Evaluation Method for Improving the Accuracy of Wave Energy Device Tests and Numerical Models

Abstract

The present invention discloses an evaluation method for improving the accuracy of wave energy device tests and numerical models, specifically related to the field of wave energy tests. The specific steps are as follows: preliminary parameter acquisition and modeling, multi-scale test design, multi-medium and multi-physical field coupling simulation, data fusion and multi-model calibration, dynamic adaptability optimization, and establishment of performance evaluation criteria. The present invention combines various technical means such as multi-scale tests, multi-physical field coupling simulation, data fusion calibration, and dynamic adaptability optimization, which can comprehensively improve the performance evaluation ability of wave energy devices; proposes a systematic test and digital model optimization process and a preliminary parameter acquisition and modeling method, which can improve the accuracy of wave energy device digital simulation and tests; proposes a more accurate and applicable wave energy device evaluation standard and process to ensure that digital simulation and physical tests can better reflect the actual performance of the prototype machine, providing strong technical support for the development and optimization of wave energy devices.

Description

Technical Field

[0001] The present invention relates to the technical field of wave energy testing, and more specifically, 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 the concern about the environmental impact of traditional energy sources, 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, the existing tests and numerical models have some limitations and cannot accurately reflect the performance of the devices under actual sea conditions.

[0003] The development of wave energy converters (WECs) requires numerical simulation and physical tests to determine their performance, stability, and reliability. However, due to the complexity of numerical and physical models and the variability of the ocean environment, current tests and numerical simulations often have difficulty fully reproducing the performance of the devices under actual sea conditions, resulting in a deviation between the model prediction results and the actual operating performance of the prototype. Therefore, by combining technical means such as multi-scale tests, multi-physics field coupling simulation, data fusion calibration, and dynamic adaptive optimization, a more accurate and applicable evaluation standard and process for wave energy devices are proposed to ensure that numerical simulation and physical tests can better reflect the actual performance of the prototype.

[0004] To address this issue, researchers have developed a series of innovative technical solutions to improve the accuracy of tests and numerical models of WECs in actual sea conditions. Multiphysics coupling modeling software such as COMSOL Multiphysics is used to synthesize various physical phenomena, thereby enhancing the accuracy of simulation results. This modeling method can simulate the dynamic responses of wave energy devices in complex marine environments, including key parameters such as the impact force of waves, the motion responses of the devices, and the 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 a 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 the responses of the device under different wave conditions, thus providing important data support for the design and optimization of the device; Study the impact of real-time control of wave forces on WEC energy absorption, improve the energy extraction efficiency through real-time control strategies such as optimal command theory and grey model, and dynamically adjust the operating parameters of the device to adapt to changing wave conditions, thereby maximizing energy capture; Adopt advanced control strategies such as model predictive control (MPC) to optimize the efficiency of the device and improve the hydrodynamic performance of the buoyant pendulum WEC through experimental and numerical analysis; Conduct semi-physical simulation experiments on submerged wave energy triboelectric nanogenerators and comprehensively study the hydrodynamic performance of WEC models using CFD methods, including viscosity correction and parameter optimization; Develop a new type of three-degree-of-freedom WEC, analyze its conversion efficiency through modeling, simulation, and experiment, and improve the conversion efficiency from wave energy to water kinetic energy. The comprehensive application of these technical solutions has significantly improved the accuracy of WEC performance evaluation, providing a solid scientific foundation for the design, optimization, and commercial deployment of wave energy converters.

