CN114329813A - Fan blade optimization design method based on Wilson model and particle swarm optimization - Google Patents
Fan blade optimization design method based on Wilson model and particle swarm optimization Download PDFInfo
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Abstract
The application provides a fan blade optimization design method based on a Wilson model and a particle swarm algorithm, and the method comprises the following steps: determining blade design parameters to be optimized, and establishing an initial blade model; equally dividing the initial blade model into a preset number of phyllines along the spanwise direction, and iteratively calculating the axial induction coefficient and the tangential induction coefficient of each airfoil section through a Wilson model; respectively calculating the torsion angle and the chord length of each airfoil according to the axial induction coefficient and the tangential induction coefficient of each airfoil; and taking the twist angle and the chord length of each wing section as input values of the particle swarm algorithm, and simultaneously re-optimizing the twist angle and the chord length of each wing section through the particle swarm algorithm. According to the method, the blades are subjected to combined optimization design through a Wilson model and a particle swarm algorithm, so that the pneumatic performance of the blades is effectively improved, and the power generation efficiency of the fan is improved.
Description
Technical Field
The application relates to the technical field of wind power generation, in particular to a fan blade optimization design method based on a Wilson model and a particle swarm algorithm.
Background
Along with the enhancement of the environmental awareness of people, the development of clean energy is more and more emphasized. The wind power generation plays an important role in the technical development of renewable novel clean energy, and particularly has a good application prospect in areas with rich wind resources.
The fan blade is an important component of the wind driven generator, and the improvement of the aerodynamic performance of the blade is a key for improving the utilization efficiency of wind energy, so that the generating efficiency of the fan is directly influenced.
In the related art, the airfoil profile and the profile of the blade are designed according to needs in blade design software, and then design parameters of the blade are optimized. However, the applicant finds that in the related art, the blade design mode is only to optimize the blade design parameters through a single optimization method, and the optimization effect is limited, so that the aerodynamic performance of the blades of the fan is poor, and the power generation efficiency of the fan is low.
Disclosure of Invention
The present application is directed to solving, at least to some extent, one of the technical problems in the related art.
Therefore, the first purpose of the application is to provide a Wilson model and particle swarm algorithm-based fan blade optimization design method, the method carries out combined optimization design on blades through the Wilson model and the particle swarm algorithm, the aerodynamic performance of the designed blades is effectively improved, after secondary optimization is carried out on design parameters of the blades through the particle swarm optimization algorithm, the wind energy utilization coefficient is remarkably improved, the wind energy utilization rate of the fan is further improved, the power generation efficiency of the fan is improved, and the production cost of wind power generation is favorably reduced under the same load requirement.
The second purpose of the application is to provide a fan blade optimization design system based on a Wilson model and a particle swarm optimization;
a third object of the present application is to propose a non-transitory computer-readable storage medium.
In order to achieve the above object, an embodiment of the first aspect of the present application is to provide a fan blade optimization design method based on a Wilson model and a particle swarm algorithm, where the method includes the following steps:
determining blade design parameters to be optimized, and establishing an initial blade model;
equally dividing the initial blade model into a preset number of phyllines along the spanwise direction, determining the airfoil profile of each phylline, and iteratively calculating the axial induction coefficient and the tangential induction coefficient of each airfoil profile through a Wilson model;
respectively calculating the torsion angle and the chord length of each airfoil according to the axial induction coefficient and the tangential induction coefficient of each airfoil;
and taking the torsion angle and the chord length of each wing section as input values of a particle swarm algorithm, and simultaneously re-optimizing the torsion angle and the chord length of each wing section through the particle swarm algorithm.
Optionally, in an embodiment of the present application, after the simultaneously re-optimizing the twist angle and the chord length of each airfoil by the particle swarm algorithm, the method further includes: and determining the chord length and the twist angle of the fan blade according to the twist angle and the chord length of each re-optimized airfoil profile, and fitting the airfoil and the profile parameters of each airfoil.
Optionally, in an embodiment of the present application, iteratively calculating the axial induction coefficient and the tangential induction coefficient of each of the airfoils by a Wilson model includes: determining initial values of the axial induction coefficient and the tangential induction coefficient; obtaining a relation between a preset inflow angle and the axial induction coefficient and the tangential induction coefficient, and obtaining an iterative formula of the preset axial induction coefficient and the preset tangential induction coefficient; and performing iterative calculation on the axial induction coefficient and the tangential induction coefficient according to the relational expression and the iterative formula until the axial induction coefficient and the tangential induction coefficient are converged.
Optionally, in an embodiment of the present application, the iterative formula of the axial induction coefficient and the tangential induction coefficient is:
wherein a is the axial induction coefficient, b is the tangential induction coefficient,
is the angle of inflow, CxIs the normal lift coefficient, CyIs the tangential lift coefficient, and sigma is the wind wheel solidity.
