OWC wave energy self-powered ocean monitoring buoy

Abstract

The application discloses an OWC wave energy self-powered ocean monitoring buoy, and belongs to the technical field of ocean buoy equipment in ship and ocean engineering. The buoy integrates the oscillating water column wave energy power generation unit in the lower cabin body, and realizes rapid and accurate prediction of the air chamber air pressure through the built-in wave-air two-phase coupling air pressure prediction model. In view of the problems that the existing CFD simulation and physical experiment for evaluating the buoy OWC air chamber air pressure are high in cost and low in efficiency, and are difficult to support the rapid iteration design of the buoy, the application equivalent the oscillating water column in the air chamber to a rigid piston, obtains key parameters through ANSYSAQWA frequency domain hydrodynamic analysis, converts the key parameters into a state space model by using a Vectfit3 vector fitting algorithm, and finally couples the air pressure prediction of the Conminga time domain equation and the adiabatic air pressure dynamic equation. The single working condition calculation time of the application is shortened to several minutes, the efficiency is improved by two orders of magnitude, the error of the air pressure peak value in the resonance region is 10% to 20%, and the application can support the rapid structure optimization, air chamber-turbine matching and power supply performance evaluation of the ocean monitoring buoy.

Description

Technical Field

[0001] This invention relates to the field of marine buoy equipment technology in shipbuilding and marine engineering, specifically to an OWC wave energy self-powered marine monitoring buoy. Background Technology

[0002] With the large-scale construction of marine observation networks, marine monitoring buoys, as the core carriers for offshore in-situ observation, face increasing demands for in-situ operating time and power supply reliability. Traditional marine monitoring buoys mostly adopt a power supply solution of "lithium battery packs + solar panels." In high-latitude sea areas, long rainy seasons, and deep-sea scenarios, the power supply continuity is insufficient, making it difficult to support the long-term continuous operation of sensors, communication modules, and positioning systems. Moreover, battery replacement and maintenance costs are high and the cycle is long.

[0003] Oscillating Water Column (OWC) devices are among the most mature and commercially promising technologies in the field of wave energy development and utilization. Their core working principle is as follows: incident waves drive a water column within an air chamber to oscillate up and down, compressing / expanding the air inside the chamber and creating a reciprocating high-pressure airflow. This airflow drives an air turbine to rotate and power a generator. Because the OWC device's wave energy capture system has no complex moving parts, and the air turbine and generator do not directly contact seawater, it boasts significant advantages such as simple structure, high operational reliability, strong resistance to wind and waves, and low construction and maintenance costs. It has already been demonstrated in numerous countries and regions worldwide.

[0004] The internal pressure of the air chamber is a core parameter determining the energy conversion efficiency of an OWC (Overflow-Cooled) device, and its dynamic changes directly affect the output power, operational stability, and service life of the air turbine. Simultaneously, the air chamber pressure is also a key input parameter for OWC device structural design, air chamber-turbine matching analysis, and control system development. Currently, methods for evaluating OWC air chamber pressure mainly fall into two categories: physical model experimental methods and numerical simulation methods. Physical model experiment method: This method simulates the pressure changes in the air chamber under different wave conditions by creating a scaled-down model in a wave tank. The results are intuitive and reliable, but it has inherent drawbacks such as long experimental cycles, high costs, and limited coverage of operating conditions. When the structural parameters of the device change, the model needs to be rebuilt and experiments conducted again, which cannot meet the needs of rapid iterative optimization in the engineering design phase.

[0005] Numerical simulation methods mainly include computational fluid dynamics (CFD) numerical simulation and simplified theoretical models. CFD numerical simulation simulates the wave-air two-phase flow field by solving the Navier-Stokes equations, capturing complex nonlinear flow phenomena. However, it involves extremely high computational demands, requiring sophisticated computer hardware; a single-condition calculation typically takes 3-8 hours, and it is highly mesh-dependent. When the gas chamber geometry changes, the mesh needs to be re-generated and the entire calculation process repeated, resulting in extremely low efficiency. Existing simplified theoretical models are mostly based on pure frequency domain analysis or linear time-domain models that ignore wave-air coupling effects. They cannot accurately characterize the dynamic two-way coupling relationship between water column motion and gas chamber pressure, exhibiting significant prediction errors in non-resonance regions or under large-amplitude wave conditions, making it difficult to meet engineering accuracy requirements.

[0006] To address the aforementioned issues, Ocean University of China proposed an improved numerical prediction method, with publication number CN114297949A and application number 202111410947.6, entitled "A Full-Process Model of an Oscillating Water Column Device Based on CFD Simulation and Its Efficiency Prediction Method." This patent application establishes a full-process CFD model of the OWC device's air chamber-turbine coupling system by simulating the damping effect of the air turbine within a momentum source term region in the air duct, thus predicting the device's energy conversion efficiency. However, this method still relies on traditional CFD time-domain numerical simulation and fails to fundamentally solve the problem of low computational efficiency: its single-condition calculation time still requires several hours, the number of grids reaches millions, and re-meshing and solving are required after changes in structural parameters; simultaneously, the turbine model of this method only supports a simplified form of the momentum source term and cannot directly integrate the flow-pressure ratio characteristic curve of the actual air turbine, making it difficult to directly apply to the air chamber-turbine matching optimization analysis in the engineering design stage.

[0007] Furthermore, while some existing studies have attempted to improve computational efficiency using frequency-time conversion techniques, they generally suffer from problems such as non-standard data preprocessing, arbitrary selection of fitting order, and inability to guarantee system stability. Moreover, a standardized engineering implementation process has not been established, making it difficult for a broad range of engineers to master and apply. Therefore, developing a rapid OWC chamber pressure prediction method that balances computational efficiency and engineering accuracy, standardizes the operation process, and is easy to apply in engineering is of significant practical importance for promoting the engineering and commercialization of wave energy technology. Summary of the Invention

[0008] The purpose of this invention is to provide an OWC wave energy self-powered marine monitoring buoy. This invention uses frequency domain hydrodynamic analysis and Vectfit 3-frequency time conversion technology to improve the prediction efficiency of OWC air chamber pressure while ensuring engineering accuracy. It is compatible with various wave energy turbine models, has a wide range of applications, and stable performance.

[0009] The technical solution adopted in this invention is as follows: An OWC (Wave Energy Self-Powered) marine monitoring buoy, characterized by comprising a main buoy body, an OWC air chamber structure, a micro air turbine power generation unit, and a pressure prediction and control module; the OWC air chamber structure is integrated into the lower part of the main buoy body, with an inlet at the bottom of the air chamber connecting to external seawater, and the top of the air chamber connected to the micro air turbine power generation unit via an air duct; the pressure prediction and control module incorporates a wave-air two-phase coupling simulation model, predicting the dynamic characteristics of the air chamber pressure through the following steps, supporting buoy power generation control and structural verification: S1. Establish a simplified dynamic model of the water column in the air chamber: The oscillating water column in the air chamber is equivalent to a rigid piston with the same cross-sectional shape as the air chamber. The three-dimensional model of the air chamber-piston system is completed based on the actual geometric dimensions of the OWC device. S2. Frequency domain analysis of hydrodynamic parameters: The gas chamber-piston system was simulated in the frequency domain using the hydrodynamic analysis software ANSYS AQWA to obtain the frequency response data of the added mass, radiation damping, wave excitation force amplitude and phase angle in the Z direction; S3. Time-domain system identification: Using the Vectfit3 vector fitting algorithm, based on the frequency domain data obtained in step S2, the state-space expressions of radiation force and excitation force in the Cummins time-domain equation are fitted respectively. S4. Wave-Air Coupling Time-Domain Simulation Modeling: Based on the state-space model obtained in step S3, a Cummins time-domain equation simulation model containing a dynamic air pressure coupling model is constructed in the Simulink environment. The target wave condition parameters are input to predict the time-domain change curve of the air pressure in the air chamber of the OWC device.

