMB-SLAM) online algorithm, based on acoustic and magnetic beacons, was proposed. The AMB-SLAM online algorithm is based on multiple randomly distributed beacons of low-frequency magnetic fields and a single fixed acoustic beacon for location and mapping. The experimental results show that the performance of the AMB-SLAM online algorithm has a high robustness. The proposed approach (the AMB-SLAM online algorithm) provides a low-complexity, low-cost, and high-precision online solution to the AUVs navigation and localization problem in featureless seawater environments. The AMB-SLAM online solution could enable AUVs to autonomously explore or autonomously intervene in featureless seawater environments, which would enable AUVs to accomplish fully autonomous survey missions.

This study discusses a method for quantitative quality assessment for the simplification of linear features. Considering the multi-scale nature of linear features, this paper combines the improved Douglas–Peucker method without threshold and the multiway tree model to construct a weighted hierarchical linear feature representation model called the Douglas–Peucker Multiway Tree (DMC-tree). Subsequently, the uncertainty computation is conducted from the root of the DMC-Tree top-down level by level to obtain the quality indexes. Then, the quality index of the whole linear feature is obtained by combining the indexes of every layer together with their weights. The results of the presented method are compared with those of the length ratio method and the Hausdorff distance method. The results show the advantages of the presented method over the others, including (1) its sensitivity to feature points of multiple scales, (2) the quantitative characteristics of the indexes, and (3) the finer granularity in assessment.

The modern discrimination of sediment is based on acoustic intensity (backscatter) information from high-resolution multibeam echo-sounder systems (MBES). The backscattering intensity, varying with the angle of incidence, reveals the characteristics of seabed sediment. In this study, we propose a novel unsupervised acoustic sediment classification method based on the K-medoids algorithm using multibeam backscattering intensity data. In this method, we use the Lurton parameters model, which is the relationship between the backscattering intensity and incidence, to obtain the backscattering angle corresponding curve, and we use the genetic algorithm to fit the curve by the least-squares method. After extracting the four relevant parameters of the model when the ideal fitting effect was achieved, we input the characteristic parameters obtained from the fitting to the K-medoids clustering model. To validate the proposed classification method, we compare it with the self-organizing map (SOM) neural network classification method under the same parameter settings. The results of the experiment show that when the seabed sediment category is less than or equal to 3, the results of the K-medoids algorithm and the SOM neural network are approximately identical. As the sediment category increases, the SOM neural network shows instability, and it is impossible to see the clear boundaries of the seabed sediment, while the K-medoids category is 5 and the seabed sediment classification is correct. After comparing with field in situ seabed sediment sampling along the MBES survey line, the sediment classification method based on K-medoids is consistent with the distribution of the field sediment sampling. The classification accuracies for bedrock, sandy clay, and silty sand are all above 90%; those for gravel and clay are nearly 80%, and the overall accuracy reaches 89.7%.

Many geoacoustic models are used to establish the relationship between the physical and acoustic properties of sediments. In this work, Bayesian inversion and model selection techniques are applied to compare combinations of three geoacoustic models and corresponding scattering models—the fluid model with the effective density fluid model (EDFM), the grain-shearing elastic model with the viscosity grain-shearing (VGS(λ)) model, and the poroelastic model with the corrected and reparametrized extended Biot–Stoll (CREB) model. First, the resolution and correlation of parameters for the three models are compared based on estimates of the posterior probability distributions (PPDs), which are obtained by Bayesian inversion using the backscattering strength data. Then, model comparison and selection techniques are utilized to assess the matching degree of model predictions and measurements qualitatively and to ascertain the Bayes factors in favor of each quantitatively. These studies indicate that the fluid and poroelastic models outperform the grain-shearing elastic model, in terms of both parameter resolution and the ability to produce predictions in agreement with measurements for sandy sediments. The poroelastic model is considered to be the best, as the inversion based on it can provide more highly resolved information of sandy sediments. Finally, the attempt to implement geoacoustic inversion with different models provides a relatively feasible remote sensing scheme for various types of sediments under unknown conditions, which needs further validation.