Low stress multidisciplinary integrated design method and system for turbine air-cooled blades

Abstract

The low-stress multidisciplinary integrated design method and system for turbine air-cooled blades belongs to the field of energy and power technology, and solves the problems in engine air-cooled turbine blades caused by complex blade structures, large temperature differences in different parts, and deformation that has a great impact on heat flow. The method includes: S1: collecting parameters of the part to be optimized of the turbine, and performing parametric design on the part to be optimized; S2: establishing a structured grid of the part to be optimized, and calculating the mass and strength of the solid domain of the part to be optimized; S3: optimizing the part to be optimized of the turbine based on the mass and strength of the solid domain and the agent model using a multi-objective genetic algorithm. The present invention is suitable for turbine integrated design scenarios.

Description

Technical Field

[0001] The invention belongs to the field of energy and power technology, and specifically relates to the field of turbine design technology. Background Art

[0002] Modern aviation turbine engines have set higher goals for the aerodynamic, structural and heat transfer designs of components. Therefore, it is necessary to adopt a multidisciplinary coupling solution method of aerodynamics, heat transfer, structure and strength according to the characteristics of hot end components to achieve a multidisciplinary comprehensive optimization method.

[0003] In the past turbine design, heat transfer and aerodynamic design were separated, which resulted in the failure to fully consider cooling and heat transfer issues in aerodynamic design, and the failure to fully consider aerodynamic performance and its impact in cooling design. This design method does not have much problem when the cooling air volume is relatively small, but for the current unconventional and high-load modern turbines, the turbine inlet temperature and cooling air volume are greatly increased. When the temperature before the turbine of an aircraft engine reaches 1800-2000K, the corresponding turbine cooling air volume must reach about 20% of the mainstream flow. If the temperature before the turbine is increased to more than 2200K, the cooling air volume needs to be increased. The application of traditional design methods will not accurately predict the actual situation of the target.

[0004] It often happens that the pressure of the mainstream in the calculation result is greater than the pressure at the outlet of the cold air channel. In this case, it is difficult to accurately determine the heat transfer, structure, and strength. For this reason, designers must adopt a more conservative design. The maximum allowable number of cycles of turbine blades under centrifugal force, aerodynamic force, thermal load, and their combined effects is often calculated by each load separately, and then the final result is determined by the safety factor. This ensures that the internal components of the designed safety value are safe and reliable, but it limits the improvement of the design level.

[0005] In addition, in the engine air-cooled turbine blades, due to the complex structure of the blades, the temperature difference between different parts is large, and the deformation also has a great influence on the heat flow. Therefore, the gas-thermal-elastic multi-field coupling problem must be considered in the design of the engine turbine cascade.

[0006] At the end of the 1990s and the beginning of this century, the level of computers has been greatly improved. Large-scale parallel computing has emerged and has been widely used in industrial design and academic research. At the same time, the level of computational mathematics as a computing tool has also been greatly improved and developed. The cycle of product updates has been continuously shortened, and the performance and quality of products have been continuously improved. As users' requirements for products continue to increase, multidisciplinary coupling optimization is no longer a strange concept. Each discipline and its branches are no longer carried out separately in the design process. The coupling matching problem with other disciplines must be considered. It is required that the key parameters and key performance indicators reach the optimal state on the basis of maintaining a certain level of each parameter. Multidisciplinary coupling design is gradually becoming a new design means and method. At the same time, multidisciplinary optimization design has attracted more extensive attention from engineering designers and academic researchers. Summary of the invention

[0007] The purpose of the present invention is to propose a low-stress multidisciplinary integrated design method, system, computer equipment and medium for turbine air-cooled blades, so as to solve the current unconventional, high-load modern turbine turbine inlet temperature and cooling air volume are greatly increased, when the temperature before the turbine of the aircraft engine reaches 1800-2000K, the corresponding turbine cooling air volume must reach about 20% of the mainstream flow rate, if the temperature before the turbine is increased to above 2200K, the cooling air volume needs to be increased, the application of traditional design methods will not be able to accurately predict the actual situation of the target problem, designers must adopt a more conservative design, the maximum allowable number of cycles of turbine blades under centrifugal force, aerodynamic force, thermal load and their combined action is often calculated by each load separately The number of cycles, and then the final result is determined by the safety factor, so as to ensure that the designed safety value internal components are safe and reliable, but it limits the improvement of the design level, and in the engine air-cooled turbine blades, due to the complex blade structure, the temperature difference between different parts is large, and the deformation also has a large impact on the heat flow, thus causing problems.

