Design method, device, electronic equipment and medium for turbine blade

Abstract

The present application relates to the field of aero-engine design, and particularly relates to a design method and device for a turbine blade, electronic equipment and medium, wherein by acquiring a plurality of related information of the turbine blade, the structure type and geometric parameters of the inner cavity cooling structure are first determined, then the complex inner cavity cooling structure is equivalent to a plurality of units by using an internal flow network model, and the heat exchange performance parameters are quickly determined in combination with the calculation coefficients in the associated database, and then the demand quantity and cold efficiency value of the cooling gas are obtained; since the process is replaced by parameterization and networkization instead of complete three-dimensional flow-heat coupling numerical simulation, the rapid evaluation and iterative adjustment of different structure types and geometric parameters can be completed in a short time, thereby avoiding the repeated calculation overhead caused by frequent modeling and mesh division, and the design efficiency of the turbine blade inner cavity cooling structure is significantly improved.

Description

Technical Field

[0001] This invention relates to the field of aero-engine design, and more specifically to a design method, apparatus, electronic equipment, and medium for turbine blades. Background Technology

[0002] The temperature of turbines in modern aero engines is constantly increasing, which places extremely high demands on the efficient cooling technology of turbine blades. Various internal cavity cooling structures / channels are introduced into the blade interior as cooling channels based on their flow channel arrangement and heat transfer characteristics, in order to achieve higher cooling effects with limited cooling gas usage. However, how to engineer these complex internal cavity cooling structures / channels for blade design is a problem that needs to be considered by those skilled in the art. Currently, the design process for blades related to internal cooling structures / channels generally adopts a combination of general-purpose commercial CFD (Computational Fluid Dynamics) software and 3D modeling software. First, based on experience or simple models, complex geometric models of internal cooling structures / channels (such as double-walled, sweating cooling, and biomimetic cooling structures / channels) for different regions of the blade are manually created in 3D modeling software. Then, the models are imported into CFD software for fluid-thermal coupling numerical simulation. The geometric parameters are repeatedly modified based on the simulation results until the design requirements are met. This design process based on full 3D CFD simulation calculation consumes huge amounts of computing resources. A single analysis often takes several days, and each modification requires remodeling and meshing, resulting in a long iteration cycle and low efficiency. Summary of the Invention

[0003] This invention provides a design method, apparatus, electronic device, and medium for turbine blades, aiming to avoid the inefficiency caused by using full three-dimensional CFD simulation calculations in the blade design process related to internal cavity cooling structures / channels.

[0004] In a first aspect, embodiments of this application provide a design method for turbine blades, the method comprising: Multiple relevant information of the turbine blade are obtained, wherein the multiple relevant information are used to characterize the structural features of the turbine blade and the airflow and temperature characteristics of the environment in which the turbine blade is located; Based on the aforementioned relevant information, the structural type and geometric parameters of the internal cavity cooling structure of the turbine blade are determined, wherein the internal cavity cooling structure is located inside the blade cavity of the turbine blade and is used to conduct cooling gas. Based on the internal flow network model and associated database of the turbine blades, the demand for cooling gas and the cooling efficiency value are determined according to the structure type and geometric parameters. The internal flow network model is used to convert the internal cavity cooling structure into multiple units and to determine the heat transfer performance parameters of the internal cavity cooling structure based on the structure type and geometric parameters. The associated database is used to store calculation coefficients associated with the structure type and geometric parameters, and the calculation coefficients are used to determine the heat transfer performance parameters. If the required amount and cooling efficiency of the cooling gas do not meet the preset conditions, the structure type and geometric parameters are adjusted according to the required amount and cooling efficiency to obtain the adjusted structure type and geometric parameters. The adjusted structural type and geometric parameters are used as the structural type and geometric parameters. The process jumps to the internal flow network model and associated database based on the turbine blade. The step of determining the cooling gas demand and cooling effect value according to the structural type and geometric parameters is executed until the cooling gas demand and cooling effect value meet the preset conditions. The structural type and geometric parameters corresponding to the required amount and cooling efficiency value of the cooling gas that meet the preset conditions are determined as the target type and target geometric parameters of the internal cavity cooling structure.

[0005] In some embodiments, determining the structural type and geometric parameters of the internal cooling structure of the turbine blade based on the plurality of relevant information includes: Based on the aforementioned relevant information, the turbine blade is divided into regions to obtain multiple characteristic regions. The external thermal boundary information of each characteristic region is different. The external thermal boundary information of the characteristic region is used to characterize the boundary heat exchange conditions of the blade outer wall surface included in the characteristic region and the heat flux density of the blade outer wall surface of the characteristic region. For each of the aforementioned feature regions, the structural type and geometric parameters of the internal cavity cooling structure of the feature region are determined.

[0006] In some implementations, the determination of the cooling gas demand and cooling efficiency value based on the internal flow network model and associated database of the turbine blades, according to the structural type and geometric parameters, includes: For each of the aforementioned feature regions, based on the internal flow network model and associated database of the turbine blade, the heat exchange between the inner cavity of the feature region and the outside of the turbine blade is determined according to the structural type and geometric parameters of the internal cavity cooling structure of the feature region. Based on the heat exchange between the inner cavity of the characteristic region and the outside of the turbine blades, determine the required amount of cooling gas and the cooling efficiency value of the characteristic region; The step of adjusting the structural type and geometric parameters based on the demand and cooling efficiency value to obtain the adjusted structural type and geometric parameters includes: For each of the aforementioned characteristic regions, the structural type and geometric parameters of the characteristic region are adjusted according to the cooling gas demand and cooling efficiency value of the characteristic region, thereby obtaining the adjusted structural type and geometric parameters of the characteristic region.

