A method and system for stall delay modeling of a wind turbine blade airfoil

Abstract

The application relates to the technical field of wind turbine aerodynamic performance calculation, and provides a method and system for modeling stall delay of a wind turbine blade airfoil. The method comprises the following steps: based on the geometric parameters of a blade formed by the same airfoil, solving the continuity equation, Navier-Stokes equation and turbulence equation of a three-dimensional discrete flow region by using a flow control region to perform flow field calculation on the wind turbine under different incoming flow wind speeds, inputting relevant parameters into a lift / drag coefficient neural network model respectively, training, and obtaining a lift / drag coefficient neural network correction model respectively. According to the method, three-dimensional aerodynamic data of different airfoils under different working conditions such as different rotating speeds are obtained by correcting two-dimensional aerodynamic data, and the precision of wind turbine aerodynamic performance prediction is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of wind turbine aerodynamic performance calculation, and in particular to a method and system for modeling stall delay of a wind turbine blade airfoil. Background Art

[0002] Blade element momentum theory is a widely used method for predicting wind turbine power. For stalled wind turbines, this method often underestimates power at high wind speeds. This is primarily due to the influence of blade rotation on the boundary layer. Currently, this phenomenon is typically corrected using a stall delay model, which can be embedded in blade element momentum theory to accurately estimate power at high wind speeds.

[0003] Commonly used models, such as the Du-Selig stall delay correction model, are derived from three-dimensional boundary layer theory. Some simplifications include the assumption that the boundary layer on the blade surface is laminar, the velocity distribution outside the blade boundary layer is linear with the flow direction, and that delayed stall is solely dependent on the tip-to-tip speed ratio. However, these assumptions are not entirely consistent with reality. Therefore, when the final correction model is used over a wide range of inlet angles of attack, the correction values ​​exhibit significant localized errors compared to the measured values. Summary of the Invention

[0004] In view of this, the present invention provides a method for modeling stall delay of a wind turbine blade airfoil to solve the technical problem in the prior art that when the stall delay effect is corrected, the power at high wind speed cannot be accurately estimated.

[0005] In a first aspect, the present invention provides a method for modeling stall delay of a wind turbine blade airfoil, comprising:

[0006] S1. Based on the geometric parameters of blades composed of the same airfoil, the flow control region is used to solve the continuity equation, Navier-Stokes equation, and turbulence equation for the three-dimensional discrete flow region to calculate the flow field of the wind turbine under different incoming wind speeds, obtaining a first calculation result. The first calculation result, the two-dimensional lift coefficient, and the geometric parameters of the blade are input into a lift coefficient neural network model for training to obtain a lift coefficient neural network correction model;

[0007] S2. Based on the geometric parameters of the blades composed of the same airfoil, the flow control area is used to solve the continuity equation, Navier-Stokes equation and turbulence equation of the three-dimensional discrete flow area to calculate the flow field of the wind turbine under different incoming wind speeds, and a second calculation result is obtained. The second calculation result, the two-dimensional drag coefficient and the geometric parameters of the blades are input into the drag coefficient neural network model for training to obtain a drag coefficient neural network correction model.

[0008] Furthermore, the geometric parameters of the blade include: local solidity c / r and twist angle β;

[0009] The calculation result 1 includes: peripheral speed ratio, three-dimensional angle of attack and three-dimensional lift coefficient;

[0010] The second calculation result includes: peripheral speed ratio, three-dimensional attack angle and three-dimensional drag coefficient.

[0011] Furthermore, it also includes:

[0012] The peripheral speed ratio is calculated based on the incoming wind speed, the blade rotation speed and the local radius;

[0013] Based on solving the continuity equation of the three-dimensional discrete flow region, after the Navier-Stokes equation and the turbulence equation converge, the velocity and pressure are obtained. Based on integrating the pressure, the three-dimensional normal force and three-dimensional tangential force on the airfoil surface are obtained. Then, according to the dimensionless transformation of the three-dimensional normal force and three-dimensional tangential force, the corresponding three-dimensional normal force coefficient C is obtained respectively. n and the three-dimensional tangential force coefficient C t , calculate and obtain the three-dimensional angle of attack;

[0014] The velocity and pressure are obtained by solving the continuity equation, Navier-Stokes equation and turbulence equation of the two-dimensional airfoil flow area. Based on the integration of the pressure, the two-dimensional normal force and the two-dimensional tangential force of the airfoil surface are obtained. The corresponding two-dimensional normal force coefficient and two-dimensional tangential force coefficient are obtained by non-dimensionalizing the two-dimensional normal force and the two-dimensional tangential force, respectively. Then, the two-dimensional lift coefficient and the two-dimensional drag coefficient are calculated based on the two-dimensional normal force coefficient and the two-dimensional tangential force coefficient.