[0010] Preferably, step S1 specifically includes: extracting key geometric parameters from the OWC device design drawings, such as the length, width, and height of the gas chamber, the location, shape, and size of the bottom inlet, the gas chamber wall thickness, and the connection method between the gas chamber and the external water body; using SolidWorks, CATIA, or ANSYS Design Modeler 3D modeling software to establish geometric models of the fixed structure of the gas chamber and the movable piston, respectively, wherein the gap between the piston and the gas chamber wall is 0.005m~0.02m, which is the minimum unit size of the subsequent hydrodynamic analysis mesh; establishing a model of the bottom inlet of the gas chamber, ensuring that the opening area and water flow channel characteristics are consistent with the actual situation, and aligning the upper surface of the piston with the default Z=0 horizontal plane of ANSYS AQWA during modeling; removing details such as chamfers, fillets, and bolt holes that have no impact on hydrodynamic analysis, and exporting the model in STEP, IGES, or X_t standard format after assembly.

[0011] Preferably, step S2 specifically includes: importing the 3D model into ANSYS AQWA, extracting the outer surfaces of all structures and suppressing the original solid model; setting the piston mass to the product of seawater density and piston volume, setting the air chamber structure as a fixed constraint, and retaining only the Z-direction motion degree of freedom for the piston; generating a surface mesh, with the global mesh size taken as 1 / 20 to 1 / 15 of the minimum incident wavelength; and performing local mesh refinement on the air chamber inlet, near the free liquid surface, and in areas of geometric abrupt changes, with the mesh size of the refined areas being equal to the global size. The mesh convergence was verified, and convergence was determined when the maximum rate of change of the added mass and radiation damping obtained in two consecutive calculations was less than 2%. The analysis frequency range was set to 0.3Hz~3.5Hz, the frequency interval was 0.05Hz~0.1Hz, and the output parameters were configured as added mass, radiation damping, wave excitation force amplitude and phase angle. The solver was then started to complete the calculation. For the frequency domain data of directional hydrodynamic parameters, the average value of the added mass at frequencies greater than 2.5 Hz is taken as the high-frequency convergence added mass. Check and correct non-physical negative values ​​in radiation damping, and export the processed data in CSV format.

[0012] Preferably, step S3 specifically includes: the additional mass, radiation damping, and high-frequency convergence additional mass obtained in step S2. The radiation impedance is constructed, and the complex form of the wave excitation force is constructed based on the amplitude and phase angle of the wave excitation force. The Vectfit3 vector fitting algorithm is used to fit the radiation impedance and the wave excitation force into rational transfer functions, respectively. An auxiliary function is introduced to transform the nonlinear fitting problem into a linear least squares problem. The poles are updated iteratively until convergence. The converged rational transfer function is converted into a standard linear state-space expression, and the state-space matrices of the radiation force system and the excitation force system are obtained respectively.

[0013] Preferably, in step S3, the expression for radiation impedance is: ,in For complex units, Angular frequency, For frequency-varying radiation damping, Add mass to the frequency-varying wave; the expression for the complex form of the wave excitation force is as follows: ,in For the amplitude of the excitation force, Let be the phase angle of the excitation force; the rational transfer function is of the form: ,in For Laplace variables, The poles of the transfer function For the transfer function residual, This is the fitting order, ranging from 6 to 12. The function represents the high-frequency asymptotic value; the auxiliary function introduced is... ,in The auxiliary residual is used; the iterative convergence condition is an auxiliary function. The maximum relative error is less than 10 .

[0014] Preferably, in step S3, the standard linear state-space expression consists of state equations and output equations, wherein the state equations describe the rate of change of the system state variables, and the output equations describe the relationship between the system output and the state variables and inputs. The four matrices of the state-space expression are the system matrix A, the input matrix B, the output matrix C, and the direct transfer matrix D.

[0015] Preferably, step S4 specifically includes: establishing the Cummins time-domain equation containing the air-pressure coupling term and the air chamber pressure dynamic equation under the adiabatic process assumption; sequentially constructing the wave input module, the excitation force state space module, the radiation force state space module, the water column motion dynamics module, and the air pressure dynamics module, and integrating them to form a complete wave-air coupling simulation model; setting simulation parameters, inputting target wave condition parameters, running the simulation to obtain the time-domain variation curve of the air chamber pressure, and extracting the peak pressure, average value, and fluctuation characteristic parameters.

[0016] Preferably, in step S4, the Cummins time-domain equation including the pressure coupling term is: ,in The mass of the piston is the equivalent water column mass. This represents the displacement of the water column in the Z direction. Here is the radiation damping kernel function. The coefficient of restitution for static water. The cross-sectional area of ​​the air chamber is... For time-domain wave excitation force, The relative air pressure in the air chamber is; the dynamic equation for the air chamber pressure is: ,in This is the absolute pressure of the air chamber. The specific heat ratio of air is taken as 1.4. This represents the volume of gas in the gas chamber under still water conditions. For turbine flow, The flow rate is the leakage flow rate in the gas chamber; the turbine flow model uses a small-hole leakage model during the validation phase. ,in For flow coefficient, The area of ​​the small hole, For air density, the flow-pressure ratio characteristic curve of the actual turbine is used in the engineering design stage.

[0017] Preferably, in step S4, the wave input module supports both regular and irregular wave inputs, with the irregular wave generated using the JONSWAP spectrum; the simulation time step is 0.01s to 0.05s, and the simulation duration is 20 to 50 wave cycles to ensure that the system reaches a steady state before extracting valid data.

[0018] Preferably, this method is applicable to fixed OWC devices with arbitrary cross-sectional shapes such as rectangles, circles, and polygons, and can be used for structural optimization design of OWC devices, gas chamber-turbine matching analysis, and rapid performance evaluation under different wave conditions.

[0019] This invention addresses the core problems of existing OWC chamber pressure assessment methods, such as low computational efficiency, poor engineering applicability, and insufficient standardization. It proposes a rapid prediction method based on wave-gas two-phase coupling. Through reasonable simplification of dynamics, frequency-time conversion technology, and modular coupled simulation, a good balance between computational efficiency and engineering accuracy is achieved. Specific beneficial effects are as follows: 1. This invention decouples the traditional CFD method's three-dimensional full-time-domain flow field solution into three stages: "frequency-domain hydrodynamic parameter extraction + time-domain system identification + coupled simulation." The single-condition calculation time is reduced from 3-8 hours using existing CFD methods to 1-5 minutes, improving computational efficiency by more than two orders of magnitude. When the device's structural parameters change, there is no need to re-grid millions of three-dimensional meshes and perform a full-process solution; only a single frequency-domain hydrodynamic analysis is required to quickly obtain new prediction results. This significantly shortens the design cycle of the OWC device and supports multi-condition parameter scanning and rapid iterative optimization during the engineering design phase.

