CN113722955B - Wind turbine generator bearing block structure optimization design method based on finite elements - Google Patents
Wind turbine generator bearing block structure optimization design method based on finite elements Download PDFInfo
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Abstract
The invention provides a wind turbine generator bearing seat structure optimization design method based on finite elements, which comprises the following steps: s1, acquiring a three-dimensional geometric model, a finite element model and a simulation analysis report of a bearing pedestal to be optimally designed; s2, establishing a reference coordinate system, taking the local geometric characteristics of the bearing seat as a displacement reference, and storing and associating the nodes to a control parameter; s3, linking the control parameters with the finite element model of the bearing seat; s4, gridding the adjusted finite element model of the bearing seat and outputting a new model; s5, integrating the process parameters and the model, collecting a plurality of sample points to perform sample calculation, and obtaining corresponding calculation results; s6, performing optimal feasible domain calculation; and S7, obtaining an optimal size optimization scheme through optimization calculation. The defects of insufficient deformation control depth, large deformation error, structural reconstruction verification workload after deformation, complex optimization technical route and low efficiency of the conventional bearing seat optimization design technology are overcome.
Description
Technical Field
The invention relates to the technical field of wind power generation, in particular to a wind turbine generator bearing seat structure optimization design method based on finite elements.
Background
The research and development of wind turbine generators in various domestic host computer factories are developed along the direction of high capacity and high conversion efficiency, the bearing seat for installing and fixing the main bearing is increased and weighted, and the problem of reducing weight, cost and efficiency, which is urgently needed to be solved by host computer manufacturers, is solved.
The design is carried out based on experience, the iteration time is long, and the design period of the product is uncontrollable; some existing optimization technologies can optimize a bearing seat with a simple structure to a certain extent through topological optimization and deformation optimization. However, for some bearing blocks with complex structures and assembly requirements, only approximate deformation can be performed, and it is difficult to accurately, effectively and deeply control detailed deformation and optimization design, and it is difficult to fully excavate a weight-reducing space, and a large number of errors are introduced due to approximate deformation to interfere with optimization of an optimization algorithm.
Disclosure of Invention
In view of the above, the invention provides a wind turbine generator bearing seat structure optimization design method based on finite elements, and overcomes the defects of insufficient deformation control depth, large deformation error, structural reconstruction verification workload after deformation, complex optimization technology route and low efficiency of the existing bearing seat optimization design technology.
The invention solves the technical problems through the following technical means: the invention provides a wind turbine generator bearing seat structure optimization design method based on finite elements, which comprises the following steps:
s1, acquiring a three-dimensional geometric model, a finite element model and a simulation analysis report of a bearing seat to be optimally designed;
s2, determining optimizable structural features and process parameter ranges of the bearing seat according to the three-dimensional geometric model and the finite element model in the step S1, establishing a reference coordinate system, taking local geometric features of the bearing seat as displacement references, selecting surface nodes of the structural features to be modified, moving the surface nodes to adjust the process parameters to limit values, and storing and relating the nodes to a control parameter;
s3, linking the control parameters in the step S2 with the finite element model of the bearing seat, and adjusting the technological parameters of each structural feature of the bearing seat by modifying the control parameters;
s4, gridding the finite element model of the bearing seat adjusted in the step S3 and outputting a new model;
s5, integrating the process parameters and the models in the steps S2 and S3, collecting a plurality of sample points for sample calculation, and obtaining corresponding calculation results;
s6, extracting the calculation result in the step S5 to perform optimal feasible domain calculation;
and S7, performing optimization calculation by taking the control parameters in the step S2 as design variables and the feasible domain boundary obtained in the step S6 as upper and lower limits, and obtaining an optimal size optimization scheme through optimization calculation.
Further, the calculation conditions of the finite element model in the step S1 include an ultimate strength condition and a fatigue accumulated damage condition.
Further, in step S2: the process parameter range comprises a chamfer angle, the thickness of a thin-wall structure and the radius of a revolving body.