Optionally, in an embodiment of the present application, simultaneously re-optimizing the twist angle and the chord length of each airfoil by the particle swarm algorithm includes: initializing a particle swarm according to the torsion angle and the chord length of each airfoil; calculating a particle adaptive value, and acquiring an individual optimal value and a global optimal value of the particles based on a preset constraint condition; updating the speed and the position of the particles through a preset optimization formula according to the individual optimal value and the global optimal value of the particles; and judging whether the convergence accuracy is met, and if the convergence accuracy is met, outputting the torsion angle and chord length of each phyllo subjected to re-optimization.
Optionally, in an embodiment of the present application, the obtaining a global optimal value of the particle includes: selecting the maximum annual power generation of a fan as an objective function, and determining a particle global optimum value according to the objective function, wherein the objective function is represented by the following formula:
where E is annual energy production, N is number of sections, P is output power, f () is probability density distribution function, and v is wind speed.
In order to achieve the above object, an embodiment of the second aspect of the present application further provides a fan blade optimization design system based on a Wilson model and a particle swarm algorithm, including the following modules:
the building module is used for determining blade design parameters to be optimized and building an initial blade model;
the first calculation module is used for equally dividing the initial blade model into a preset number of phyllines along the spanwise direction, determining the airfoil profile of each phylline, and iteratively calculating the axial induction coefficient and the tangential induction coefficient of each airfoil profile through a Wilson model;
the second calculation module is used for calculating the torsion angle and the chord length of each airfoil according to the axial induction coefficient and the tangential induction coefficient of each airfoil;
and the optimization module is used for taking the torsion angle and the chord length of each airfoil as input values of the particle swarm algorithm, and simultaneously re-optimizing the torsion angle and the chord length of each airfoil through the particle swarm algorithm.
Optionally, in an embodiment of the present application, the optimization module is further configured to: and determining the chord length and the twist angle of the fan blade according to the twist angle and the chord length of each re-optimized airfoil profile, and fitting the airfoil and the profile parameters of each airfoil.
Optionally, in an embodiment of the present application, the first calculating module is specifically configured to: determining initial values of the axial induction coefficient and the tangential induction coefficient; obtaining a relation between a preset inflow angle and the axial induction coefficient and the tangential induction coefficient, and obtaining an iterative formula of the preset axial induction coefficient and the preset tangential induction coefficient; and performing iterative calculation on the axial induction coefficient and the tangential induction coefficient according to the relational expression and the iterative formula until the axial induction coefficient and the tangential induction coefficient are converged.
The technical scheme provided by the embodiment of the application at least has the following beneficial effects: this application is according to factors such as the pneumatic characteristic of the stationarity of fan operation and blade, confirms the relevant parameter of blade design, optimizes fan blade parameter through Wilson model earlier, obtains chord length and the torsion angle of optimizing back blade, wherein through equally dividing the blade into a plurality of cross-sections, the operating mode that the design parameter is applicable in the blade design of different grade type respectively to every cross-section. And then, the blade design parameters after the Wilson model optimization are used as input values of the particle swarm optimization, secondary optimization is carried out on the blade design parameters through the particle swarm optimization, the blade is subjected to combined optimization design through the Wilson model and the particle swarm optimization, the aerodynamic performance of the designed blade is effectively improved, after the secondary optimization is carried out on the blade design parameters through the particle swarm optimization, the wind energy utilization coefficient is obviously improved, the wind energy utilization rate of the fan is further improved, the power generation efficiency of the fan is improved, and the production cost of wind power generation is favorably reduced under the same load requirement.
In order to implement the foregoing embodiments, the third aspect of the present application further provides a non-transitory computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the Wilson model and particle swarm algorithm-based fan blade optimization design method in the foregoing embodiments is implemented.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flow chart of a fan blade optimization design method based on a Wilson model and a particle swarm optimization algorithm provided by an embodiment of the present application;
FIG. 2 is a flow chart illustrating a specific method for optimizing blade design parameters through a Wilson model according to an embodiment of the present disclosure;
FIG. 3 is a flowchart illustrating a specific method for re-optimizing design parameters of each leaf element by a particle swarm optimization, according to an embodiment of the present disclosure;
FIG. 4 is a schematic diagram illustrating a variation curve of a wind energy utilization factor with wind speed according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a fan blade optimization design system based on a Wilson model and a particle swarm optimization, which is provided by an embodiment of the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
Aiming at the technical problems that in the related art, only a single optimization method is used, for example, only a Wilson model is used for blade design and optimization, the optimization effect is limited, and the aerodynamic performance of the blades of the fan is poor, the Wilson model and particle swarm algorithm-based fan blade optimization design method is provided, and the blades are subjected to combined optimization design through the Wilson model and the particle swarm algorithm, so that the aerodynamic performance of the blades is effectively improved.