[0008] A low stress multidisciplinary integrated design method for turbine air-cooled blades proposed by the present invention includes:

[0009] S1: collecting parameters of the part of the turbine to be optimized, and performing parameterized design on the part to be optimized;

[0010] S2: Establish structured grids of the parts to be optimized according to the experimental design, and calculate the mass and strength of the solid domain of the parts to be optimized;

[0011] S3: According to the mass and strength of the solid domain, based on the approximate model, a multi-objective genetic algorithm is used to optimize the part of the turbine to be optimized.

[0012] Furthermore, a preferred solution is provided: the turbine portion to be optimized includes turbine blades, a mortise and tenon structure, and an edge plate structure.

[0013] Furthermore, a preferred scheme is provided: the parameters of the part to be optimized include turbine blade parameters, tenon structure parameters and edge plate structure parameters; the turbine blade parameters include: blade profile data, blade outer surface pressure, Mach number distribution, blade outer surface heat transfer coefficient and temperature; the tenon structure parameters include: tenon tooth circumferential angle, tenon tooth contact center point position; the edge plate structure parameters include: edge plate thickness.

[0014] Furthermore, a preferred solution is provided: S2 includes:

[0015] S2.1: Divide the geometry of the part to be optimized into a plurality of quadrilaterals according to the experimental design;

[0016] S2.2: Solve the simplified Laplace equation based on the quadrilateral to obtain a coarse grid;

[0017] S2.3: optimizing and encrypting the coarse grid to obtain a two-dimensional structured grid;

[0018] S2.4: stretching the two-dimensional grid to obtain a three-dimensional structured grid;

[0019] S2.5: Integrate each grid in the three-dimensional structured grid to obtain the volume of each grid;

[0020] S2.6: Calculate the mass and strength of the solid domain of the part to be optimized based on the density of the part to be optimized and the volume of each grid.

[0021] Furthermore, a preferred solution is provided: the simplified Laplace equation is:

[0022]

[0023] Among them, p i0 is the coordinate value vector of the grid node, n is the number of grid nodes adjacent to the grid node, p ij is the coordinate value vector of the grid nodes adjacent to this grid node.

[0024] Furthermore, a preferred solution is provided: the multi-objective optimization is implemented by a multi-objective genetic algorithm, and the approximate model adopts a Kriging approximate model.

[0025] Furthermore, a preferred solution is provided: S3 includes:

[0026] S3.1: Experimental design using Latin hypercube sampling;

[0027] S3.2: Construct the Kriging approximation model and obtain the optimal value;

[0028] S3.3: using the optimal value to update the Kriging approximation model until the optimal solution of the model is reached;

[0029] S3.4: The optimal solution obtained in S3.3 is used as the evaluation function of the genetic algorithm, and the multi-objective genetic algorithm is used to optimize the part of the turbine to be optimized, so as to obtain the Pareto optimal solution and complete the optimization.

[0030] The present invention also proposes a computer device, comprising a memory and a processor, wherein a computer program is stored in the memory. When the processor runs the computer program stored in the memory, the processor executes a low-stress multidisciplinary integrated design method for turbine air-cooled blades described in any combination of the above-mentioned schemes.

[0031] The present invention also proposes a computer-readable storage medium, which is used to store a computer program, and the computer program executes a low-stress multidisciplinary integrated design method for turbine air-cooled blades described in any combination of the above-mentioned schemes.

[0032] The present invention also proposes a low stress multidisciplinary integrated design system for turbine air-cooled blades, comprising:

[0033] Parametric design device: used to collect parameters of the turbine part to be optimized and perform parametric design on the part to be optimized;

[0034] Calculation device: used for establishing a structured grid of the part to be optimized, and calculating the mass and strength of the solid domain of the part to be optimized;

[0035] Optimization device: used for optimizing the part of the turbine to be optimized according to the mass and strength of the solid domain, based on an approximate model and using a multi-objective genetic algorithm.

