CN113239491A - Multi-parameter optimization design method for box body reinforcing ribs in wind power gear box - Google Patents
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Abstract
The invention discloses a multi-parameter optimization design method for a box body reinforcing rib in a wind power gear box, which relates to the technical field of wind power.A three-dimensional model of a middle box body and an upper and a lower two-stage gear rings connected with the middle box body is established by taking the key size of the reinforcing rib as a design variable and is assembled; solving the maximum equivalent stress and mass of the middle box body under 18 groups of samples by orthogonal design and finite element simulation; describing the relation between the design variable x of the complex structure and the optimization target y through an approximate function y (x) obtained by a response surface method; global search is carried out on the feasible domain of the approximate function through a genetic algorithm to obtain an optimal design variable combination; rounding the design variables and carrying out finite element verification; by a response surface method of a genetic algorithm, the optimal design variable can be quickly obtained; and rounding the optimal design variable to obtain a design variable suitable for processing, and carrying out finite element simulation verification, thereby reducing the time cost and the production cost.
Description
Technical Field
The invention belongs to the technical field of wind power, and particularly relates to a multi-parameter optimization design method for a box body reinforcing rib in a wind power gear box.
Background
In a wind generating set, because the rotating speed of a wind wheel is very low and can not reach the rotating speed required by the power generation of a generator, the generator needs to obtain the corresponding rotating speed through the acceleration of a wind power gear box, and the wind power gear box plays a vital role in wind power equipment as an important core component. The wind power gear box generally adopts a structure of a one-stage planetary gear train and a two-stage parallel gear train or a structure of a two-stage planetary gear and a one-stage parallel gear train. Wherein, a connecting mechanism is required to be arranged between each gear train for transmitting power between the gear trains of different stages. And the wind power gear box has large transmission torque, complex working condition and high reliability requirement, so that a connecting mechanism between different gear trains of the wind power gear box has high torque transmission capacity and reliability, and simultaneously, good transmission stability is required to be ensured so as to meet the performance requirement of the whole wind power gear box.
Patent document CN110725939A discloses a wind power gear box, which comprises a planet carrier, a flange and a tubular shaft. Specifically, the flange can be fixed connection on the planet carrier, is provided with the locking hole on the flange. The end of the tubular shaft is provided with a stop part, and the stop part is matched with the stop hole and can be fixedly arranged in the stop hole in a penetrating mode, so that the tubular shaft can rotate synchronously with the planet carrier. According to the wind power gear box provided by the invention, the flange is fixedly connected to the planet carrier, the stop hole is formed in the flange, the stop part is arranged at the end part of the tubular shaft, and the stop part cannot rotate relative to the stop hole when being arranged in the stop hole in a penetrating manner, so that the tubular shaft and the planet carrier can synchronously rotate through the flange. The fixing mode between the tubular shaft and the planet carrier does not need a stop block, so that the number of parts is reduced, the structure is simpler, the installation is easy, and the production cost is low.
In the prior art, when the middle box body is designed, a large number of samples are often needed, so that the calculation cost is high, meanwhile, the quality of the middle box body is large, the manufacturing cost of the middle box body is high, and in order to solve the problems, the multi-parameter optimization design method for the reinforcing ribs of the box body in the wind power gear box is provided.
Disclosure of Invention
The invention aims to provide a multi-parameter optimization design method for a box body reinforcing rib in a wind power gear box.
The technical problem to be solved by the invention is as follows: how to minimize the quality of the middle box body through reasonable design so as to achieve the effect of reducing cost, and the optimal design variable can be quickly obtained through a response surface method of a genetic algorithm; and rounding the optimal design variable to obtain a design variable suitable for processing, and verifying the rounded design variable by using finite element simulation, so that a large amount of time cost is saved for practical engineering application, and the production cost of the middle box body can be reduced.