[0015] Furthermore, the three-dimensional angle of attack is calculated by the following steps, including:

[0016] (1) Let the initial value of the first axial induction factor be 0, and the initial value of the first circumferential induction factor be 0;

[0017] (2) calculating the inflow angle Φ based on the initial value of the first axial induction factor and the initial value of the first circumferential induction factor;

[0018] (3) calculating the tip and root loss coefficients according to the inflow angle, then calculating the second axial induction factor and the second circumferential factor, and determining whether the second axial induction factor is less than a first set threshold value,

[0019] The first set threshold value includes: 0.4;

[0020] If so, then according to the tip and root loss coefficients, the local radius, number of blades and normal force coefficient C n, calculate the third axial induction factor and the third circumferential induction factor;

[0021] If not, the three-dimensional tangential force coefficient C is obtained from fluid dynamics. t and the three-dimensional normal force coefficient C n , and based on the blade tip and blade root loss coefficients, calculating the third axial induction factor and the third circumferential induction factor;

[0022] (4) determining whether the absolute value of the difference between the second axial induction factor and the third axial induction factor is less than a second set threshold, and determining whether the absolute value of the difference between the second circumferential induction factor b and the third circumferential induction factor c is less than the second set threshold,

[0023] If so, the three-dimensional lift coefficient, three-dimensional drag coefficient and three-dimensional angle of attack are calculated based on the three-dimensional tangential force coefficient and the three-dimensional normal force coefficient;

[0024] If not, assign the third axial induction factor to the axial induction factor a; assign the third circumferential induction factor to the second circumferential induction factor b, and return to step (2) for the next round of iterative calculation until convergence.

[0025] Furthermore, the flow field calculation of the wind turbine under different incoming wind speeds includes:

[0026] S11. Set the blade flow calculation domain;

[0027] S12. Divide the flow calculation domain into a network so that each grid node corresponds to a flow control area;

[0028] S13. Set the boundary conditions of the flow calculation domain;

[0029] S14. Using the flow control region, the continuity equation, the Navier-Stokes equation, and the turbulence equation of the three-dimensional discrete flow region are solved until all equations obtain converged solutions to obtain the pressure.

[0030] Furthermore, the S11 includes:

[0031] Based on the symmetry of the rotation domain of a two-blade or three-blade wind turbine, a single-blade flow calculation domain is taken. The inlet of the flow calculation domain is 5R away from the wind rotor, the outlet of the flow calculation domain is 20R away from the wind rotor, and it extends 5R along the blade span direction. The flow calculation domain consists of two semi-cylinders and the wind rotor blade. The radius of the outer cylinder is 5R, the radius of the inner cylinder is the blade root radius, and the length of the two cylinders is 25R.

[0032] Furthermore, the S13 includes:

[0033] The flow calculation domain consists of blades and two semi-cylinders. The inlet velocity is given through the inlet of the flow control area, and different wind speeds are set from 5 m / s to 25 m / s. The outlet pressure of the flow control area is given, and the side is given a no-slip wall condition. The rotating domain is set to the rotation speed Ω under the corresponding wind speed, the rotation speed of the stationary domain is 0, and the data transfer at the interface between the rotating domain and the stationary domain is processed according to the mixed plane method.

[0034] Furthermore, obtaining a converged solution also includes obtaining a speed Turbulent kinetic energy k and turbulent frequency ω.

[0035] Furthermore, in said S14, after said pressure is obtained, it further comprises: inputting real-time data into said lift coefficient neural network correction model and said drag coefficient neural network correction model respectively to obtain a three-dimensional lift coefficient C l3D and the three-dimensional drag coefficient C D3D , and combined with the blade element momentum theory to calculate the aerodynamic performance of the wind turbine, the three-dimensional power and three-dimensional axial thrust are obtained.