[0036] Compared with the prior art, the advantages of the present invention are:

[0037] The method proposed in the present invention adopts the NSGA-II algorithm, which solves the problems of high computational complexity, no management strategy, and the need to specify the sharing radius in the traditional NSGA algorithm. The NSGA-II algorithm adopted reduces the computational complexity, proposes the congestion degree and the congestion degree comparison operator, replaces the fitness sharing strategy that needs to specify the sharing radius, and is used as the winning criterion in the same-level comparison after quick sorting, so that the individuals in the quasi-Pareto domain can be expanded to the entire Pareto domain and evenly distributed, maintaining the diversity of the population, introducing the elite strategy, and expanding the sampling space. The parent population and the child population generated by it are combined to compete together to produce the next generation population, which is conducive to keeping the excellent individuals in the parent generation into the next generation, and by storing all individuals in the population in layers, the best individuals will not be lost, and the population level will be rapidly improved.

[0038] The method adopts the Kriging approximation model, based on the structural analysis theory, to make an unbiased optimal estimate of the design region and construct an approximate response surface, that is, to minimize the variance of the estimation error. The Kriging approximation model has flexibility and accuracy for highly nonlinear problems.

[0039] The present invention establishes a low-stress multidisciplinary integrated design system for turbine air-cooled blades, explains the mutual coupling effect of multiple disciplines, and the coupling effect law on the design of air-cooled turbine blades, introduces advanced system design and optimization concepts and methods, and builds a more scientific and accurate low-stress multidisciplinary integrated coupling design platform to achieve global and systematic high integration to achieve overall optimization of the overall parameters of turbine blades. Based on the multidisciplinary coupling design system, a new cooling layout design method for air-cooled turbine blades has been developed to achieve accurate prediction and control of local temperature gradients to achieve the effect of reducing blade stress.

[0040] The present invention is applicable to turbine integrated design scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A flowchart of a turbine cooling optimization design method according to implementation mode 1;

[0042] Figure 2 This is a flowchart of S3 described in Implementation Method 1;

[0043] Figure 3 This is a basic flow chart of NSGA-II described in Implementation Mode 2. DETAILED DESCRIPTION

[0044] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] Implementation Method 1

[0046] Reference Figure 1 , Figure 2 This embodiment will be described.

[0047] The turbine cooling optimization design method proposed in this embodiment performs multidisciplinary optimization design on turbine blades, mortise and tenon structures, and edge plate combinations. The mass and strength of the solid domain are calculated through a grid generation program using ANSYS-APDL. According to the requirements, a single-objective optimization GA, a multi-objective optimization NSGA II, and corresponding approximate model methods (response surface and Kriging interpolation) are written to achieve the multidisciplinary optimization design of the entire turbine cooling. The main flow chart is as follows: Figure 1 As shown, the turbine cooling optimization design method comprises:

[0048] S1: Collect parameters of the turbine part to be optimized, and perform parameterized design on the part to be optimized; the turbine part to be optimized includes: turbine blades, tenon joint structure and edge plate structure; the parameters of the part to be optimized include turbine blade parameters, tenon joint structure parameters and edge plate structure parameters; the turbine blade parameters include: blade profile data, blade outer surface pressure, Mach number distribution, blade outer surface heat transfer coefficient and temperature; the tenon joint structure parameters include: tenon tooth circumferential angle, tenon tooth contact center point position; the edge plate structure parameters include: edge plate thickness, etc.;

[0049] S2: Establish structured grids of the parts to be optimized according to the experimental design, and calculate the mass and strength of the solid domain of the parts to be optimized;

[0050] S2.1: Divide the geometry of the part to be optimized into a plurality of quadrilaterals according to the experimental design;

[0051] S2.2: Solve the simplified Laplace equation based on the quadrilateral to obtain a coarse grid;

[0052] S2.3: optimizing and encrypting the coarse grid to obtain a two-dimensional structured grid;

[0053] S2.4: stretching the two-dimensional grid to obtain a three-dimensional structured grid;

[0054] S2.5: Integrate each grid in the three-dimensional structured grid to obtain the volume of each grid;

[0055] S2.6: Calculate the mass and strength of the solid domain of the part to be optimized according to the density of the part to be optimized and the volume of each grid;

[0056] S3: optimizing the part of the turbine to be optimized by using a multi-objective genetic algorithm based on a proxy model according to the mass and strength of the solid domain;

[0057] S3.1: Experimental design using Latin hypercube sampling;

[0058] S3.2: Construct the Kriging approximation model and obtain the optimal value;

[0059] S3.3: using the optimal value to update the Kriging approximation model until the optimal solution of the model is reached;

[0060] S3.4: The optimal solution obtained in S3.3 is used as the evaluation function of the genetic algorithm, and the multi-objective genetic algorithm is used to optimize the part of the turbine to be optimized, so as to obtain the Pareto optimal solution and complete the optimization.