Further, in step S7, the optimization calculation includes: s71, taking the control parameters in the step S2 as design variables, and taking the feasible domain boundary obtained in the step S6 as an upper limit and a lower limit; s72, setting the maximum stress of the selected working condition as a maximum value for constraint; s73, minimizing the volume as an optimization target; and S74, carrying out numerical value optimizing calculation by using a Hooke-Jeeves algorithm to obtain an optimal size optimization scheme.
Further, after the step S7, the method also comprises the step S8 of outputting the optimization scheme model of the step S7, modifying the working condition to carry out fatigue verification, adjustment and verification until the fatigue verification, the adjustment and the verification are met, and then the optimization scheme is used as a final optimization scheme; and S9, carrying out geometric reconstruction on the final optimization scheme to obtain the final optimization design.
According to the technical scheme, the invention has the beneficial effects that: the invention provides a wind turbine generator bearing seat structure optimization design method based on finite elements, which comprises the following steps: s1, acquiring a three-dimensional geometric model, a finite element model and a simulation analysis report of a bearing pedestal to be optimally designed; s2, establishing a reference coordinate system, taking the local geometric characteristics of the bearing seat as a displacement reference, and storing and associating the nodes to a control parameter; s3, linking the control parameters with the finite element model of the bearing seat; s4, gridding the adjusted finite element model of the bearing seat and outputting a new model; s5, integrating the process parameters and the model, collecting a plurality of sample points to perform sample calculation, and obtaining corresponding calculation results; s6, performing optimal feasible domain calculation; and S7, obtaining an optimal size optimization scheme through optimization calculation. The defects of insufficient deformation control depth, large deformation error, workload verification of structure reconstruction after deformation and complex and low-efficiency optimization technical route of the conventional bearing seat optimization design technology are overcome.
Drawings
In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a symmetrical cross-sectional view of the bearing seat base geometry;
FIG. 3 is a grid model of a bearing block base configuration;
FIG. 4 is a comparison graph before and after adjustment of chamfer angle parameters;
FIG. 5 is a comparison graph of the thickness parameter of the first thickness of the revolution solid before and after adjustment;
FIG. 6 is a comparison graph of the thickness parameter before and after adjustment of the second thickness;
FIG. 7 is a comparison graph of thickness parameter adjustment before and after thickness three;
FIG. 8 is a comparison graph of the thickness parameter before and after adjustment of the thickness four;
FIG. 9 is a comparison graph of thickness parameters before and after adjustment of thickness five;
FIG. 10 is a diagram illustrating the optimization results;
reference numerals: 1-thickness one; 2-thickness two; 3-thickness three; 4-thickness four; 5-thickness five; 6-chamfering angle; 11-mounting a boss; 12-mounting a boss; 13-hoisting holes; 14-a gearbox mounting; 15-mounting a boss; 16-a front bearing mount; 17- -rear bearing mount.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and therefore are only examples, and the protection scope of the present invention is not limited thereby.
Referring to fig. 1 to 10, the invention provides a wind turbine generator bearing seat structure optimization design method based on finite elements, which includes the following steps:
s1, as shown in figure 2, acquiring a three-dimensional geometric model, a finite element model and a simulation analysis report of a bearing pedestal to be optimally designed; the finite element model calculation working conditions comprise an ultimate strength working condition and a fatigue accumulated damage working condition, and performance data under the working conditions are calculated.