[0005] However, there are significant shortcomings in the numerical simulation and testing of wave energy devices in the existing technology. In terms of accuracy, the lack of multi-scale tests, insufficient multiphysics coupling simulation, and imperfect data fusion calibration make it difficult to comprehensively present the performance of wave energy devices at different scales, which may lead to deviations in the evaluation of the actual performance of the devices, affecting accurate judgment and reducing the reliability of the evaluation. In terms of applicability, the lack of dynamic adaptability optimization makes it difficult to make real-time adjustments and optimizations according to different marine environmental conditions and device operating states, greatly limiting the performance of wave energy devices under various complex working conditions. In terms of technical means, the existing technology fails to fully integrate various advanced technical means and appears relatively single and limited when solving the problem of wave energy device performance evaluation. On the other hand, individual test or simulation methods often have limitations and cannot comprehensively consider the influence of various factors on the performance of wave energy devices. Summary of the Invention

[0006] To overcome the above-mentioned defects of the prior art, embodiments of the present invention provide 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 simulation 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 often have difficulty fully restoring 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 machine.

[0007] To achieve the above object, the present invention provides the following technical solutions: An evaluation method for improving the accuracy of wave energy device tests and numerical models, the specific steps are as follows:

[0008] S1. Preliminary parameter acquisition and modeling;

[0009] S1.1. Key parameters: Collect relevant data on wave characteristics, fluid characteristics, meteorological conditions, device design parameters, PTO system parameters, and material properties;

[0010] S1.2. Preliminary modeling: Construct a preliminary model including a hydrodynamic model, a structural mechanics model, and an energy conversion model;

[0011] S2. Multi-scale test design: After the preliminary modeling in step S1 is completed, design a series of multi-scale physical tests to verify and calibrate the accuracy of the numerical model and further optimize the design parameters:

[0012] S2.1. Small-scale test: Conduct tests using a scaled-down physical model in a laboratory wave flume / tank;

[0013] S2.2. Medium-scale test: Conduct further verification tests in an artificial marine environment or a medium-scale test site;

[0014] S2.3. Full-scale test: Conduct full-scale prototype tests in the predetermined installation sea area of the device; Through long-term observation, evaluate the comprehensive performance of the device in the real environment;

[0015] S3. Multi-medium multi-physical field coupling simulation: Construct a multi-physical field coupling model, integrate factors such as structural dynamics, fluid dynamics, and electromagnetic fields, and comprehensively simulate the working process of the device;

[0016] S4. Data fusion and multi-model calibration: Combine the test results of different scales with the numerical simulation data, use data fusion methods for multiple rounds of model calibration to reduce model prediction deviations; Based on the full-scale test data, perform the final calibration of the model, and use the reverse optimization algorithm to adjust the key parameters of the CFD model and the multi-medium multi-physical field coupling model to ensure the maximum consistency between the simulation results and the measured results;

[0017] S5, Dynamic Adaptive Optimization: Develop a machine learning-based dynamic adaptive optimization algorithm to optimize control strategies using real-time monitoring data; dynamically adjust key parameters such as turbine speed and float movement amplitude according to changes in wave environment to improve the energy capture efficiency of the device during actual operation;

[0018] The dynamic adaptive optimization algorithm usually involves multiple control equations and formulas, which are used to describe the operating state of the wave energy conversion device (WEC) and how to adjust according to environmental changes; the following are the main control equations and formulas:

[0019] Turbine speed control equation: , where, is the turbine speed at time , is the wave power, is the set speed, is the power change;

[0020] Float movement amplitude control equation: , where, is the movement amplitude of the float at time , is the wave height, is the wave period, is the set movement amplitude;

[0021] Energy capture efficiency evaluation:

[0022] , where, is the energy capture efficiency at time , is the power output of the device, is the wave power;

[0023] Wave power estimation: , where, is the density of water, is the acceleration due to gravity, is the wave energy propagation speed, is the characteristic width of the wave energy device;

[0024] Machine learning model optimization: , where, represents the model parameters, is the learning rate, is the loss function, is the gradient of the loss function with respect to the parameters;

[0025] Adaptive adjustment mechanism: , where, is at time is the control input, is the loss function, is the expected output, is the system model, is the system state;

[0026] S6. Establish a performance evaluation criterion: Combine the multi-scale test and numerical simulation results to establish a comprehensive performance evaluation criterion for the wave energy device, including dimensions such as energy conversion efficiency, structural reliability, environmental adaptability, and life-cycle cost-effectiveness; Continuously improve the evaluation criterion through continuous data collection and model adjustment in actual applications to adapt to the changing environment and technological requirements.

