Multi-working-condition performance matching scaling fan blade design method based on power mapping

Abstract

The invention discloses a multi-working-condition performance matching scaling fan blade design method based on power mapping, and relates to the field of fan blade design. Determining a Reynolds number under the scale of the scaled fan model to establish a low Reynolds number airfoil profile set, and performing performance evaluation on airfoils in the low Reynolds number airfoil profile set to select an optimal airfoil profile; setting thrust matching objective functions under various working conditions based on the selected optimal airfoil to perform parameter optimization, redefining a matching mechanism of the power performance of the scaled fan and the prototype to set a power mapping matching objective function, and integrating all the objective functions to generate a comprehensive optimization objective function to perform scaled fan blade parameter optimization. Blade parameter optimization is carried out by adopting an optimization algorithm with a new search position self-verification process; according to the multi-working-condition performance matching scaled fan blade design method based on power mapping, the power mapping mode and the multi-working-condition thrust performance matching mode are combined, and complete reproduction of scaled fan model aerodynamic performance on a standard prototype fan is achieved.

Description

Technical Field

[0001] This invention relates to the field of wind turbine blade design, and in particular to a method for designing scaled-down wind turbine blades based on power mapping and multi-condition performance matching. Background Technology

[0002] Scaled-down wind turbine models are primarily used for trial operation in engineering environments and pre-production unit testing, as well as for theoretical research and verification in laboratory environments. Current scaled-down wind turbine model design relies on the Froude similarity criterion, which inevitably leads to a reduction in the Reynolds number, resulting in aerodynamic performance of the scaled-down model blades that is far below design targets. Currently, the field mainly addresses this issue using three methods: replacing the Froude similarity criterion with a velocity scaling similarity criterion, using other devices to replace the model blades to generate the required aerodynamic forces, and redesigning the model blades. The first method disrupts the dynamic similarity between the scaled-down wind turbine model and the prototype during the design process, making it more suitable for situations with limited experimental wind generation conditions. The second method is complex and can only reproduce axial forces (i.e., rotor thrust), making it more suitable for situations with highly specific experimental objectives. The third method has a clear technical route and can achieve better results in terms of operating conditions and load distribution, thus having wider applicability.

[0003] Redesigning scaled-down wind turbine model blades involves two parts: airfoil replacement and parameter optimization. Current methods, for the first part, only select a low Reynolds number airfoil to replace the initial blade airfoil based on others' conclusions or experience, without sufficient comparison and selection of the replacement airfoil. For the second part, they only focus on the total thrust performance of the rotor or the thrust distribution of the blades under a single operating condition, without considering the actual operation of the prototype under multiple operating conditions, and ignore the role of the power coefficient in scaled-down blades of floating wind turbines. The simplification of these two processes in the redesign of model blades inevitably leads to a shift in their aerodynamic performance, failing to fully meet the fundamental application requirements of scaled-down wind turbine models in the performance similarity mapping of prototypes. Summary of the Invention

[0004] The purpose of this invention is to provide a multi-condition performance matching design method for scaled-down wind turbine blades based on power mapping. By combining power mapping and multi-condition thrust performance matching, the aerodynamic performance of the scaled-down wind turbine model is completely reproduced to that of the prototype wind turbine. This effectively achieves the airfoil optimization in the redesign of the scaled-down wind turbine model blades, effectively offsets the influence of the Reynolds number effect, and realizes parameter optimization of the scaled-down wind turbine model blades under multiple conditions and with multiple performance characteristics.

[0005] To achieve the above objectives, this invention provides a multi-condition performance matching scaled-down wind turbine blade design method based on power mapping, comprising the following steps: S1. Determine the Reynolds number at the scale of the scaled-down wind turbine model and establish a set of low Reynolds number airfoils; S2. Evaluate the performance of different airfoils in the low Reynolds number airfoil set of S1 and select the optimal airfoil; S3. Based on the optimal airfoil selected in S2, set the thrust matching objective function under various working conditions to optimize parameters; S4. Based on the optimal airfoil selected in S2, redefine the matching mechanism between the power performance of the scaled-down wind turbine model and the prototype, and set the power mapping matching objective function. S5, the thrust matching objective function of S3 and the power mapping matching objective function of S4 are combined to generate a comprehensive optimization objective function to optimize the parameters of the model blades of the scaled-down wind turbine model; S6. The optimal blade data is obtained by using the GOA optimization algorithm with a new search position self-verification process to optimize the blade parameters.