S2, determining the optimizable structural characteristics and the technological parameter range of the bearing seat according to the three-dimensional geometric model and the finite element model in the step S1, wherein the technological parameter range comprises: the method comprises the steps of chamfering angles, the thickness of a thin-wall structure and the radius of a revolving body, establishing a reference coordinate system, taking the local geometric characteristics of a bearing seat as a displacement reference, selecting a surface node of the structural characteristics to be modified, moving the surface node to adjust the process parameters to be limiting values, and storing and relating the nodes to a control parameter; the movement of all nodes is directly controlled to realize the precise adjustment of the structure size in each position according to the process characteristics, and a control body is not adopted to roughly deform the grid of the envelope area. The method has the advantages that accurate adjustment of characteristics is directly achieved based on finite element model grid nodes, accurate control can be effectively conducted on all types of structural characteristics, complex characteristics including thin walls, chamfers, holes and other narrow and irregular regions can be effectively conducted in parameter optimization aiming at the characteristic structure size of the bearing seat of the wind turbine generator, the adjustment process is achieved by directly controlling the displacement of the nodes, other characteristics are restrained, technological requirements are met, and the appearance difference between an adjusted simulation model and an actual processing structure is guaranteed to be within an error allowable range. Directly controlling and restraining the movement of all nodes to realize the structure size adjustment; a coordinate system and a reference system of a characteristic surface are introduced to define node movement; methods of influencing cell regions are introduced to define local adjustment of features; a displacement constraint method is introduced to keep the technological characteristics of the appearance unchanged in the automatic structure optimization calculation process.
Specifically, according to the bearing seat basic model simulation report and the structure thereof in the step S1, the structural characteristics which can be optimally adjusted and the technological parameter ranges thereof (such as chamfer angle, thin-wall structure thickness and revolving body radius) are determined; the method comprises the steps of referencing a coordinate system, geometric features, constraining nodes of other features, selecting surface nodes of structural features to be modified and units to be influenced by the surface nodes, moving the surface nodes, adjusting process parameters of the features to be limited allowable values, storing and associating the node displacement groups to a control parameter, and modifying the process parameters of the finite element model through adjusting the parameters. In the adjustment process, a coordinate system and geometric characteristics are introduced as displacement references, a selected influence unit area is introduced, and a method for guaranteeing process characteristics through the reference system and displacement constraints is introduced, so that the node level accurate adjustment is directly performed on the finite element model structure, the difference between the approximate grid deformation and the actual structure is avoided, and the repeated reconstruction and verification of the optimized structure are not required.
A freedom degree constraint system is introduced, and a node can be selected to carry out freedom degree constraint, so that the influence of local characteristic adjustment on an accessory structure is avoided. The constraint fillet is tangent to two chamfered surfaces in the chamfering angle change process; and the constraint thin-wall structure region is always in a torus and the like in the deformation process.
S3, linking the control parameters in the step S2 with the finite element model of the bearing seat, and adjusting the technological parameters of each structural feature of the bearing seat by modifying the control parameters;
specifically, the structural characteristic parameters of 1 chamfer and 5 thicknesses are selected for adjustment. As shown in fig. 4, the adjustment of the chamfer angle parameter is shown as an example. Firstly, using general finite element pretreatment software, and selecting nodes on a chamfer surface as points to be moved; secondly, selecting a grid of the chamfer area as a grid to be influenced by the movement of the node; thirdly, selecting nodes of other surfaces, restraining the movement of the nodes, and ensuring that the other characteristic surfaces are not influenced in the process of adjusting the characteristic size of the chamfer; fourthly, creating a cylindrical coordinate system of the rotation center of the bearing seat; fifthly, two surfaces connected with the fillet are created, wherein one bearing seat is provided with an inner cylindrical surface, and the other right circular ring surface; sixthly, chamfering the two surfaces again, wherein the fillet radius is 250mm (serving as the smallest fillet possible in the process); and seventhly, projecting the nodes on the chamfer surface onto a new chamfer surface, realizing the accurate adjustment of the fillet parameters of the chamfer, influencing the deformation of the unit, storing the displacement of all the nodes at the moment, and relating the displacement to a control parameter. This modification of the fillet radius can thus be achieved by modifying the control parameters.