The fan blade optimization design method and system based on the Wilson model and the particle swarm algorithm, which are provided by the embodiment of the invention, are described in detail below with reference to the attached drawings.
Fig. 1 is a flowchart of a fan blade optimization design method based on a Wilson model and a particle swarm algorithm according to an embodiment of the present application, and as shown in fig. 1, the method includes the following steps:
The design parameters of the blades can include various blade airfoil parameters, appearance parameters and other parameters which need to be determined and optimized when the wind turbine blades are designed, the design parameters of the blades directly determine various characteristics such as aerodynamic characteristics, running stability and the like of the blades in actual running, and the aerodynamic performance of the blades can be improved by reasonably optimizing the design parameters of the blades.
It should be noted that the blade design of the wind turbine is mainly divided into a blade airfoil design and a blade profile design. The design of the blade airfoil, namely the shape design of the axial cross section of the blade, comprises the design and optimization of various airfoil parameters, and the design of the blade appearance, namely the design of the three-dimensional curved surface of the blade. In order to describe the blade optimization design method more clearly, some blade design parameters to be optimized determined by the present application are explained first.
Wherein, for blade airfoil design, the blade design parameters may include: an upper airfoil surface, i.e. an airfoil top camber line; a lower airfoil surface, i.e. an airfoil lower camber line; chord length, i.e., the distance from the leading edge to the trailing edge of the airfoil; the plane of rotation of the rotor, i.e. the horizontal plane around which the rotor rotates; the twist angle, namely the pitch angle, is the included angle between the chord of the blade and the rotating plane of the wind wheel; angle of attack, i.e. the angle between the relative wind speed and the blade chord; the inflow angle, i.e. the angle between the relative wind speed and the plane of rotation, in this example, the relative wind speed refers to the vector containing the magnitude and direction of the speed.
It should be further noted that, since the twist angle and the chord length of the blade have a large influence on the aerodynamic performance of the blade, and some other design parameters can be determined according to the twist angle and the chord length, for convenience of clear and concise description, the optimized design of the twist angle and the chord length is mainly used as an exemplary illustration of the optimized design of the blade design parameters in the embodiment of the present application.
In the embodiment of the present application, the initial blade model may be a blade model established in blade design software, for example, the initial model may be established in profile airfoil design software, and design parameters of the initial blade model are optimized during optimal design of a blade, so as to complete optimal design of the blade.
The spanwise direction may be a direction in which the blade extends from the leading edge of the airfoil to the trailing edge of the airfoil, the axial direction is a direction of a rotation central axis direction of each of the blading, that is, a direction common to the central axis, and the tangential direction refers to a direction of a tangent line of each of the blading. The leaf element is each of the segments obtained by equally dividing the blade into a predetermined number of parts, and it is understood that each leaf element is a three-dimensional segment, and each leaf element after the segment corresponds to a two-dimensional cross section, i.e. the airfoil profile of the leaf element.
The method is based on the maximum optimization target of the maximum wind energy utilization coefficient under the designed wind speed, and not only is the influence of blade tip loss and the lift-drag ratio of an airfoil profile on the optimal performance of the blade combined when the blade is designed, but also the performance of a wind wheel under the non-designed working condition is combined, so that the blade design parameters are optimized through the Wilson model firstly, and the wind energy conversion efficiency of the designed blade can be improved.
Specifically, the blades are firstly equally divided, and the number of equally divided blades can be determined according to the actual requirements of blade design, for example, according to the size of the blades, which is not limited herein. After an initial blade airfoil model is equally divided into a preset number of phyllines along the spanwise direction, airfoil parameters are respectively optimally designed for the airfoil of each phylline, specifically, an axial induction coefficient and a tangential induction coefficient of each airfoil are calculated firstly, and then the airfoil parameters of the airfoil are calculated through the axial induction coefficient and the tangential induction coefficient.
In an embodiment of the application, an axial induction coefficient and a tangential induction coefficient of each airfoil section are calculated through a Wilson model in an iterative mode, the method comprises the following steps of determining initial values of the axial induction coefficient and the tangential induction coefficient, obtaining a preset relation between an inflow angle and the axial induction coefficient and the tangential induction coefficient and an iterative formula of the preset axial induction coefficient and the preset tangential induction coefficient, and then performing iterative calculation on the axial induction coefficient and the tangential induction coefficient according to the relation and the iterative formula until the axial induction coefficient and the tangential induction coefficient converge.
Specifically, in the present example, with the axial induction coefficient and the tangential induction coefficient as variables for iteration, first, the initial values of the axial induction coefficient a and the tangential induction coefficient b may be calculated by the following formulas:
b＝10e12r/R/λ0wherein λ is0Is the tip speed ratio, R is the length from the section to the center of rotation, and R is the blade section radius. Wherein the tip speed ratio λ0Is the ratio of the linear velocity of the tip of the blade to the wind speed,the method can be determined by combining the design requirements of the blades and the wind energy condition of the fan, and the radius of the section of each blade can be equally divided, and then different sections are measured to obtain the length of the radius of the section.