[0061] Specifically:

[0062] The simplified Laplace equation is:

[0063]

[0064] Among them, p i0 is the coordinate value vector of the grid node, n is the number of grid nodes adjacent to the grid node, p ij is the coordinate value vector of the grid nodes adjacent to this grid node.

[0065] This implementation adopts a non-uniform rational Bezier curve method for parameterization.

[0066] Bezier curve arc generation method, the mathematical description of NURB curve is:

[0067]

[0068] Among them, P i is the control point position vector, ω i is the weight of the i-th control point. The shape of the curve is determined by P i With ω i Joint control. i,n (t) is the Bernstein basis function:

[0069]

[0070] Using this method, program users can use a parametric design method to achieve different structures by changing input parameters. This method can also generate complex and changeable curves with fewer parameters. The use of non-uniform rational Bezier curve method can greatly improve the program's freedom and robustness.

[0071] The following uses the parametric design of the mortise and tenon structure as an example:

[0072] 1) According to the parameterization standard of mortise and tenon joint structure, the parametric generation of key geometry of mortise and tenon joint structure is realized;

[0073] 2) The chamfers and other parts are generated using the three-point second-order Bezier curve method, and the fillets in the top area are generated using the four-point third-order Bezier curve method;

[0074] 3) According to the cutting angle and the length of the tenon, the three-dimensional tenon joint structure can be scanned.

[0075] 4) According to the geometry of the mortise and tenon joint structure, it can be input into UG and can also be used for automatic generation of 2D and 3D meshes.

[0076] 5) The number of tenon teeth can be changed.

[0077] On this basis, the two-dimensional structured mesh division of the mortise and tenon structure is determined, and the standard form of the Laplace equation is simplified to the following formula for each internal node, that is, the coordinate value of each node is equal to the average value of the coordinate values ​​of the adjacent mesh nodes:

[0078]

[0079] After such simplification, it becomes very easy to calculate the internal mesh nodes based on the given boundary nodes, and the program has good versatility, avoiding the complex mesh generation control algorithm in the traditional Laplace equation method.

[0080] Laplace mesh solving first divides the mesh into several quadrilaterals according to geometry, which is called topological division. Then, the simplified Laplace equation is solved based on the quadrilaterals to obtain a rough mesh. On this basis, the mesh is optimized and encrypted to obtain the final mesh.

[0081] Implementation Method 2

[0082] Reference Figure 3 This embodiment will be described.

[0083] The non-dominated sorting genetic algorithm (NSGA) has been applied to many problems, but NSGA still has some problems:

[0084] a) High computational complexity: Because it is O(mN3) (m is the number of objective functions and N is the population size), the calculation is quite time-consuming when the population is large.

[0085] b) No elite strategy: The elite strategy can speed up the execution of the algorithm and, to a certain extent, ensure that the satisfactory solution that has been found is not lost.

[0086] c) The sharing radius needs to be specified.

[0087] The NSGA-II used in this implementation method improves the above defects in the following three aspects:

[0088] 1. A fast non-dominated sorting method is proposed to reduce the computational complexity of the algorithm from the original O(mN3) to O(mN2), where m is the number of objective functions and N is the population size.

[0089] The specific method of fast non-dominated sorting is as follows: Assuming the population is P, the algorithm needs to calculate two parameters np and Sp for each individual p in P, where np is the number of individuals that dominate individual p in the population, and Sp is the set of individuals dominated by individual p in the population. Traversing the entire population, the total computational complexity of these two parameters is O(Nn2).

[0090] The main steps are:

[0091] (1) Find all individuals in the population with np=0 and save them in the current set F1;

[0092] (2) For each individual i in the current set F1, the set of individuals it controls is Si. Traverse each individual i in Si and execute n_i=n_i-1. If n_i=0, save individual i in the set H.

