A design optimization method for an oscillating water column wave energy device

Abstract

The present invention discloses a design optimization method for an oscillating water column wave energy device, belonging to the technical field of ocean wave energy utilization. The specific steps are as follows: Step S1: Determine the modeling parameters of the device to be optimized, perform parametric modeling on the wave energy device, and obtain the parametric modeling of the wave energy device; Step S2: Select the air chamber width in the modeling parameters as the target optimization parameter, and set other modeling parameters as constants; Step S3: Input the parametric modeling into the fluid mechanics software, calculate the corresponding air pressure value P1(t) and gas flow velocity value V(t) of the wave energy device, and form a discrete point group; Step S4: Fit the discrete point group to construct a surrogate model; Step S5: Use the surrogate model for the final optimization design. By adopting the above method, the present invention can meet the optimization scenarios of multiple parameters and multiple objectives, and provide a new method for the design optimization of wave energy devices.

Description

Technical Field

[0001] The invention relates to the technical field of ocean wave energy utilization technology, and in particular to a design optimization method for an oscillating water column wave energy device. Background Art

[0002] Wave energy in the ocean has huge development potential, and wave energy has a wide range of application scenarios, such as forming an integrated system with floating breakwaters to reduce costs and increase efficiency, forming multi-energy complementarity with offshore wind power and offshore photovoltaics, and providing energy for islands and reefs far from land to promote regional economic development. The development and utilization technology of wave energy conversion devices is still in the research stage. How to improve the wave capture efficiency of the device and how to improve the production capacity of the device under complex sea conditions are the current research focuses. The oscillating water column wave energy device is widely used because of its simple structure and the contact between key components and seawater. The core of the device is the air chamber in contact with seawater and the turbine turbine for power generation. The water column in the air chamber produces reciprocating motion due to the periodic motion of the waves, thereby compressing the air in the air chamber, forming an airflow at the exhaust port on the top of the air chamber, driving the turbine device to rotate, and then driving the power generation device to generate electricity.

[0003] Optimizing the device's external structure and the number of air chambers can effectively improve the device's wave capture ability, but currently most methods simply optimize the air hole width, draft, and air chamber width manually. The design optimization efficiency is low, the optimization degree is low, and the effect is poor, which cannot meet existing needs. Summary of the invention

[0004] The purpose of the present invention is to provide a design optimization method for an oscillating water column wave energy device, which can utilize multi-platform linkage to provide a better method for the optimal design of the wave energy device.

[0005] To achieve the above object, the present invention provides a design optimization method for an oscillating water column wave energy device, comprising the following steps:

[0006] Step S1: determining the modeling parameters of the wave energy device to be optimized, the modeling parameters including five parameters, namely, the width of the air hole of the wave energy device, the wall thickness of the wave energy device, the draft of the wave energy device, the height of the internal air column of the wave energy device and the width of the air chamber of the wave energy device; performing parametric modeling on the wave energy device in a three-dimensional modeling software to obtain a parametric modeling of the wave energy device;

[0007] Step S2: Set any one of the modeling parameters as the target optimization parameter, and set the other four modeling parameters as constants to obtain u target optimization parameters of the wave energy device, defined as a u ;

[0008] Step S3: input the parameterized modeling in step S1 into the fluid mechanics software, and calculate the corresponding air pressure value P1(t) and gas flow rate value V(t) of the wave energy device according to different target optimization parameters to form a discrete point group;

[0009] Step S4: Use Gaussian process to fit the discrete point group and construct a proxy model;

[0010] Step S5: Use the proxy model to perform the final optimization design. When the convergence conditions are met, the optimized target optimization parameters are obtained.

[0011] Preferably, in step S2, the air chamber width is used as the target optimization parameter, and the air chamber width of the wave energy device is obtained by obtaining the air chamber widths at different height positions according to the same distance.

[0012] Preferably, in step S3, the process of fluid mechanics calculation is as follows:

[0013] In the parameterized model in step S1, a number of sample points are collected according to the uniform Latin hypercube principle. One sample point includes a set of modeling parameter values. The value of the target optimization parameter in each set of modeling parameters is different. The fluid mechanics parameters are set and calculations are performed on different sample points. The calculation results are the internal air pressure value P1(t) of the device and the gas flow rate value V(t) at the pore. The modeling parameters, air pressure values ​​and gas flow rate values ​​are integrated to form a discrete point group.

[0014] Preferably, the fluid mechanics parameter settings in step S3 include grid settings, physical boundary settings and operating condition parameter settings, wherein the grid settings Δx and Δy are 1 / 80 of the wavelength, Δz is 1 / 20 of the amplitude, the grid is encrypted on the object surface and the free liquid surface, the physical boundaries include the velocity inlet, the pressure outlet and the wall, and the operating condition parameters include the water depth, the incident wave height and the incident wave period.