[0027] In a preferred embodiment, the parameters in the step S1.1 specifically include:

[0028] Wave characteristics: Wave height, wavelength, wave period, and wave direction data; The data includes long-term observation data and extreme sea condition data, covering different seasons and weather conditions;

[0029] Fluid characteristics: Collect data on the flow velocity, flow direction, and tidal changes related to the sea area to understand the potential hydrodynamic loads;

[0030] Meteorological conditions: Collect meteorological data on the wind speed and wind direction in this sea area, which will affect the formation and energy density of waves;

[0031] Device design parameters: Include the structural dimensions and shapes of the energy capture bodies;

[0032] PTO system parameters: Turbine specifications in the pneumatic system, parameters of each component in the hydraulic system, design rules of the rack and pinion in the mechanical system, etc.;

[0033] Material properties: According to the marine environment, select materials with anti-corrosion, fatigue resistance, and high-strength characteristics, and obtain relevant physical and chemical property data;

[0034] The models in the step S1.2 specifically include:

[0035] Hydrodynamic model: Establish a hydrodynamic model based on the obtained key data to simulate the interaction between waves and the device structure, including the calculation and identification of acting forces such as wave forces, buoyancy, and viscous forces;

[0036] Structural mechanics model: Establish a structural model through finite element method analysis to calculate the stress distribution, deformation, and fatigue of the device under wave action;

[0037] Energy conversion model: According to the energy conversion system and working principle of the device, establish an energy conversion model to simulate the power output characteristics of the energy conversion devices in the pneumatic system / hydraulic system / mechanical system.

[0038] In a preferred embodiment, the small-scale test in step S2.1 is mainly used to verify the wave force calculation of the numerical model, the structural motion response, and the basic energy conversion efficiency;

[0039] Parameter sensitivity analysis: By adjusting the key parameters of the model (such as floater size, PTO system parameters, etc.), study the influence of these parameters on the device performance, and compare with the results of the numerical model;

[0040] The mesoscale test in step S2.2: The test model at this stage is closer to the actual device, and is mainly used to test the stability of the device, the response of the PTO system, and the energy conversion performance under complex wave conditions;

[0041] 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;

[0042] The full-system integration test in the full-scale test of step S2.3: Test all subsystems of the prototype device, including the collaborative work of the structure, power transmission, control system, etc., to ensure the reliability and adaptability of the entire device.

[0043] In a preferred embodiment, step S3 specifically includes:

[0044] S3.1, Fluid-structure interaction simulation: Simulate the interaction between waves and the device structure, and analyze the structural force and fatigue conditions;

[0045] S3.2, Multiphase simulation: Conduct multiphase simulation for different wave energy devices. For example, in an oscillating water column (OWC) type device, simulate the gas-liquid two-phase flow in the air chamber, and predict the power output of the turbine and the pressure fluctuation in the air chamber;

[0046] S3.3, Multi-directional wave simulation: Introduce multi-directional waves and complex wave conditions for simulation, and consider the influence of waves in different directions and frequencies on the device performance.

[0047] In a preferred embodiment, step S4 specifically includes:

[0048] S4.1, Parameter calibration: Use the small-scale and mesoscale test data to calibrate the key parameters in the numerical model, such as the fluid resistance coefficient, turbine efficiency curve, etc.;

[0049] S4.2, Model correction: Introduce the full-scale test data into the numerical model, and through reverse optimization and sensitivity analysis, conduct multiple rounds of correction and optimization on the model to ensure that the numerical simulation results can better match the measured data.