Then, iteration is performed on the initial value of the induction coefficient. Firstly, determining the relationship between the inflow angle and the axial induction coefficient and the tangential induction coefficient, and specifically calculating the inflow angle through the following formula:
where φ is the inflow angle, a is the axial induction coefficient, b is the tangential induction coefficient, V0Is the design wind speed, Ω is the blade rotational angular velocity, and r is the length of the section to the center of rotation. In the formula, V0And omega can be determined according to actual design requirements, r can be obtained through measurement, and the inflow and outflow angle can be calculated according to the input axial induction coefficient and the input tangential induction coefficient under the condition that the three parameters are considered to be known.
And secondly, determining an iterative formula of an axial induction coefficient and a tangential induction coefficient, wherein as a possible implementation mode, the iterative formula of the induction coefficients is provided in the application, and the iterative formula is specifically as follows:
wherein a is the axial induction coefficient, b is the tangential induction coefficient,
is the angle of inflow, CxIs the normal lift coefficient, CyIs the tangential lift coefficient, and sigma is the wind wheel solidity.
The wind wheel solidity is the ratio of the sum of the projection areas of the blades on the rotation plane of the wind wheel to the swept area of the wind wheel, and can be measured through experiments. The normal lift coefficient and the tangential lift coefficient can be determined according to a change curve of the lift-drag ratio of the airfoil along with the attack angle, namely the normal lift coefficient and the tangential lift coefficient can be determined according to the lift-drag ratio curve when the attack angle of the blade is calculated. In a specific implementation, in an embodiment of the present application, when an attack angle of a blade is calculated, data such as kinematic viscosity and density of air in an environment where a fan is located may be obtained from wind energy data of an actual application area of the blade to be designed, the obtained data such as kinematic viscosity and density of air in the environment where the fan is located is input into design software, the obtained data such as reynolds number and the like are input into the design software to simulate the environment where the fan is actually located, two-dimensional aerodynamic analysis is performed on a blade airfoil, a variation curve of a lift-to-drag ratio of the airfoil with the attack angle is obtained according to an analysis result, an attack angle corresponding to a maximum lift-to-drag ratio is found in the curve, the attack angle is taken as a design attack angle, and a normal lift coefficient and a tangential lift coefficient are analyzed according to the lift-to drag ratio curve. Therefore, in the embodiment, the actual environment of the fan can be simulated based on the aerodynamic characteristics of the airfoil profile, the two-dimensional aerodynamic analysis is carried out on the airfoil profile of the blade, the designed attack angle is determined, and the accuracy and the applicability of the attack angle design are improved.
Under the condition that the normal lift coefficient, the tangential lift coefficient and the wind wheel real degree are regarded as known, the axial induction coefficient and the tangential induction coefficient can be respectively calculated according to the input inflow angle.
And thirdly, determining initial values of the axial induction coefficient a and the tangential induction coefficient b, substituting the initial values into the calculation formula of the inflow angle in the first step to calculate an initial inflow angle, substituting the initial inflow angle into the iterative formula in the second step to calculate a new group of axial induction coefficient a and tangential induction coefficient b, and calculating a new inflow angle according to the new induction coefficient by using the calculation formula of the inflow angle, so as to repeat the step until the values of the axial induction coefficient a and the tangential induction coefficient b are converged to obtain the optimal solution of the axial induction coefficient and the tangential induction coefficient.
Therefore, through the iteration mode of the embodiment of the application, the optimal solution of the axial induction coefficient and the tangential induction coefficient of the airfoil of any one of the phyllotaxis can be iteratively calculated, and the axial induction coefficient and the tangential induction coefficient of each airfoil are sequentially calculated through the method.
And 103, respectively calculating the torsion angle and the chord length of each airfoil according to the axial induction coefficient and the tangential induction coefficient of each airfoil.
In the embodiment of the application, the inflow angle of each airfoil is calculated according to the axial induction coefficient and the tangential induction coefficient respectively, and the torsion angle and the chord length of the blade are calculated by combining the inflow angle. As a possible implementation manner, the inflow angle of each leaf element can be calculated by the above calculation formula of the inflow angle. In some embodiments of the application, an inflow angle can be preset according to the actual wind direction under the operating environment of the fan.
Further, after the inflow angle is calculated, the torsion angle of each airfoil is calculated through the following formula:
wherein, theta is a twist angle,
is the inflow angle and alpha is the angle of attack. From the above description of the embodiments, it can be seen that the angle of attack can be determined according to the variation curve of the lift-drag ratio with the angle of attack, and after the inflow angle is calculated, the torsion angle can be calculated according to the formula.
Further, the chord length of each airfoil element is then calculated by the following formula:
wherein B is the number of phyllines, CLIs the lift coefficient.