[0020] 2. Based on linear potential flow theory and rigorous numerical processing specifications, this invention achieves stable prediction errors of peak gas pressure in the gas chamber within 10%~20% under wave conditions near the water column resonant frequency, which is of utmost concern in engineering design. The average error is less than 10%, fully meeting the accuracy requirements for preliminary design, structural strength verification, and gas chamber-turbine matching analysis of OWC devices. The errors mainly stem from the neglect of viscous and nonlinear effects by linear potential flow theory, falling within an acceptable range for engineering applications. Furthermore, the accuracy can be further improved by introducing simple nonlinear correction coefficients.

[0021] 3. This invention clearly defines standardized operating procedures for the entire process from 3D modeling, mesh generation, data preprocessing to simulation calculation: including a global mesh size of 1 / 20 to 1 / 15 of the minimum incident wavelength, a local refinement zone size of 1 / 2 to 1 / 3 of the global size, mesh convergence verification criteria of less than 2% for added mass and radiation damping change rate, a fitting order of 6 to 12, a simulation step size of 0.01 s to 0.05 s, and a simulation duration of 20 to 50 wave cycles. Engineers do not need to master complex CFD theories and numerical methods; they can quickly complete chamber pressure prediction by following the standardized procedures, significantly lowering the technical threshold.

[0022] 4. Unlike existing technologies that only support simplified momentum source terms or orifice venting models, the simulation model of this invention is compatible with both turbine models: the orifice venting model simplifies the experimental setup during the verification phase, while the flow-pressure ratio characteristic curves of actual air turbines can be directly imported during the engineering design phase, achieving integrated matching analysis of the air chamber and the turbine. This feature allows this method to be directly applied to the engineering design phase of OWC devices, rather than being limited to scientific research verification.

[0023] 5. This invention is applicable to fixed OWC devices with arbitrary cross-sectional shapes such as rectangles, circles, and polygons. By adding the floating body's motion degrees of freedom to the Cummins time-domain equations, it can be directly extended to floating OWC devices. In addition to predicting chamber pressure, this method can be further extended to calculate key performance parameters of OWC devices such as wave energy capture efficiency, turbine output power, and annual power generation. It can also be combined with intelligent optimization algorithms such as genetic algorithms and particle swarm optimization to achieve multi-objective optimization of chamber geometric parameters, and with turbine control algorithms for system-level performance optimization.

[0024] 6. This invention employs the Vectfit3 vector fitting algorithm for frequency-time conversion. This algorithm automatically ensures that the fitted transfer function is causally stable, avoiding the non-physical poles and numerical divergence problems that may occur in traditional frequency-time conversion methods. Furthermore, this invention explicitly requires linear interpolation correction for non-physical negative radiation damping appearing in the frequency domain calculation, further improving the numerical stability and reliability of the system. Attached Figure Description

[0025] Figure 1 The curves show the comparison between the simulation results and experimental measurements of the relative air pressure in the air chamber when the incident wave height is 5 cm and the period is 2 s. The curves have been aligned from the 4th zero crossing point to eliminate the influence of the initial transient. Figure 2 The curves show the comparison between the simulation results and experimental measurements of the relative air pressure in the air chamber when the incident wave height is 5 cm and the period is 2.2 s. The curves have been aligned from the 4th zero crossing point to eliminate the influence of the initial transient. Figure 3 The curves show the comparison between the simulation results and experimental measurements of the relative air pressure in the air chamber when the incident wave height is 5 cm and the period is 2.5 s. The curves have been aligned from the 4th zero crossing point to eliminate the influence of the initial transient. Figure 4 This is a schematic diagram of the overall process of the OWC wave energy self-powered marine monitoring buoy of the present invention; Figure 5 This is a schematic diagram of the overall structure and sensor arrangement of the OWC device involved in this invention; Figure 6 for Figure 5A partial functional diagram illustrating the working principle of the OWC device shown. Figure 7 This is a structural block diagram of the Simulink wave-air coupling simulation model used in this invention.

[0026] In the diagram, 1-air turbine, 2-generator, 3-liquid level sensor, 4-differential pressure sensor, 5-air chamber, 6-air chamber / water column chamber. Detailed Implementation

[0027] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0028] This invention proposes an OWC wave energy self-powered marine monitoring buoy. Its core is to integrate an OWC wave energy power generation unit into the lower cabin of the buoy and carry a rapid prediction model of air chamber pressure based on wave-air two-phase coupling. Under the premise of ensuring engineering accuracy, it can realize efficient evaluation of the buoy's power generation performance and structural load, and support the rapid iterative design of the buoy.

[0029] The overall structure of the buoy is as follows: Figure 5 As shown, the system mainly consists of the buoy's main hull, an OWC (Overhead Air Control) chamber structure, a miniature air turbine 1, a generator 2, a liquid level sensor 3, a differential pressure sensor 4, and a pressure prediction and control module. The air chamber 5 and the air chamber water column chamber 6 are two parts of the same cavity, connected by a free liquid surface. Incident waves enter the air chamber water column chamber 6 through the bottom inlet, driving the water column inside to oscillate up and down, compressing or expanding the air in the air chamber 5, forming a reciprocating high-pressure airflow. This airflow drives the miniature air turbine 1 to rotate and powers the generator 2 to generate electricity, supplying power to the monitoring and communication equipment onboard the buoy. The pressure prediction and control module incorporates a wave-air coupling simulation model, which can quickly predict the dynamic characteristics of the air chamber pressure based on the target sea state, providing a basis for buoy structural design, turbine matching, and power generation control.

[0030] The overall implementation process of the air pressure prediction of this invention is as follows: Figure 4 As shown, the process includes four core steps: S1 establishing a three-dimensional model of the "piston-chamber" dual-body system; S2 performing frequency domain hydrodynamic simulation calculations using hydrodynamic analysis software; S3 converting the frequency domain hydrodynamic data into a time domain state-space model; and S4 performing simulation solutions using the pre-built Simulink coupled model. The OWC device structure and sensor arrangement involved in this invention are as follows... Figure 5As shown, 1 is an air turbine, 2 is a generator, 3 is a liquid level sensor, 4 is a differential pressure sensor, 5 is an air chamber, and 6 is an air chamber with a water column. Its working principle is that the incident wave enters from the rear of the air chamber with a water column 6, driving the water column inside the air chamber to oscillate up and down, thereby compressing or expanding the air in the air chamber 5, forming a reciprocating high-pressure airflow that drives the air turbine 1 to rotate, thus driving the generator 2 to generate electricity. It should be noted that the air chamber 5 and the air chamber with a water column 6 are not two independent areas, but rather two parts of the same cavity connected by a free liquid surface. Figure 6 for Figure 5 A partial functional diagram illustrates the complete energy conversion process: the incident wave enters the air chamber from below, the water column oscillates up and down within the air chamber to compress the air, and the air flows back and forth at the outlet of the air chamber. Figure 7 The diagram shows the Simulink simulation model, where ① is the Cummins time-domain equation model, ② is the pressure dynamic equation model, and ③ is the orifice venting model (the orifice venting model is used to replace the actual air turbine model in the verification stage of this invention to simplify the experimental setup and verification process).

