CN115935808A - Turbine blade cross rib cooling structure parameterization optimization design method and system - Google Patents
Turbine blade cross rib cooling structure parameterization optimization design method and system Download PDFInfo
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Abstract
A turbine blade cross rib cooling structure parameterization optimization design method and a system belong to the field of impeller machinery, and solve the problems that in the traditional turbine blade cross rib cooling structure optimization design process, a limited number of times, repeatability and empirical design test schemes need to be manually optimized, so that the workload is huge, the optimization result has certain discreteness, and the optimal result is difficult to find, and the invention has the key points that: the method comprises the steps of dividing a turbine blade air film into four regions, respectively carrying out optimization design on the four regions to obtain optimization design variables, carrying out UG (Unigraphics) parameterized modeling on the four regions according to the optimization design variables, carrying out automatic grid division on the model, carrying out CFD (computational fluid dynamics) calculation and post-processing, extracting a target function, judging the target function, outputting an optimization result, and finishing the optimization design method. The method is suitable for the optimized design of the internal cross rib structure of the gas turbine blade.
Description
Technical Field
The invention relates to the field of impeller machinery, in particular to a parameterization optimization design method for a cross rib cooling structure of a turbine blade.
Background
In order to improve the thermal efficiency of modern gas turbines, the turbine inlet temperature is increased, and it is necessary to cool the inside of the turbine blade to ensure the turbine can work stably and reliably at high temperature, and it is urgent to find an excellent internal cooling structure. The cross-rib cooling structure is widely adopted for internal cooling of turbine blades due to its good cooling performance.
The cross-rib channels, also known as Matrix coating channels (Matrix coating channels) or grid rib channels, are one form of internal Cooling. The upper and lower wall surfaces of the channel are provided with mutually staggered fins to divide the whole internal cooling channel into a plurality of upper and lower sub-channels. The air flow, as it passes through each sub-channel, is deflected to the next sub-channel. In the turning position, strong impact and rotation between the airflow and the wall surface of the rib occur, and strong eddy is generated to take away heat on the wall surface of the rib. The cooling mode has the advantages that the flow of the cold air can be greatly increased, the cold air is in full contact with the wall surface, and the heat exchange characteristic is good. Meanwhile, the fins are arranged in a staggered mode, so that the excellent heat conducting performance of the fins can be exerted to the maximum extent, and the temperature distribution of the surface of the blade is more uniform.
The performance parameters for measuring the quality of the cross rib channel mainly comprise two. One is to characterize the heat exchange enhancement ratio Nu/Nu of the convective heat exchange capability 0 (ii) a Secondly, the parameter flow resistance coefficient ratio f/f for representing the pressure loss of the airflow flowing through the cross rib channel 0 . For a particular type of cross rib, the higher the heat transfer enhancement ratio, the lower the flow resistance ratio under the same conditions, and the better the overall performance of the cross rib. In order to characterize the overall performance of the cross-ribs, an overall heat transfer factor TPF was introduced, which is defined as:
when the flow parameters are fixed, for a certain type of cross rib, the comprehensive heat exchange performance is mainly influenced by the structure and arrangement mode of the cross rib.
In the traditional optimization design process of the turbine blade cross rib cooling structure, a limited number of times, repeatability and experience of manual optimization are required to design a test scheme, so that the workload is huge, the optimization result has certain discreteness, and the optimal result is difficult to find.
Under the condition of not changing flow parameters, the optimized design of the cross rib cooling structure can optimize the comprehensive heat exchange performance of the cross ribs, greatly enhance the heat exchange capability of the cross rib cooling structure, reduce the surface temperature of the blades and enable the overall performance of the gas turbine to be higher on the first floor.
Disclosure of Invention
The invention aims to provide a parameterization optimization design method for a cooling structure of a cross rib of a turbine blade, which solves the problems that in the optimization design process of the cooling structure of the cross rib of the traditional turbine blade, a design test scheme with limited times, repeatability and experience is required to be manually optimized, so that the workload is huge, the optimization result has certain discreteness, and the optimal result is difficult to find.