[0006] Preferably, in S1, the lift coefficient of each airfoil in the low Reynolds number airfoil cluster is calculated. The specific formula is as follows: ; in, Lift is the resultant aerodynamic force perpendicular to the direction of the incoming airflow when an airfoil moves through the air. It represents the kinetic energy possessed per unit volume of air. air density, The velocity of the airfoil relative to the air. The planar area of ​​the airfoil is the projected area seen when the airfoil is viewed directly from the direction of the incoming flow. drag coefficient The specific formula is as follows: ; in, Resistance is the resultant aerodynamic force that acts in the opposite direction to the incoming airflow when an object moves through the air.

[0007] Preferably, the specific process of S2 is as follows: S21. The specific calculation formula for the peak value of each airfoil based on the maximum lift coefficient is as follows: ; in, Indicates the current airfoil The score of the peak item, Indicates the current airfoil Peak value and These represent the current airfoils. Minimum and maximum values ​​within the peak; S22. The specific calculation formula for the score of the valley value item for each airfoil based on the maximum drag coefficient is as follows: ; in, Indicates the current airfoil The rating of the valley value item, Indicates the current airfoil Valley value, and These represent the current airfoils. The minimum and maximum values ​​within the valleys; S23. The specific calculation formula for the lift-to-drag ratio of each airfoil based on the maximum lift-to-drag ratio is as follows: ; in, Indicates the current airfoil The score of the peak item, Indicates the current airfoil Peak value and These represent the current airfoils. Minimum and maximum values ​​within the peak; S24. Based on the calculated final score, determine the optimal airfoil to replace the original airfoil. The specific formula for calculating the final score is as follows: ; in, This indicates the final score for the current airfoil.

[0008] Preferably, in S3, the actual operating environment of the wind turbine is complex, and the unit needs to operate under multiple operating conditions. Based on the optimal airfoil and its performance parameters obtained in S2, the thrust coefficient of the corresponding wind turbine can be calculated by combining blade element momentum theory with blade parameters. In order to meet the thrust matching of multiple operating conditions, the thrust matching objective function of parameter optimization is set as follows: ; in, This represents the thrust matching objective function. This indicates the current tip speed ratio operating condition. and These represent the rotor thrust coefficients of the prototype wind turbine and the scaled-down wind turbine model corresponding to the current blade parameters, respectively.

[0009] Preferably, the specific process of S4 is as follows: S41. The power mapping method redefines the matching mechanism between the power performance of the scaled-down wind turbine model and the prototype. The specific formula is as follows: ; in, This represents the rotor power coefficient of the scaled-down wind turbine model after mapping. This represents the rotor power coefficient of a scaled-down wind turbine model. Indicates the power mapping index; S42. Using the mapped power coefficient as the model evaluation index, the multi-condition power matching power mapping objective function is constructed as follows: ; in, This represents the power mapping matching objective function. This represents the rotor power coefficient of the prototype wind turbine.

[0010] Preferably, in S5, the thrust is matched to the objective function of S3. Power mapping matching objective function with S4 Combined into a single comprehensive optimization objective function The specific expression is as follows: .

[0011] Preferably, the specific process for optimizing blade parameters in S6 is as follows: S61. In the optimization algorithm GOA with a self-verification process for new search positions, set an upper limit on the number of searches and set the model blade airfoil. and Set the value, the tip speed ratio condition to be searched, and the thrust coefficient and power coefficient of the prototype wind turbine rotor corresponding to the tip speed ratio condition; S62. Fit the chord length and installation angle of the prototype wind turbine blades into a fourth-order polynomial and a second-order polynomial, respectively. Use the coefficients of the two polynomials as design variables and the design variables as the initial search points. S63. Determine the constraints for the blade chord length and installation angle and convert them into constraints for the search point. S64. Based on the blade element momentum theory, obtain the thrust coefficient and power coefficient of the model blade at the current search point under the target tip speed ratio condition, and calculate the function value of the comprehensive optimization objective function as a reference value. S65. Generate the next new search point that satisfies the constraints. Calculate the function value of the new search point using the same calculation process as S63-S64. Compare the reference value and the function value of the new search point. Select the minimum value as the new reference value. Repeat the above search process until the search count reaches the upper limit. S66. When the number of searches reaches the upper limit, the search point corresponding to the current reference value is defined as the optimal search point and converted into the optimal blade chord length and installation angle data output.