[0036] The real-time data includes two-dimensional angle of attack, two-dimensional lift coefficient and two-dimensional drag coefficient, peripheral speed ratio under different working conditions, local solidity and torsion angle.

[0037] In a second aspect, the present invention provides a system for modeling stall delay of a wind turbine blade airfoil, comprising: a lift correction module and a drag correction module,

[0038] The lift correction module is used to calculate the flow field of the wind turbine under different incoming wind speeds based on the geometric parameters of the blades composed of the same airfoil, by solving the continuity equation, Navier-Stokes equation and turbulence equation of the three-dimensional discrete flow region in the discrete region, to obtain a calculation result 1, and input the calculation result 1, the two-dimensional lift coefficient and the geometric parameters of the blade into the lift coefficient neural network model for training to obtain a lift coefficient neural network correction model;

[0039] The drag correction module is used to calculate the flow field of the wind turbine under different incoming wind speeds based on the geometric parameters of the blades composed of the same airfoil, by solving the continuity equation, Navier-Stokes equation and turbulence equation of the three-dimensional discrete flow region, to obtain a second calculation result, and input the second calculation result, the two-dimensional drag coefficient and the geometric parameters of the blades into the drag coefficient neural network model for training to obtain a drag coefficient neural network correction model.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] 1. The present invention uses a neural network model to establish and train in MATLAB, making the training process simpler and more direct;

[0042] 2. The present invention obtains three-dimensional aerodynamic data of different airfoils under different speeds and other working conditions by correcting two-dimensional aerodynamic data, thereby improving the accuracy of wind turbine aerodynamic performance prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the present invention, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0044] Figure 1 This is a flow chart of a method for modeling stall delay of a wind turbine blade airfoil provided by an embodiment of the present invention;

[0045] Figure 2 is a calculation flow chart of the blade element momentum theory provided by an embodiment of the present invention;

[0046] Figure 3 This is a calculation flow chart of the modified model embedded momentum blade element theory provided by an embodiment of the present invention;

[0047] Figure 4 This is a flow chart for extracting the three-dimensional angle of attack and the three-dimensional lift and drag coefficient when the wind rotor rotates, provided by an embodiment of the present invention;

[0048] Figure 5 This is a block diagram of a system for modeling stall delay of a wind turbine blade airfoil provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In the following description, specific details such as particular system structures and techniques are provided for purposes of illustration, not limitation, to facilitate a thorough understanding of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.

[0050] A method for modeling stall delay of a wind turbine blade airfoil according to the present invention will be described in detail below with reference to the accompanying drawings.

[0051] Figure 1 This is a flow chart of a method for modeling stall delay of a wind turbine blade airfoil provided by an embodiment of the present invention.

[0052] like Figure 1 As shown, the method includes:

[0053] S1. Based on the geometric parameters of blades composed of the same airfoil, the flow control region is used to solve the continuity equation, Navier-Stokes equation, and turbulence equation for the three-dimensional discrete flow region to calculate the flow field of the wind turbine under different incoming wind speeds, obtaining a first calculation result. The first calculation result, the two-dimensional lift coefficient, and the geometric parameters of the blade are input into a lift coefficient neural network model for training to obtain a lift coefficient neural network correction model;

[0054] S2. Based on the geometric parameters of the blades composed of the same airfoil, the flow control area is used to solve the continuity equation, Navier-Stokes equation and turbulence equation of the three-dimensional discrete flow area to calculate the flow field of the wind turbine under different incoming wind speeds, and a second calculation result is obtained. The second calculation result, the two-dimensional drag coefficient and the geometric parameters of the blades are input into the drag coefficient neural network model for training to obtain a drag coefficient neural network correction model.

[0055] Among them, the lift coefficient neural network correction model and the drag coefficient neural network correction model are both airfoil stall delay models. Currently, airfoil stall delay models also include: Snel. model and Du-Selig model.

[0056] The training of the neural network is carried out in MATLAB. The present invention adopts MATLAB as the training environment, making the training process simpler and easier.

[0057] The lift coefficient neural network correction model and the drag coefficient neural network correction model jointly correct the airfoil stall.

[0058] Among them, CFD stands for Computational Fluid Dynamics.