A turbine blade cross-rib cooling structure parametric optimization design method, the method comprising:
s1: dividing the turbine blade air film partitioned cross rib cooling structure into four regions;
s2: optimally designing a first area of the four areas: extracting the characteristic design parameters of the first area, obtaining optimized design variables according to the characteristic design parameters of the first area, and setting the optimized design variables to obtain set optimized design variables;
s3: carrying out UG (user generated Unit) parameterized modeling on the first area according to the set optimal design variable;
s4: using an ICEM script file to automatically grid and divide the parameterized cross rib model;
s5: using CCL language in CFX software to: preprocessing the boundary condition of the calculation domain; calling a CFX solver, and performing solving calculation on the background of the calculation domain to obtain a solving result; compiling a CCL language, performing post-processing on the solving result, and outputting an objective function;
s6: judging the objective function, and outputting an optimization result when the objective function meets the design requirement; when the objective function does not meet the design requirement, optimizing the optimized design variables by using an optimization algorithm, and repeating S3-S6 until the optimized objective function meets the design requirement;
s7: and repeating the steps S2-S6, and carrying out optimization design on the rest of the four regions until the objective functions of the cross rib cooling structure meet the requirements, thereby completing the turbine blade cross rib cooling structure parameterized optimization design method.
Preferably, the four regions include: a forward region, a mid region, a rearward region, and a rearward region.
Preferably, the characteristic design parameters include a rib width ω, a rib pitch t, and a rib inclination β of the intersecting rib.
Preferably, the optimal design variables are set as: setting the upper value limit, the lower value limit and the initial value of the optimized design variable; the upper limit of the rib width is 2mm, the lower limit of the rib width is 1.2mm, and the initial value is 1.6mm; the upper limit of the rib spacing is 1.8mm, the lower limit of the rib spacing is 1mm, and the initial value is 1.4mm; the upper limit of the inclination angle of the rib is 40 degrees, the lower limit of the inclination angle of the rib is 20 degrees, and the initial value of the inclination angle of the rib is 30 degrees.
Preferably, the parametrically modeled parameters include: the rib pitch t, the rib height h, the rib width omega, the rib inclination angle beta and the number of the ribs N, wherein the parameters are expressed as y = (t, N, h, omega, beta).
Preferably, the constraint conditions of the parametric modeling are the length of the flow channel, the total length of the rib row and the arrangement direction of the ribs.
Preferably, the pre-treatment comprises: automatic setting and turbulence model selection.
Preferably, the optimization algorithm is a multi-island genetic algorithm.
Preferably, the objective function is the integrated heat transfer factor TPF,
wherein Nu is Nu 0 In order for a stationary pipe to develop a completely turbulent knoop value,
f is the flow resistance coefficient of the cross rib channel, f 0 Is the coefficient of friction of a smooth channel.
The invention also provides a turbine blade cross rib cooling structure parameterized and optimized design system, which comprises a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes a turbine blade cross rib cooling structure parameterized and optimized design method in any item.
The invention has the beneficial effects that:
the invention carries out automatic optimization operation on the optimized design parameters through an optimization algorithm; the UG parametric modeling method is used for realizing automatic reading of the cooling structure model of the cross ribs in the turbine blade, self-defining change of parameters and automatic output of a new model in a certain format; realizing automatic mesh division of a calculation model through a script file in ICEM software; the method comprises the following steps of finishing preprocessing operations such as automatic setting of a boundary condition of a computational domain, selection of a turbulence model and the like by utilizing a CCL language in CFX; background solution calculation of the calculation domain is realized by calling a CFX solver; and (4) realizing the post-processing of the solved result through the written CCL language, and outputting an objective function required by optimization. Through the steps, the automatic optimization process of the cooling structure parameters of the cross ribs in the turbine blade can be realized, the final optimization result is output, the optimal or suboptimal scheme of the cooling structure of the cross ribs can be stably and quickly obtained, time and labor are saved, and the beneficial effects of economy and high efficiency are generated.