[0012] Preferably, the constraints on the blade chord length and installation angle in S63 are as follows: S631, The chord length of the model blade is a positive value; S632. The tip chord length of the model blade is not greater than the chord length of the center plane of the fourth blade segment from the end of the prototype wind turbine blade. S633, the installation angle of the model blade is positive; S634. The installation angle of the first blade element segment of the model blade shall not be greater than the installation angle of the first blade element segment of the prototype wind turbine blade.

[0013] Therefore, the multi-condition performance matching scaled-down wind turbine blade design method based on power mapping described above has the following advantages compared with the prior art: This application enables the scaled-down wind turbine model to reproduce the aerodynamic performance of the prototype wind turbine in terms of both thrust and power. It constructs evaluation indicators, compares the aerodynamic performance of low Reynolds number airfoils, and effectively achieves airfoil optimization in the redesign of scaled-down wind turbine model blades. Based on the power mapping method, it effectively offsets the influence of the Reynolds number effect and realizes parameter optimization of scaled-down wind turbine model blades under multiple operating conditions and with multiple performance characteristics.

[0014] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0015] Figure 1 This is an overall flowchart of the multi-condition performance matching scaled-down wind turbine blade design method based on power mapping of the present invention; Figure 2 This is a lift coefficient curve of the AG14 airfoil used in the embodiment of the multi-condition performance matching scaled-down wind turbine blade design method based on power mapping of the present invention; Figure 3 This is a drag coefficient curve of the AG14 airfoil used in the embodiment of the multi-condition performance matching scaled-down wind turbine blade design method based on power mapping of the present invention; Figure 4 This is a curve of the thrust coefficient of each blade under different tip speed ratio conditions obtained in the embodiment of the multi-condition performance matching scaled-down wind turbine blade design method based on power mapping of the present invention. Figure 5 The diagram shows the power coefficient curves of each blade under different tip speed ratio conditions obtained in the embodiments of the multi-condition performance matching scaled-down wind turbine blade design method based on power mapping of the present invention. Detailed Implementation

[0016] In the description of this invention, it should be noted that the terms "upper," "lower," "inner," "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship in which the product of this invention is usually placed when in use. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention.