[0031] Step 1: Establish a simplified dynamic model of the water column in the air chamber: The core of this step is to rationally simplify the dynamics of the core moving component of the OWC device—the oscillating water column—based on the applicability conditions of linear potential flow theory, and establish a three-dimensional geometric model that can be used for subsequent hydrodynamic analysis. Under small-amplitude wave incidence conditions (usually defined as the ratio of incident wave height to water depth in the air chamber being less than 0.2), the water column in the air chamber mainly undergoes overall up-and-down reciprocating translational motion. Shear deformation, surface ripples, and turbulence effects inside the water column can be ignored. Therefore, it can be equivalent to a rigid piston with a cross-sectional shape completely consistent with the air chamber. This simplification assumption is the basis for the rapid calculation of this method. Its physical essence is to ignore the relative motion inside the water column, simplifying the motion of the water column to a single-degree-of-freedom rigid body motion, thereby reducing the complex three-dimensional flow field problem to a one-dimensional dynamic problem. For large-amplitude wave conditions, the prediction accuracy can be further improved by introducing nonlinear correction coefficients in the subsequent time-domain equations, but this invention mainly focuses on the small-amplitude operating wave conditions most common in engineering design.

[0032] The specific implementation process must be carried out strictly in accordance with the following steps: ① Extraction of Key Geometric Parameters: All core geometric parameters affecting hydrodynamic characteristics are extracted from the design drawings of the OWC device, including but not limited to: the length L, width W, and height H inside the air chamber; the location, shape, and size of the bottom inlet; the wall thickness of the air chamber; and the connection method between the air chamber and the external water body. For the rectangular air chamber used in the verification of this invention, the inlet is usually located on the side of the bottom of the air chamber, and its area is generally 30% to 60% of the cross-sectional area of ​​the air chamber. This ratio is the optimal range summarized from engineering practice, ensuring smooth water flow in and out of the air chamber while avoiding excessive head loss. For circular or polygonal air chambers, the shape of the inlet can be adjusted accordingly to be circular or polygonal, but its area ratio still follows the above principle.

[0033] ② 3D Geometric Modeling: General-purpose 3D modeling software is used to create geometric models of the fixed gas chamber structure and the movable water column (equivalent piston). The following three key technical points require special attention during modeling: First, the cross-sectional shape of the piston must be completely consistent with the cross-sectional shape inside the gas chamber to ensure the accuracy of the equivalence. Second, a certain gap needs to be reserved between the piston and the gas chamber wall. The value of this gap should be consistent with the minimum mesh element size used in subsequent hydrodynamic analysis, typically 0.005m to 0.02m. This gap serves two purposes: one is to simulate the frictional effect between the water column and the gas chamber wall, and the other is to avoid numerical singularities caused by wall overlap in hydrodynamic calculations. Third, since the default free surface in professional hydrodynamic analysis software is located at the Z=0 plane, the upper surface of the piston must be strictly aligned with the Z=0 plane during modeling; otherwise, it will lead to errors in hydrostatic pressure calculations, thus affecting the accuracy of all subsequent hydrodynamic parameters. If the piston position is accidentally set incorrectly during modeling, it can be corrected by translating the model as a whole during the subsequent hydrodynamic software preprocessing stage.

[0034] ③ Boundary Condition Modeling: Accurately model the inlet at the bottom of the air chamber, ensuring that the area, shape, and location of the opening are completely consistent with the actual design. The geometric characteristics of the inlet are a key factor determining the efficiency of wave energy entering the air chamber, directly affecting the oscillation amplitude of the water column and the magnitude of the air pressure in the air chamber. Unnecessary simplifications to the inlet should be avoided during modeling, such as changing rounded corners to right angles, as these small geometric changes can have a significant impact on the local flow field.

[0035] ④ Model Simplification and Export: The initially established 3D model undergoes geometric cleanup, removing all details that have no impact on hydrodynamic analysis, including chamfers, fillets, bolt holes, welds, and stiffeners. These details are typically much smaller than the wavelength of the incident wave (generally less than 1 / 100th of the wavelength), and their impact on the overall hydrodynamic characteristics is negligible, but they significantly increase the mesh size and reduce computational efficiency. After cleanup, the air chamber fixing structure and the movable piston are assembled as a single unit, and then exported in a common 3D model format such as STEP, IGES, or X_t for import into subsequent hydrodynamic analysis software.

[0036] Step 2: Analyze the frequency domain of hydrodynamic parameters: The purpose of this step is to obtain the hydrodynamic parameters of the air chamber-piston system under wave action at different frequencies, including added mass, radiation damping, and wave excitation force and its phase angle. These parameters are the core physical quantities describing the interaction between the water column and the waves, and are also the basis for subsequent time-domain system identification and coupled simulation. This invention adopts a frequency domain analysis method based on linear potential flow theory. This method assumes that the fluid is inviscid, incompressible, and irrotational. By solving the Laplace equation, the velocity potential of the flow field is obtained, and then the hydrodynamic forces acting on the piston are calculated. Compared with the time-domain CFD method, the frequency domain analysis method has the advantages of fast calculation speed, high accuracy, and the ability to obtain parameters across the entire frequency range in one operation. It is currently the international standard method for obtaining hydrodynamic parameters of floating bodies.

[0037] The specific implementation steps are as follows: ① Model Preprocessing: Import the 3D model exported in the previous step into the professional hydrodynamic analysis software. After importing, first check the model to ensure it has no geometric defects, such as gaps, overlapping surfaces, or non-manifold edges. These defects can lead to subsequent mesh generation failures or inaccurate calculation results. Then, perform the outer surface extraction operation: select all solid models, execute the "Generate Outer Surface" command, and then suppress the original solid models. This is because frequency domain hydrodynamic analysis software usually only performs mesh generation and calculation on the outer surface of the structure; the solid model itself does not participate in the solution. If the piston's upper surface is not aligned with the Z=0 plane during modeling, translate the entire model along the Z-axis at this stage to ensure the piston's upper surface is precisely aligned with the Z=0 plane.

[0038] ② Setting physical properties and boundary conditions: Set the piston's mass: ,in The density of seawater is usually taken as 1025 kg / m³. 3 , Let represent the volume of the piston. The piston's center of gravity is located at its geometric center to ensure stability of motion. The air chamber structure is set as a fixed constraint because, in actual operation, the air chamber of the OWC device is fixed to the seabed or coastline and will not generate any movement. The piston is set as a floating body, retaining only the translational degree of freedom in the Z direction, while restricting the other five degrees of freedom (translation in the X and Y directions and rotation about the X, Y, and Z axes). This is entirely consistent with our previous assumption that the water column is equivalent to a rigid piston that only moves up and down. The upper surface of the piston is operated with an air cover, with drag set to 0, simulating the interface between the air and water column in the air chamber.

[0039] ③ Mesh generation and convergence verification: Surface mesh generation: Mesh quality is a key factor determining the accuracy of hydrodynamic calculations; therefore, the size and quality of the mesh must be strictly controlled. The global mesh size should be 1 / 20 to 1 / 15 of the minimum wavelength of the incident wave to ensure that the wave does not experience significant numerical dissipation during propagation. For areas such as the edge of the air chamber inlet, near the free surface, and regions with abrupt geometric changes, local mesh refinement is required. The mesh size in these refined regions should be 1 / 2 to 1 / 3 of the global size, as the flow field changes most drastically in these areas, requiring a finer mesh to accurately capture the details of the flow field.