Drawings
FIG. 1 is a flow chart of a cross-rib cooling structure optimization design method according to an embodiment
FIG. 2 is a schematic structural view of a turbine blade cross-rib cooling structure according to a second embodiment after the structure has been divided into a forward region, a mid region, an aft region, and an aft region;
fig. 3 is a schematic diagram of geometric design parameters of a front cross rib according to an eleventh embodiment, where characteristic parameters of a cross rib include a rib width w, a rib pitch t, and a rib inclination angle β, and cross rib channel region control parameters include a trapezoidal cross section length a, a trapezoidal cross section width b, a trapezoidal cross section height L, and a cross rib region height h, and the cross rib channel region control parameters are kept unchanged and only cross rib characteristic design parameters are changed in a cross rib optimization design process. (ii) a
Fig. 4 is a schematic view of an automatically divided front cross-rib grid according to an eleventh embodiment, including a partially enlarged view of the cross-rib grid;
fig. 5 is a schematic diagram of design parameters of a central cross rib geometry according to an eleventh embodiment, wherein channel control parameters in a cross rib region are kept unchanged, and only characteristic geometric parameters of a cross rib region, such as rib width w, rib spacing t and rib inclination angle β, are changed;
fig. 6 is a structural model of a rear cross rib according to the eleventh embodiment, which has a structure similar to that of a middle cross rib, and only the rib width w, the rib spacing t and the rib inclination angle β of the prototype rear cross rib are changed during the optimization process, and the rest of the design parameters are kept unchanged;
fig. 7 is a tail cross rib model according to the eleventh embodiment, which has a structure similar to that of a tail cross rib, and in the same way, only the rib width w, the rib spacing t and the rib inclination angle β of the prototype tail cross rib are changed in the optimization process, and the rest of the design parameters are kept unchanged;
fig. 8 is a flowchart of the multi-island genetic algorithm according to the eleventh embodiment.
Detailed Description
The first implementation mode comprises the following steps: this embodiment is explained with reference to fig. 1;
the parameterized and optimized design method for the cooling structure of the cross ribs of the turbine blade in the embodiment comprises the following steps of:
s1: dividing the turbine blade cross-rib cooling structure into four regions;
s2: optimally designing a first area of the four areas: extracting the characteristic design parameters of the first area, obtaining an optimized design variable according to the characteristic design parameters of the first area, and setting the optimized design variable to obtain a set optimized design variable;
s3: carrying out UG (user generated Unit) parameterized modeling on the first area according to the set optimal design variable;
s4: automatically meshing the parameterized cross rib model by using an ICEM script file;
s5: using CCL language in CFX software: preprocessing the boundary condition of the calculation domain; calling a CFX solver, and performing solving calculation on the background of the calculation domain to obtain a solving result; compiling a CCL language, performing post-processing on the solving result, and outputting an objective function;
s6: judging the objective function, and outputting an optimization result when the objective function meets the design requirement; when the objective function does not meet the design requirement, optimizing the optimized design variables by using an optimization algorithm, and repeating S3-S6 until the objective function obtained by optimization meets the design requirement;
s7: and repeating the steps S2-S6, and carrying out optimization design on the rest areas in the different areas until the objective functions of the cross rib cooling structure meet the requirements, so as to complete the parameterization optimization design of the turbine blade cross rib cooling structure.
Specifically, the method comprises the following steps:
and S4, in the automatic mesh division of the parameterized cross rib model, the mesh type is an unstructured mesh.
According to the parameterized and optimized design method for the cooling structure of the cross ribs of the turbine blade, the optimized design parameters are automatically optimized through an optimization algorithm; the UG parametric modeling method is used for realizing automatic reading of the cooling structure model of the cross ribs in the turbine blade, self-defining change of parameters and automatic output of a new model in a certain format; realizing automatic mesh division of a calculation model through a script file in self-written ICEM software; the method comprises the following steps of finishing preprocessing operations such as automatic setting of a boundary condition of a computational domain, selection of a turbulence model and the like by utilizing a CCL language in CFX; the background solution calculation of the calculation domain is realized by calling a CFX solver; and (4) realizing the post-processing of the solved result through the written CCL language, and outputting an objective function required by optimization. Through the steps, the automatic optimization process of the cooling structure parameters of the crossed ribs in the turbine blade can be realized, the final optimization result is output, the optimal or suboptimal scheme of the cooling structure of the crossed ribs can be stably and quickly obtained, time and labor are saved, and the beneficial effects of economy and high efficiency are generated.