[0017] Example like Figure 1 As shown, the multi-condition performance matching scaled-down wind turbine blade design method based on power mapping of the present invention includes the following steps: S1. Determine the Reynolds number at the scale of the scaled-down wind turbine model and establish a set of low Reynolds number airfoils; The lift coefficients of each airfoil in the low Reynolds number airfoil set are calculated. The specific formula is as follows: ; in, Lift is the resultant aerodynamic force perpendicular to the direction of the incoming airflow when an airfoil moves through the air. It represents the kinetic energy possessed per unit volume of air. air density, The velocity of the airfoil relative to the air is the vacuum speed. The planar area of ​​the airfoil is the projected area seen when the airfoil is viewed directly from the direction of the incoming flow. drag coefficient The specific formula is as follows: ; in, Resistance is the resultant aerodynamic force that acts in the opposite direction to the incoming airflow when an object moves through the air. S2. Evaluate the performance of different airfoils in the low Reynolds number airfoil set of S1 and select the optimal airfoil; The specific process is as follows: S21. The specific calculation formula for the peak value of each airfoil based on the maximum lift coefficient is as follows: ; in, Indicates the current airfoil The score of the peak item, Indicates the current airfoil Peak value and These represent the current airfoils. Minimum and maximum values ​​within the peak; S22. The specific calculation formula for the score of the valley value item for each airfoil based on the maximum drag coefficient is as follows: ; in, Indicates the current airfoil The rating of the valley value item, Indicates the current airfoil Valley value, and These represent the current airfoils. The minimum and maximum values ​​within the valleys; S23. The specific calculation formula for the lift-to-drag ratio of each airfoil based on the maximum lift-to-drag ratio is as follows: ; in, Indicates the current airfoil The score of the peak item, Indicates the current airfoil Peak value and These represent the current airfoils. Minimum and maximum values ​​within the peak; S24. Based on the calculated final score, determine the optimal airfoil to replace the original airfoil. The specific formula for calculating the final score is as follows: ; in, This indicates the final score for the current airfoil; S3. Based on the optimal airfoil selected in S2, set the thrust matching objective function under various working conditions to optimize parameters; The actual operating environment of wind turbines is complex, and the units need to operate under multiple conditions. Based on the optimal airfoil and its performance parameters obtained from S2, the thrust coefficient of the corresponding wind turbine can be calculated by combining blade element momentum theory with blade parameters. To meet the thrust matching requirements under multiple operating conditions, the thrust matching objective function for parameter optimization is set as follows: ; in, This represents the thrust matching objective function. This indicates the current tip speed ratio operating condition. and These represent the rotor thrust coefficients of the prototype wind turbine and the scaled-down wind turbine model corresponding to the current blade parameters, respectively. S4. Based on the optimal airfoil selected in S2, redefine the matching mechanism between the power performance of the scaled-down wind turbine model and the prototype, and set the power mapping matching objective function. The specific process is as follows: S41. The power mapping method redefines the matching mechanism between the power performance of the scaled-down wind turbine model and the prototype. The specific formula is as follows: ; in, This represents the rotor power coefficient of the scaled-down wind turbine model after mapping. This represents the rotor power coefficient of a scaled-down wind turbine model. Indicates the power mapping index; S42. Using the mapped power coefficient as the model evaluation index, the multi-condition power matching power mapping objective function is constructed as follows: ; in, This represents the power mapping matching objective function. This represents the rotor power coefficient of the prototype wind turbine; S5, the thrust matching objective function of S3, and the power mapping matching objective function of S4 are combined to generate a comprehensive optimization objective function for parameter optimization of the scaled-down wind turbine model blades; the thrust matching objective function of S3 is then used to optimize the parameters of the blades. Power mapping matching objective function with S4 Combined into a single comprehensive optimization objective function The specific expression is as follows: S6. The optimal blade is obtained by optimizing the blade parameters using the GOA optimization algorithm with a new search position self-verification process. The specific process for optimizing blade parameters is as follows: S61. In the optimization algorithm GOA with a self-verification process for new search positions, set an upper limit on the number of searches and set the model blade airfoil. and Set the value, the tip speed ratio condition to be searched, and the thrust coefficient and power coefficient of the prototype wind turbine rotor corresponding to the tip speed ratio condition; S62. Fit the chord length and installation angle of the prototype wind turbine blades into a fourth-order polynomial and a second-order polynomial, respectively. Use the coefficients of the two polynomials as design variables and the design variables as the initial search points. S63. Determine the constraints for the blade chord length and installation angle and convert them into constraints for the search point. The constraints on the blade chord length and installation angle are as follows: S631, The chord length of the model blade is a positive value; S632. The tip chord length of the model blade is not greater than the chord length of the center plane of the fourth blade segment from the end of the prototype wind turbine blade. S633, the installation angle of the model blade is positive; S634. The installation angle of the first blade element segment of the model blade shall not be greater than the installation angle of the first blade element segment of the prototype wind turbine blade. S64. Based on the blade element momentum theory, obtain the thrust coefficient and power coefficient of the model blade at the current search point under the target tip speed ratio condition, and calculate the function value of the comprehensive optimization objective function as a reference value. S65. Generate the next new search point that satisfies the constraints. Calculate the function value of the new search point using the same calculation process as S63-S64. Compare the reference value and the function value of the new search point. Select the minimum value as the new reference value. Repeat the above search process until the search count reaches the upper limit. S66. When the number of searches reaches the upper limit, the search point corresponding to the current reference value is defined as the optimal search point and converted into the optimal blade chord length and installation angle data output.

