Evaluation method for improving accuracy of wave energy device test and numerical model

Abstract

The invention discloses an evaluation method for improving the accuracy of a wave energy device test and a numerical model, and particularly relates to the field of wave energy tests. The method comprises the following specific steps of preliminary parameter acquisition and modeling, multi-scale test design, multi-medium multi-physical field coupling simulation, data fusion and multi-model calibration, dynamic adaptability optimization and performance evaluation standard establishment. According to the invention, multiple technical means such as multi-scale test, multi-physical field coupling simulation, data fusion calibration, dynamic adaptability optimization and the like are combined, so that the performance evaluation capability of the wave energy device can be comprehensively improved; a systematized test and digital-analog optimization process and a preliminary parameter acquisition and modeling method are provided, so that the digital-analog simulation and test accuracy of the wave energy device can be improved; the invention provides a wave energy device evaluation standard and process with higher accuracy and applicability, so as to ensure that digital-analog simulation and physical tests can better reflect the actual performance of a prototype, and provide powerful technical support for the development and optimization of the wave energy device.

Description

Technical Field

[0001] The present invention relates to the technical field of wave energy testing, and more particularly to an evaluation method for improving the accuracy of wave energy device testing and numerical models. Background Art

[0002] With the growth of energy demand and concerns about the environmental impact of traditional energy, the development of renewable energy has become increasingly important. As a renewable energy source, wave energy has the advantages of high energy density and wide distribution, but its development faces many technical challenges. The performance evaluation of wave energy conversion devices requires accurate tests and numerical models. However, existing tests and numerical models have some limitations and cannot accurately reflect the performance of the device under actual sea conditions.

[0003] The development of wave energy converter (WEC) requires numerical simulation and physical tests to determine its performance, stability and reliability. However, due to the complexity of numerical and physical models and the variability of the marine environment, current tests and numerical simulations often fail to fully restore the performance of the device under actual sea conditions, resulting in a deviation between the model prediction results and the actual operating performance of the prototype. Therefore, a more accurate and applicable wave energy device evaluation standard and process is proposed by combining multi-scale tests, multi-physics field coupling simulation, data fusion calibration and dynamic adaptive optimization. This ensures that numerical simulation and physical tests can better reflect the actual performance of the prototype.

[0004] To solve this problem, researchers have developed a series of innovative technical solutions to improve the accuracy of WEC tests and numerical models under actual sea conditions. Multi-physics coupling modeling software such as COMSOL Multiphysics is used to integrate multiple physical phenomena to improve the accuracy of simulation results. This modeling method can simulate the dynamic response of wave energy devices in complex marine environments, including wave impact force, device motion response, and key parameters such as energy conversion efficiency. Combining computer simulation and semi-physical simulation test technology of actual power generation systems, the wave action is reproduced in real time through the hydraulic drive system to evaluate the power generation performance of the device. This method can not only simulate the dynamic changes of waves, but also simulate the response of the device under different wave conditions, thus providing important data support for the design and optimization of the device. The influence of real-time control of wave forces on WEC energy absorption is studied, and the energy extraction efficiency is improved through real-time control strategies such as optimal command theory and grey model, and the operating parameters of the device are dynamically adjusted to adapt to the changing wave conditions to maximize energy capture. Advanced control strategies such as model predictive control (MPC) are used to optimize the efficiency of the device, and the hydrodynamic performance of the buoyant pendulum WEC is improved through experiments and numerical analysis. Semi-physical simulation experiments of submerged wave energy friction nano-power generation devices are carried out, and the hydrodynamic performance of the WEC model is comprehensively studied using CFD methods, including viscosity correction and parameter optimization. A new type of three-degree-of-freedom WEC is developed, and its conversion efficiency is analyzed through modeling, simulation and experiments to improve the conversion efficiency of wave energy to kinetic energy of water. The combined application of these technical solutions has significantly improved the accuracy of WEC performance evaluation and provided a solid scientific basis for the design, optimization and commercial deployment of wave energy converters.