The purpose of the invention can be realized by the following technical scheme: a multi-parameter optimization design method for box body reinforcing ribs in a wind power gear box specifically comprises the following steps:
the method comprises the following steps: establishing a three-dimensional model of the middle box body and the upper and lower two-stage gear rings connected with the middle box body by taking the key size of the reinforcing rib as a design variable, and assembling;
step two: solving the maximum equivalent stress and mass of the middle box body under 18 groups of samples by orthogonal design and finite element simulation;
step three: describing the relation between the design variable x of the complex structure and the optimization target y through an approximate function y (x) obtained by a response surface method;
step four: global search is carried out on the feasible domain of the approximate function through a genetic algorithm to obtain an optimal design variable combination;
step five: rounding the design variables and carrying out finite element verification;
the specific obtaining process of the relation between the design variable x and the optimization target y in the third step comprises the following steps:
step S1: performing response surface analysis on the design variables and the optimization target to obtain a mathematical model; the mathematical model comprises an approximation function and a constraint condition;
step S2: by the formula
Obtaining an approximation function y (x), wherein i and j both represent each parameter in the critical dimension, and n is a positive integer; α 0, α i and α ij are undetermined coefficients;
step S3: and (3) performing precision test on the approximation function y (x) obtained in the step (S2), wherein the specific test process comprises the following steps:
step S31: the sample values obtained by finite element simulation calculation for 18 groups of sample points are marked as ypWherein p is 1, 2, … …, 18;
step S32: by the formula
Obtaining an average sample value YP of each group of sample points;
step S33: substituting the 18 groups of sample values into an approximation function y (x) to obtain an approximation YJ of the 18 groups of sample pointsp；
Step S34: by the formula
The precision coefficient RSME of the approximation function y (x) is obtained.
Further, the critical dimensions of the reinforcing ribs in the first step include number, thickness, length of the first section, length of the second section, height of the first section, and the number, thickness, length of the first section, length of the second section, height of the first section, and height of the second section are respectively marked as x1, x2, x3, x4, x5, and x 6; the acquisition mode of the design variable is to carry out discrete processing on the key size of the reinforcing rib and obtain discrete parameters.
Further, the discrete parameters are used for parameter modeling, and the established models are assembled to obtain an assembly drawing of the middle box body.
Further, in the second step, the specific process for acquiring the maximum equivalent stress and the mass of the middle box body comprises the following steps:
step D1: recording an upper limit value, a lower limit value and a middle value of each parameter in the critical dimension as horizontal factors;
step D2: matching 6 parameters in the critical dimension with an upper limit value, a lower limit value and 3 middle horizontal factors corresponding to the 6 parameters through orthogonal design, thereby obtaining 18 groups of sample points;
step D3: and obtaining the maximum equivalent stress and mass of the middle box body through finite element simulation calculation.
Further, the specific process of the fourth step and the fifth step comprises the following steps:
step Y1: by the formula SY (x) y (x) -C0Obtaining a fitness function SY (x), C0The weight of the middle box body under each initial variable is obtained;
step Y2: optimizing and iterating the genetic algorithm through MATLAB to obtain an optimal design variable; step Y3: and rounding the optimal design variable to obtain a design variable suitable for processing, and verifying the rounded design variable by using finite element simulation.
The invention has the beneficial effects that: by a response surface method of a genetic algorithm, the optimal design variable can be quickly obtained; and rounding the optimal design variable to obtain a design variable suitable for processing, and verifying the rounded design variable by using finite element simulation, so that a large amount of time cost is saved for practical engineering application, and the production cost of the middle box body can be reduced.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic block diagram of a multi-parameter optimization design method for box reinforcing ribs in a wind power gear box.
Detailed Description
As shown in fig. 1, a multi-parameter optimization design method for box body reinforcing ribs in a wind power gear box specifically comprises the following steps:
the method comprises the following steps: establishing a three-dimensional model of the middle box body and the upper and lower two-stage gear rings connected with the middle box body by taking the key size of the reinforcing rib as a design variable, and assembling;
step two: solving the maximum equivalent stress and mass of the middle box body under 18 groups of samples by orthogonal design and finite element simulation;
step three: describing the relation between the design variable x of the complex structure and the optimization target y through an approximate function y (x) obtained by a response surface method;
step four: global search is carried out on the feasible domain of the approximate function through a genetic algorithm to obtain an optimal design variable combination;
step five: and (4) rounding the design variables (actual processing requirements) and carrying out finite element verification.