[0015] Preferably, in step S4, the process of constructing the proxy model through the Gaussian process is as follows:

[0016] The distribution of a discrete point group is converted into a function distribution, which is determined by the mean function and the covariance function.

[0017] The surrogate model expression of the Gaussian process is as follows:

[0018] η(a1,a2,a3…a u )~N(μ(a),K(a i ,a j ));

[0019]

[0020]

[0021] In the above formula, η(a1,a2,a3…a u ) is the efficiency objective function, μ(a) is the mean function vector, K(a i ,a j ) is the covariance function matrix, σ and l are hyperparameters, a i and a j are two different sample point data on the Gaussian process continuous domain.

[0022] Preferably, in step S5, the process of optimizing the proxy model is as follows:

[0023] Taking power generation efficiency as the goal of the wave energy device, the optimal parameters are searched through genetic algorithm, and the process of calculating power generation efficiency is as follows:

[0024] The average power is calculated as follows:

[0025] Q1(t)=V(t)×A;

[0026]

[0027] In the above formula, represents the air volume flow rate of the air chamber per unit time, V(t) represents the gas flow rate value, A represents the cross-sectional area of ​​the pore, E represents the average power of the device, n represents the number of cycles for obtaining continuous data, T represents the wave period, P1(t) represents the pressure in the air chamber, and the calculation formula for the device efficiency is as follows:

[0028]

[0029] In the above formula, η represents the efficiency of the device, E is the average power of the device, E0 is the wave energy flux rate of the incident wave per unit width, y0 is the width of the device perpendicular to the incident wave direction, L is the incident wavelength, ρ is the density of water, g is the gravitational acceleration, H is the incident wave height, T is the wave period, k is the wave number, and h is the water depth of the tank;

[0030] The formula for the optimal solution of the target optimization parameters is as follows:

[0031] Max:η(a1,a2,a3…,a u );

[0032] In the above formula, each target optimization parameter is set with a corresponding value range.

[0033] Therefore, the present invention adopts the above-mentioned design optimization method of an oscillating water column wave energy device, which has the following advantages:

[0034] 1. The method of the present invention establishes a full-process optimization platform of "model parameters-CFD calculation-data post-processing-fitting proxy model-target optimization", which can meet multi-parameter and multi-target optimization scenarios and provide a new method for the design optimization of wave energy devices.

[0035] 2. The method of the present invention uses computational fluid dynamics to calculate sample points for high-fidelity simulation of liquid viscosity and gas dissipation, establishes a gas-liquid Euler two-phase numerical water tank, and uses Stokes fifth-order waves to simulate experimental wave conditions, which can meet the requirements of a high-precision hydrodynamic-pneumatic-PTO system coupling model.

[0036] 3. The method of the present invention establishes a proxy model to build a coupling bridge between the structural design parameters and the objective function. The proxy model is used for subsequent optimization design and global search for the optimal solution, which greatly improves the efficiency of the whole process simulation while ensuring the simulation accuracy.

[0037] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a flow chart of a design optimization method of an oscillating water column wave energy device of the present invention;

[0039] Figure 2 It is a front view of an experimental device in a design optimization method of an oscillating water column wave energy device of the present invention;

[0040] Figure 3 A bottom view of an experimental device in a design optimization method for an oscillating water column wave energy device of the present invention;

[0041] Figure 4 Schematic diagram of sample points in a design optimization method for an oscillating water column wave energy device of the present invention

[0042] Figure 5 A pressure comparison diagram before and after optimization in a design optimization method for an oscillating water column wave energy device of the present invention;

[0043] Figure 6 A flow velocity comparison diagram before and after optimization in a design optimization method for an oscillating water column wave energy device of the present invention;

[0044] Figure 7 This is an initial experimental device diagram in a design optimization method of an oscillating water column wave energy device of the present invention;

[0045] Figure 8 This is a diagram of an optimized experimental device in a design optimization method for an oscillating water column wave energy device of the present invention. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations. The specific model specifications need to be selected and determined according to the actual specifications of the device, and the specific selection calculation method adopts the existing technology in the field, so it will not be described in detail.