[0005] However, existing technologies have significant shortcomings in the numerical simulation and testing of wave energy devices. In terms of accuracy, the lack of multi-scale tests, insufficient multi-physics field coupling simulation, and imperfect data fusion calibration make it difficult to fully present the performance of wave energy devices at different scales, which may lead to deviations in the evaluation of the actual performance of the device, affect accurate judgments, and reduce the reliability of the evaluation. In terms of applicability, the lack of dynamic adaptive optimization makes it difficult to make real-time adjustments and optimizations based on different marine environmental conditions and device operating conditions, which greatly limits the performance of wave energy devices under various complex working conditions. In terms of technical means, existing technologies have failed to fully integrate a variety of advanced technical means, and appear to be relatively single and limited in solving the problem of wave energy device performance evaluation. On the other hand, individual tests or simulation methods often have limitations and cannot comprehensively consider the impact of various factors on the performance of wave energy devices. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an evaluation method for improving the accuracy of wave energy device tests and numerical models. The technical problem to be solved by the present invention is that the development of wave energy conversion devices requires numerical simulations and physical tests to determine their performance, stability and reliability; due to the complexity of numerical models and physical models and the variability of the marine environment, current tests and numerical simulations are often difficult to fully restore the performance of the device under actual sea conditions, resulting in a deviation between the model prediction results and the actual operating performance of the prototype.

[0007] To achieve the above object, the present invention provides the following technical solution: an evaluation method for improving the accuracy of wave energy device tests and numerical models, the specific steps are as follows: S1, preliminary parameter acquisition and modeling; S1.1. Key parameters: Collect and obtain relevant data on wave characteristics, fluid characteristics, meteorological conditions, device design parameters, PTO system parameters and material properties; S1.2. Preliminary modeling: Construct a preliminary model including fluid dynamics model, structural mechanics model and energy conversion model; S2. Multi-scale test design: After the initial modeling is completed in step S1, a series of multi-scale physical tests are designed to verify and calibrate the accuracy of the numerical model and further optimize the design parameters: S2.1. Small-scale testing: Testing using a reduced-scale physical model in a laboratory wave tank / pond; S2.2, Mesoscale test: further verification test in artificial ocean environment or mesoscale test field; S2.3, Full-scale test: Conduct full-scale prototype test in the sea area where the device is scheduled to be installed; evaluate the comprehensive performance of the device in the real environment through long-term observation; S3, Multi-media and multi-physics coupling simulation: Construct a multi-physics coupling model, integrate factors such as structural dynamics, fluid dynamics and electromagnetic fields, and fully simulate the working process of the device; S4. Data fusion and multi-model calibration: Combine the test results of different scales with the numerical simulation data, use the data fusion method to perform multiple rounds of model calibration, and reduce the model prediction deviation; perform the final calibration of the model based on the full-scale test data, and use the reverse optimization algorithm to adjust the key parameters of the CFD model and the multi-media multi-physics field coupling model to ensure the maximum consistency between the simulation results and the measured results; S5. Dynamic adaptive optimization: Develop a dynamic adaptive optimization algorithm based on machine learning, and use real-time monitoring data to optimize the control strategy; dynamically adjust key parameters such as turbine speed and float movement amplitude according to changes in the wave environment to improve the energy capture efficiency of the device in actual operation; Dynamic adaptive optimization algorithms usually involve multiple control equations and formulas that are used to describe the operating state of the wave energy converter (WEC) and how to adjust according to environmental changes; the following are the main control equations and formulas: Turbine speed control equation: ,in, It is the turbine in time The rotation speed, is the wave power, is the set speed, is the power change; The control equation of the float motion amplitude is: ,in, is the float in time The range of motion, is the wave height, is the wave period, is the set range of motion; Energy capture efficiency evaluation: ,in, It's in time The energy capture efficiency, is the power output of the device, is the wave power; Wave power estimation: ,in, is the density of water, is the acceleration due to gravity, is the speed of wave energy propagation, is the characteristic width of the wave energy device; Machine Learning Model Optimization: ,in, represents the model parameters, is the learning rate, is the loss function, is the gradient of the loss function with respect to the parameters; Adaptive adjustment mechanism: ,in, It's in time The control input, is the loss function, is the expected output, is the system model, is the system status; S6. Establish performance evaluation standards: Combine multi-scale tests and numerical simulation results to establish comprehensive performance evaluation standards for wave energy devices, including dimensions such as energy conversion efficiency, structural reliability, environmental adaptability, and life cycle cost-effectiveness. Through continuous data collection and model adjustment in actual applications, continuously improve the evaluation standards to adapt to changing environmental and technical needs.