The key dimensions of the strengthening ribs in the first step comprise the number, the thickness, the first section length, the second section length, the first section height and the second section height, and the number, the thickness, the first section length, the second section length, the first section height and the second section height are respectively marked as x1, x2, x3, x4, x5 and x 6; the obtaining mode of the design variable is to carry out discrete processing on the key size of the reinforcing rib and obtain discrete parameters;
the discrete parameters are used for parameter modeling, and the established model is assembled to obtain an assembly drawing of the middle box body;
in the second step, the specific process for acquiring the maximum equivalent stress and the mass of the middle box body comprises the following steps:
step D1: recording an upper limit value, a lower limit value and a middle value of each parameter in the critical dimension as horizontal factors;
step D2: matching 6 parameters in the critical dimension with an upper limit value, a lower limit value and 3 middle horizontal factors corresponding to the 6 parameters through orthogonal design, thereby obtaining 18 groups of sample points;
step D3: and obtaining the maximum equivalent stress and mass of the middle box body through finite element simulation calculation.
The specific obtaining process of the relation between the design variable x and the optimization target y in the third step comprises the following steps:
step S1: performing response surface analysis on the design variables and the optimization target to obtain a mathematical model; the mathematical model comprises an approximation function and a constraint condition;
step S2: by the formula
Obtaining an approximation function y (x), wherein i and j both represent each parameter in the critical dimension, and n is a positive integer; α 0, α i and α ij are undetermined coefficients; alpha 0, alpha i and alpha ij are obtained by MATLAB;
step S3: and (3) performing precision test on the approximation function y (x) obtained in the step (S2), wherein the specific test process comprises the following steps:
step S31: the sample values obtained by finite element simulation calculation for 18 groups of sample points are marked as ypWherein p is 1, 2, … …, 18;
step S32: by the formula
Obtaining an average sample value YP of each group of sample points;
step S33: substituting the 18 groups of sample values into an approximation function y (x) to obtain an approximation YJ of the 18 groups of sample pointsp；
Step S34: by the formula
Obtaining a precision coefficient RSME of an approximation function y (x);
the constraint condition is the allowable Von Mises stress of the middle box body.
The genetic algorithm is an adaptive global optimization probability search algorithm which simulates evolution law of the biological world, and the specific processes of the fourth step and the fifth step comprise the following steps:
step Y1: by the formula SY (x) y (x) -C0Obtaining a fitness function SY (x), C0The weight of the middle box body under each initial variable is obtained;
step Y2: optimizing and iterating the genetic algorithm through MATLAB to obtain an optimal design variable;
step Y3: rounding the optimal design variable to obtain a design variable suitable for processing, verifying the rounded design variable by using finite element simulation, and quickly obtaining the optimal design variable by a response surface method of a genetic algorithm; and rounding the optimal design variable to obtain a design variable suitable for processing, and verifying the rounded design variable by using finite element simulation, so that a large amount of time cost is saved for practical engineering application, and the production cost of the middle box body can be reduced.
The working principle is as follows: and (3) forming the key size of the reinforcing rib: number, thickness, length, height, respectively labeled x1, x2, x3, x4, x5, and x 6; the acquisition mode of the design variable is to carry out discrete processing on the key size of the reinforcing rib and obtain discrete parameters; performing parameter modeling through discrete parameters, and assembling the established model to obtain an assembly drawing of the middle box body; recording an upper limit value, a lower limit value and a middle value of each parameter in the critical dimension as horizontal factors, and matching 6 parameters in the critical dimension with the upper limit value, the lower limit value and the middle value of 3 horizontal factors corresponding to the 6 parameters through orthogonal design to obtain 18 groups of sample points; and obtaining the maximum equivalent stress and mass of the middle box body through finite element simulation calculation. Performing response surface analysis on the design variables and the optimization target to obtain a mathematical model; by the formula
Obtaining an approximation function y (x); then, the approximation function y (x) is subjected to precision test, specifically, the sample value obtained by finite element simulation calculation of 18 groups of sample points is marked as yp(ii) a By the formula
Obtaining an average sample value YP of each group of sample points; substituting the 18 groups of sample values into an approximation function y (x) to obtain an approximation YJ of the 18 groups of sample pointsp(ii) a Then by the formula
The precision coefficient RSME of the approximation function y (x) is obtained. The genetic algorithm is an adaptive global optimization probability search algorithm which simulates evolution law of the biological world and is evolved by the formula SY (x) y (x) -C0Obtaining a fitness function SY (x), C0The weight of the middle box body under each initial variable is obtained; optimizing and iterating the genetic algorithm through MATLAB to obtain an optimal design variable; rounding the optimal design variable to obtain a design variable suitable for processing, verifying the rounded design variable by using finite element simulation, and quickly obtaining the optimal design variable by a response surface method of a genetic algorithm; and rounding the optimal design variable to obtain a design variable suitable for processing, and verifying the rounded design variable by using finite element simulation, so that a large amount of time cost is saved for practical engineering application, and the production cost of the middle box body can be reduced.