In fig. 4, the initial maximum chamfer radius before the position a is adjusted is 0.4325m, and the control parameter =0; b, adjusting the position to ensure that the minimum chamfer radius is 0.25m and the control parameter =1;
in fig. 5, the c position is the initial maximum thickness, and the control parameter =0; d is the minimum thickness, control parameter =1;
the e position in fig. 6 is the initial maximum thickness control parameter =0; f is the minimum thickness, and the control parameter =1;
the g position in fig. 7 is the initial maximum thickness control parameter =0; h is the minimum thickness, control parameter =1;
in fig. 8, the i position is the initial maximum thickness control parameter =0; j is the minimum thickness, control parameter =1;
the k position in fig. 9 is the initial maximum thickness control parameter =0; l is minimum thickness, control parameter =1;
the same method is adopted to obtain other 5 thickness characteristic adjusted node displacement groups, and the node displacement groups are associated to respective control parameters, except that the node moves directly along the radial direction of the bearing seat cylindrical coordinate system by the same displacement, so that the surface nodes of all the characteristics under adjustment are accurately ensured to be circumferentially symmetrical along the cylindrical coordinate system.
Table 1 shows the value ranges of the determined characteristic adjustment control parameters. TABLE 1
S4, gridding the finite element model of the bearing seat adjusted in the step S3 and outputting a new model; the finite element preprocessing software is automatically called by editing the script, and the parameters are controlled by modifying the characteristics to adjust the structural characteristic parameters. And optimizing the quality of the adjusted bearing seat grid, outputting a new model, wherein the position of the surface node of the model is unchanged, and the quality of the internal units is completely optimized so as to avoid the calculation failure caused by the grid quality problem due to deformation. The edited script is used for opening a modeling program and reading a model, submitting a structural characteristic size adjusting instruction and a size adjusting numerical value to the modeling program so as to execute model characteristic size adjustment, then executing grid instruction adjustment, and outputting the model with the latest adjusted characteristic size after the model characteristic size adjustment is finished. The finite element preprocessing software used in the method is hypermesh.
And editing a script to realize automatic calling of finite element preprocessing software, performing quality optimization on the adjusted bearing seat grid, and outputting an adjusted bearing seat grid model. Through the deformation execution script program, a bearing seat finite element model which is adjusted by means of finite element pretreatment software can be submitted for grid optimization, a new model is output, the position of a node on the surface of the model is unchanged, and the internal unit quality is completely optimized so as to avoid the calculation failure caused by the grid quality problem due to deformation. The edited script is used for batch processing of files for opening the modeling program and reading the model, and is submitted to the script in step S4.
S5, integrating the process parameters and the models in the steps S2 and S3, collecting a plurality of sample points to perform sample calculation, and obtaining corresponding calculation results;
specifically, an edited script program and a template model file are sequentially integrated through a self-editing script or a test design component by means of software; and the automatic sequential execution of bearing seat structure adjustment, grid quality optimization, model calculation and stress and quality post-processing output is realized.
Taking 6 characteristic control parameters as design variables, wherein the upper limit and the lower limit of the characteristic control parameters are variable ranges, sampling the design variables by using an optimized Latin hypercube algorithm to obtain 10 control parameter value combinations, and uniformly distributing 10 values of each control parameter in the upper limit and the lower limit;
the experimental design component generates 9-13 design matrixes by adopting an optimized Latin hypercube method. The bearing block mainly bears load in a central area through assembly with the bearing, the mounting bosses on two sides are supported by the main frame, the form of a force transmission path is single and uniform, and meanwhile, the size and the mass of the main frame and the bearing block in the wind turbine generator set are huge, so that the influence and the sensitivity of size parameters of each area of the bearing block on the performance of each working condition are not highly nonlinear, and a general relevant curve is a monotonous and gentle curve. Therefore, each optimized size variable only needs to uniformly collect 9-13 points within an allowable range, namely 9-13 sample points, and sample calculation is carried out for 9-13 times;
and (4) submitting and executing each sample combination, completing design sampling of the bearing seat, and calculating sensitivity information of each stress and quality to each control parameter through a mathematical algorithm.