The second embodiment: this embodiment is explained with reference to fig. 2;
this embodiment is a further illustration of the four regions described in the parametric optimization design method for a turbine blade cross-rib cooling structure described in the first embodiment.
The four regions described in this embodiment include: a forward region, a mid region, a rearward region, and a rearward region.
Specifically, the method comprises the following steps:
the turbine blade cross rib cooling structure is divided into a front region, a middle region, a rear region and a tail region according to the distance between the regions and a cooling working medium inlet, wherein the front region is closest to the inlet, and the tail region is farthest from the inlet. The principle of zoning is that zones are zoned according to the rib inclination angle β of the intersecting ribs, as shown in fig. 2, and the intersecting rib elements of each zone have the same rib inclination angle.
The cooling structure of the cross ribs of the turbine blade is divided into four areas, a block optimization design idea is adopted, and the optimization design platform is utilized to carry out optimization design on the cross rib structures of the four areas respectively, so that the optimization efficiency can be improved.
The third implementation mode comprises the following steps:
the present embodiment is a further illustration of the characteristic design parameters described in the turbine blade cross rib cooling structure parameterized and optimized design method described in the first embodiment.
The characteristic design parameters described in the present embodiment include the rib width ω, the rib pitch t, and the rib inclination β of the intersecting rib.
Specifically, the method comprises the following steps:
the parameters described in this embodiment are parameters that affect the performance of the cross ribs, i.e., the main parameters of the (objective function) comprehensive heat transfer factor TPF, and the reason why the cross rib channel region control parameters a, b, h, and L are not used as optimization variables is that the size of the geometric region of the cross ribs is fixed.
The fourth embodiment:
the present embodiment is a further example of setting the optimized design variables described in the method for parametrically optimizing the design of the turbine blade cross rib cooling structure according to the first embodiment or the third embodiment.
In this embodiment, the optimal design variables are set as: setting the upper value limit, the lower value limit and the initial value of the optimized design variable; the upper limit of the rib width is 2mm, the lower limit of the rib width is 1.2mm, and the initial value is 1.6mm; the upper limit of the rib spacing is 1.8mm, the lower limit of the rib spacing is 1mm, and the initial value is 1.4mm; the upper limit of the inclination angle of the rib is 40 degrees, the lower limit of the inclination angle of the rib is 20 degrees, and the initial value of the inclination angle of the rib is 30 degrees.
Specifically, the method comprises the following steps:
the initial value of the embodiment is the cross rib parameter of the turbine blade, because the optimization design only optimizes the characteristic parameter in a small range, if the parameter change is too severe, the original structure is distorted, and the initial value cannot be applied to the original blade.
The fifth embodiment:
the present embodiment is a further illustration of the parametric modeling described in the method for the parametric optimization design of the turbine blade cross rib cooling structure described in the first embodiment.
The parameters of the parametric modeling according to the present embodiment include: the rib pitch t, the rib height h, the rib width omega, the rib inclination angle beta and the number of the ribs N, wherein the parameters are expressed as y = (t, N, h, omega, beta).
Embodiment six:
the present embodiment is a further illustration of the parametric modeling described in the method for the parametric optimization design of the turbine blade cross rib cooling structure described in the first embodiment.
The constraint conditions of the parametric modeling in this embodiment are the length of the flow channel, the total length of the rib row, and the arrangement direction of the ribs.
Specifically, the method comprises the following steps:
the length of the flow channel affects the height L of the trapezoidal section, the total length of the rib row affects the height h of the crossed rib area, and the arrangement direction of the fins needs to be consistent with the height direction of the blades.
The reason for modeling the constraints described in this embodiment is that the optimization result of the cross rib is ultimately applied to a turbine blade whose geometry is certain, and the cross rib area must be limited to the inside of the blade to apply the cross rib to the blade.
Embodiment seven:
the present embodiment is further exemplified by the ICEM script file described in the method for parametrically optimizing and designing a turbine blade cross rib cooling structure according to the first embodiment.
The ICEM script file in the embodiment is a self-written ICEM script file.