The above formulas are all calculated by removing dimensions and taking numerical values thereof, the formula is a formula which is obtained by acquiring a large amount of data and performing software simulation to obtain the closest real situation, and the preset parameters and the preset threshold value in the formula are set by the technical personnel in the field according to the actual situation or obtained by simulating a large amount of data.
The foregoing is illustrative and explanatory of the structure of the invention, and various modifications, additions or substitutions in a similar manner to the specific embodiments described may be made by those skilled in the art without departing from the structure or scope of the invention as defined in the claims. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Claims (5)
1. A multi-parameter optimization design method for box body reinforcing ribs in a wind power gear box is characterized by comprising the following steps:
the method comprises the following steps: establishing a three-dimensional model of the middle box body and the upper and lower two-stage gear rings connected with the middle box body by taking the key size of the reinforcing rib as a design variable, and assembling;
step two: solving the maximum equivalent stress and mass of the middle box body under 18 groups of samples by orthogonal design and finite element simulation;
step three: describing the relation between the design variable x of the complex structure and the optimization target y through an approximate function y (x) obtained by a response surface method;
step four: global search is carried out on the feasible domain of the approximate function through a genetic algorithm to obtain an optimal design variable combination;
step five: rounding the design variables and carrying out finite element verification;
the specific obtaining process of the relation between the design variable x and the optimization target y in the third step comprises the following steps:
step S1: performing response surface analysis on the design variables and the optimization target to obtain a mathematical model; the mathematical model comprises an approximation function and a constraint condition;
step S2: by the formula
Obtaining an approximation function y (x), where i and j are both tabulatedEach parameter in the critical dimension is shown, and n is a positive integer; α 0, α i and α ij are undetermined coefficients;
step S3: and (3) performing precision test on the approximation function y (x) obtained in the step (S2), wherein the specific test process comprises the following steps:
step S31: the sample values obtained by finite element simulation calculation for 18 groups of sample points are marked as ypWherein p is 1, 2, … …, 18;
step S32: by the formula
Obtaining an average sample value YP of each group of sample points;
step S33: substituting the 18 groups of sample values into an approximation function y (x) to obtain an approximation YJ of the 18 groups of sample pointsp；
Step S34: by the formula
The precision coefficient RSME of the approximation function y (x) is obtained.
2. The multiparameter optimization design method for reinforcing ribs of a box body in a wind power gear box as claimed in claim 1, wherein the key dimensions of the reinforcing ribs in the first step comprise number, thickness, first section length, second section length, first section height and second section height, and the number, thickness, first section length, second section length, first section height and second section height are respectively marked as x1, x2, x3, x4, x5 and x 6; the acquisition mode of the design variable is to carry out discrete processing on the key size of the reinforcing rib and obtain discrete parameters.
3. The multi-parameter optimization design method of the box body reinforcing ribs in the wind power gear box is characterized in that the discrete parameters are used for parameter modeling, and the established models are assembled to obtain an assembly drawing of the box body.
4. The multi-parameter optimization design method for the reinforcing ribs of the box body in the wind power gear box is characterized in that in the second step, the specific obtaining process of the maximum equivalent stress and the mass of the box body comprises the following steps:
step D1: recording an upper limit value, a lower limit value and a middle value of each parameter in the critical dimension as horizontal factors;
step D2: matching 6 parameters in the critical dimension with an upper limit value, a lower limit value and 3 middle horizontal factors corresponding to the 6 parameters through orthogonal design, thereby obtaining 18 groups of sample points;
step D3: and obtaining the maximum equivalent stress and mass of the middle box body through finite element simulation calculation.
5. The multi-parameter optimization design method for the box body reinforcing rib in the wind power gear box is characterized in that the specific processes of the fourth step and the fifth step comprise the following steps:
step Y1: by the formula SY (x) y (x) -C0Obtaining a fitness function SY (x), C0The weight of the middle box body under each initial variable is obtained;
step Y2: optimizing and iterating the genetic algorithm through MATLAB to obtain an optimal design variable;
step Y3: and rounding the optimal design variable to obtain a design variable suitable for processing, and verifying the rounded design variable by using finite element simulation.
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