S6, extracting the calculation result in the step S5 to perform optimal feasible domain calculation;
specifically, based on the input and output data of part of the test sample points obtained in the step S5, 1/5 of the length of the allowable range of each optimized size variable is used as a variable (continuous translation), the maximum stress of the sample in the range is used as a main evaluation index, the weight of the maximum stress is 4, the maximum and minimum mass of the bearing seat is used as a next-level evaluation index, the weight of the maximum stress is 1, and an optimal feasible region is calculated, that is, the optimal feasible region of each optimized size variable is obtained through 10 calculations of a sparse test design method.
And S7, performing optimization calculation by taking the control parameters in the step S2 as design variables and the feasible domain boundary obtained in the step S6 as upper and lower limits, and obtaining an optimal size optimization scheme through optimization calculation.
S71, taking the control parameters in the step S2 as design variables, and taking the feasible domain boundary obtained in the step S6 as an upper limit and a lower limit; s72, setting the maximum stress of the selected working condition as a maximum value for constraint; s73, minimizing the volume as an optimization target; and S74, carrying out numerical value optimizing calculation by using a Hooke-Jeeves algorithm to obtain an optimal size optimization scheme.
Table 2 shows comparison of values of characteristic control parameters before and after optimization, and Table 2 shows
And S8, outputting the optimization scheme model obtained in the step S7, and modifying the working condition to perform fatigue verification, adjustment and verification until the condition is met, namely, taking the optimization scheme as a final optimization scheme.
And S9, carrying out geometric reconstruction on the final optimization scheme to obtain the final optimization design.
Table 3 shows the comparison of the main performance analysis results of the optimized front and rear bearing blocks, the weight reduction is realized by 8.12 tons, and the stress and the fatigue are increased to some extent but the strength requirements are met, and Table 3 shows that
In conclusion, the optimized rear bearing seat reduces the mass from 53.15t to 45.03t, reduces the mass by 15.3%, slightly increases the fatigue accumulated damage, slightly reduces the ultimate working condition strength of the bearing seat, but still meets the design requirements, and fully excavates the weight reduction potential of the bearing seat.
In the scheme, because the grid nodes are the minimum units of the finite element model, the method can effectively and accurately control and optimize all types of structural features, wherein the complex features comprise thin walls, chamfers, openings and other narrow and irregular regions.
According to the method, a freedom degree constraint system is introduced, the freedom degree constraint can be carried out on the selected nodes, and the influence of local feature adjustment on the accessory structure is avoided. The constraint fillet keeps tangency with two surfaces to be chamfered in the chamfering angle change process; the constraint thin-wall structure area is always positioned on a torus and the like in the deformation process
The method adopts various reference systems to adjust the complex structure, and ensures the structural change and the composite process requirement. The size of the inner diameter of the revolving body is ensured to be always displaced along the radial direction in the inner diameter adjusting process by referring to a cylindrical coordinate system; the surface nodes can also be projected and changed directly to the geometric surface as a reference.
The method introduces a grid influence area method, can directly specify the units to be influenced in the structure adjustment, and the units other than the selected units are not influenced, so as to conveniently realize local feature deformation.
The method can quickly realize accurate adjustment of the characteristics and the size of the result based on the finite element model, and ensure that the appearance difference between the changed simulation model and the actual processing structure is within an error allowable range. Compared with the existing approximate rough deformation optimized by the control body, the method has the advantages that: the advantage of model structure precision avoids the interference to the optimization algorithm, and the result of the calculated sample directly represents the simulation result of the actual manufacturing structure; the complex detailed characteristics can be accurately controlled, so that the space for optimizing the quality and the performance of the structure can be deeply excavated; the reference system and the freedom degree constraint technology are adopted, the explosion structure meets the manufacturing process requirement, the structural performance of the optimized design expresses the performance of the actual manufacturing structure, reconstruction and verification are not needed after each optimization, only the final optimization scheme needs to be geometrically reconstructed, the workload is greatly reduced, the process is simplified, and the efficiency and the optimization effect are improved.