[0059] In the inner span of a wind turbine blade, the airfoil is usually thicker and often operates at a larger angle of attack, making it prone to stall. The critical angle of attack at which stall occurs in a two-dimensional airfoil is smaller than the critical angle of attack at which stall occurs in a rotating airfoil. This phenomenon is called stall delay. This is reflected in the aerodynamic performance of the airfoil. The two-dimensional lift coefficient and two-dimensional drag coefficient corresponding to the same angle of attack are significantly different from the three-dimensional lift coefficient and three-dimensional drag coefficient under three-dimensional conditions. Usually, an airfoil stall delay model is required to make corrections so that the three-dimensional lift coefficient and three-dimensional drag coefficient can be obtained based on the two-dimensional lift coefficient and the two-dimensional drag coefficient. Based on the above analysis, the three-dimensional lift coefficient is related to the local solidity, the local peripheral speed ratio, the three-dimensional angle of attack, the twist angle, and the two-dimensional lift coefficient, while the three-dimensional drag coefficient is related to the local solidity, the local peripheral speed ratio, the angle of attack, the twist angle, and the two-dimensional drag coefficient.

[0060] Figure 2This is a calculation flow chart of the blade element momentum theory provided by an embodiment of the present invention.

[0061] Figure 3 This is a calculation flow chart of the modified model embedded momentum blade element theory provided by an embodiment of the present invention.

[0062] The above airfoil stall delay correction model is combined with the blade element momentum theory to predict the aerodynamic performance of wind turbines, such as power and axial thrust. The calculation process of the blade element momentum theory is as follows: Figure 2 As shown, the geometric parameters of the input blade include the distribution of c / r and the twist angle β along the span direction, the blade pitch angle ε, and the aerodynamic performance parameters of the airfoil include the angle of attack, the three-dimensional lift coefficient and the three-dimensional drag coefficient, and finally the power can be output.

[0063] The local solidity includes: chord length and local radius.

[0064] The geometric parameters of the blade include: local solidity and twist angle;

[0065] The calculation result 1 includes: peripheral speed ratio, three-dimensional angle of attack and three-dimensional lift coefficient;

[0066] The second calculation result includes: peripheral speed ratio, three-dimensional attack angle and three-dimensional drag coefficient.

[0067] Among them, the local solidity and torsion angle are known.

[0068] The peripheral speed ratio is calculated based on the incoming wind speed, the blade rotation speed and the local radius;

[0069] Among them, the incoming wind speed, blade rotation speed and local radius are known.

[0070] Figure 4 This is a flow chart for extracting the three-dimensional angle of attack and the three-dimensional lift-drag coefficient when the wind rotor rotates, provided by an embodiment of the present invention.

[0071] Based on solving the continuity equation of the three-dimensional discrete flow area, after the Navier-Stokes equation and the turbulence equation are converged, the velocity and pressure are obtained. Based on integrating the pressure, the three-dimensional normal force and three-dimensional tangential force on the airfoil surface are obtained. Then, based on the dimensionless transformation of the three-dimensional normal force and three-dimensional tangential force, the corresponding three-dimensional normal force coefficient and three-dimensional tangential force coefficient are respectively obtained, and the three-dimensional angle of attack and its corresponding three-dimensional lift coefficient and three-dimensional drag coefficient are calculated.

[0072] The three-dimensional angle of attack is calculated by the following steps, including:

[0073] (1) Let the initial value of the first axial induction factor be 0, and the initial value of the first circumferential induction factor be 0;

[0074] (2) calculating the inflow angle based on the initial value of the first axial induction factor and the initial value of the first circumferential induction factor;

[0075] (3) calculating the tip and root loss coefficients according to the inflow angle, then calculating the second axial induction factor a and the second circumferential factor b, and determining whether the second axial induction factor a is less than a first set threshold value,

[0076] Among them, the first set threshold includes: 0.4.