Therefore, the inflow angle, the torsion angle and the chord length of each airfoil are sequentially calculated by using the obtained optimal solution of the axial induction coefficient and the tangential induction coefficient in the mode until the chord length and the torsion angle of all the airfoils are calculated.
For more clearly describing a specific implementation process of parameter optimization through a Wilson model in the present application, a specific method for optimizing blade design parameters through the Wilson model, as shown in fig. 2, is described below, where the method includes:
and S21, inputting blade design parameters to be optimized.
S22, the entire blade is cut into n sections, and subsequent calculations are performed for each section.
This step translates to an algorithmic representation as: and For is 1: n, wherein n is the number of sections.
And S23, calculating the tip speed ratio and the blade section radius.
S24, initializing the axial induction coefficient a and the tangential induction coefficient b.
And S25, calculating a and b by using an iterative formula until convergence.
And S26, calculating the torsion angle and the chord length of the current section according to the a and the b.
S27, switch to the next section, and return to S23 to repeat the calculation.
This step translates to an algorithmic representation as: i ═ i + 1.
And S28, outputting torsion angles and chord lengths of all the sections.
And step 104, taking the torsion angle and the chord length of each wing section as input values of the particle swarm optimization, and simultaneously re-optimizing the torsion angle and the chord length of each wing section through the particle swarm optimization.
The particle swarm optimization algorithm is a random search algorithm based on swarm cooperation, each particle learns two values all the time in the process of searching for an optimal solution, one is an individual historical optimal value, the other is a swarm historical optimal value, in the process of searching for an optimal position, the speed and the position of the particle are required to be iterated and adjusted continuously based on the two optimal values, and then the particle is gradually close to the optimal position.
It should be noted that, according to the blade optimization design method of the present application, a corresponding twist angle and a corresponding chord length are generated for each airfoil of each leaf element, and each leaf element is equally divided by the same blade, so that the twist angle and the chord length of the airfoil of all leaf elements can be regarded as a particle population, and thus, in the embodiment of the present application, the chord length and the twist angle can be optimized again by combining a particle swarm optimization on the basis of an optimization result output by a Wilson model.
In order to more clearly explain the specific implementation process of applying for parameter optimization through a particle swarm optimization, a specific method for re-optimizing the twist angle and the chord length of each airfoil through the particle swarm optimization is provided in the embodiment of the present application, and as shown in fig. 3, the method includes the following steps:
and S31, initializing the particle swarm according to the twist angle and the chord length of each airfoil.
Specifically, the twist angle and chord length of the airfoil profile of all the phylloids output by the Wilson model are used as input values of a particle swarm algorithm, and the speed and the position of the particle are initialized. In the method, the initialization is carried out according to the optimization result obtained by the Wilson model, so that the randomness of particle initialization can be reduced, and the optimal solution can be searched by fewer iterations.
In some embodiments of the present application, other design parameters of each airfoil, for example, the length from the cross section to the center of gyration, may also be combined as a design variable of the particle swarm algorithm, that is, design parameters other than the twist angle and the chord length may also be optimized simultaneously in this example.
And S32, calculating the particle adaptive value, and acquiring the particle individual optimal value and the particle global optimal value based on the preset constraint condition.
The adaptive value is an objective function value obtained by substituting the position of the particle into the objective function, and the adaptive value is directly related to the distance of the optimal solution.
Specifically, an adaptive value is calculated for each particle in the particle population, and under a preset constraint condition, an optimal solution, namely an individual historical optimal value, found by each particle per se and an optimal solution, namely a population historical optimal value, found by the whole population are obtained.
In one embodiment of the present application, the chord length C of each airfoil is selectediAngle of torsion betaiAnd the length r from the cross section to the center of rotationiFor the design variable X, denoted X ═ c1,c2,…,cn；β1,β2,…,βn；r1,r2,…,rn]Then X satisfies the following constraint:
wherein, cminAnd cmaxTo optimize the minimum and maximum chord length, β, within the intervalminAnd betamaxTo optimize the minimum and maximum twist angle, r, within the intervalminAnd rmaxThe minimum and maximum lengths of the blade section in the optimized interval are obtained. The optimization interval is determined by a maximum threshold and a minimum threshold, each threshold can be determined according to an optimization result output by a Wilson model, it can be understood that the optimization result output by the Wilson model is used as an input, and when particle swarm optimization is performed, the determined optimal solution is obviously within the range of the optimization result output by the Wilson model, so that the maximum value and the minimum value in each type of parameter can be determined after the parameters of each airfoil output by the Wilson model are sorted, and the thresholds can be determined. Therefore, the particle swarm algorithm constraint condition is determined by combining the optimization result output by the Wilson model.