[0008] In a preferred embodiment, the parameters in step S1.1 specifically include: Wave characteristics: wave height, wavelength, wave period and wave direction data; data includes long-term observation data and extreme sea conditions data, covering different seasons and weather conditions; Fluid characteristics: Collect data on flow velocity, flow direction, and tidal changes related to the sea area to understand potential hydrodynamic loads; Meteorological conditions: Collect meteorological data on wind speed and direction in the sea area, which will affect wave formation and energy density; Device design parameters: including the energy capture body structure size and shape; PTO system parameters: turbine specifications in pneumatic systems, parameters of components in hydraulic systems, gear and rack design rules in mechanical systems, etc. Material properties: Select materials with corrosion resistance, fatigue resistance and high strength according to the marine environment, and obtain relevant physical and chemical performance data; The model in step S1.2 specifically includes: Fluid dynamics model: Based on the key data obtained, a fluid dynamics model is established to simulate the interaction between waves and device structures, including the calculation and identification of wave forces, buoyancy forces, and viscosity forces; Structural mechanics model: The structural model is established through finite element analysis to calculate the stress distribution, deformation and fatigue of the device under the action of waves; Energy conversion model: Based on the energy conversion system and working principle of the device, an energy conversion model is established to simulate the power output characteristics of the energy conversion device of the pneumatic system / hydraulic system / mechanical system.

[0009] In a preferred embodiment, the small-scale test in step S2.1 is mainly used to verify the wave force calculation, structural motion response and basic energy conversion efficiency of the numerical model; Parameter sensitivity analysis: By adjusting the key parameters of the model (such as float size, PTO system parameters, etc.), the impact of these parameters on the device performance is studied and compared with the results of the numerical model; Mesoscale test in step S2.2: The test model in this stage is closer to the actual device and is mainly used to test the stability of the device under complex wave conditions, the response of the PTO system and the energy conversion performance; Dynamic response test: simulate complex sea conditions (including multi-directional random waves, tidal current superposition, etc.), evaluate the dynamic response characteristics and energy conversion performance of the device, and conduct detailed data collection; The full-system integration test in the full-scale test in step S2.3 is to test the coordinated working of all subsystems of the prototype device, including structure, power transmission, control system, etc., to ensure the reliability and adaptability of the entire device.

[0010] In a preferred embodiment, the step S3 specifically includes: S3.1, Fluid-Structure Interaction Simulation: Simulate the interaction between waves and device structures, and analyze the stress and fatigue of the structure; S3.2. Multi-media simulation: Multi-media simulation is performed for different wave energy devices. For example, in oscillating water column (OWC) devices, the gas-liquid two-phase flow in the air chamber is simulated to predict the power output of the turbine and the pressure fluctuation in the air chamber. S3.3. Multi-directional wave simulation: Introduce multi-directional waves and complex wave condition simulation, and consider the impact of waves of different directions and frequencies on the performance of the device.

[0011] In a preferred embodiment, the step S4 specifically includes: S4.1. Parameter calibration: Use small-scale and medium-scale test data to calibrate key parameters in the numerical model, such as fluid resistance coefficient, turbine efficiency curve, etc. S4.2. Model correction: Introduce full-scale test data into the numerical model, and perform multiple rounds of model correction and optimization through reverse optimization and sensitivity analysis to ensure that the numerical simulation results can better match the measured data.

[0012] In a preferred embodiment, in step S5, historical data and environmental prediction are combined to establish an intelligent decision-making system to provide a basis for future operation optimization: S5.1. Data-driven control optimization: Based on real-time collected marine environment data and device operation data, the device operation control strategy is optimized through machine learning algorithms, such as turbine speed adjustment, float movement amplitude optimization, etc. S5.2. Adaptive adjustment mechanism: Select appropriate environmental change monitoring sensors to continuously monitor various key parameters in the marine environment, such as wave height, wave period, water flow speed, etc., and transmit accurate data to the control system of the device; establish a dynamic adjustment mechanism and optimize the energy conversion efficiency evaluation; through continuous monitoring, adjustment and optimization, the device can always maintain a high energy conversion efficiency under different environmental conditions.

[0013] In a preferred embodiment, the evaluation criteria of the energy conversion efficiency in step S6 are: considering the energy conversion efficiency under different wave conditions, using a standardized efficiency index, such as the annual average energy capture efficiency (Annual Energy Production Efficiency, AEPE); the annual average energy capture efficiency refers to the ratio of the average value of the effective operating energy capture efficiency within a year to the theoretical maximum energy that can be obtained from the waves within the time period; the specific formula is as follows: This efficiency is one of the key indicators for evaluating the performance of wave energy converters. A higher AEPE means that the wave energy device can capture and convert wave energy more effectively under different wave conditions. It is one of the important indicators for measuring the performance of wave energy devices.