[0018] In the specific implementation process, the technical solution of this application was implemented on the NREL-5MW floating wind turbine model at a scale of 1:80. 767 airfoils, including those from the AG series and NACA series with an airfoil thickness of less than 8%, were selected to form the low Reynolds number airfoil set described in S1. The scoring calculation method proposed in technical solution S2 of this application was used to obtain the highest score. The AG14 airfoil has a speed of 0.7286, and its performance curve is as follows: Figure 2 and Figure 3 As shown, the optimal AG14 airfoil obtained in S2 is used to design a scaled-down model blade for a single airfoil. The mechanisms of multi-condition performance matching (S3) and power mapping (S4) are introduced respectively. The thrust matching objective function and the power mapping matching objective function generated by the two mechanisms are integrated to obtain the comprehensive optimization objective function in S5. Then, based on the blade chord length and installation angle optimization scheme proposed in S6, an iterative optimization based on the GOA optimization algorithm is used to obtain a comprehensive optimization objective function value. The design scheme for the 0.4338 model blade was used, and the final thrust and power reproduction results of the model blade were obtained as follows: Figure 4 and Figure 5 As shown.

[0019] Therefore, the present invention adopts the multi-condition performance matching scaled-down wind turbine blade design method based on power mapping described above. By combining power mapping and multi-condition thrust performance matching, the aerodynamic performance of the scaled-down wind turbine model is completely reproduced to that of the prototype wind turbine. This effectively achieves the airfoil optimization in the redesign of the scaled-down wind turbine model blade, effectively offsets the influence of the Reynolds number effect, and realizes the parameter optimization of the scaled-down wind turbine model blade under multiple conditions and with multiple performance characteristics.

[0020] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for designing scaled-down wind turbine blades based on power mapping and multi-condition performance matching, characterized in that, Includes the following steps: S1. Determine the Reynolds number at the scale of the scaled-down wind turbine model and establish a set of low Reynolds number airfoils; S2. Evaluate the performance of different airfoils in the low Reynolds number airfoil set of S1 and select the optimal airfoil; S3. Based on the optimal airfoil selected in S2, set the thrust matching objective function under various working conditions to optimize parameters; S4. Based on the optimal airfoil selected in S2, redefine the matching mechanism between the power performance of the scaled-down wind turbine model and the prototype, and set the power mapping matching objective function. S5, the thrust matching objective function of S3 and the power mapping matching objective function of S4 are combined to generate a comprehensive optimization objective function to optimize the parameters of the model blades of the scaled-down wind turbine model; S6. The optimal blade data is obtained by using the GOA optimization algorithm with a new search position self-verification process to optimize the blade parameters.

2. The multi-condition performance matching scaled-down wind turbine blade design method based on power mapping according to claim 1, characterized in that: S1 calculates the lift coefficients of each airfoil in the low Reynolds number airfoil group at the corresponding Reynolds number. The specific formula is as follows: ； in, Lift is the resultant aerodynamic force perpendicular to the direction of the incoming airflow when an airfoil moves through the air. It represents the kinetic energy possessed per unit volume of air. air density, The velocity of the airfoil relative to the air. The planar area of ​​the airfoil is the projected area seen when the airfoil is viewed directly from the direction of the incoming flow. drag coefficient The specific formula is as follows: ； in, Resistance is the resultant aerodynamic force that acts in the opposite direction to the incoming airflow when an object moves through the air.

3. The multi-condition performance matching scaled-down wind turbine blade design method based on power mapping according to claim 2, characterized in that: The specific process of S2 is as follows: S21. The specific calculation formula for the peak value of each airfoil based on the maximum lift coefficient is as follows: ； in, Indicates the current airfoil The score of the peak item, Indicates the current airfoil Peak value and These represent the current airfoils. Minimum and maximum values ​​within the peak; S22. The specific calculation formula for the score of the valley value item for each airfoil based on the maximum drag coefficient is as follows: ； in, Indicates the current airfoil The rating of the valley value item, Indicates the current airfoil Valley value, and These represent the current airfoils. The minimum and maximum values ​​within the valleys; S23. The specific calculation formula for the lift-to-drag ratio of each airfoil based on the maximum lift-to-drag ratio is as follows: ； in, Indicates the current airfoil The score of the peak item, Indicates the current airfoil Peak value and These represent the current airfoils. Minimum and maximum values ​​within the peak; S24. Based on the calculated final score, determine the optimal airfoil to replace the original airfoil. The specific formula for calculating the final score is as follows: ； in, This indicates the final score for the current airfoil.