[0093] (3) The individuals obtained in F1 are recorded as the individuals in the first non-dominated layer, and H is taken as the current set. The above operations are repeated until the entire population is classified.

[0094] 2. Crowding degree and crowding degree comparison operator are proposed to replace the fitness sharing strategy that requires specifying the sharing radius, and are used as the winning criteria in the same-level comparison after quick sorting, so that individuals in the quasi-Pareto domain can be expanded to the entire Pareto domain and evenly distributed, maintaining the diversity of the population.

[0095] Crowding refers to the density of individuals around a given individual in a population, which can be intuitively represented as the sum of the length and width of the largest rectangle surrounding the individual n. The crowding degree of individual n is represented by nd.

[0096] The algorithm for the congestion degree n is as follows:

[0097] In the non-dominated sorting genetic algorithm with elite strategy, the calculation of crowding degree is an important link to ensure population diversity. Its function pseudo code is as follows:

[0098] Let n_d=0,n=1,2,…,N

[0099] For each objective function, sort the population based on the objective function, set the crowding degree of the two individuals at the boundary to be infinite, that is, "1"_"d""=""N"_"d""=∞", calculate n_d=n_d+(f_m(i+1)-f_m(i-1)), n=2,3,…,N-1

[0100] There are some small problems here. The setting of infinite crowding is not at both ends of the boundary but at one end. If it is a minimum problem, it is the minimum end. Among them, the crowding distance nd of the individual is ultimately divided by the range of the individual in this layer on the objective function (the difference between the maximum and minimum values ​​of the individual in this layer on the objective function value).

[0101] 3. Introduce elite strategy to expand sampling space. Combining the parent population with the offspring population it produces to compete together to produce the next generation population is conducive to keeping the excellent individuals in the parent generation into the next generation, and by storing all individuals in the population in layers, the best individuals will not be lost, and the population level will be rapidly improved.

[0102] In this embodiment, the multi-objective genetic algorithm (i.e., NSGA-II algorithm) used in S3 is a multi-objective optimization algorithm based on a genetic algorithm, and is a multi-objective optimization based on the discussion of the Pareto optimal solution. The process is as follows: Figure 3 As shown in the figure: First, an initial population of size N is randomly generated. After non-dominated sorting, the first generation of offspring population is obtained through the three basic operations of selection, crossover and mutation of the genetic algorithm; secondly, starting from the second generation, the parent population and the offspring population are merged to perform fast non-dominated sorting. At the same time, the crowding degree of individuals in each non-dominated layer is calculated, and suitable individuals are selected to form a new parent population according to the non-dominated relationship and the crowding degree of the individuals; finally, a new offspring population is generated through the basic operations of the genetic algorithm; and so on, until the conditions for the end of the program are met.

[0103] NSGA-II is an improvement on the conventional genetic algorithm. The key steps are as follows:

[0104] 1. Design of fast non-dominated sorting operator

[0105] The key to the design of multi-objective optimization problems is to find the Pareto optimal solution set. The fast non-dominated sorting in the NSGA-II algorithm is to stratify the population according to the non-inferior solution level of the individual, and its role is to guide the search to the direction of the Pareto optimal solution set. It is a cyclic fitness value classification process. First, the non-dominated solution set in the population is found, which is recorded as the first non-dominated layer F. All its individuals are assigned a non-dominated order "i"_"rank""=1" (where "i"_"rank" is the non-dominated order value of individual i) and removed from the entire population; then continue to find the non-dominated solution set in the remaining population, recorded as the second non-dominated layer F2, and the individuals are assigned a non-dominated order "i"_"rank""=2"; continue in this way until the entire population is stratified and the individuals in the same layer have the same non-dominated order "i"_"rank".

[0106] 2. Design of individual crowding distance operator

[0107] In order to be able to perform selective sorting within individuals with the same "i"_"rank", NSGA-II proposes the concept of individual crowding distance. The crowding distance of individual i is the distance between the two individuals i+1 and i-1 adjacent to i in the target space, and its calculation steps are:

[0108] a) Initialize the distance of individuals in the same layer: Let L[i]_d = 0 (where L[i]_d represents the crowding distance of any individual i).

[0109] b) For individuals in the same layer, arrange them in ascending order according to the mth objective function value: so that individuals on the edge of the sorting have a selection advantage. Given a large number W, let L[0]_d=L[i]_d=0.