[0040] Mesh convergence verification is a necessary step to ensure the reliability of the calculation results. The specific method involves gradually reducing the mesh size and repeating the hydrodynamic calculations. When the maximum rate of change of the added mass and radiation damping obtained from two consecutive calculations is less than 2%, the mesh is considered to have converged, and this mesh size ensures the accuracy of the calculation results. In this embodiment, when the mesh size is reduced from 0.02m to 0.015m, the maximum rate of change of the added mass is 1.3%, and the maximum rate of change of the radiation damping is 1.1%, both less than 2%. Therefore, a global mesh size of 0.02m is ultimately adopted.

[0041] ④ Solving for parameter configuration and calculation: Set the analysis frequency range: This range should cover the main wave frequencies and water column resonance frequencies of the target sea area, typically set to 0.3Hz~3.5Hz. The frequency interval should be 0.05Hz~0.1Hz to ensure accurate capture of the system's resonance peaks. When configuring output parameters, select the Z-direction added mass, radiation damping, wave excitation force amplitude, and its phase angle; these are all necessary data for subsequent system identification. Start the solver for calculation. During the calculation process, monitor the solution log and promptly address issues such as numerical divergence and singularities.

[0042] ⑤ Post-processing and data preprocessing: After the calculation is completed, extract the added mass in the Z direction. Radiation damping Wave excitation force amplitude and its phase angle The frequency domain data is obtained and exported in CSV format. The exported data then undergoes quality checks and preprocessing; this step directly affects the accuracy of subsequent system identification. Determine the high-frequency convergence added quality The physical meaning of added mass is that when a buoy accelerates in a fluid, it drags a portion of the surrounding fluid along with it; the mass of this dragged fluid is called added mass. Theoretically, as the frequency approaches infinity, the added mass converges to a constant value. In practice, the average value of the added mass at frequencies greater than 2.5 Hz is usually taken as the added mass. Because the fluctuation of the added mass is very small within this frequency range, it can be considered to have converged. In this embodiment, when the frequency is greater than 2.5Hz, the added mass fluctuates around 60kg, therefore, we take... .

[0043] Correcting for non-physically negative radiation damping: Due to errors in numerical calculations, radiation damping may appear negative at certain frequencies, which is clearly contrary to physical laws (radiation damping represents energy dissipation and cannot be negative). In such cases, correction is needed through linear interpolation or smoothing.

[0044] Verify the rationality of the RAO curve: Calculate the response amplitude operator (RAO) curve of the water column and check whether its resonant frequency is consistent with the theoretically calculated value. If the deviation exceeds 5%, it is necessary to check whether there are problems with the model settings or mesh quality.

[0045] Step 3: Identify the time-domain system: The core of this step is to use the Vectfit3 vector fitting algorithm to convert the frequency-domain hydrodynamic parameters obtained in the previous step into state-space expressions that can be used for time-domain simulation. Frequency-domain analysis can only obtain the system's response under steady-state sinusoidal excitation, while actual ocean waves are stochastic and unsteady; therefore, it is necessary to convert the frequency-domain data to the time domain for analysis. The Cummins time-domain equations are classic time-domain equations describing the motion of a floating body in waves. The radiation force term contains a convolution integral, representing the memory effect of radiation damping, i.e., the influence of the floating body's past motion on the current force. Directly calculating this convolution integral is very time-consuming, especially in long-term simulations, where the computational load increases linearly with the simulation time. Therefore, it is necessary to use system identification techniques to represent the radiation force and excitation force as state-space models, which allows for efficient solutions in the simulation environment, with computational load independent of simulation time.

[0046] The Vectfit3 algorithm is an advanced algorithm widely used internationally in the field of system identification. It can accurately fit the frequency domain response function to a rational transfer function and has advantages such as fast convergence speed, good numerical stability, and automatic guarantee of system causal stability. The specific implementation is as follows: ① Frequency domain data preprocessing: Construction of radiation impedance: radiation impedance Defined as the ratio of radiation force to piston velocity, its expression is: ; In the formula: For complex units, satisfying ; Angular frequency, and frequency The relationship is ; For frequency-varying radiation damping; Add mass to frequency-varying frequencies; Add quality to high-frequency convergence.

[0047] Introduction The purpose is to ensure that when the frequency approaches infinity, the imaginary part of the radiation impedance approaches zero, so that the fitted transfer function is causally stable.

[0048] Constructing the complex form of wave excitation force: The excitation force, expressed in terms of amplitude and phase, is converted into a complex form for subsequent fitting calculations. Its expression is: (2); In the formula: The amplitude of the wave excitation force; The phase angle of the wave excitation force.

[0049] ②Vectfit3 vector fitting: radiation impedance and wave excitation force Each is fitted with a rational transfer function. The general form of a rational transfer function is: ; In the formula: For Laplace variables; These are the poles of the transfer function; The residual of the transfer function is represented by units determined by the dimensions of the input and output. The fitting order is used. This represents the high-frequency asymptotic value of the function, and its unit is determined by the input and output dimensions.

[0050] Directly solving the fitting problem of the rational transfer function described above is a nonlinear optimization problem because of the poles. Located in the denominator, it is difficult to solve using the conventional least squares method. To address this issue, the Vectfit3 algorithm introduces an auxiliary function. This transforms the nonlinear fitting problem into a linear least squares problem. The auxiliary function takes the form: ; In the formula: The auxiliary residual is dimensionless. After introducing the auxiliary function, the original fitting problem can be transformed into: ; Simplifying the above equation, we get: ; In the formula: For the first The frequency domain simulation value at each frequency point, i.e. or At this point, if the extreme point is... If the parameter is fixed, then the above equation becomes a problem concerning the parameter. , and The linear equations can be solved using the weighted least squares method. For each frequency point... By separating the real and imaginary parts of the equation, we obtain: ; in It is taking the real part of the complex equation. This refers to taking the imaginary part of a complex equation. Custom weights.

[0051] Rewrite the above system of equations in matrix form ,in: matrix The dimension is The rows correspond to the real and imaginary parts of the equations, and the columns correspond to the basis functions. , and constant term ; vector Let the vector be the parameters to be determined. vector This is the vector of the right-hand side.

[0052] The above system of linear equations is solved using the weighted least squares method to minimize the error function: ; In the formula: The fitted transfer function is obtained in the th... The value of each frequency point; This represents the total number of frequency points.

[0053] Iteratively update the extreme points: Solve to obtain the auxiliary residuals Then, calculate the auxiliary function. The zeros are then used as poles for the next iteration. This linear solution and pole update process is repeated until the auxiliary function... The maximum relative error is less than At this point, the iteration can be considered to have converged. This method of continuously updating the poles through iteration ensures that the fitted transfer function has high accuracy.

[0054] Fitting order The choice of fitting order needs to balance accuracy and computational efficiency. Experience shows that for fitting the hydrodynamic parameters of an OWC device, a fitting order of 6 to 12 is sufficient to achieve high accuracy. If the order is too low, the fitting error will be large; if the order is too high, it will not only increase the computational load but may also lead to overfitting, causing non-physical poles in the transfer function. In this embodiment, the fitting order is set to 8, and after 5 iterations, the maximum relative error of the auxiliary function is... less than This satisfies the convergence requirement.

[0055] ③State space transition: The converged rational transfer function is converted into a standard linear state-space expression. The state-space model is the most commonly used model form in time-domain simulation, and its expression is: ; In the formula: Let be a state vector with dimension . ; The first derivative of the state vector, with dimension . ; For system input; This refers to the system's output. The system matrix has dimensions of . ; The input matrix has dimensions of . ; The output matrix has a dimension of 1. To directly pass the matrix, the dimension is...