[0077] If so, then according to the tip and root loss coefficients, the local radius, number of blades and normal force coefficient C n , calculate the third axial induction factor a new and third circumferential induction factor b new ;

[0078] If not, the three-dimensional tangential force coefficient C is obtained from fluid dynamics. t and the three-dimensional normal force coefficient C n , and based on the blade tip and blade root loss coefficients, the third axial induction factor a is calculated new and the third circumferential induction factor b new ;

[0079] (4) Determine the difference between the second axial induction factor a and the third axial induction factor a new Whether the absolute value of the difference between the second circumferential induction factor b and the third circumferential induction factor b is less than the second set threshold, it is judged that the second circumferential induction factor b and the third circumferential induction factor b are new whether the absolute value of the difference is less than the second set threshold,

[0080] If so, then according to the three-dimensional tangential force coefficient C t , three-dimensional normal force coefficient C n , calculate and obtain the three-dimensional lift coefficient C l3d , three-dimensional resistance coefficient C d2d and three-dimensional angle of attack;

[0081] If not, assign the third axial induction factor to the axial induction factor a; assign the third circumferential induction factor to the second circumferential induction factor b, and return to step (2) for the next round of iterative calculation until convergence.

[0082] The flow field calculation of the wind turbine under different incoming wind speeds includes:

[0083] S11. Set the blade flow calculation domain;

[0084] Based on the symmetry of the rotation domain of a two-blade or three-blade wind turbine, a single-blade flow calculation domain is taken. The inlet of the flow calculation domain is 5R away from the wind rotor, the outlet of the flow calculation domain is 20R away from the wind rotor, and it extends 5R along the blade span direction. The flow calculation domain consists of two semi-cylinders and the wind rotor blade. The radius of the outer cylinder is 5R, the radius of the inner cylinder is the blade root radius, and the length of the two cylinders is 25R.

[0085] S12. Divide the flow calculation domain into a network so that each grid node corresponds to a flow control area;

[0086] The flow calculation domain is divided into grids, including using structured grids or unstructured grids.

[0087] S13. Set the boundary conditions of the flow calculation domain;

[0088] The S13 includes:

[0089] The flow calculation domain consists of blades and two semi-cylinders. The inlet velocity is given through the inlet of the flow control area, and different wind speeds are set from 5 m / s to 25 m / s. The outlet pressure of the flow control area is given, and the side is given a no-slip wall condition. The rotating domain is set to the rotation speed Ω under the corresponding wind speed, the rotation speed of the stationary domain is 0, and the data transfer at the interface between the rotating domain and the stationary domain is processed according to the mixed plane method.

[0090] Among them, the inlet is the velocity inlet, which is perpendicular to the wind wheel rotation plane, the outlet is set as the pressure outlet, and the external field cylindrical surface is set as the free slip wall boundary.

[0091] S14. Using the flow control region, the continuity equation, the Navier-Stokes equation, and the turbulence equation of the three-dimensional discrete flow region are solved until all equations obtain converged solutions to obtain the pressure.

[0092] The obtaining of the converged solution also includes obtaining the speed Turbulent kinetic energy k and turbulent frequency ω.

[0093] The S14 includes:

[0094] The flow control region is used to solve the continuity equation of the three-dimensional discrete flow region:

[0095]

[0096] Navier-Stokes equations: (x, y, z directions)

[0097]

[0098] Where ρ is the density, U j is the velocity component, xi is the coordinate axis, p is the pressure, τ is the molecular stress tensor, S M is the source term (the source terms in the rotating domain and the stationary domain are different).

[0099] Turbulence equations:

[0100]

[0101]

[0102]

[0103] Where k and ω represent the turbulent kinetic energy and turbulent frequency respectively, μ is the dynamic viscosity coefficient, and μ t is the turbulent viscosity coefficient, P k represents the turbulence production term, P kb represents the buoyancy production term, P kω represents the additional buoyancy production term, β′, α, β, σk, and σω are all empirical constants.

[0104] The continuity equations, Navier-Stoker equations, and turbulence equations for the three-dimensional discrete flow domain are discretized within the flow control domain to produce a set of difference equations. After solving these difference equations, the numerical solutions obtained do not fully satisfy the difference equations. The difference between the left and right sides of each equation is called the residual of the flow control domain. The residuals of the flow control domain are accumulated across the entire solution domain to obtain the residual of the corresponding equation. When the residuals of all the above equations are less than a set threshold of three, the entire solution process is considered converged, and the pressure is obtained.

[0105] The third set threshold value includes: 1e-4.