In an embodiment of the application, when the global optimal value of the particle is obtained, the maximum annual energy production of the fan can be selected as an objective function, and the global optimal value of the particle is determined according to the objective function, wherein the objective function is expressed by the following formula:
where E is annual energy production, N is number of sections, P is output power, f () is probability density distribution function, and v is wind speed. In this example, by substituting design parameters of each airfoil into the objective function and comparing the magnitude of annual energy production, a particle global optimum can be determined.
Therefore, the individual optimal value and the global optimal value of the particles which meet the constraint condition are determined.
And S33, updating the speed and the position of the particles through a preset optimization formula according to the individual optimal value and the global optimal value of the particles.
Specifically, the particle updates its velocity and position by tracking the determined individual optimal value and the particle global optimal value. As a possible way, the speed can be updated first by the following optimization formula:
V’＝V+c1*rand*(pbest-P)+c2*rand*(gbest-P)
where V' is the updated velocity, V is the velocity before update, c1 and c2 are preset learning factors, pbest is the particle individual optimum, gbest is the particle global optimum, and P is the position before update.
Further, the location is updated by the following optimization formula:
P’＝P+V
where P' is the updated position.
Therefore, in the above manner, the optimal solution is searched in each iteration, and the speed and the position of the particle are updated.
S34, determining whether the convergence accuracy is satisfied, if not, returning to S32, and if so, executing S35.
And S35, outputting the twist angle and the chord length of each phyllo after being re-optimized.
Specifically, the preset convergence accuracy is taken as a termination condition, iteration is stopped after the convergence accuracy is judged to be met, and the twist angle and the chord length of each optimized phyllo are output. As a possible implementation manner, iteration times may be set, where the iteration times are determined according to the convergence accuracy, and after the convergence accuracy is determined according to an actual requirement, the iteration times are determined based on the convergence accuracy greater than a preset convergence accuracy, so that after the iteration times are reached, it can be determined that the convergence accuracy is met, and then an optimization result is output.
Therefore, the blade is subjected to combined optimization design through a Wilson model and a particle swarm algorithm, the twist angle and the chord length of each wing type are optimized again, the blade is designed, and the aerodynamic performance of the blade can be effectively improved.
It should be noted that the examples of the present application are provided hereinIn the chord length C of each airfoiliAngle of torsion betaiAnd the length r from the cross section to the center of rotationiFor the variable, the obtained optimized output is the design parameters such as the twist angle and the chord length of each wing profile, so that the optimized twist angle and chord length of each wing profile can be simultaneously output, the optimization in sequence is avoided, and the efficiency of optimizing the design parameters of the blade is improved.
Further, after the twist angle and the chord length of each airfoil section are further optimized through a particle swarm algorithm, the design of the blade can be finally completed according to the obtained optimized blade design parameters. In the embodiment of the present application, according to the calculated chord length and twist angle of the airfoil of each lutein, the chord length and twist angle of the whole blade can be determined, and then the airfoil and the profile parameters of each lutein are fitted, where the airfoil of each lutein includes an upper airfoil and a lower airfoil of the airfoil, and the profile parameters of each lutein includes a three-dimensional curved surface of each lutein, and the like. In this example, the chord length and the twist angle of the relevant parameters of the airfoil profile are calculated, then the upper airfoil surface and the lower airfoil surface of each section are fitted, the airfoil profile of the blade can be obtained after fitting, and the optimal design of the airfoil profile of the blade is completed. And moreover, the three-dimensional curved surface of each leaf element is fitted, so that the whole appearance of the blade is fitted and determined, namely after the wing profile parameters are fitted and optimized, the three-dimensional curved surface of the blade can be obtained through the fitting of a plurality of segments, and the appearance of the blade can be optimized and designed after corresponding adjustment is carried out according to the three-dimensional curved surface.
In an embodiment of the present application, in practical applications, a blade designed by the method for optimally designing a fan blade based on a Wilson model and a particle swarm algorithm of the present application and a blade optimally designed by using the Wilson method alone are tested, a test result is shown in fig. 4, an abscissa in the figure is a wind speed, and an ordinate is a wind energy utilization coefficient, and as can be seen from fig. 4, compared with a method for optimally designing a blade by using the Wilson method alone, a wind energy utilization coefficient of a fan using a blade designed by using the method of the present application is greater than a wind energy utilization coefficient of a fan using a blade optimally designed alone no matter what wind speed. Therefore, the combined optimization design of the Wilson method and the particle swarm algorithm is applied, so that the wind energy utilization coefficient of the blade is effectively improved, and the generating efficiency of the fan is improved.