[0056] The conversion from a rational transfer function to a state-space model can be achieved through various methods, such as the controllable canonical form and the observable canonical form. In practice, the conversion can be directly performed using built-in functions in mathematical software, eliminating the need for manual calculation. After the conversion, the state-space matrices of the radiation force system and the excitation force system are obtained, respectively. In this embodiment, the state-space matrices of the excitation force system and the radiation force system are as follows: State-space matrix of the excitation system (8th order): System Matrix (8 rows and 8 columns):

[0057]

[0058]

[0059]

[0060] The above are the correlation matrices A, B, C, and D of the incentive system.

[0061]

[0062]

[0063]

[0064]

[0065] The above are the correlation matrices A, B, C, and D of the radiative force system.

[0066] Step 4: Simulation Modeling of Wave-Air Coupling in the Time Domain The core of this step is to build a time-domain simulation model in the Simulink environment that incorporates wave-air two-phase coupling, achieving a complete simulation from wave input to air chamber pressure output. The operation of the OWC device is a typical strongly coupled process: the incident wave drives the water column to oscillate up and down, the motion of the water column changes the volume of the air chamber, causing changes in the air pressure inside the air chamber; and the changes in air chamber pressure, in turn, act on the water column, hindering its motion. Therefore, this two-way coupling effect must be fully considered in the model to accurately predict the dynamic changes in air chamber pressure. If this coupling effect is ignored, the prediction results will have large errors, especially near the resonant frequency.

[0067] This invention uses the Cummins time-domain equation to describe the motion of the water column and the gas pressure dynamic equation based on the adiabatic process of an ideal gas to describe the change of gas pressure in the gas chamber. By coupling these two equations, a complete wave-gas coupling system is constructed.

[0068] ① Establishment of core control equations: The motion of the water column is described using the Cummins time-domain equations. These equations are derived based on frequency-domain hydrodynamic parameters and consider the memory effect of radiation damping. They are among the most accurate time-domain equations currently available for describing the motion of a floating body in waves. The expression is: ; In the formula: The mass of the piston is the equivalent water column mass. water column Acceleration in the direction of; Let be the radiation damping kernel function, and its expression is: It describes the memory effect of radiation damping; for Water column velocity in the direction; The coefficient of restitution for static water. ,in For the density of water, Let gravitational acceleration be (take) ), This refers to the cross-sectional area of ​​the air chamber. water column Displacement in direction, with upward being the positive direction; For time-domain wave excitation force; This is the relative air pressure of the air chamber, which is the difference between the absolute air pressure of the air chamber and the atmospheric pressure. This represents the cross-sectional area of ​​the air chamber.

[0069] The physical meanings of the terms in the Cummins time-domain equations are as follows: The first term on the left is the equivalent mass inertial force, including the mass of the water column itself and the high-frequency added mass; the second term is the radiation damping force, representing the energy consumed by the outward radiation of waves due to the water column's motion. The convolution integral reflects the memory effect of radiation damping, that is, the influence of the water column's past motion on the current force; the third term is the hydrostatic restoring force, which is the force generated by gravity to bring the water column back to its equilibrium position when it deviates from the equilibrium position. On the right, the first term is the excitation force of the waves on the water column, which is the source of the water column's motion; the second term is the force exerted by the air pressure in the air chamber on the water column, which is a coupling term and reflects the reaction of air pressure on the water column's motion.

[0070] The dynamic equation for air pressure within the chamber is derived based on the assumption of an adiabatic process for an ideal gas. Since the compression and expansion of air within the chamber are extremely rapid (typically on the order of milliseconds), heat exchange is negligible and can therefore be considered an adiabatic process. According to the law of conservation of mass, the rate of change of air mass within the chamber equals the mass of air flowing into the chamber minus the mass of air flowing out. Combining the ideal gas law and the adiabatic process equation, the dynamic equation for air pressure can be derived: ; In the formula: This refers to the absolute pressure of the air chamber. For the specific heat ratio of air, take . ; This represents the gas volume in the gas chamber under still water conditions. This represents the rate of change of air chamber volume due to the movement of the water column, which is essentially the volumetric flow rate of air flowing into or out of the air chamber. When the water column moves upward, the air chamber volume decreases, air flows out, and the flow rate is negative; when the water column moves downward, the air chamber volume increases, air flows in, and the flow rate is positive. The volumetric flow rate through the air turbine; The leakage volume flow rate of the gas chamber is very small and can be ignored for a well-sealed gas chamber.

[0071] In the model validation phase, to simplify the experimental setup, a small-orifice venting model is typically used to replace the air turbine. The small-orifice venting model is a commonly used simplified model in engineering and can effectively simulate the flow-pressure ratio characteristics of a turbine. Its expression is: ; In the formula: The flow coefficient of the orifice is usually taken as... In this embodiment, ; Let be the area of ​​the small hole; The density of air is usually taken as ; This represents the relative air pressure in the air chamber.

[0072] During the engineering design phase, to obtain more accurate results, the flow-pressure ratio characteristic curve of an actual air turbine should be used instead of the orifice discharge model. The flow-pressure ratio characteristic curve of the turbine is usually provided by the turbine manufacturer or obtained through experimental measurement.

[0073] ②Simulink model building: according to Figure 7 The structure shown is used to build a Simulink simulation model, which mainly consists of three core modules: Module 1: Cummins Time-Domain Equation Model: This module is the core of the entire simulation model, integrating the state-space matrices of the excitation force system and radiation force system obtained in the previous step. Its input is the time-domain sequence of the waves, and its output is the displacement, velocity, and acceleration of the water column. Internally, based on the Cummins time-domain equations, the module comprehensively considers the excitation force, radiation damping force, hydrostatic restoring force, and air pressure force to solve for the motion state of the water column.

[0074] Module 2: Dynamic Equation Model of Air Pressure: This module takes the velocity of the water column as input and calculates the absolute and relative air pressure of the air chamber based on the dynamic equation of air pressure. The module internally considers the change in air chamber volume with the displacement of the water column, as well as the adiabatic compression and expansion processes of air.

[0075] Module 3: Orifice Leakage Model: The input of this module is the relative air pressure of the air chamber. The air flow rate through the orifice is calculated based on the orifice leakage model, and the flow rate is fed back to the air pressure dynamic equation model to form a closed-loop coupling.

[0076] When building a model, special attention must be paid to the consistency of the signal dimensions and signs, as this is crucial to ensuring the model's correct operation. For example, when the water column moves upward, the air chamber volume decreases, the air pressure increases, and the orifice discharge flow rate is positive; when the water column moves downward, the air chamber volume increases, the air pressure decreases, and the orifice discharge flow rate is negative. If the signs are set incorrectly, it will cause the model to diverge or produce completely erroneous results.

[0077] ③ Simulation parameter settings and operation: Simulation parameters should be set as follows: The simulation time step should be between 0.01s and 0.05s. A time step that is too large will lead to numerical instability, while a time step that is too small will increase the computational load. In this embodiment, a time step of 0.01s is used, which ensures both numerical stability and computational efficiency. The simulation duration should be between 20 and 50 wave cycles to ensure the system reaches a steady state before extracting data from the steady-state phase for analysis, thus avoiding the influence of the initial transient process on the results. In this embodiment, a simulation duration of 40s is used, which is equivalent to 20 cycles for a wave with a period of 2s, sufficient for the system to reach a steady state.