Compared with the existing wind turbine generator bearing block optimization strategy, the method has the following advantages:
1. the sample size is small, and the calculated amount is small. The existing optimization strategy comprises large sample size test design, approximate modeling, optimization calculation, optimization scheme reconstruction and reconstruction geometric verification. The large sample amount calculation is more precise to excavate the design space, but based on the characteristics that the bearing singleness of a bearing seat of the wind turbine generator and the influence of the size parameters of each area on the performance of each working condition are generally monotonous and gentle curves, the low sample amount experimental design calculation is executed, 9-13 design matrixes are generated by adopting an optimized Latin hypercube method, and the optimal feasible area can be preliminarily determined by performing 9-13 times of sample calculation; in the existing test design, 36 sample points are needed for 7-variable sample calculation, 43 sample points are needed for 2-order approximation model establishment, 50 sample points are needed for 4-order approximation model establishment, and the number of samples is more, so that the number of sample calculation failures is higher.
2. Based on the characteristic that the influence of the size parameters of each area on the performance of each working condition is generally a monotonous and gentle curve, a Hooke-Jeeves algorithm is adopted, the step length is set, optimization is carried out for about 10 times, and the optimal solution or the adjacent solution thereof can be found, namely, the test design and optimization calculation only need 19 to 23 times of simulation calculation.
An approximate modeling method is not introduced in the method, so that the proxy model error and the follow-up optimization model reconstruction verification work are avoided, the workload is greatly reduced, the flow is simplified, and the efficiency is improved.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (5)
1. A wind turbine generator bearing block structure optimization design method based on finite elements is characterized by comprising the following steps: the method comprises the following steps:
s1, acquiring a three-dimensional geometric model, a finite element model and a simulation analysis report of a bearing pedestal to be optimally designed;
s2, determining optimizable structural features and process parameter ranges of the bearing seat according to the three-dimensional geometric model and the finite element model in the step S1, establishing a reference coordinate system, taking local geometric features of the bearing seat as displacement references, selecting surface nodes of the structural features to be modified, moving the surface nodes to adjust the process parameters to limit values, and storing and relating the nodes to a control parameter;
s3, linking the control parameters in the step S2 with the finite element model of the bearing seat, and adjusting the process parameters of each structural feature of the bearing seat by modifying the control parameters;
s4, gridding the finite element model of the bearing seat adjusted in the step S3 and outputting a new model;
s5, integrating the process parameters and the models in the steps S2 and S3, collecting a plurality of sample points for sample calculation, and obtaining corresponding calculation results;
s6, extracting the calculation result in the step S5 to perform optimal feasible domain calculation;
and S7, performing optimization calculation by taking the control parameters in the step S2 as design variables and the feasible domain boundary obtained in the step S6 as upper and lower limits, and obtaining an optimal size optimization scheme through optimization calculation.
2. The wind turbine generator bearing seat structure optimization design method based on finite elements according to claim 1, wherein the calculation conditions of the finite element model in the step S1 include an ultimate strength condition and a fatigue accumulated damage condition.
3. The wind turbine bearing seat structure optimization design method based on finite elements according to claim 1, wherein in step S2: the process parameter range comprises a chamfer angle, the thickness of a thin-wall structure and the radius of a revolving body.
4. The wind turbine generator bearing support structure optimization design method based on finite elements as claimed in claim 1, wherein in step S7, the optimization calculation includes: s71, taking the control parameters in the step S2 as design variables, and taking the feasible domain boundary obtained in the step S6 as an upper limit and a lower limit; s72, setting the maximum stress of the selected working condition as a maximum value for constraint; s73, minimizing the volume as an optimization target; and S74, carrying out numerical value optimizing calculation by using a Hooke-Jeeves algorithm to obtain an optimal size optimization scheme.
5. The wind turbine generator bearing seat structure optimization design method based on finite elements according to claim 1, characterized by further comprising, after the step S7, S8, outputting the optimization scheme model of the step S7, modifying the working conditions to perform fatigue verification, adjustment and verification until the requirements are met, and then taking the result as a final optimization scheme; and S9, carrying out geometric reconstruction on the final optimization scheme to obtain a final optimization design.
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