[0014] Evaluation criteria for structural reliability: In terms of fatigue analysis, accurately predict the fatigue life of key structural components under different sea conditions, set reasonable safety factors, and evaluate fatigue damage accumulation; in terms of structural stress monitoring, reasonably arrange monitoring points, ensure monitoring accuracy, set early warning and alarm values, and analyze stress data; comprehensively establish a reliability index system, conduct regular evaluation and maintenance, and continuously improve evaluation standards and methods to ensure the reliability and safety of wave energy device structures.

[0015] In a preferred embodiment, the evaluation criteria for environmental adaptability in step S6 are: in calm sea conditions, energy conversion efficiency, operational stability and control system response are considered; in moderate sea conditions, the focus is on evaluating wave impact resistance, energy conversion stability and reliability; in severe sea conditions, attention is paid to structural strength and stability, safety protection mechanism and survivability; in complex wave conditions, emphasis is placed on wave direction adaptability, irregular wave response and dynamic stability, and these aspects are combined to provide an important reference for the design, optimization and operation of wave energy devices.

[0016] In a preferred embodiment, the evaluation criteria for the cost-effectiveness of the entire life cycle in step S6: the evaluation criteria for the entire life cycle of the wave energy device include consideration of the initial investment in equipment, engineering construction and planning and design costs, attention to daily, fault repair and upgrade costs in maintenance costs, and energy benefits assessed from the perspective of power generation, electricity sales revenue and environmental value. The comprehensive use of cost-benefit analysis, technical performance evaluation and environmental impact assessment provides a scientific evaluation for the entire life cycle of the device.

[0017] Technical effects and advantages of the present invention: The present invention combines multiple technical means such as multi-scale experiments, multi-physical field coupling simulation, data fusion calibration and dynamic adaptive optimization to comprehensively improve the performance evaluation capability of wave energy devices; proposes more accurate and applicable wave energy device evaluation standards and processes to ensure that digital simulation and physical experiments better reflect the actual performance of the prototype; proposes a systematic test and digital simulation optimization process and preliminary parameter acquisition and modeling method to improve the accuracy of digital simulation and testing of wave energy devices; proposes a more accurate and applicable wave energy device evaluation standard and process to ensure that digital simulation and physical experiments can better reflect the actual performance of the prototype, providing strong technical support for the development and optimization of wave energy devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is the overall system flow chart of the present invention.

[0019] Figure 2 This is a preliminary parameter acquisition and modeling module diagram of the present invention.

[0020] Figure 3 This is a module diagram for the multi-scale experimental design of the present invention.

[0021] Figure 4 This is a diagram of the multi-media multi-physics field coupling simulation module of the present invention.

[0022] Figure 5 This is a diagram of the data fusion and multi-model calibration module of the present invention.

[0023] Figure 6 This is a diagram of the dynamic adaptive optimization module of the present invention.

[0024] Figure 7 This is a module diagram for establishing performance evaluation standards of the present invention. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] The present invention provides Figure 1The evaluation method for improving the accuracy of wave energy device tests and numerical models shown in the figure can significantly improve the accuracy of numerical simulation and testing of wave energy devices. Combining multi-scale tests, multi-physics field coupling simulation, data fusion calibration and dynamic adaptive optimization and other technical means, a more accurate and applicable wave energy device evaluation standard and process is proposed to ensure that numerical simulation and physical tests can better reflect the actual performance of the prototype, providing strong technical support for the development and optimization of wave energy devices. The specific steps are as follows:

[0027] S1. Preliminary parameter acquisition and modeling (e.g. Figure 2 ); S1.1. Key parameters; Wave characteristics: wave height, wavelength, wave period, wave direction, etc.; data should include long-term observation data and extreme sea conditions data, covering different seasons and weather conditions; Fluid characteristics: Collect data related to the sea area, such as flow velocity, flow direction, tidal changes, etc., to understand the potential hydrodynamic loads; Meteorological conditions: collect meteorological data such as wind speed and direction in the sea area, which will affect the formation and energy density of waves; Device design parameters: including the energy capture body structure size and shape; PTO system parameters: turbine specifications in pneumatic systems, parameters of components in hydraulic systems, gear and rack design rules in mechanical systems, etc. Material properties: Select materials with corrosion resistance, fatigue resistance and high strength according to the marine environment, and obtain relevant physical and chemical performance data; S1.2, preliminary modeling; Fluid dynamics model: Based on the key data obtained, a fluid dynamics model is established to simulate the interaction between waves and device structures, including the calculation and identification of wave forces, buoyancy forces, and viscosity forces; Structural mechanics model: The structural model is established through finite element analysis to calculate the stress distribution, deformation and fatigue of the device under the action of waves; Energy conversion model: According to the energy conversion system and working principle of the device, an energy conversion model is established to simulate the power output characteristics of energy conversion devices such as pneumatic system / hydraulic system / mechanical system; S2. Multiscale experimental design (e.g. Figure 3 ); After the preliminary modeling is completed, a series of multi-scale physical tests are designed to verify and calibrate the accuracy of the numerical model and further optimize the design parameters; (1) Small-scale test: A test is conducted in a laboratory wave flume / tank using a reduced-scale physical model. This test is mainly used to verify the wave force calculation, structural motion response and basic energy conversion efficiency of the numerical model. Parameter sensitivity analysis: By adjusting the key parameters of the model (such as float size, PTO system parameters, etc.), the impact of these parameters on the device performance is studied and compared with the results of the numerical model; (2) Mesoscale test: Further verification tests are carried out in artificial ocean environments or mesoscale test fields. The test model at this stage is closer to the actual device and is mainly used to test the stability of the device under complex wave conditions, the response of the PTO system, and the energy conversion performance. Dynamic response test: simulate complex sea conditions (including multi-directional random waves, tidal current superposition, etc.), evaluate the dynamic response characteristics and energy conversion performance of the device, and conduct detailed data collection; (3) Full-scale test: Conduct full-scale prototype test in the sea area where the device is scheduled to be installed; evaluate the comprehensive performance of the device in a real environment through long-term observation; Full system integration test: Test all subsystems of the prototype device, including the coordinated working of structure, power transmission, control system, etc., to ensure the reliability and adaptability of the entire device; S3, multi-media multi-physics coupling simulation (such as Figure 4 ); Construct a multi-physics coupling model, integrate factors such as structural dynamics, fluid dynamics and electromagnetic fields, and fully simulate the working process of the device; (1) Fluid-structure interaction simulation: simulate the interaction between waves and device structures, and analyze the stress and fatigue of the structure; (2) Multi-media simulation: Multi-media simulation is performed for different wave energy devices. For example, in an oscillating water column (OWC) device, the gas-liquid two-phase flow in the air chamber is simulated to predict the power output of the turbine and the pressure fluctuation in the air chamber. (3) Multi-directional wave simulation: Introducing multi-directional waves and complex wave condition simulation, considering the impact of waves of different directions and frequencies on device performance; S4. Data fusion and multi-model calibration (e.g. Figure 5 ); Combine the test results of different scales with the simulation data, and use the data fusion method to perform multiple rounds of model calibration to reduce the model prediction bias; The final calibration of the model is carried out based on the full-scale test data, and the key parameters of the CFD model and the multi-media multi-physics field coupling model are adjusted using the inverse optimization algorithm to ensure the maximum consistency between the simulation results and the measured results; (1) Parameter calibration: Using small-scale and mesoscale test data, key parameters in the numerical model are calibrated, such as fluid resistance coefficient, turbine efficiency curve, etc. (2) Model calibration: Introduce full-scale test data into the numerical model, and perform multiple rounds of calibration and optimization on the model through reverse optimization and sensitivity analysis to ensure that the numerical simulation results can better match the measured data; S5, Dynamic Adaptive Optimization (e.g. Figure 6 ); Develop a dynamic adaptive optimization algorithm based on machine learning and use real-time monitoring data to optimize the control strategy; dynamically adjust key parameters such as turbine speed and float movement amplitude according to changes in the wave environment to improve the energy capture efficiency of the device in actual operation; Dynamic adaptive optimization algorithms usually involve multiple control equations and formulas that are used to describe the operating state of the wave energy converter (WEC) and how to adjust according to environmental changes; the following are the main control equations and formulas: Turbine speed control equation: ,in, It is the turbine in time The rotation speed, is the wave power, is the set speed, is the power change; The control equation of the float motion amplitude is: ,in, Is the float in time The range of motion, is the wave height, is the wave period, is the set range of motion; Energy capture efficiency evaluation: ,in, It's in time The energy capture efficiency, is the power output of the device, is the wave power; Wave power estimation: ,in, is the density of water, is the acceleration due to gravity, is the speed of wave energy propagation, is the characteristic width of the wave energy device; Machine Learning Model Optimization: ,in, represents the model parameters, is the learning rate, is the loss function, is the gradient of the loss function with respect to the parameters; Adaptive adjustment mechanism: ,in, It's in time The control input, is the loss function, is the expected output, is the system model, is the system status; Combine historical data and environmental forecasts to establish an intelligent decision-making system to provide a basis for future operation optimization; (1) Data-driven control optimization: Based on real-time collected marine environment data and device operation data, the device operation control strategy is optimized through machine learning algorithms, such as turbine speed adjustment and float movement amplitude optimization; (2) Adaptive adjustment mechanism: Select appropriate environmental change monitoring sensors to continuously monitor various key parameters in the marine environment, such as wave height, wave period, water flow speed, etc., and transmit accurate data to the device's control system; establish a dynamic adjustment mechanism and optimize the energy conversion efficiency evaluation; through continuous monitoring, adjustment and optimization, the device can always maintain a high energy conversion efficiency under different environmental conditions; S6. Establish performance evaluation criteria (e.g. Figure 7 ); Combine the results of multi-scale tests and numerical simulations to establish comprehensive performance evaluation criteria for wave energy devices, including dimensions such as energy conversion efficiency, structural reliability, environmental adaptability, and life cycle cost-effectiveness; Through continuous data collection and model adjustment in actual applications, the evaluation criteria are continuously improved to adapt to the changing environment and technical needs; (1) Energy conversion efficiency evaluation Definition: Wave energy conversion efficiency is the rate at which a wave energy device converts wave energy into usable energy; Evaluation criteria: Considering the energy conversion efficiency under different wave conditions, standardized efficiency indicators are used, such as the annual average energy capture efficiency (AEPE). The annual average energy capture efficiency refers to the ratio of the average value of the effective operating energy capture efficiency within a year to the theoretical maximum energy that can be obtained from the waves during this period. The specific formula is as follows: This efficiency is one of the key indicators for evaluating the performance of wave energy converters. A higher AEPE means that the wave energy device can capture and convert wave energy more effectively under different wave conditions. It is one of the important indicators for measuring the performance of wave energy devices.