[0078] The incident wave parameters were set according to Table 1, using a regular sine wave input with a wave height of 5 cm and periods of 2 s, 2.2 s, and 2.5 s, respectively. These three periods cover the range near the water column resonant frequency, which is the most common operating condition in the actual operation of the OWC device.

[0079] Table 1 Incident wave parameters:

[0080] Example: Prediction and Verification of Chamber Pressure under Regular Wave Conditions To verify the effectiveness of the method described in this invention, this embodiment takes a rectangular OWC device in a laboratory wave tank as the research object, performs air chamber pressure prediction under regular wave conditions, and compares the prediction results with the experimental measurement results.

[0081] Experimental setup parameters: The experimental setup used in this embodiment is installed in a wave tank measuring 30m long, 1.5m wide, and 1.2m deep. A push-plate wave generator is installed at one end of the tank, capable of generating high-precision regular and irregular waves. The specific parameters of the OWC device are as follows: Air chamber: Rectangular body, with an internal length of... ,Width ,high cross-sectional area of ​​the air chamber ; Bottom water inlet: rectangular, dimensions The area is approximately Equivalent piston: cross-sectional area ,thickness piston mass ; Still water height: Therefore, the gas volume in the gas chamber under static water conditions ; Orifice parameters: A small orifice with a diameter of [missing information] is installed at the top of the air chamber. The small holes are used to simulate the venting effect of an air turbine; the area of ​​the small holes is... ; Measurement System: A differential pressure sensor is installed at the top of the air chamber to measure the relative air pressure within the chamber, with a range of [range missing]. The accuracy is The level sensor is installed in the air chamber and is used to measure the displacement of the water column, with a range of [missing information]. The accuracy is The sensor's sampling frequency is set to... .

[0082] The calculation process for predicting air chamber pressure according to the four steps described in this invention is as follows: Create a 3D model of the gas chamber and piston: Use general-purpose 3D modeling software to create the geometric models of the gas chamber and piston. The upper surface of the piston is flush with the Z=0 plane, and the gap between the piston and the gas chamber wall is set to 0.01m. After removing all chamfers, fillets, bolt holes, and other detailed features, export the model in X_t format.

[0083] Frequency Domain Hydrodynamic Analysis: The exported model is imported into professional hydrodynamic analysis software. The outer surface is extracted, and the solid model is suppressed. The air chamber is set as a fixed constraint, and the piston retains only the Z-direction motion degree of freedom. The piston mass is set to 8.35 kg, and the centroid coordinates are (0, 0, 0.015 m). Surface meshing is performed, with a global mesh size of 0.02 m. Local refinement is applied to the piston surface and inlet region, with a mesh size of 0.01 m in the refined region, generating a total of 12456 surface elements. Mesh convergence is verified to confirm that the mesh meets the requirements. The analysis frequency range is set to 0.5 Hz to 3.3 Hz, with a frequency interval of 0.05 Hz, and the solver is started for calculation. After the calculation is completed, the hydrodynamic parameters in the Z-direction are extracted, and the high-frequency convergence additional mass is determined. The data quality was found to be good.

[0084] Time-domain system identification: The preprocessed frequency domain data is imported into mathematical software, and the Vectfit3 algorithm code is written for system identification. The fitting order is set to 8, and convergence occurs after 5 iterations. The converged rational transfer function is converted into a state-space model to obtain the state-space matrices of the excitation force system and the radiation force system.

[0085] Simulink Simulation: A wave-air coupled simulation model was built in the Simulink environment, integrating the aforementioned state-space matrix. The simulation time step was set to 0.01s, and the simulation duration was 40s. Three regular waves with different periods were input, and the simulation was run to obtain the time-domain curves of the relative air pressure in the air chamber.

[0086] Results Verification and Analysis: First, the natural frequency of the water column was calculated. The formula is as follows: ; Substituting the parameters, we get: ; The corresponding inherent period is approximately The three cycles selected in this embodiment ( , , All of these conditions are greater than the natural period, belonging to the sub-resonance region, which is the most common operating condition in the actual operation of the OWC device. The simulated pressure curves are compared with the experimentally measured pressure curves. During the comparison, the two curves are aligned starting from the fourth zero-crossing point to eliminate the influence of the initial transient process. The comparison results are shown in Table 2 and... Figure 1 , Figure 2 , Figure 3 As shown.

[0087] Table 2 Comparison of experimental and simulation results:

[0088] Figure 1 The curves show the comparison between simulated and experimental air pressure at a period of 2 seconds. Figure 2 The comparison curve is for a period of 2.2s. Figure 3 The comparison curves are for a period of 2.5 seconds. The comparison results show that: The three curves show a completely consistent overall trend with a phase difference of less than 5 degrees, indicating that this method can accurately capture the dynamic changes in air pressure.

[0089] With incident wave frequencies close to the resonant frequency at 2s and 2.2s periods, the prediction error for peak air pressure is less than 10%; with a 2.5s period, the error is approximately 15.5%, and the overall error range is 10%~20%, which is consistent with the accuracy expected by this invention.

[0090] The method described in this invention is applicable to fixed OWC devices with arbitrary cross-sectional shapes such as rectangles, circles, and polygons. For floating OWC devices, its application can be extended simply by adding the degrees of freedom of the floating body to the Cummins time-domain equations and considering the coupling effect between the floating body's motion and the water column's motion. This method can not only be used to predict the air chamber pressure but can also be further extended to calculate key performance parameters of OWC devices such as wave energy capture efficiency, turbine output power, and annual power generation, providing comprehensive technical support for the overall design and optimization of OWC devices.

[0091] In practical engineering applications, this method can be combined with intelligent optimization algorithms such as genetic algorithms and particle swarm optimization to perform multi-parameter, multi-objective optimization of the geometric parameters of the gas chamber (such as length, width, height, inlet size, and inlet position) to obtain the optimal wave energy capture efficiency. Furthermore, this method can be combined with turbine control algorithms to optimize the matching between the gas chamber and the turbine, thereby improving the efficiency and operational stability of the entire power generation system.

[0092] In summary, the OWC (Wave Energy Self-Powered) marine monitoring buoy provided by this invention achieves rapid and accurate prediction of air chamber pressure through reasonable dynamic simplification and advanced system identification technology. This solves the problems of high calculation costs and low efficiency associated with traditional methods, and has significant engineering application value. The standardized operation procedure of this method is easy for engineers to master and use, significantly shortening the design cycle of OWC devices, reducing design costs, and promoting the engineering and commercial application of wave energy technology.

[0093] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention. For example, the hydrodynamic analysis software and simulation software used in the present invention can be replaced with other software with the same functions, and the fitting algorithm can also adopt other algorithms capable of frequency domain to time domain conversion. These substitutions do not depart from the technical essence of the present invention.