[0047] Example

[0048] like Figure 1-Figure 3 As shown, the present invention provides a design optimization method for an oscillating water column wave energy device, comprising the following steps:

[0049] Step S1: determining the modeling parameters of the wave energy device to be optimized, the modeling parameters including five parameters, namely, the width of the air hole of the wave energy device, the wall thickness of the wave energy device, the draft of the wave energy device, the height of the internal air column of the wave energy device and the width of the air chamber of the wave energy device; performing parametric modeling on the wave energy device in a three-dimensional modeling software to obtain a parametric modeling of the wave energy device;

[0050] Step S2: Set any one of the modeling parameters as the target optimization parameter, and set the other four modeling parameters as constants to obtain u target optimization parameters of the wave energy device, defined as a u ;

[0051] Step S3: Input the parametric modeling in step S1 into the fluid mechanics software, and calculate the corresponding air pressure value P1(t) and gas flow rate value V(t) of the wave energy device according to different target optimization parameters to form a discrete point group; the calculation process is as follows:

[0052] In the parameterized model in step S1, a number of sample points are collected according to the uniform Latin hypercube principle, and one sample point includes a set of modeling parameter values. The value of the target optimization parameter in each set of modeling parameters is different, and the fluid mechanics parameter setting is performed, and the parameter setting includes the setting of the grid, the setting of the physical boundary and the setting of the operating parameters, wherein the grid setting Δx and Δy are 1 / 80 of the wavelength, Δz is 1 / 20 of the amplitude, and the grid is encrypted on the surface of the object and the free liquid surface. The physical boundaries include the velocity inlet, the pressure outlet and the wall, and the operating parameters include the water depth, the incident wave height and the incident wave period; and then the different sample points are calculated, and the calculation results are the internal air pressure value P1(t) of the device and the gas flow rate value V(t) at the pore, and the modeling parameters, air pressure values ​​and gas flow rate values ​​are integrated to form a discrete point group.

[0053] Step S4: Use Gaussian process to fit the discrete point group, build a proxy model, and transform the distribution of the discrete point group into the distribution of the function, which is determined by the mean function and covariance function.

[0054] The proxy model expression is as follows:

[0055] η(a1,a2,a3…a u )~N(μ(a),K(a i ,a j ));

[0056]

[0057] In the above formula, η(a1,a2,a3…a u ) is the efficiency objective function, μ(a) is the mean function vector, K(a i ,a j ) is the covariance function matrix, σ and l are hyperparameters, a i and a j are two different sample point data on the Gaussian process continuous domain.

[0058] Step S5: Use the proxy model to perform the final optimization design. When the convergence conditions are met, the optimized target optimization parameters are obtained. The optimization design process is as follows:

[0059] Taking power generation efficiency as the goal of the wave energy device, the optimal parameters are searched through genetic algorithm, and the process of calculating power generation efficiency is as follows:

[0060] The average power is calculated as follows:

[0061] Q1(t)=V(t)×A;

[0062]

[0063] In the above formula, Q1(t) represents the air volume flow rate of the air chamber per unit time, V(t) represents the gas flow rate value, A represents the cross-sectional area of ​​the pore, E represents the average power of the device, n represents the number of cycles for obtaining continuous data, T represents the wave period, P1(t) represents the pressure in the air chamber, and the calculation formula for the device efficiency is as follows:

[0064]

[0065] In the above formula, η represents the efficiency of the device, E is the average power of the device, E0 is the wave energy flux rate of the incident wave per unit width, y0 is the width of the device perpendicular to the incident wave direction, L is the incident wavelength, ρ is the density of water, g is the gravitational acceleration, H is the incident wave height, T is the wave period, k is the wave number, and h is the water depth of the tank;

[0066] The formula for the optimal solution of the target optimization parameters is as follows:

[0067] Max:η(a1,a2,a3…,a u );

[0068] In the above formula, each target optimization parameter is set with a corresponding value range.

[0069] The specific process is as follows: By selecting any one of the modeling parameters as the target optimization parameter, in this embodiment, the air chamber width is set as the target optimization parameter, and the air chamber width of the wave energy device is obtained by obtaining the air chamber width at different height positions according to the same distance, and the obtained results are as follows: Figure 4 As shown in the figure, five air chamber widths are collected, namely a1, a2, a3, a4, and a5; the remaining items are obtained as needed when used as target optimization parameters. The power generation efficiency is calculated by parametric modeling and fluid mechanics, so as to screen out the solution of the target optimization parameters, thereby improving the calculation effect, and using the Gaussian model to form a corresponding proxy model. Using the proxy model calculation can effectively improve the efficiency of optimization. The calculation results are shown in Figure 5-6 As shown in the figure, the air pressure value P1(t) and the gas flow rate value V(t) of the wave energy device before and after optimization are calculated and compared, which shows that the optimized structure has better advantages. The structural diagram of the initial wave energy device is shown in Figure 7 As shown in the figure, the final optimized structure is as follows Figure 8 shown.