[0050] In a preferred embodiment, in step S5, a historical data and environmental prediction are combined to establish an intelligent decision-making system, providing a basis for future operation optimization:

[0051] S5.1. Data-driven control optimization: Based on the real-time collected marine environment data and device operation data, optimize the operation control strategy of the device through machine learning algorithms, such as turbine speed adjustment, floater movement amplitude optimization, etc.;

[0052] 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 velocity, 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.

[0053] In a preferred embodiment, the evaluation criteria for energy conversion efficiency in step S6: Consider the energy conversion efficiency under different wave conditions, and adopt standardized efficiency indicators, such as Annual Energy Production Efficiency (AEPE); The annual average energy capture efficiency refers to the ratio of the mean value of the effective operation energy capture efficiency within one year to the theoretical maximum energy that can be obtained from waves during this 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, and it is one of the important indicators for measuring the performance of wave energy devices.

[0054] Evaluation criteria for structural reliability: 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 the accumulation of fatigue damage; In terms of structural stress monitoring, reasonably arrange monitoring points to ensure monitoring accuracy, set warning and alarm values, and analyze stress data; Comprehensively establish a reliability index system, regularly evaluate and maintain, and continuously improve the evaluation criteria and methods to ensure the reliability and safety of the wave energy device structure.

[0055] In a preferred embodiment, the evaluation criteria for environmental adaptability in step S6: Under calm sea conditions, consider energy conversion efficiency, operation stability, and control system response; In medium sea conditions, focus on evaluating the anti-wave impact ability, energy conversion stability, and reliability; In severe sea conditions, pay attention to structural strength and stability, safety protection mechanisms, and survivability; Under complex wave conditions, focus on examining wave direction adaptability, irregular wave response, and dynamic stability. Combining these aspects provides important references for the design, optimization, and operation of wave energy devices.

[0056] In a preferred embodiment, the evaluation criteria for the life-cycle cost-effectiveness in step S6 are as follows: The life-cycle evaluation criteria for wave energy devices include considering the costs of equipment, engineering construction, and planning and design for the initial investment, paying attention to the daily, fault repair, and upgrade and transformation costs in the maintenance costs, evaluating the energy benefits from the power generation, electricity sales revenue, and environmental value, and comprehensively applying cost-benefit analysis, technical performance evaluation, and environmental impact assessment to provide a scientific evaluation for the entire life cycle of the device.

[0057] Technical effects and advantages of the present invention:

[0058] The present invention combines multiple technical means such as multi-scale experiments, multi-physical field coupling simulations, data fusion calibration, and dynamic adaptability optimization to comprehensively improve the performance evaluation ability of wave energy devices; proposes more accurate and applicable evaluation criteria and processes for wave energy devices to ensure that numerical simulations and physical experiments better reflect the actual performance of the prototype; proposes a systematic experimental and digital simulation optimization process and a preliminary parameter acquisition and modeling method to improve the accuracy of numerical simulations and experiments of wave energy devices; proposes a more accurate and applicable evaluation criteria and process for wave energy devices to ensure that numerical simulations 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. Description of the drawings

[0059] Figure 1 It is the overall system flow chart of the present invention.

[0060] Figure 2 It is the module diagram for obtaining preliminary parameters and modeling of the present invention.

[0061] Figure 3 It is the module diagram for multi-scale experimental design of the present invention.

[0062] Figure 4 It is the module diagram for multi-medium multi-physical field coupling simulation of the present invention.

[0063] Figure 5 It is the module diagram for data fusion and multi-model calibration of the present invention.

[0064] Figure 6 It is the module diagram for dynamic adaptability optimization of the present invention.