Specifically, the method comprises the following steps:
the ICEM script file first sets the cross-ribbed section and then sets the mesh size and boundary layer of the set section. The ICEM script can realize automatic division of the unstructured grid.
The eighth embodiment:
the present embodiment is a further illustration of the pre-treatment described in the turbine blade cross rib cooling structure parametric optimization design method described in the first embodiment.
The pretreatment according to the present embodiment includes: automatic setting, turbulence model selection and the like.
Specifically, the method comprises the following steps:
and the automatic setting of the boundary conditions of the calculation domain, the selection of a turbulence model and other preprocessing processes are completed by using a CCL (short pass filter matrix) language in the CFX.
The ninth embodiment:
the present embodiment is a further illustration of the objective function described in the method for the parametric optimization design of the turbine blade cross rib cooling structure according to the first embodiment.
The objective function in this embodiment is a comprehensive heat transfer factor TPF, which specifically includes:
specifically, the method comprises the following steps:
the integrated heat transfer factor TPF can characterize the relationship between convective heat transfer capacity and pressure loss.
Embodiment ten:
the present embodiment is a further illustration of the optimization algorithm described in the method for the parameterized and optimized design of the cross-rib cooling structure of the turbine blade according to the first embodiment.
The optimization algorithm described in this embodiment is a multi-island genetic algorithm.
Specifically, the method comprises the following steps:
obtaining optimized design schemes by setting relevant parameters of a multi-island genetic algorithm and continuously carrying out iterative computation on the optimized schemes until the objective function meets the design requirements, and outputting an optimized result to obtain the optimal design scheme of the cross ribs.
Embodiment eleven: this embodiment will be described with reference to fig. 2, 4, 5, 6, and 7;
the present embodiment is a further illustration of the optimization algorithm described in the method for the parameterized and optimized design of the cross-rib cooling structure of the turbine blade according to the first embodiment.
Firstly, the internal cross rib cooling structure of the high-pressure turbine guide vane is divided into four regions, namely a front region, a middle region, a rear region and a tail region. Firstly, optimizing a front cross rib cooling structure, wherein the optimization objective function is the comprehensive heat exchange factor TPF of the cross rib structure, and is defined as:
selecting the rib width w, the rib spacing t and the rib inclination angle beta for controlling the characteristic shape of the crossed ribs as optimized design parameters, and setting the value range and the initial value of the optimized design variables, wherein the value range of the rib width is 1.2mm-2mm, the value range of the rib spacing is 1mm-1.8mm, and the value range of the rib inclination angle is 20-40 degrees; the initial value of rib width was taken to be 1.6mm, the initial value of rib pitch was taken to be 1.4mm, and the initial value of rib inclination was taken to be 30 °. For the middle cross rib, the range of the rib width is 0.9mm-1.5mm, the range of the rib spacing is 1mm-1.6mm, and the range of the rib inclination angle is 40-50 degrees; the initial value of the rib width was taken to be 1.2mm, the initial value of the rib pitch was taken to be 1.3mm, and the initial value of the rib inclination angle was taken to be 45 °. For the rear cross rib, the width of the rib ranges from 0.7mm to 1.1mm, the spacing of the rib ranges from 0.4mm to 0.8mm, and the inclination angle of the rib ranges from 60 degrees to 70 degrees; the initial value of rib width was taken to be 0.9mm, the initial value of rib pitch was taken to be 0.6mm, and the initial value of rib inclination was taken to be 65 °. For the tail cross ribs, the value range of the rib width is 0.6mm-1mm, the value range of the rib spacing is 0.3mm-1.6mm, and the value range of the rib inclination angle is 75-85 degrees; the initial value of the rib width was taken to be 0.8mm, the initial value of the rib pitch was taken to be 0.5mm, and the initial value of the rib inclination angle was taken to be 80 °.