Claims

1. An OWC (Wave Energy Self-Powered) marine monitoring buoy, characterized in that, The system includes a buoy main hull, an OWC (Open Water Control) chamber structure, a micro air turbine power generation unit, and a pressure prediction and control module. The OWC chamber structure is integrated into the lower part of the buoy main hull, with an inlet at the bottom connecting to external seawater, and the top of the chamber connected to the micro air turbine power generation unit via an air duct. The pressure prediction and control module incorporates a wave-air two-phase coupling simulation model, predicting the dynamic characteristics of the chamber pressure through the following steps to support buoy power generation control and structural verification: S1. Establish a simplified dynamic model of the water column in the air chamber: The oscillating water column in the OWC air chamber is equivalent to a rigid piston with the same cross-sectional shape as the air chamber. Based on the actual geometric dimensions of the buoy air chamber, a three-dimensional model of the air chamber-piston system is completed. S2. Frequency domain analysis of hydrodynamic parameters: The gas chamber-piston system was simulated in the frequency domain using the hydrodynamic analysis software ANSYS AQWA to obtain the frequency response data of the added mass, radiation damping, wave excitation force amplitude and phase angle in the heave motion direction; S3. Time-domain system identification: Using the Vectfit3 vector fitting algorithm, based on the frequency domain data obtained in step S2, the state-space expressions of radiation force and excitation force in the Cummins time-domain equation are fitted respectively. S4. Wave-Air Coupled Time-Domain Simulation Prediction: Based on the state-space model obtained in step S3, the Cummins time-domain equation and the adiabatic pressure dynamic equation are coupled to construct a wave-air coupled simulation model. The target sea state and wave state parameters are input, and the time-domain variation curve of the air chamber pressure is output.

2. The OWC wave energy self-powered marine monitoring buoy according to claim 1, characterized in that, Step S1 specifically includes: extracting key geometric parameters from the OWC device design drawings, such as the length, width, and height of the gas chamber, the location, shape, and size of the bottom inlet, the gas chamber wall thickness, and the connection method between the gas chamber and the external water body; using SolidWorks, CATIA, or ANSYS Design Modeler 3D modeling software to establish geometric models of the fixed structure of the gas chamber and the movable piston, respectively, wherein the gap between the piston and the gas chamber wall is 0.005m~0.02m, which is the minimum unit size of the subsequent hydrodynamic analysis mesh; establishing a model of the bottom inlet of the gas chamber, ensuring that the opening area and water flow channel characteristics are consistent with the actual situation, and aligning the upper surface of the piston with the default Z=0 horizontal plane of ANSYS AQWA during modeling; removing details such as chamfers, fillets, and bolt holes that do not affect the hydrodynamic analysis, and exporting the model in STEP, IGES, or X_t standard format after assembly.

3. The OWC wave energy self-powered marine monitoring buoy according to claim 1, characterized in that, Step S2 specifically includes: importing the 3D model into ANSYS AQWA, extracting the outer surfaces of all structures and suppressing the original solid model; setting the piston mass to the product of seawater density and piston volume, setting the air chamber structure as a fixed constraint, and retaining only the Z-direction motion degree of freedom for the piston; generating a surface mesh, with the global mesh size taken as 1 / 20 to 1 / 15 of the minimum incident wavelength; and performing local mesh refinement on the air chamber inlet, near the free liquid surface, and in areas of geometric abrupt changes, with the mesh size of the refined areas being equal to the global size. The mesh convergence was verified, and convergence was determined when the maximum rate of change of the added mass and radiation damping obtained in two consecutive calculations was less than 2%. The analysis frequency range was set to 0.3Hz~3.5Hz, the frequency interval was 0.05Hz~0.1Hz, and the output parameters were configured as added mass, radiation damping, wave excitation force amplitude and phase angle. The solver was then started to complete the calculation. For the frequency domain data of directional hydrodynamic parameters, the average value of the added mass at frequencies greater than 2.5 Hz is taken as the high-frequency convergence added mass. Check and correct non-physical negative values ​​in radiation damping, and export the processed data in CSV format.

4. The OWC wave energy self-powered marine monitoring buoy according to claim 1, characterized in that, Step S3 specifically includes: the additional mass, radiation damping, and high-frequency convergence additional mass obtained in step S2. The radiation impedance is constructed, and the complex form of the wave excitation force is constructed based on the amplitude and phase angle of the wave excitation force. The Vectfit3 vector fitting algorithm is used to fit the radiation impedance and the wave excitation force into rational transfer functions, respectively. An auxiliary function is introduced to transform the nonlinear fitting problem into a linear least squares problem. The poles are updated iteratively until convergence. The converged rational transfer function is converted into a standard linear state-space expression, and the state-space matrices of the radiation force system and the excitation force system are obtained respectively.

5. The OWC wave energy self-powered marine monitoring buoy according to claim 4, characterized in that, In step S3, the expression for radiation impedance is: ,in For complex units, Angular frequency, For frequency-varying radiation damping, Add mass to the frequency-varying wave; the expression for the complex form of the wave excitation force is as follows: ,in For the amplitude of the excitation force, Let be the phase angle of the excitation force; the rational transfer function is of the form: ,in For Laplace variables, The poles of the transfer function For the transfer function residual, This is the fitting order, ranging from 6 to 12. The function represents the high-frequency asymptotic value; the auxiliary function introduced is... ,in The auxiliary residual is used; the iterative convergence condition is an auxiliary function. The maximum relative error is less than 10 -4 .

6. The OWC wave energy self-powered marine monitoring buoy according to claim 4, characterized in that, In step S3, the standard linear state-space expression consists of state equations and output equations. The state equations describe the rate of change of the system state variables, and the output equations describe the relationship between the system output and the state variables and inputs. The four matrices of the state-space expression are the system matrix A, the input matrix B, the output matrix C, and the direct transfer matrix D.

7. The OWC wave energy self-powered marine monitoring buoy according to claim 1, characterized in that, Step S4 specifically includes: establishing the Cummins time-domain equation containing the air-pressure coupling term and the air chamber pressure dynamic equation under the adiabatic process assumption; sequentially constructing the wave input module, excitation force state space module, radiation force state space module, water column motion dynamics module, and air pressure dynamics module, integrating them to form a complete wave-air coupling simulation model; setting simulation parameters, inputting target wave condition parameters, running the simulation to obtain the time-domain variation curve of air chamber pressure, and extracting the peak pressure, average value, and fluctuation characteristic parameters.

8. The OWC wave-powered self-powered marine monitoring buoy according to claim 7, characterized in that, In step S4, the Cummins time-domain equation containing the pressure coupling term is: ,in The mass of the piston is the equivalent water column mass. This represents the displacement of the water column in the Z direction. Here is the radiation damping kernel function. The coefficient of restitution for static water. The cross-sectional area of ​​the air chamber is... For time-domain wave excitation force, The relative air pressure in the air chamber is; the dynamic equation for the air chamber pressure is: ,in This is the absolute pressure of the air chamber. The specific heat ratio of air is taken as 1.

4. This represents the volume of gas in the gas chamber under still water conditions. For turbine flow, The flow rate is the leakage flow rate in the gas chamber; the turbine flow model uses a small-hole leakage model during the validation phase. ,in For flow coefficient, The area of ​​the small hole, For air density, the flow-pressure ratio characteristic curve of the actual turbine is used in the engineering design stage.

9. The OWC wave energy self-powered marine monitoring buoy according to claim 7, characterized in that, In step S4, the wave input module supports both regular and irregular wave inputs, with irregular waves generated using JONSWAP spectrum. The simulation time step is set to 0.01s to 0.05s, and the simulation duration is set to 20 to 50 wave cycles to ensure that the system reaches a steady state before extracting valid data.

10. The OWC wave energy self-powered marine monitoring buoy according to claim 1, characterized in that, This method is applicable to fixed OWC devices with arbitrary cross-sectional shapes such as rectangles, circles, and polygons. It can be used for structural optimization design of OWC devices, gas chamber-turbine matching analysis, and rapid performance evaluation under different wave conditions.

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