[0106] The two-dimensional lift coefficient and the two-dimensional drag coefficient are obtained by solving the continuity equation of the two-dimensional discrete flow region, the Navier-Stokes equation and the turbulence equation, and obtaining the pressure. Based on the pressure, the airfoil surface pressure is obtained. The normal force and the tangential force are obtained by integrating the airfoil surface pressure. Then, the corresponding normal force coefficient C is obtained by non-dimensionalizing the normal force and the tangential force. n and the tangential force coefficient C t , and then according to the normal force coefficient C n and the tangential force coefficient C t Calculated.

[0107] C L，2D =C n cosΦ+C t sinΦ

[0108] C D，2D =C n sinΦ-Ct cosΦ

[0109] Among them, C L，2D represents the two-dimensional lift coefficient, C L，2D represents the two-dimensional drag coefficient.

[0110] The two-dimensional lift coefficient and the two-dimensional drag coefficient are obtained by using CFD or by using a wind tunnel experiment, by placing the airfoil in a wind tunnel and measuring the lift and drag.

[0111] The two-dimensional attack angle is obtained based on the inflow angle Φ.

[0112] In the S14, after obtaining the pressure, the method further includes: inputting the real-time data into the lift coefficient neural network correction model and the drag coefficient neural network correction model respectively to obtain a three-dimensional lift coefficient and a three-dimensional drag coefficient, and calculating the aerodynamic performance of the wind turbine in combination with the blade element momentum theory to obtain a three-dimensional power and a three-dimensional axial thrust.

[0113] The real-time data includes two-dimensional angle of attack, two-dimensional lift coefficient and two-dimensional drag coefficient, peripheral speed ratio under different working conditions, local solidity and torsion angle.

[0114] By correcting the two-dimensional aerodynamic data, three-dimensional aerodynamic data of different airfoils under different speeds and other working conditions are obtained, which improves the accuracy of wind turbine aerodynamic performance prediction.

[0115] Among them, blade element momentum theory is one of the main methods for blade design and aerodynamic performance calculation of wind turbines.

[0116] The present invention uses a neural network model to establish and train in MATLAB, making the training process simpler and more direct; by correcting two-dimensional aerodynamic data, three-dimensional aerodynamic data of different airfoils under different speeds and other working conditions are obtained, thereby improving the accuracy of wind turbine aerodynamic performance prediction.

[0117] Figure 5 This is a block diagram of a system for modeling stall delay of a wind turbine blade airfoil provided by an embodiment of the present invention.

[0118] Based on the same concept, the present invention also provides a system for modeling stall delay of wind turbine blade airfoil, comprising: a lift correction module and a drag correction module,

[0119] The lift correction module is used to calculate the flow field of the wind turbine under different incoming wind speeds by solving the continuity equation, Navier-Stokes equation and turbulence equation of the three-dimensional discrete flow region based on the geometric parameters of the blades composed of the same airfoil, using the flow control region to obtain a first calculation result, input the first calculation result, the two-dimensional lift coefficient and the geometric parameters of the blades into a lift coefficient neural network model for training to obtain a lift coefficient neural network correction model;

[0120] The drag correction module is used to calculate the flow field of the wind turbine under different incoming wind speeds based on the geometric parameters of the blades composed of the same airfoil, using the flow control area to solve the continuity equation, Navier-Stokes equation and turbulence equation of the three-dimensional discrete flow area, and input the calculated two-dimensional drag coefficient and the geometric parameters of the blades into the drag coefficient neural network model for training to obtain the drag coefficient neural network correction model.

[0121] All of the above optional technical solutions can be combined in any way to form optional embodiments of the present application, and will not be described in detail here.

[0122] It should be understood that the order of execution of the steps in the above embodiments does not necessarily mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0123] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents. However, these modifications or replacements do not deviate from the essence of the corresponding technical solutions of the embodiments of the present invention.