[0065] Figure 7 It is the module diagram for establishing performance evaluation criteria of the present invention. Detailed implementation manners

[0066] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0067] The present invention provides an evaluation method for improving the accuracy of wave energy device tests and numerical models as Figure 1 shown, which can significantly improve the accuracy of wave energy device numerical simulation and tests; combining technical means such as multi-scale tests, multi-physical field coupling simulation, data fusion calibration, and dynamic adaptive optimization, a more accurate and applicable evaluation standard and process for wave energy devices are proposed to ensure that numerical simulation and physical tests can better reflect the actual performance of the prototype machine, providing strong technical support for the development and optimization of wave energy devices. The specific steps are as follows:

[0068] S1. Obtaining preliminary parameters and modeling (such as Figure 2 );

[0069] S1.1. Key parameters;

[0070] Wave characteristics: wave height, wavelength, wave period, wave direction, etc.; the data should include long-term observation data and extreme sea condition data, covering different seasons and weather conditions;

[0071] Fluid characteristics: collect data such as flow velocity, flow direction, and tidal changes related to the sea area to understand potential hydrodynamic loads;

[0072] Meteorological conditions: collect meteorological data such as wind speed and wind direction in this sea area, which will affect the formation and energy density of waves;

[0073] Device design parameters: including the structural size and shape of the energy capture body;

[0074] PTO system parameters: turbine specifications in the pneumatic system, parameters of each component in the hydraulic system, design rules of the rack and pinion in the mechanical system, etc.;

[0075] Material properties: select materials with anti-corrosion, fatigue resistance, and high-strength characteristics according to the marine environment, and obtain relevant physical and chemical property data;

[0076] S1.2. Preliminary modeling;

[0077] Fluid dynamics model: based on the obtained key data, establish a fluid dynamics model to simulate the interaction between waves and the device structure, including the calculation and identification of acting forces such as wave forces, buoyancy, and viscous forces;

[0078] Structural mechanics model: Establish a structural model through finite element method analysis to calculate the stress distribution, deformation, and fatigue of the device under wave action;

[0079] Energy conversion model: Based on the energy conversion system and working principle of the device, establish an energy conversion model to simulate the power output characteristics of energy conversion devices such as pneumatic systems / hydraulic systems / mechanical systems, etc.;

[0080] S2. Multi-scale experimental design (such as Figure 3 );

[0081] After the preliminary modeling is completed, design a series of multi-scale physical experiments to verify and calibrate the accuracy of the numerical model and further optimize the design parameters;

[0082] (1) Small-scale experiment: Conduct experiments using a scaled-down physical model in a laboratory wave flume / tank; This experiment is mainly used to verify the wave force calculation, structural motion response, and basic energy conversion efficiency of the numerical model;

[0083] Parameter sensitivity analysis: By adjusting the key parameters of the model (such as buoy size, PTO system parameters, etc.), study the influence of these parameters on the device performance and compare with the results of the numerical model;

[0084] (2) Medium-scale experiment: Conduct further verification experiments in an artificial marine environment or a medium-scale test site; The test model at this stage is closer to the actual device and is mainly used to test the stability of the device, the response of the PTO system, and the energy conversion performance under complex wave conditions;

[0085] 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;

[0086] (3) Full-scale experiment: Conduct full-scale prototype experiments in the predetermined installation sea area of the device; Through long-term observation, evaluate the comprehensive performance of the device in the real environment;

[0087] Full-system integration test: Test all subsystems of the prototype device, including the coordinated operation of the structure, power transmission, control system, etc., to ensure the reliability and adaptability of the entire device;

[0088] S3. Multi-medium multi-physical field coupling simulation (such as Figure 4 );

[0089] Build a multi-physical field coupling model, integrate factors such as structural dynamics, fluid dynamics, and electromagnetic fields, and comprehensively simulate the working process of the device;

[0090] (1)Fluid-structure interaction simulation: Simulate the interaction between waves and the device structure, and analyze the structural forces and fatigue conditions;

[0091] (2)Multi-medium simulation: Conduct multi-medium simulations for different wave energy devices. For example, in oscillating water column (OWC) devices, simulate the two-phase gas-liquid flow in the air chamber, and predict the power output of the turbine and the pressure fluctuations in the air chamber;

[0092] (3)Multi-directional wave simulation: Introduce multi-directional waves and complex wave conditions for simulation, and consider the influence of waves in different directions and frequencies on the device performance;