And compiling a self-programming (C + +) program combined with UG by utilizing UG secondary development according to the set initial values of the cross ribs, and realizing the parametric modeling of the cooling structure of the cross ribs in the turbine blade. For the crossed ribs, the main parameters of the ribs include the rib spacing t, the rib height h, the rib width w, the rib inclination angle β and the number N of the ribs, and the parameters can be expressed as y = (t, N, h, ω, β), and the constraint conditions are the length of the flow channel, the total length of the rib row and the arrangement direction of the ribs. Fig. 4 shows the established anterior cross-rib modeling model, the characteristic parameter definitions of which have been indicated in the figure. Fig. 5 is a model of the established middle cross rib, fig. 6 is a model of the rear cross rib, and fig. 7 is a model of the rear cross rib.
And carrying out mesh division on the established model. The invention realizes the automatic generation of the grid by using ICEM, and realizes the automatic grid division of the parameterized model by calling the script file by using the script function in the ICEM. Fig. 4 shows a schematic view of a front cross-rib grid.
The CCL language in the CFX is used for realizing the functions of preprocessing, such as automatic setting of the boundary condition of the computational domain, selection of a turbulence model and the like; the background solution calculation of the calculation domain is realized by calling a CFX solver; and (4) realizing the post-processing of the solved result by writing a CCL language and outputting an objective function required by optimization.
And judging the objective function obtained by optimization, outputting an optimization result if the objective function meets the design requirement, and otherwise, optimizing the optimization design variables by using an optimization algorithm. The optimization algorithm adopts a multi-island genetic algorithm, for front cross ribs, the size of a subgroup of the optimization algorithm is 3, the number of islands is 3, the total evolution is 10 generations, the cross rate is 0.9, the variation rate is 0.01, the number of elite per generation is 1, and the total number of individuals is 90; for the middle cross rib, the subgroup scale is 3, the island number is 3, the total evolution is 10 generations, the cross rate is 0.9, the variation rate is 0.01, the number of elite in each generation is 1, and the total number is 90 individuals; for the posterior cross rib, the subgroup scale is 3, the island number is 3, the total evolution is 10 generations, the cross rate is 0.9, the variation rate is 0.01, the number of elite in each generation is 1, and the total number of 90 individuals is counted; for the tail cross rib, the subgroup size is 3, the island number is 3, a total of 10 generations of evolution are performed, the cross rate is 0.9, the variation rate is 0.01, the number of elite in each generation is 1, and 90 individuals are counted.
And repeating the steps, and continuously carrying out optimization design on the middle cross rib cooling structure, the rear cross rib cooling structure and the tail cross rib cooling structure until the objective functions of all the cross rib cooling structures meet the requirements. The final cross rib structure parameters obtained by the optimization design are as follows: the rib width of the front cross rib is 1.995mm, the rib spacing is 1.29mm, the rib inclination angle is 37.8 degrees, the optimized comprehensive heat exchange factor of the front cross rib reaches 0.197, and is improved by 24.68 percent compared with the prototype; the optimized rib width of the middle crossed ribs is 1.361mm, the rib spacing is 1.398mm, the rib inclination angle is 48.4 degrees, the comprehensive heat exchange factor is 0.246, and the improvement is 0.41 percent compared with the prototype; the optimized rib width of the rear cross rib is 0.8mm, the rib spacing is 0.62mm, the rib inclination angle is 69.2 degrees, the comprehensive heat exchange factor reaches 0.259, and is improved by 0.39 percent compared with the prototype; the optimized rib width of the tail cross rib is 0.945mm, the rib spacing is 0.48mm, the rib inclination angle is 84.7 degrees, the comprehensive heat exchange factor is 0.258, and the improvement is 3.49 percent compared with the prototype.
In conclusion, the method for parametrically and optimally designing the cooling structure of the cross ribs of the turbine blade is realized. Compared with the traditional method for manually optimizing the cooling structure of the cross ribs in the turbine, the optimization design method can realize the parameterized modeling and automatic output of the cooling structure model of the cross ribs in the turbine, the automatic generation of grids, the automatic CFD calculation and post-processing, and the automatic optimization process under a certain optimization strategy. The method is used for fully automating the optimization process, and the user can realize the automatic optimization of the cross rib cooling structure only by setting the value range and the initial value of the optimization design parameters, so that the optimal or suboptimal scheme of the cross rib cooling structure can be stably and quickly obtained, and the economic and efficient beneficial effects are generated.
