The bionic robotic fish is one of the special autonomous underwater vehicles (AUV), whose path planning is crucial for many applications including underwater environment detection, archaeology, pipeline leak detection, and so on. However, the uncertain ocean currents increase the difficulty of path planning for bionic robotic fish in practice. In this paper, the path energy consumption is selected as the objective function for path planning, path safety factor, and smoothness are considered as the constraint conditions. The kinematic model is established for bionic robotic fish and, considering the uncertainty of ocean currents, a “min-max” robust optimization problem is proposed in the light of the normal optimization model of path planning for bionic robotic fish. The co-evolutionary genetic algorithm is presented to solve the robust optimization problem with two populations; one population represents the solutions and the other represents the uncertain ocean currents. The objective of the proposed algorithm is to find a robust solution that has the best worst-case performance over a set of possible ocean currents. Multiple experiments indicate that the proposed algorithm is very effective for path planning for bionic robotic fish with ocean currents.

With the further exploitation of offshore resources, there are more and more offshore oil and gas fields which cannot meet the production capacity requirements. So, it becomes extremely urgent to pay attention to the decommissioning of the exploitation equipment in abandoned offshore fields. A new decommissioning solution is offered by the double-ship integrated offshore platform decommissioning equipment comes. However, as the equipment will inevitably bear the combined actions of various dynamic and static loads during operation, the strength and stability of the overall unit and the connections between different modules will be greatly challenged by the complex ocean. Firstly, the dynamic characteristics of the integrated decommissioning system are analyzed in this paper. Mathematical modeling of the lifting arm system is established based on the unit characteristics matrix, and a dynamic equation of the flexible lifting arm unit and system is developed based on Lagrange’s equation and solved through numerical calculation. Secondly, modal analysis and transient analysis of the lifting arm in specific working conditions are performed according to the prototype parameters of the designed decommissioning system. Finally, according to the principle of similitude, a hydrodynamic experiment method is proposed with an integrated decommissioning multi-dimensional vibration test bench. The decommissioning system model test bench is designed and built to perform the dynamic response test, and this paper compares the test results and the simulation results for verification. The comparison verifies that the theoretical analysis and the tests prove each other valid and the results are accurate, meaning this work provides a powerful theoretical reference and offers effective research methods for future studies on super-large-scale integrated decommissioning equipment.

Microbubble cleaning technology has the characteristics of environmental protection and low energy consumption and has been initially applied in the field of oil sludge cleaning. For the controllability of microbubbles in the cleaning process, this paper adopted the numerical simulation method to quantitatively study the growth and collapse mechanism of bubbles in the cleaning process, the influence law of flow field, and parameter changes on the dynamic behavior of bubbles from the perspective of micro-dynamics. It was found that the dynamic characteristics of the flow field around the bubble in the free field show a symmetrical distribution, while the motion of the fluid near the wall is blocked by the wall, and the flow field between the bubble and the wall becomes irregular. It was also found that the large amplitude pressure changes in microbubbles produced a “plastic hinge” alternating impact on the wall, the bubble collapse, and wall pulsation pressure change are mainly controlled by the driving pressure. The liquid temperature mainly affects the dynamic characteristics of the bubble by causing a change in the viscosity of the medium. Under the condition of P_d = 0.1 MPa and γ = 1.5, when the liquid viscosity decreases from 0.110 to 0.00314 Pa·s, the maximum pressure affected by the first bubble collapse increases from 200.71 to 317.74 Kpa. Compared with the bubble distribution, the impact of increasing the number of bubbles on the wall is more significant. Under the condition of γ = 1 and P_d = 0.1 MPa, compared with the single bubble, the pulse pressure of nine bubbles’ collapse on the wall increases by 100 KPa. The research results can provide technical support for the development of microbubble cleaning technology.

Based on the key module-lifting arm system, based on the principle of similarity and the hydrodynamic experimental method of a multi-dimension vibration test platform, an experimental platform for dismantling equipment is designed and built. Subsequently, the motion control model of the six-degrees-of-freedom platform is established. The three-ring control model of a servo electric cylinder is established, and the active heave compensation control of a servo electric cylinder is realized by combining position control theory. Based on the co-simulation of ADAMS and Simulink, the co-simulation system of the integrated dismantling equipment experimental platform is designed and built, and the simulation system is tested and verified. Finally, simulation and experimental verification are carried out based on the experimental platform and co-simulation system. The results show that the heave compensation rate reaches 58.3% in third-class sea conditions, 61.2% in fourth-class sea conditions, and 62.4% in fifth-class sea conditions. The integrated dismantling scheme of super large offshore oilfield facilities is feasible but, in order to ensure the safety and reliability of the operation, a heave compensation system needs to be added. The error between the simulation results and the experimental results is about 15%. Based on the analysis of external interference factors in the experiment, the error results are within a reasonable range, which proves that the experimental platform, the co-simulation system of the experimental platform, and the heave compensation strategy are accurate and effective. This study, for the first time in China, provides an effective experimental platform and co-simulation platform for the design and optimization of the integrated dismantling equipment of super large offshore oilfield facilities and lays a good research foundation for the construction and engineering demonstration of subsequent equipment.

Damage induced by a low-velocity impact can reduce the stability and reliability of structures. In this study, a novel low-velocity impact region identification method based on the spectral peak frequency (SPF) and support vector machine (SVM) is proposed to identify the low-velocity impact regions on a steel cantilever beam. A low-velocity impact region identification system of the cantilever beam is established by applying fiber Bragg grating (FBG) sensors, and only 2 sensors are used in this system. The power spectral density functions of the impact response signal are smoothed using the linear weighting method to remove pseudospectral peak frequencies, and then, SPFs are extracted as the features. For 25 low-velocity impact regions with dimensions of 30 mm × 10 mm, the results show that the recognition rate obtained by the proposed method is 100% and the feature vector consisting of the first two SPFs with the largest amplitude has the highest recognition rate. Through the comparative study, it is found that the recognition rate of SVM is higher than that of the probabilistic neural network (PNN) and extreme learning machine (ELM) for low-velocity impact area recognition of cantilever beams. As a result, the low-velocity impact region identification method of this paper can be applied to the real-time health monitoring of cantilever beam structures.