[0093] S4. Data fusion and multi-model calibration (such as Figure 5 );

[0094] Combine the test results at different scales with the numerical simulation data, and use the data fusion method to conduct multi-round model calibration to reduce the model prediction deviation;

[0095] Based on the full-scale test data, conduct the final calibration of the model. Use the reverse optimization algorithm to adjust the key parameters of the CFD model and the multi-medium multi-physics field coupling model to ensure the maximum consistency between the simulation results and the measured results;

[0096] (1)Parameter calibration: Use the small-scale and medium-scale test data to calibrate the key parameters in the numerical model, such as the fluid resistance coefficient, turbine efficiency curve, etc.;

[0097] (2)Model correction: Introduce the full-scale test data into the numerical model, and through reverse optimization and sensitivity analysis, conduct multi-round correction and optimization of the model to ensure that the numerical simulation results can better match the measured data;

[0098] S5. Dynamic adaptive optimization (such as Figure 6 );

[0099] Develop a dynamic adaptive optimization algorithm based on machine learning, and use real-time monitoring data to optimize the control strategy; According to the changes in the wave environment, dynamically adjust the key parameters such as the turbine speed and the movement amplitude of the float to improve the energy capture efficiency of the device during actual operation;

[0100] The dynamic adaptive optimization algorithm usually involves multiple control equations and formulas, which 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:

[0101] Turbine speed control equation: , where, is the speed of the turbine at time , is the wave power, is the set rotational speed, is the power change;

[0102] Float movement amplitude control equation: , where, is the movement amplitude of the float at time ; is the wave height, is the wave period, is the set movement amplitude;

[0103] Energy capture efficiency evaluation:

[0104] , where, is the energy capture efficiency at time ; is the power output by the device, is the wave power;

[0105] Wave power estimation: , where, is the density of water, is the acceleration due to gravity, is the wave energy propagation speed, is the characteristic width of the wave energy device;

[0106] Machine learning model optimization: , where, represents the model parameters, is the learning rate, is the loss function, is the gradient of the loss function with respect to the parameters;

[0107] Adaptive adjustment mechanism: , where, is the control input at time ; is the loss function, is the desired output, is the system model, is the system state;

[0108] Combined with historical data and environmental predictions, an intelligent decision-making system is established to provide a basis for future operation optimization;

[0109] (1) Data-driven control optimization: Based on real-time collected ocean environment data and device operation data, optimize the device's operation control strategy through machine learning algorithms, such as turbine rotational speed adjustment, float movement amplitude optimization, etc.;

[0110] (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;

[0111] S6. Establish performance evaluation criteria (e.g. Figure 7 );

[0112] 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;

[0113] 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;

[0114] (1) Energy conversion efficiency evaluation

[0115] Definition: Wave energy conversion efficiency is the rate at which a wave energy device converts wave energy into usable energy;

[0116] 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.

[0117] (2) Structural reliability evaluation

[0118] 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;

[0119] Evaluation criteria: In terms of fatigue analysis, accurately predict the fatigue life of key structural components under different sea conditions, set reasonable safety factors, and evaluate the accumulation of fatigue damage; in terms of structural stress monitoring, reasonably arrange monitoring points, ensure monitoring accuracy, set warning and alarm values, and analyze stress data; comprehensively establish a reliability index system, regularly evaluate and maintain it, and continuously improve the evaluation criteria and methods to ensure the reliability and safety of the wave energy device structure;

[0120] (3)Environmental adaptability evaluation

[0121] Definition: The environmental adaptability of a wave energy device refers to the ability of the device to maintain stable operation, efficiently convert wave energy into available energy under different sea conditions (such as extreme weather and complex wave conditions), and be able to withstand various impacts brought by environmental factors during long-term use without serious failures or significant performance degradation;