[0028] (2) Structural reliability evaluation Definition: During the design service life of the wave energy device, fatigue analysis and real-time stress monitoring of the structure are performed to ensure that the structure can operate continuously and stably under various marine environmental conditions without structural failure caused by fatigue damage or excessive stress, thereby achieving the intended function; Evaluation criteria: In terms of fatigue analysis, accurately predict the fatigue life of key structural components under different sea conditions, set a reasonable safety factor, and evaluate fatigue damage accumulation; in terms of structural stress monitoring, reasonably arrange monitoring points, ensure monitoring accuracy, set early warning and alarm values, and analyze stress data; comprehensively establish a reliability index system, conduct regular evaluation and maintenance, and continuously improve evaluation standards and methods to ensure the reliability and safety of wave energy device structures; (3) Environmental adaptability evaluation Definition: The environmental adaptability of a wave energy device refers to the device's ability to maintain stable operation and efficiently convert wave energy into usable energy under different sea conditions (such as extreme weather and complex wave conditions), and to withstand various impacts of environmental factors without serious failure or significant performance degradation during long-term use; Evaluation criteria: In calm sea conditions, energy conversion efficiency, operational stability and control system response are considered; in moderate sea conditions, the focus is on assessing wave impact resistance, energy conversion stability and reliability; in severe sea conditions, attention is paid to structural strength and stability, safety protection mechanisms and survivability; in complex wave conditions, emphasis is placed on wave direction adaptability, irregular wave response and dynamic stability. These aspects are combined to provide important references for the design, optimization and operation of wave energy devices; (4) Full life cycle cost-benefit evaluation Definition: The life cycle assessment of wave energy devices is a method to evaluate the sustainability and comprehensive performance of wave energy devices in terms of technology, economy and environment by comprehensively considering the initial investment, maintenance cost and energy benefits of the wave energy devices throughout the entire process from planning and design, manufacturing and installation, operation and use to decommissioning and disposal. By analyzing and evaluating each stage of the life cycle, it provides a basis for the optimal design, efficient operation and reasonable decision-making of wave energy devices. Evaluation standards: The evaluation standards for the entire life cycle of wave energy devices include consideration of the initial investment in equipment, engineering construction and planning and design costs, attention to daily, fault repair and upgrade costs in maintenance costs, assessment of energy benefits from power generation, electricity sales revenue and environmental value, and comprehensive use of cost-benefit analysis, technical performance evaluation and environmental impact assessment to provide a scientific evaluation of the entire life cycle of the device.