In summary, according to the Wilson model and particle swarm algorithm-based fan blade optimization design method, relevant parameters of blade design are determined according to factors such as stability of fan operation and aerodynamic characteristics of blades, fan blade parameters are optimized through the Wilson model to obtain chord lengths and torsion angles of optimized blades, the blades are equally divided into a plurality of sections, and the optimized design parameters are applicable to working conditions of different types of blade designs aiming at each section. And then, the blade design parameters after the Wilson model optimization are used as input values of the particle swarm optimization, secondary optimization is carried out on the blade design parameters through the particle swarm optimization, the blade is subjected to combined optimization design through the Wilson model and the particle swarm optimization, the aerodynamic performance of the designed blade is effectively improved, after the secondary optimization is carried out on the blade design parameters through the particle swarm optimization, the wind energy utilization coefficient is obviously improved, the wind energy utilization rate of the fan is further improved, the power generation efficiency of the fan is improved, and the production cost of wind power generation is favorably reduced under the same load requirement.
In order to implement the above embodiment, the present application further provides a Wilson model and particle swarm optimization-based fan blade optimization design system, and fig. 5 is a schematic structural diagram of a Wilson model and particle swarm optimization-based fan blade optimization design system provided in the embodiment of the present application, and as shown in fig. 5, the system includes an establishing module 100, a first calculating module 200, a second calculating module 300, and an optimizing module 400.
The building module 100 is configured to determine blade design parameters to be optimized, and build an initial blade model.
The first calculation module 200 is configured to equally divide the initial blade model into a preset number of phylls along the spanwise direction, determine an airfoil profile of each phyll, and iteratively calculate an axial induction coefficient and a tangential induction coefficient of each airfoil profile through a Wilson model.
And the second calculating module 300 is used for calculating the torsion angle and the chord length of each airfoil according to the axial induction coefficient and the tangential induction coefficient of each airfoil.
And the optimization module 400 is configured to take the twist angle and the chord length of each airfoil as input values of the particle swarm optimization, and simultaneously re-optimize the twist angle and the chord length of each airfoil through the particle swarm optimization.
Optionally, in an embodiment of the present application, the optimization module 400 is further configured to determine a chord length and a twist angle of the wind turbine blade according to the re-optimized twist angle and chord length of each airfoil, and to fit the airfoil and profile parameters of each of the plurality of airfoils.
Optionally, in an embodiment of the present application, the first calculation module 200 is specifically configured to determine initial values of the axial induction coefficient and the tangential induction coefficient; acquiring a preset relational expression of an inflow angle, an axial induction coefficient and a tangential induction coefficient, and acquiring an iterative formula of the preset axial induction coefficient and the preset tangential induction coefficient; and carrying out iterative calculation on the axial induction coefficient and the tangential induction coefficient according to the relational expression and the iterative formula until the axial induction coefficient and the tangential induction coefficient are converged.
Optionally, in an embodiment of the present application, the iterative formula of the axial induction coefficient and the tangential induction coefficient is:
wherein a is the axial induction coefficient, b is the tangential induction coefficient,
is the angle of inflow, CxIs the normal lift coefficient, CyIs the tangential lift coefficient, and sigma is the wind wheel solidity.
Optionally, in an embodiment of the present application, the optimization module 400 is specifically configured to initialize the particle swarm according to the twist angle and chord length of each airfoil; calculating a particle adaptive value, and acquiring an individual optimal value and a global optimal value of the particles based on a preset constraint condition; updating the speed and the position of the particles through a preset optimization formula according to the individual optimal value and the global optimal value of the particles; and judging whether the convergence accuracy is met, and if the convergence accuracy is met, outputting the torsion angle and chord length of each wing section after re-optimization.
Optionally, in an embodiment of the present application, the obtaining a global optimal value of the particle includes: selecting the maximum annual power generation of the fan as an objective function, and determining a particle global optimum value according to the objective function, wherein the objective function is represented by the following formula:
where E is annual energy production, N is number of sections, P is output power, f () is probability density distribution function, and v is wind speed.
It should be noted that the explanation of the embodiment of the fan blade optimization design method based on the Wilson model and the particle swarm algorithm is also applicable to the system of the embodiment, and is not repeated here.
To sum up, the Wilson model and particle swarm algorithm-based fan blade optimal design system of the embodiment of the application determines relevant parameters of blade design according to factors such as stability of fan operation and aerodynamic characteristics of the blades, optimizes fan blade parameters through the Wilson model to obtain chord length and twist angle of the optimized blades, wherein the blades are equally divided into a plurality of sections, and the optimal design parameters are applicable to working conditions of different types of blade designs respectively for each section. And then, the blade design parameters after the Wilson model optimization are used as input values of the particle swarm optimization, secondary optimization is carried out on the blade design parameters through the particle swarm optimization, the blade is subjected to combined optimization design through the Wilson model and the particle swarm optimization, the aerodynamic performance of the designed blade is effectively improved, after the secondary optimization is carried out on the blade design parameters through the particle swarm optimization, the wind energy utilization coefficient is obviously improved, the wind energy utilization rate of the fan is further improved, the power generation efficiency of the fan is improved, and the production cost of wind power generation is favorably reduced under the same load requirement.