[0124] The spirit and scope of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for modeling stall delay of a wind turbine blade airfoil, characterized in that: include: S1. Based on the geometric parameters of blades composed of the same airfoil, the flow control region is used to solve the continuity equation, Navier-Stokes equation, and turbulence equation for the three-dimensional discrete flow region to calculate the flow field of the wind turbine under different incoming wind speeds, obtaining a first calculation result. The first calculation result, the two-dimensional lift coefficient, and the geometric parameters of the blade are input into a lift coefficient neural network model for training to obtain a lift coefficient neural network correction model; S2. Based on the geometric parameters of the blades formed by the same airfoil, the flow control region is used to solve the continuity equation, Navier-Stokes equation, and turbulence equation for the three-dimensional discrete flow region to calculate the flow field of the wind turbine under different incoming wind velocities, obtaining a second calculation result. The second calculation result, the two-dimensional drag coefficient, and the geometric parameters of the blade are input into a drag coefficient neural network model for training to obtain a drag coefficient neural network correction model. The geometric parameters of the blade include: local solidity c and twist angle β; The calculation result 1 includes: peripheral speed ratio, three-dimensional angle of attack and three-dimensional lift coefficient; The second calculation result includes: peripheral speed ratio, three-dimensional attack angle and three-dimensional drag coefficient; Also includes: The peripheral speed ratio is calculated based on the incoming wind speed, the blade rotation speed and the local radius; Based on solving the continuity equation of the three-dimensional discrete flow region, after the Navier-Stokes equation and the turbulence equation converge, the velocity and pressure are obtained. Based on integrating the pressure, the three-dimensional normal force and three-dimensional tangential force on the airfoil surface are obtained. Then, according to the dimensionless transformation of the three-dimensional normal force and three-dimensional tangential force, the corresponding three-dimensional normal force coefficient C is obtained respectively. n and the three-dimensional tangential force coefficient C t , calculate and obtain the three-dimensional angle of attack; The velocity and pressure are obtained by solving the continuity equation, Navier-Stokes equation and turbulence equation of the two-dimensional airfoil flow area. Based on the integration of the pressure, the two-dimensional normal force and the two-dimensional tangential force of the airfoil surface are obtained. The corresponding two-dimensional normal force coefficient and two-dimensional tangential force coefficient are obtained by non-dimensionalizing the two-dimensional normal force and the two-dimensional tangential force, respectively. Then, the two-dimensional lift coefficient and the two-dimensional drag coefficient are calculated based on the two-dimensional normal force coefficient and the two-dimensional tangential force coefficient.

2. The method according to claim 1, characterized in that The three-dimensional angle of attack is calculated by the following steps, including: (1) Let the initial value of the first axial induction factor be 0, and the initial value of the first circumferential induction factor be 0; (2) calculating an inflow angle Φ based on the initial value of the first axial induction factor and the initial value of the first circumferential induction factor; (3) calculating the tip and root loss coefficients according to the inflow angle, then calculating the second axial induction factor and the second circumferential induction factor, and determining whether the second axial induction factor is less than a first set threshold value, The first set threshold value includes: 0.4; If so, then according to the tip and root loss coefficients, the local radius, number of blades and normal force coefficient C n , calculate the third axial induction factor and the third circumferential induction factor; If not, the three-dimensional tangential force coefficient C is obtained from fluid dynamics. t and the three-dimensional normal force coefficient C n , and based on the blade tip and blade root loss coefficients, calculating the third axial induction factor and the third circumferential induction factor; (4) determining whether the absolute value of the difference between the second axial induction factor and the third axial induction factor is less than a second set threshold, and determining whether the absolute value of the difference between the second circumferential induction factor and the third circumferential induction factor is less than the second set threshold, If so, the three-dimensional lift coefficient, three-dimensional drag coefficient and three-dimensional angle of attack are calculated based on the three-dimensional tangential force coefficient and the three-dimensional normal force coefficient; If not, assign the third axial induction factor to the second axial induction factor; assign the third circumferential induction factor to the second circumferential induction factor, and return to step (2) for the next round of iterative calculation until convergence.

3. The method according to claim 1, characterized in that The flow field calculation of the wind turbine under different incoming wind speeds includes: S11. Set the blade flow calculation domain; S12. Divide the flow calculation domain into a network so that each grid node corresponds to a flow control area; S13. Set the boundary conditions of the flow calculation domain; S14. Using the flow control region, the continuity equation, the Navier-Stokes equation, and the turbulence equation of the three-dimensional discrete flow region are solved until all equations obtain converged solutions to obtain the pressure.