[0029] In summary: Combining multiple technologies to ensure accuracy: other technologies may only focus on the impact of a single factor or some factors on the performance of wave energy devices, while the present invention combines multi-scale tests, multi-physics field coupling simulation, data fusion calibration and other technical means; multi-scale tests can fully reflect the performance of devices at different scales, avoiding inaccurate device performance evaluation due to the one-sidedness of single-scale analysis; multi-physics field coupling simulation can accurately present the real working state of the device in a complex marine environment, which is closer to the actual situation than other technologies; data fusion calibration can effectively eliminate the difference between simulation and test data, and improve the reliability of device performance evaluation; Dynamic adaptive optimization enables wave energy devices to be adjusted and optimized in real time according to different marine environmental conditions and device operating status; for example, under different wave conditions, current speed and other environmental changes, the device can automatically adjust the working mode and parameter settings to achieve the best energy conversion efficiency; in addition, more accurate and applicable evaluation standards and processes can ensure that digital simulation and physical tests better reflect the actual performance of the prototype; this means that during the development and optimization process, the performance of the device can be evaluated more accurately, problems can be discovered and improved in a timely manner, thereby improving the efficiency and quality of the entire development process; Comprehensive technical means provide systematic and comprehensive support for the development and optimization of wave energy devices; starting from parameter acquisition and modeling, in-depth analysis is carried out through multi-scale experiments and multi-physics field coupling simulation, and then data fusion calibration is used to improve accuracy, and finally applicability is enhanced through dynamic adaptive optimization; this series of technical solutions cooperate with each other to form a complete technical system, which provides strong technical support for the development of wave energy devices and helps to promote the advancement and application of wave energy technology.

[0030] Finally, a few points should be explained: First, in the description of this application, it should be noted that, unless otherwise specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, which may refer to mechanical connection or electrical connection, or internal communication between two components, or direct connection. "upper", "lower", "left", "right", etc. are only used to indicate relative positional relationships. When the absolute position of the object being described changes, the relative positional relationship may change; Secondly: In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other; Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An evaluation method for improving the accuracy of wave energy device tests and numerical models, characterized in that: The specific steps are as follows: S1, preliminary parameter acquisition and modeling; S1.

1. Key parameters: Collect relevant data; S1.2, Preliminary Modeling: Build a preliminary model; S2. Multi-scale test design: After the initial modeling in step S1 is completed, a series of multi-scale physical tests are designed to further optimize the design parameters: S2.1, Small-scale test: Tests are conducted in the laboratory using reduced-scale physical models; S2.2, Mesoscale test: further verification test in artificial ocean environment or mesoscale test field; S2.3, Full-scale test: Conduct full-scale prototype test in the sea area to evaluate the comprehensive performance of the device in the real environment; S3. Multi-media multi-physics coupling simulation: Construct a multi-physics coupling model to fully simulate the working process of the device; S4, data fusion and multi-model calibration: combining test results at different scales with digital simulation data; Final calibration of the model based on full-scale test data; S5. Dynamic adaptive optimization: Develop a dynamic adaptive optimization algorithm based on machine learning and use real-time monitoring data to optimize control strategies; S6. Establish performance evaluation standards: Combine multi-scale tests and numerical simulation results to establish comprehensive performance evaluation standards for wave energy devices.

2. The evaluation method for improving the accuracy of wave energy device tests and numerical models according to claim 1, characterized in that: The parameters in step S1.1 specifically include: Wave characteristics: wave height, wavelength, wave period and wave direction data; data includes long-term observation data and extreme sea conditions data, covering different seasons and weather conditions; Fluid characteristics: Collect data on flow velocity, flow direction, and tidal changes related to the sea area to understand potential hydrodynamic loads; Meteorological conditions: collect meteorological data on wind speed and direction in the sea area; Device design parameters: including the energy capture body structure size and shape; PTO system parameters: turbine specifications in pneumatic systems, parameters of components in hydraulic systems, and gear rack design rules in mechanical systems; Material properties: Select materials with corrosion resistance, fatigue resistance and high strength according to the marine environment, and obtain relevant physical and chemical performance data; The model in step S1.2 specifically includes: Fluid dynamics model: Based on the key data obtained, a fluid dynamics model is established to simulate the interaction between waves and device structures, including the calculation and identification of wave forces, buoyancy and viscosity forces; Structural mechanics model: The structural model is established through finite element analysis to calculate the stress distribution, deformation and fatigue of the device under the action of waves; Energy conversion model: Based on the energy conversion system and working principle of the device, an energy conversion model is established to simulate the power output characteristics of the energy conversion device of the pneumatic system / hydraulic system / mechanical system.