In order to implement the above embodiments, the present application further proposes a non-transitory computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the Wilson model and particle swarm algorithm-based fan blade optimization design method as described in any of the above embodiments.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (10)
1. A fan blade optimization design method based on a Wilson model and a particle swarm algorithm is characterized by comprising the following steps:
determining blade design parameters to be optimized, and establishing an initial blade model;
equally dividing the initial blade model into a preset number of phyllines along the spanwise direction, determining the airfoil profile of each phylline, and iteratively calculating the axial induction coefficient and the tangential induction coefficient of each airfoil profile through a Wilson model;
respectively calculating the torsion angle and the chord length of each airfoil according to the axial induction coefficient and the tangential induction coefficient of each airfoil;
and taking the torsion angle and the chord length of each wing section as input values of a particle swarm algorithm, and simultaneously re-optimizing the torsion angle and the chord length of each wing section through the particle swarm algorithm.
2. The method of claim 1, further comprising, after said simultaneously re-optimizing twist and chord length of each of said airfoils by said particle swarm optimization,:
and determining the chord length and the twist angle of the fan blade according to the twist angle and the chord length of each re-optimized airfoil profile, and fitting the airfoil and the profile parameters of each airfoil.
3. The method of claim 1 or 2, wherein said iteratively calculating an axial induction coefficient and a tangential induction coefficient for each of said airfoils by a Wilson model comprises:
determining initial values of the axial induction coefficient and the tangential induction coefficient;
obtaining a relation between a preset inflow angle and the axial induction coefficient and the tangential induction coefficient, and obtaining an iterative formula of the preset axial induction coefficient and the preset tangential induction coefficient;
and performing iterative calculation on the axial induction coefficient and the tangential induction coefficient according to the relational expression and the iterative formula until the axial induction coefficient and the tangential induction coefficient are converged.
4. The method of claim 3, wherein the axial induction coefficient and the tangential induction coefficient are iterated through the following equations:
wherein a is the axial induction coefficient, b is the tangential induction coefficient,
is the angle of inflow, CxIs the normal lift coefficient, CyIs the tangential lift coefficient, and sigma is the wind wheel solidity.
5. The method of claim 1, wherein said simultaneously re-optimizing twist angle and chord length of each said airfoil by said particle swarm algorithm comprises:
initializing a particle swarm according to the torsion angle and the chord length of each airfoil;
calculating a particle adaptive value, and acquiring an individual optimal value and a global optimal value of the particles based on a preset constraint condition;
updating the speed and the position of the particles through a preset optimization formula according to the individual optimal value and the global optimal value of the particles;
and judging whether the convergence accuracy is met, and if the convergence accuracy is met, outputting the torsion angle and chord length of each wing section after being re-optimized.
6. The method of claim 5, wherein the obtaining a global optimal value of particles comprises:
selecting the maximum annual power generation of a fan as an objective function, and determining a particle global optimum value according to the objective function, wherein the objective function is represented by the following formula:
where E is annual energy production, N is number of sections, P is output power, f () is probability density distribution function, and v is wind speed.
7. The utility model provides a fan blade optimal design system based on Wilson's model and particle swarm algorithm which characterized in that includes:
the building module is used for determining blade design parameters to be optimized and building an initial blade model;
the first calculation module is used for equally dividing the initial blade model into a preset number of phyllines along the spanwise direction, determining the airfoil profile of each phylline, and iteratively calculating the axial induction coefficient and the tangential induction coefficient of each airfoil profile through a Wilson model;
the second calculation module is used for calculating the torsion angle and the chord length of each airfoil according to the axial induction coefficient and the tangential induction coefficient of each airfoil;
and the optimization module is used for taking the torsion angle and the chord length of each airfoil as input values of the particle swarm algorithm, and simultaneously re-optimizing the torsion angle and the chord length of each airfoil through the particle swarm algorithm.
8. The system of claim 7, wherein the optimization module is further configured to:
and determining the chord length and the twist angle of the fan blade according to the twist angle and the chord length of each re-optimized airfoil profile, and fitting the airfoil and the profile parameters of each airfoil.
9. The system of claim 7 or 8, wherein the first computing module is specifically configured to:
determining initial values of the axial induction coefficient and the tangential induction coefficient;
obtaining a relation between a preset inflow angle and the axial induction coefficient and the tangential induction coefficient, and obtaining an iterative formula of the preset axial induction coefficient and the preset tangential induction coefficient;
and performing iterative calculation on the axial induction coefficient and the tangential induction coefficient according to the relational expression and the iterative formula until the axial induction coefficient and the tangential induction coefficient are converged.
10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements a Wilson model and particle swarm algorithm based fan blade optimization design method as claimed in any one of claims 1-6.
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| CN117744283B (en) * | 2024-02-20 | 2024-04-30 | 陕西空天信息技术有限公司 | Design method, device, equipment and computer storage medium for compressor |
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