4. The method according to claim 3, characterized in that The S11 includes: Based on the symmetry of the rotation domain of a two-blade or three-blade wind turbine, a single-blade flow calculation domain is taken. The inlet of the flow calculation domain is 5R away from the wind rotor, the outlet of the flow calculation domain is 20R away from the wind rotor, and it extends 5R along the blade span direction. The flow calculation domain consists of two semi-cylinders and the wind rotor blade. The radius of the outer cylinder is 5R, the radius of the inner cylinder is the blade root radius, and the length of the two cylinders is 25R.

5. The method according to claim 4, characterized in that The S13 includes: The flow calculation domain consists of blades and two semi-cylinders. The inlet velocity is given through the inlet of the flow control area, and different wind speeds are set from 5 m / s to 25 m / s. The outlet pressure of the flow control area is given, and the side is given a no-slip wall condition. The rotating domain is set to the rotation speed Ω under the corresponding wind speed, the rotation speed of the stationary domain is 0, and the data transfer at the interface between the rotating domain and the stationary domain is processed according to the mixed plane method.

6. The method according to claim 3, characterized in that In the step S14, obtaining a converged solution also includes obtaining a speed Turbulent kinetic energy k and turbulent frequency ω.

7. The method according to claim 3, characterized in that In the step S14, after obtaining the pressure, the method further includes: inputting the real-time data into the lift coefficient neural network correction model and the drag coefficient neural network correction model respectively to obtain the three-dimensional lift coefficient C l3D and the three-dimensional drag coefficient C D3D , and combined with the blade element momentum theory to calculate the aerodynamic performance of the wind turbine, the three-dimensional power and three-dimensional axial thrust are obtained. The real-time data includes two-dimensional angle of attack, two-dimensional lift coefficient and two-dimensional drag coefficient, peripheral speed ratio under different working conditions, local solidity and torsion angle.

8. A system for modeling stall delay of wind turbine blade airfoils, characterized in that: include: Lift correction module and drag correction module, The lift correction module is used to calculate the flow field of the wind turbine under different incoming wind speeds based on the geometric parameters of the blades composed of the same airfoil, by solving the continuity equation, Navier-Stokes equation and turbulence equation of the three-dimensional discrete flow region in the discrete region, to obtain a calculation result 1, and input the calculation result 1, the two-dimensional lift coefficient and the geometric parameters of the blade into the lift coefficient neural network model for training to obtain a lift coefficient neural network correction model; The drag correction module is configured to calculate the flow field of the wind turbine under different incoming wind speeds based on the geometric parameters of the blades formed by the same airfoil, by solving the continuity equation, Navier-Stokes equation, and turbulence equation of the three-dimensional discrete flow region, to obtain a second calculation result, and input the second calculation result, the two-dimensional drag coefficient, and the geometric parameters of the blades into a drag coefficient neural network model for training to obtain a drag coefficient neural network correction model; The geometric parameters of the blade include: local solidity c and twist angle β; The calculation result 1 includes: peripheral speed ratio, three-dimensional angle of attack and three-dimensional lift coefficient; The second calculation result includes: peripheral speed ratio, three-dimensional attack angle and three-dimensional drag coefficient; Also includes: The peripheral speed ratio is calculated based on the incoming wind speed, the blade rotation speed and the local radius; Based on solving the continuity equation of the three-dimensional discrete flow region, after the Navier-Stokes equation and the turbulence equation converge, the velocity and pressure are obtained. Based on integrating the pressure, the three-dimensional normal force and three-dimensional tangential force on the airfoil surface are obtained. Then, according to the dimensionless transformation of the three-dimensional normal force and three-dimensional tangential force, the corresponding three-dimensional normal force coefficient C is obtained respectively. n and the three-dimensional tangential force coefficient C t , calculate and obtain the three-dimensional angle of attack; The velocity and pressure are obtained by solving the continuity equation, Navier-Stokes equation and turbulence equation of the two-dimensional airfoil flow area. Based on the integration of the pressure, the two-dimensional normal force and the two-dimensional tangential force of the airfoil surface are obtained. The corresponding two-dimensional normal force coefficient and two-dimensional tangential force coefficient are obtained by non-dimensionalizing the two-dimensional normal force and the two-dimensional tangential force, respectively. Then, the two-dimensional lift coefficient and the two-dimensional drag coefficient are calculated based on the two-dimensional normal force coefficient and the two-dimensional tangential force coefficient.

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