[0110] c) For the individuals in the middle of the sort, find the crowding distance:

[0111] L[i]_d=L[i]_d+(L[i+1]_m-L[i+1]_m) / (f_m^max-f_m^min)

[0112] (Wherein, L[i+1]_m is the mth objective function value of the i+1th individual, and f_m^max and f_m^min are the maximum and minimum values ​​of the mth objective function in the set, respectively).

[0113] d) Repeat the above operation for different objective functions: obtain the crowding distance L[i]_d of individual i.

[0114] 3. Main circulation part

[0115] (1) Randomly initialize the starting population P0, perform non-dominated sorting on P0, and initialize the rank value of each individual.

[0116] (2) t = 0

[0117] (3) Individuals are selected from Pt through the binary tournament method, and crossover and mutation operations are performed to generate a new generation population Qt.

[0118] (4) By merging Pt and Qt, a combined population Rt = Pt∪Qt is generated.

[0119] (5) Perform non-dominated sorting on Rt and select N individuals through exclusion and elite retention strategies to form a new generation population Pt+1.

[0120] (6) Jump to step 3 and loop until the end condition is met.

[0121] The test function of NSGA-Ⅱ is:

[0122] ZDT1 function:

[0123]

[0124] ZDT3 functions:

[0125]

[0126] Implementation method three:

[0127] This implementation method provides a detailed introduction to the construction of the approximate model:

[0128] The construction of the approximate model includes four steps: experimental design, sample point simulation calculation, function fitting, and evaluation of the accuracy of the approximate model. The specific steps are as follows:

[0129] 1) Select design variables and value ranges, and use experimental design to generate several sample points within the parameter range, that is, several groups of input data;

[0130] 2) Obtain output data corresponding to the input data through virtual prototype simulation;

[0131] 3) Reconstruct the input and output data according to the principle of approximate model construction, and establish an approximate model that expresses the functional relationship between input and output;

[0132] 4) Determine whether the approximate model is usable by performing error accuracy analysis on the approximate model. The approximate model that meets the accuracy requirements can be used in the optimization design process to replace complex and time-consuming simulation programs to solve the objective function and obtain the optimized design parameters, thereby achieving the purpose of controlling the calculation cost. Otherwise, increase the sample points, refit, or improve the model.

[0133] The Kriging approximation model (Kriging proxy model) adopted in this embodiment is also called the Kriging interpolation estimation method. It is based on the structural analysis theory, performs unbiased optimal estimation of the design area, and constitutes an approximate response surface, that is, minimizes the variance of the estimation error. By constructing the covariance function, the Kriging model can approximate the objective function value of the unknown point, that is, the estimated output value. The Kriging model is used as an alternative to finite element simulation calculations to accurately simulate the input-output relationship, thereby achieving accurate interpolation and auxiliary simulation model optimization operations. In the case of complex and time-consuming finite element simulation model calculations, the Kriging proxy model is the best proxy model. For highly nonlinear problems, the Kriging proxy model has flexibility and accuracy.

[0134] Although various Kriging models have been successfully applied to some optimization problems, in the field of electrical engineering, there are still problems with insufficient fitting accuracy and feasibility for strongly nonlinear multidimensional functions.

[0135] The present embodiment uses Latin hypercube sampling to screen the optimal values ​​of the quality and strength of the solid domain. When using Latin hypercube technology to sample from multiple variables, it is important to maintain independence between variables. The value sampled for one variable needs to be independent of the value sampled for other variables (unless, of course, correlation is specifically desired). Independence is maintained by randomly selecting sampling intervals for each variable. In a certain iteration, variable #1 is sampled from stratum #4, variable #2 is sampled from stratum #22, and so on. This ensures randomness and independence and avoids unintentional correlation between variables.

[0136] As a more efficient sampling method, Latin Hypercube offers significant benefits in sampling efficiency and run time (due to the reduced number of iterations). These benefits are particularly evident in PC-based simulation environments. Latin Hypercube is also helpful for analysis of cases where the input probability distribution contains low-probability outcomes. By forcing the sampling in the simulation to include outliers, Latin Hypercube sampling ensures that outliers are accurately represented in the simulation output.