[0070] Therefore, the present invention adopts a design optimization method for an oscillating water column wave energy device. For the target optimization parameters of the selected wave energy device, a parameter building method can be used in combination with fluid mechanics for simulation. By summarizing the simulation results, the power generation efficiency is used as a convergence adjustment, and a proxy model is established using a Gaussian model. The selected target optimization parameters are optimized through the proxy model to obtain the output of the corresponding optimized design.

[0071] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A design optimization method for an oscillating water column wave energy device, characterized in that: The following steps are involved: Step S1: determining the modeling parameters of the wave energy device to be optimized, the modeling parameters including five parameters, namely, the width of the air hole of the wave energy device, the wall thickness of the wave energy device, the draft of the wave energy device, the height of the internal air column of the wave energy device and the width of the air chamber of the wave energy device; performing parametric modeling on the wave energy device in a three-dimensional modeling software to obtain a parametric modeling of the wave energy device; Step S2: Set any one of the modeling parameters as the target optimization parameter, and set the other four modeling parameters as constants to obtain u target optimization parameters of the wave energy device, defined as a u ; Step S3: Input the parametric modeling in step S1 into the fluid mechanics software, and calculate the corresponding air pressure value P1(t) and gas flow rate value V(t) of the wave energy device according to different target optimization parameters to form a discrete point group; the process of fluid mechanics calculation is as follows: In the parameterized model in step S1, a number of sample points are collected according to the uniform Latin hypercube principle. One sample point includes a set of modeling parameter values. The value of the target optimization parameter in each set of modeling parameters is different. The fluid mechanics parameters are set. The calculation results of different sample points are the internal air pressure value P1(t) of the device and the gas flow rate value V(t) at the pore. The modeling parameters, air pressure value and gas flow rate value are integrated to form a discrete point group. The parameter setting includes the setting of the grid, the setting of the physical boundary and the setting of the working condition parameters. The grid setting Δx and Δy are 1 / 80 of the wavelength, and Δz is 1 / 20 of the amplitude. The grid is encrypted on the surface of the object and the free liquid surface. The physical boundaries include the velocity inlet, the pressure outlet and the wall surface. The working condition parameters include the water depth, the incident wave height and the incident wave period. Step S4: Use Gaussian process to fit the discrete point group to construct a proxy model. The process of constructing the proxy model through Gaussian process is as follows: The distribution of a discrete point group is converted into a function distribution, which is determined by the mean function and the covariance function. The surrogate model expression of the Gaussian process is as follows: η(a1,a2,a3…a u )~N(μ(a),K(a i ,a j )); In the above formula, η(a1,a2,a3…a u ) is the efficiency objective function, μ(a) is the mean function vector, K(a i ,a j ) is the covariance function matrix, σ and l are hyperparameters, a i and a j are two different sample point data on the Gaussian process continuous domain; Step S5: Use the proxy model to perform the final optimization design. When the convergence conditions are met, the optimized target optimization parameters are obtained.

2. The design optimization method of an oscillating water column wave energy device according to claim 1, characterized in that: In step S2, the air chamber width is used as the target optimization parameter, and the air chamber width of the wave energy device is obtained by obtaining the air chamber width at different height positions according to the same distance.

3. The design optimization method of an oscillating water column wave energy device according to claim 1, characterized in that: In step S5, the process of optimizing the proxy model is as follows: Taking power generation efficiency as the goal of the wave energy device, the optimal parameters are searched through genetic algorithm, and the process of calculating power generation efficiency is as follows: The average power is calculated as follows: Q1(t)=V(t)×A; In the above formula, represents the air volume flow rate of the air chamber per unit time, V(t) represents the gas flow rate value, A represents the cross-sectional area of ​​the pore, E represents the average power of the device, n represents the number of cycles for obtaining continuous data, T represents the wave period, P1(t) represents the pressure in the air chamber, and the calculation formula for the device efficiency is as follows: In the above formula, η represents the efficiency of the device, E is the average power of the device, E0 is the wave energy flux rate of the incident wave per unit width, y0 is the width of the device perpendicular to the incident wave direction, L is the incident wavelength, ρ is the density of water, g is the gravitational acceleration, H is the incident wave height, T is the wave period, k is the wave number, and h is the water depth of the tank; The formula for the optimal solution of the target optimization parameters is as follows: <h2 style=";text-align:left;direction:ltr">Max:η(a1,a2,a3…,a<h2 style=";text-align:left;direction:ltr"> u <h2 style=";text-align:left;direction:ltr"> ); In the above formula, each target optimization parameter is set with a corresponding value range.

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