4. The multi-condition performance matching scaled-down wind turbine blade design method based on power mapping according to claim 3, characterized in that: The actual operating environment of the S3 wind turbine is complex, and the unit needs to operate under multiple operating conditions. Based on the optimal airfoil and its performance parameters obtained in S2, the thrust coefficient of the corresponding wind turbine can be calculated by combining blade element momentum theory with blade parameters. In order to meet the thrust matching requirements of multiple operating conditions, the thrust matching objective function for parameter optimization is set as follows: ； in, This represents the thrust matching objective function. This indicates the current tip speed ratio operating condition. and These represent the rotor thrust coefficients of the prototype wind turbine and the scaled-down wind turbine model corresponding to the current blade parameters, respectively.

5. The multi-condition performance matching scaled-down wind turbine blade design method based on power mapping according to claim 1, characterized in that: The specific process of S4 is as follows: S41. The power mapping method redefines the matching mechanism between the power performance of the scaled-down wind turbine model and the prototype. The specific formula is as follows: ； in, This represents the rotor power coefficient of the scaled-down wind turbine model after mapping. This represents the rotor power coefficient of a scaled-down wind turbine model. Indicates the power mapping index; S42. Using the mapped power coefficient as the model evaluation index, the multi-condition power matching power mapping objective function is constructed as follows: ； in, This represents the power mapping matching objective function. This represents the rotor power coefficient of the prototype wind turbine.

6. The multi-condition performance matching scaled-down wind turbine blade design method based on power mapping according to claim 5, characterized in that: The thrust matching objective function of S3 in S5 Power mapping matching objective function with S4 Combined into a single comprehensive optimization objective function The specific expression is as follows: 。 7. The multi-condition performance matching scaled-down wind turbine blade design method based on power mapping according to claim 1, characterized in that: The specific process for optimizing blade parameters in S6 is as follows: S61. In the optimization algorithm GOA with a self-verification process for new search positions, set an upper limit on the number of searches and set the model blade airfoil. and Set the value, the tip speed ratio condition to be searched, and the thrust coefficient and power coefficient of the prototype wind turbine rotor corresponding to the tip speed ratio condition; S62. Fit the chord length and installation angle of the prototype wind turbine blades into a fourth-order polynomial and a second-order polynomial, respectively. Use the coefficients of the two polynomials as design variables and the design variables as the initial search points. S63. Determine the constraints for the blade chord length and installation angle and convert them into constraints for the search point. S64. Based on the blade element momentum theory, obtain the thrust coefficient and power coefficient of the model blade at the current search point under the target tip speed ratio condition, and calculate the function value of the comprehensive optimization objective function as a reference value. S65. Generate the next new search point that satisfies the constraints. Calculate the function value of the new search point using the same calculation process as S63-S64. Compare the reference value and the function value of the new search point. Select the minimum value as the new reference value. Repeat the above search process until the search count reaches the upper limit. S66. When the number of searches reaches the upper limit, the search point corresponding to the current reference value is defined as the optimal search point and converted into the optimal blade chord length and installation angle data output.

8. The multi-condition performance matching scaled-down wind turbine blade design method based on power mapping according to claim 7, characterized in that: The constraints on the blade chord length and installation angle in S63 are as follows: S631, The chord length of the model blade is a positive value; S632. The tip chord length of the model blade is not greater than the chord length of the center plane of the fourth blade segment from the end of the prototype wind turbine blade. S633, the installation angle of the model blade is positive; S634. The installation angle of the first blade element segment of the model blade shall not be greater than the installation angle of the first blade element segment of the prototype wind turbine blade.

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