[0137] When low-probability outcomes are important, it is also helpful to run an analysis that simulates only the effects of low-probability events on the output distribution. In this case, the model simulates only the occurrence of low-probability outcomes—given a 100% probability. This allows the low-probability outcomes to be isolated and their consequences to be studied directly.

[0138] Implementation Method 4

[0139] This embodiment describes the experimental simulation results.

[0140] Experiments show that the highest temperature before optimization occurs at the tip of the leading edge of the blade, reaching 1283.74 degrees Celsius. The lowest temperature occurs at the root of the leading edge of the blade, inside the cooling channel, with a minimum value of 891.89 degrees Celsius. The average temperature of all nodes is 1047.19 degrees Celsius.

[0141] The highest temperature after optimization occurs at the tip of the leading edge of the blade, reaching 1286.02 degrees Celsius, which is 2.55 degrees Celsius higher than the result before optimization. The lowest temperature occurs at the root of the leading edge of the blade, inside the cooling channel, with a minimum value of 891.89 degrees Celsius, which is 3.64 degrees Celsius lower than the temperature before optimization. According to the average temperature of the output, it is about 1.32 degrees Celsius lower than the temperature before optimization.

[0142] Those skilled in the art will appreciate that the above are only preferred embodiments of the present invention, and the various embodiments of the present disclosure and / or the features described in the claims may be combined or combined in various ways, even if such combinations or combinations are not explicitly described in the present disclosure. It is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art may still modify the technical solutions described in the aforementioned embodiments, or perform equivalent substitutions on some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

[0143] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0144] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

[0145] Those skilled in the art will appreciate that the embodiments of the present disclosure may be provided as methods, systems or computer program products. Therefore, the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present disclosure may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0146] The present disclosure is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram and the combination of processes and / or blocks in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including an instruction device, which implements the functions specified in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0147] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0148] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present disclosure rather than to limit its protection scope. Although the present disclosure has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that after reading the present disclosure, those skilled in the art can still make various changes, modifications or equivalent substitutions to the specific implementation methods of the invention, but these changes, modifications or equivalent substitutions are all within the protection scope of the disclosed claims to be approved.

Claims

1. A low stress multidisciplinary integrated design method for turbine air-cooled blades, characterized in that: The method comprises: S1: Collect the parameters of the turbine part to be optimized and perform parameterized design on the turbine part to be optimized, including: The parameterization is based on the non-uniform rational Bezier curve method, and the Bezier curve arc generation method is used. The mathematical description of the NURB curve is: Among them, P i is the control point position vector, ω i is the weight of the i-th control point, and the shape of the curve is determined by P i With ω i Joint control, Bi,n(t) is the Bernstein basis function; According to the parametric standard of mortise and tenon joint structure, the parametric generation of key geometry of mortise and tenon joint structure is realized. The chamfered part is generated by three-point second-order Bezier curve method, and the fillet of the top area is generated by four-point third-order Bezier curve method. According to the cutting angle and the length of the tenon, the three-dimensional tenon joint structure is obtained by scanning; According to the geometry of the tenon joint structure, it is input into UG for automatic generation of two-dimensional and three-dimensional meshes, and the number of tenon teeth can be changed; Determine the two-dimensional structured mesh division of the mortise and tenon structure, and simplify the standard form of the Laplace equation for each internal node to the following formula, that is, let the coordinate value of each node be equal to the average value of the coordinate values ​​of the adjacent mesh nodes: S2: Establish a structured grid of the part to be optimized and calculate the mass and strength of the solid domain of the part to be optimized; The S2 includes: S2.1: dividing the structured grid of the part to be optimized into a plurality of quadrilaterals; S2.2: Solve the simplified Laplace equation based on the quadrilateral to obtain a coarse grid; S2.3: optimizing and encrypting the coarse grid to obtain a two-dimensional structured grid; S2.4: stretching the two-dimensional structured grid to obtain a three-dimensional structured grid; S2.5: Integrate each grid in the three-dimensional structured grid to obtain the volume of each grid; S2.6: Calculate the mass and strength of the solid domain of the part to be optimized according to the density of the part to be optimized and the volume of each grid; S3: According to the mass and strength of the solid domain, based on the approximate model, a multi-objective genetic algorithm is used to optimize the part of the turbine to be optimized.