[0122] Evaluation criteria: Under calm sea conditions, consider energy conversion efficiency, operation stability, and control system response; in medium sea conditions, focus on evaluating the anti-wave impact ability, energy conversion stability, and reliability; in severe sea conditions, pay attention to structural strength and stability, safety protection mechanisms, and survival ability; under complex wave conditions, focus on examining wave direction adaptability, irregular wave response, and dynamic stability. Synthesize these aspects to provide important references for the design, optimization, and operation of wave energy devices;

[0123] (4)Life cycle cost-benefit evaluation

[0124] Definition: The life cycle evaluation of a wave energy device is a method to comprehensively consider its initial investment, maintenance costs, and energy benefits during the entire process from planning and design, manufacturing and installation, operation and use to decommissioning and disposal, in order to evaluate the sustainability and comprehensive performance of the device in terms of technology, economy, and environment; by analyzing and evaluating each stage of the life cycle, provide a basis for the optimal design, efficient operation, and reasonable decision-making of wave energy devices;

[0125] Evaluation criteria: The life cycle evaluation criteria of wave energy devices include considering the costs of equipment, engineering construction, and planning and design of the initial investment, paying attention to the daily, fault repair, and upgrade and transformation costs in the maintenance costs, evaluating the energy benefits from power generation, electricity sales revenue, and environmental value, and comprehensively using cost-benefit analysis, technical performance evaluation, and environmental impact assessment to provide a scientific evaluation for the entire life cycle of the device.

[0126] In summary:

[0127] Ensuring accuracy through the combination of multiple technologies: Other technologies may only focus on the impact of single or partial factors on the performance of wave energy devices, while the present invention combines technical means such as multi-scale experiments, multi-physical field coupling simulations, and data fusion calibration. Multi-scale experiments can comprehensively reflect the performance of the device at different scales, avoiding inaccurate evaluation of the device performance caused by the one-sidedness of single-scale analysis. Multi-physical field coupling simulations can accurately present the real working state of the device in a complex marine environment, being closer to the actual situation compared with other technologies. Data fusion calibration can effectively eliminate the differences between simulation and experimental data, improving the reliability of the device performance evaluation.

[0128] Dynamic adaptive optimization enables the wave energy device to make real-time adjustments and optimizations according to different marine environmental conditions and device operating states. For example, under different environmental changes such as wave conditions and sea current speeds, 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 criteria and processes can ensure that numerical simulations and physical experiments 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.

[0129] The comprehensive technical means provide systematic and all-round 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-physical field coupling simulations, then data fusion calibration is used to improve accuracy, and finally dynamic adaptive optimization is used to enhance applicability. This series of technical solutions cooperate with each other to form a complete technical system, providing strong technical support for the development of wave energy devices and contributing to the progress and application of wave energy technology.

[0130] Finally, the following points should be noted: First, in the description of this application, it should be noted that unless otherwise specified and limited, the terms "installation", "connection", and "coupling" should be understood in a broad sense. It can be a mechanical connection or an electrical connection, or the internal communication of two components. It can be directly connected. The terms "upper", "lower", "left", "right", etc. are only used to represent relative position relationships. When the absolute position of the object being described changes, the relative position relationship may change.

[0131] Second: In the attached drawings of the disclosed embodiments of the present invention, only the structures related to the disclosed embodiments are involved. For other structures, reference can be made to the general design. Without conflict, the same and different embodiments of the present invention can be combined with each other.

[0132] Finally, the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within 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; 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: 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 the energy conversion device of the pneumatic system / hydraulic system / mechanical system; 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; 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 device performance; S4, data fusion and multi-model calibration: combine the test results of different scales with the simulation data; perform the final calibration of the model based on the full-scale test data; 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 calibration: Introduce full-scale test data into the numerical model, and perform multiple rounds of calibration and optimization of 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: 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: According to the marine environment, select materials with corrosion resistance, fatigue resistance and high strength characteristics, and obtain relevant physical and chemical performance data.

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 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.

5. The evaluation method for improving the accuracy of wave energy device tests and numerical models according to claim 4, 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.

6. 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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