3. The evaluation method for improving the accuracy of wave energy device tests and numerical models according to claim 1, characterized in that: The small-scale test in step S2.1 is used to verify the wave force calculation, structural motion response and basic energy conversion efficiency of the numerical model; Parameter sensitivity analysis: By adjusting the key parameters of the model, the influence of these parameters on the device performance is studied and compared with the results of the numerical model; Mesoscale test in step S2.2: The test model in this stage is closer to the actual device and is used to test the stability of the device under complex wave conditions, the response of the PTO system and the energy conversion performance; Dynamic response test: simulate complex sea conditions, evaluate the dynamic response characteristics and energy conversion performance of the device, and conduct detailed data collection; The full system integration test in the full-scale test in step S2.3: testing all subsystems of the prototype device, including the coordinated working of the structure, power transmission, and control system, to ensure the reliability and adaptability of the entire device.

4. The evaluation method for improving the accuracy of wave energy device tests and numerical models according to claim 1, characterized in that: The step S3 specifically includes: S3.1, Fluid-Structure Interaction Simulation: Simulate the interaction between waves and device structures, and analyze the stress and fatigue of the structure; S3.2, Multi-media simulation: Multi-media simulation is performed for different wave energy devices to predict the power output of the turbine and the pressure fluctuations in the air chamber; S3.

3. Multi-directional wave simulation: Introduce multi-directional waves and complex wave condition simulation, and consider the impact of waves of different directions and frequencies on the performance of the device.

5. The evaluation method for improving the accuracy of wave energy device tests and numerical models according to claim 1, characterized in that: The step S4 specifically includes: S4.

1. Parameter calibration: Use small-scale and mesoscale test data to calibrate key parameters in the numerical model; S4.

2. Model correction: Introduce full-scale test data into the numerical model, and perform multiple rounds of model correction and optimization through reverse optimization and sensitivity analysis to ensure that the numerical simulation results can better match the measured data.

6. The evaluation method for improving the accuracy of wave energy device tests and numerical models according to claim 1, characterized in that: The dynamic adaptive optimization algorithm in step S5 involves a plurality of control equations and formulas, which are used to describe the operating state of the wave energy conversion device and how to adjust it according to environmental changes; the following are the main control equations and formulas: Turbine speed control equation: ,in, It is the turbine in time The rotation speed, is the wave power, is the set speed, is the power change; The control equation of the float motion amplitude is: ,in, is the float in time The range of motion, is the wave height, is the wave period, is the set range of motion; Energy capture efficiency evaluation: ,in, It's in time The energy capture efficiency, is the power output of the device, is the wave power; Wave power estimation: ,in, is the density of water, is the acceleration due to gravity, is the speed of wave energy propagation, is the characteristic width of the wave energy device; Machine Learning Model Optimization: ,in, represents the model parameters, is the learning rate, is the loss function, is the gradient of the loss function with respect to the parameters; Adaptive adjustment mechanism: ,in, It's in time The control input, is the loss function, is the expected output, is the system model, Is the system status.

7. The evaluation method for improving the accuracy of wave energy device tests and numerical models according to claim 6, characterized in that: In step S5, historical data and environmental prediction are combined to establish an intelligent decision-making system to provide a basis for future operation optimization: S5.

1. Data-driven control optimization: Based on real-time collected marine environment data and device operation data, the device operation control strategy is optimized through machine learning algorithms; S5.2, Adaptive adjustment mechanism: Select appropriate environmental change monitoring sensors to continuously monitor various key parameters in the marine environment and transmit accurate data to the control system of the device; Establish a dynamic adjustment mechanism and optimize the energy conversion efficiency evaluation; through continuous monitoring, adjustment and optimization, the device can always maintain a high energy conversion efficiency under different environmental conditions.

8. The evaluation method for improving the accuracy of wave energy device tests and numerical models according to claim 1, characterized in that: The evaluation criteria for energy conversion efficiency evaluation in step S6 are: considering the energy conversion efficiency under different wave conditions, using a standardized efficiency index; the specific formula is as follows: , which represents the ratio of the average energy capture efficiency of the effective operation of the wave energy converter over a one-year period to the theoretical maximum energy that can be obtained from the waves during that period.

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