2. A low stress multidisciplinary integrated design method for turbine air-cooled blades according to claim 1, characterized in that: The turbine parts to be optimized include turbine blades, mortise and tenon structures and edge plate structures.

3. A low stress multidisciplinary integrated design method for turbine air-cooled blades according to claim 2, characterized in that: The parameters of the part to be optimized include turbine blade parameters, tenon joint structure parameters and edge plate structure parameters; The turbine blade parameters include: blade profile data, blade outer surface pressure, Mach number distribution, blade outer surface heat transfer coefficient and temperature; the mortise and tenon structural parameters include: tenon tooth circumferential angle, tenon tooth contact center point position; the edge plate structural parameters include: edge plate thickness.

4. The method for low stress multidisciplinary integrated design of turbine air-cooled blades according to claim 1, characterized in that: The simplified Laplace equation is: Among them, p i0 is the coordinate value vector of the grid node, n is the number of grid nodes adjacent to the grid node, p ij is the coordinate value vector of the grid nodes adjacent to this grid node.

5. The method for low stress multidisciplinary integrated design of turbine air-cooled blades according to claim 1, characterized in that: The multi-objective optimization is implemented by a multi-objective genetic algorithm, and the approximate model is a Kriging approximate model.

6. A method for low stress multidisciplinary integrated design of turbine air-cooled blades according to claim 5, characterized in that: The S3 includes: S3.1: Use Latin hypercube sampling to screen for experimental design; S3.2: Construct the Kriging approximation model and obtain the optimal value; S3.3: using the optimal value to update the Kriging approximation model until the optimal solution of the model is reached; S3.4: The optimal solution obtained in S3.3 is used as the evaluation function of the genetic algorithm, and the multi-objective genetic algorithm is used to optimize the part of the turbine to be optimized, so as to obtain the Pareto optimal solution and complete the optimization.

7. A computer device, characterized in that: It comprises a memory and a processor, wherein a computer program is stored in the memory, and when the processor runs the computer program stored in the memory, the processor executes a low-stress multidisciplinary integrated design method for turbine air-cooled blades according to any one of claims 1-6.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store a computer program, and the computer program executes a low-stress multidisciplinary integrated design method for turbine air-cooled blades according to any one of claims 1 to 6.

9. A low stress multidisciplinary integrated design system for turbine air-cooled blades, characterized in that: The system comprises: Parametric design device: used to collect parameters of the turbine part to be optimized and perform parametric design on the part to be optimized, including: The parameterization is based on the non-uniform rational Bezier curve method, and the Bezier curve arc generation method is used. The mathematical description of the NURB curve is: Among them, P i is the control point position vector, ω i is the weight of the i-th control point, and the shape of the curve is determined by P i With ω i Joint control, Bi,n(t) is the Bernstein basis function; According to the parametric standard of mortise and tenon joint structure, the parametric generation of key geometry of mortise and tenon joint structure is realized. The chamfered part is generated by three-point second-order Bezier curve method, and the fillet of the top area is generated by four-point third-order Bezier curve method. According to the cutting angle and the length of the tenon, the three-dimensional tenon joint structure is obtained by scanning; According to the geometry of the tenon joint structure, it is input into UG for automatic generation of two-dimensional and three-dimensional meshes, and the number of tenon teeth can be changed; Determine the two-dimensional structured mesh division of the mortise and tenon structure, and simplify the standard form of the Laplace equation for each internal node to the following formula, that is, let the coordinate value of each node be equal to the average value of the coordinate values ​​of the adjacent mesh nodes: Computational device: used to establish a structured grid of the part to be optimized, and calculate the quality and strength of the solid domain of the part to be optimized, including: Dividing the structured grid of the part to be optimized into a plurality of quadrilaterals; The coarse grid is obtained by solving the simplified Laplace equation based on the quadrilateral; Optimizing and encrypting the coarse grid to obtain a two-dimensional structured grid; Stretching the two-dimensional structured grid to obtain a three-dimensional structured grid; Integrating each grid in the three-dimensional structured grid to obtain the volume of each grid; Calculate the mass and strength of the solid domain of the part to be optimized according to the density of the part to be optimized and the volume of each grid; Optimization device: used for optimizing the part of the turbine to be optimized according to the mass and strength of the solid domain, based on the agent model and using a multi-objective genetic algorithm.

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