Claims (10)
1. A method for parametrically optimizing the design of a turbine blade cross-rib cooling structure, the method comprising:
s1: dividing the turbine blade air film partitioned cross rib cooling structure into four regions;
s2: optimally designing a first area of the four areas: extracting the characteristic design parameters of the first area, obtaining an optimized design variable according to the characteristic design parameters of the first area, and setting the optimized design variable to obtain a set optimized design variable;
s3: carrying out UG (user generated Unit) parameterized modeling on the first area according to the set optimal design variable;
s4: using an ICEM script file to automatically grid and divide the parameterized cross rib model;
s5: using CCL language in CFX software to: preprocessing the boundary condition of the calculation domain; calling a CFX solver, and performing solving calculation on the background of the calculation domain to obtain a solving result; compiling a CCL language, performing post-processing on the solving result, and outputting an objective function;
s6: judging the objective function, and outputting an optimization result when the objective function meets the design requirement; when the objective function does not meet the design requirement, optimizing the optimized design variables by using an optimization algorithm, and repeating S3-S6 until the objective function obtained by optimization meets the design requirement;
s7: and repeating the steps S2-S6, and carrying out optimization design on the rest of the four regions until the objective functions of the cross rib cooling structure meet the requirements, thereby completing the turbine blade cross rib cooling structure parameterized optimization design method.
2. The parametric optimization design method for the cooling structure of the cross ribs of the turbine blade as claimed in claim 1, wherein the four regions comprise: a forward region, a mid region, a rearward region, and a rearward region.
3. The parameterized and optimized design method for the turbine blade cross rib cooling structure according to claim 1, wherein the characteristic design parameters include a rib width ω, a rib spacing t and a rib inclination angle β of the cross rib.
4. A turbine blade cross rib cooling structure parameterized and optimized design method according to claim 1 or 3, characterized in that the optimized design variables are set as: setting the upper value limit, the lower value limit and the initial value of the optimized design variable; the upper limit of the rib width is 2mm, the lower limit of the rib width is 1.2mm, and the initial value is 1.6mm; the upper limit of the rib spacing is 1.8mm, the lower limit of the rib spacing is 1mm, and the initial value is 1.4mm; the upper limit of the inclination angle of the rib is 40 degrees, the lower limit of the inclination angle of the rib is 20 degrees, and the initial value of the inclination angle of the rib is 30 degrees.
5. The method of claim 1, wherein the parametrically modeled parameters comprise: the rib pitch t, the rib height h, the rib width omega, the rib inclination angle beta and the number of the ribs N, wherein the parameters are expressed as y = (t, N, h, omega, beta).
6. The method as claimed in claim 1, wherein the constraints of parametric modeling are runner length, total length of rib row and arrangement direction of ribs.
7. The parametric optimization design method for the cooling structure of the cross ribs of the turbine blade as claimed in claim 1, wherein the pre-processing comprises: automatic setting and turbulence model selection.
8. The parameterized and optimized design method for turbine blade cross-rib cooling structures according to claim 1, characterized in that the optimization algorithm is a multi-island genetic algorithm.
9. The parameterized and optimized design method for turbine blade cross rib cooling structure of claim 1, wherein the objective function is a comprehensive heat exchange factor TPF,
wherein Nu is Nu 0 Nurseel number for complete development of turbulent flow for a stationary pipe, f is the flow resistance coefficient of the cross-ribbed channel, f 0 Is the coefficient of friction of the smooth channel.
10. A turbine blade cross-rib cooling structure parameterized and optimized design system, characterized in that the system comprises a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes a turbine blade cross-rib cooling structure parameterized and optimized design method according to any one of claims 1 to 9.
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| CN116976201A (en) * | 2023-07-10 | 2023-10-31 | 哈尔滨工业大学 | Self-programming parameterized modeling method and modeling system for micro turbine blade of breathing machine, computer readable storage medium and electronic equipment |
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2022
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116976201A (en) * | 2023-07-10 | 2023-10-31 | 哈尔滨工业大学 | Self-programming parameterized modeling method and modeling system for micro turbine blade of breathing machine, computer readable storage medium and electronic equipment |
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