[0007] In some embodiments, the heat transfer performance parameters of the internal cavity cooling structure include the convective heat transfer coefficient of the inner wall of the turbine blade; The internal flow network model and associated database based on the turbine blades determine the cooling gas demand and cooling efficiency value according to the structural type and geometric parameters, including: Based on the internal flow network model and associated database of the turbine blade, the convective heat transfer coefficient and heat transfer area of ​​the inner wall of the turbine blade are determined according to the structural type and geometric parameters. The amount of heat exchanged between the inner cavity of the turbine blade and the outside of the turbine blade is determined based on the convective heat transfer coefficient and heat transfer area of ​​the inner wall. Based on the heat exchange, the required amount and cooling efficiency of the cooling gas are determined.

[0008] In some embodiments, determining the required amount of cooling gas and the cooling efficiency value based on the heat exchange includes: The external thermal boundary information of the turbine blade and the internal thermal boundary information of the turbine blade corresponding to the structural type and geometric parameters are determined. The external thermal boundary information of the turbine blade is used to characterize the boundary heat exchange conditions of the outer wall surface of the turbine blade and the heat flux density of the outer wall surface of the turbine blade. The internal thermal boundary information of the turbine blade is used to characterize the boundary heat exchange conditions of the inner wall surface of the turbine blade and the heat flux density of the inner wall surface of the turbine blade. The required amount and cooling efficiency of the cooling gas are determined based on the heat exchange, the external thermal boundary information, and the internal thermal boundary information of the turbine blades corresponding to the structural type and geometric parameters.

[0009] In some embodiments, the number of target types and target geometric parameters of the internal cavity cooling structure is multiple; After determining the structural type and geometric parameters corresponding to the required amount and cooling efficiency value of the cooling gas that meet the preset conditions as the target type and target geometric parameters of the cavity cooling structure, the method further includes: For each set of target types and target geometric parameters, determine the curve of the cooling effect value of the cooling gas corresponding to the target type and target geometric parameters as a function of the required amount of cooling gas; Based on the variation curves of the target type and target geometric parameters in each group, the optimal type and optimal geometric parameters are determined among multiple groups of target types and target geometric parameters.

[0010] In some embodiments, after determining the structural type and geometric parameters corresponding to the required amount of cooling gas and the cooling efficiency value that meet the preset conditions as the target type and target geometric parameters of the cavity cooling structure, the method further includes: The external thermal boundary information of the turbine blade and the internal thermal boundary information of the turbine blade corresponding to the target type and target geometric parameters are determined. The external thermal boundary information of the turbine blade is used to characterize the boundary heat exchange conditions of the outer wall surface of the turbine blade and the heat flux density of the outer wall surface of the turbine blade. The internal thermal boundary information of the turbine blade is used to characterize the boundary heat exchange conditions of the inner wall surface of the turbine blade and the heat flux density of the inner wall surface of the turbine blade. Based on the external thermal boundary information and the internal thermal boundary information of the turbine blade corresponding to the target type and target geometric parameters, the two-dimensional steady-state heat conduction control equation of the turbine blade is solved to obtain the solution result. Based on the solution results, the temperature field cloud map of the turbine blade, the maximum temperature of the turbine blade, and the average temperature of the turbine blade are determined. Based on the temperature field cloud map, the highest temperature, and the average temperature, the target type and target geometric parameters are verified to obtain verification results. The verification results are used to characterize whether the target type and target geometric parameters meet the temperature control requirements of the turbine blade.

[0011] Secondly, embodiments of this application provide a design apparatus for turbine blades, the apparatus comprising: The acquisition module is used to acquire multiple related information of the turbine blade, wherein the multiple related information are used to characterize the structural features of the turbine blade and the airflow and temperature characteristics of the environment in which the turbine blade is located; The first determining module is used to determine the structural type and geometric parameters of the internal cavity cooling structure of the turbine blade based on the multiple relevant information, wherein the internal cavity cooling structure is located in the blade cavity of the turbine blade and is used to conduct cooling gas. The second determining module is used to determine the demand for cooling gas and the cooling efficiency value based on the internal flow network model of the turbine blade and the associated database, according to the structure type and geometric parameters. The internal flow network model is used to convert the internal cavity cooling structure into multiple units and to determine the heat transfer performance parameters of the internal cavity cooling structure based on the structure type and geometric parameters. The associated database is used to store calculation coefficients associated with the structure type and geometric parameters, and the calculation coefficients are used to determine the heat transfer performance parameters. The adjustment module is used to adjust the structure type and geometric parameters according to the demand and cooling efficiency value when the demand and cooling efficiency value of the cooling gas do not meet the preset conditions, so as to obtain the adjusted structure type and geometric parameters. The iterative module is used to take the adjusted structure type and geometric parameters as the structure type and geometric parameters, jump to the internal flow network model and associated database based on the turbine blade, and execute the step of determining the demand amount and cooling effect value of the cooling gas according to the structure type and geometric parameters until the demand amount and cooling effect value of the cooling gas meet the preset conditions. The third determining module is used to determine the structural type and target geometric parameters corresponding to the required amount and cooling effect value of the cooling gas that meet the preset conditions as the target type and target geometric parameters of the internal cavity cooling structure.

[0012] Thirdly, embodiments of this application provide an electronic device, which includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When a processor executes a program stored in a memory, it implements the steps of the turbine blade design method provided in the first aspect of the embodiments of this application.

[0013] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the turbine blade design method provided in the first aspect of embodiments of this application.

[0014] In this embodiment, addressing the inefficiency of existing design processes combining full 3D CFD and 3D modeling software, the structural type and geometric parameters of the internal cooling structure are first determined by acquiring multiple relevant information about the turbine blade. Then, the complex internal cooling structure is equivalent to multiple elements using an internal flow network model, and the heat transfer performance parameters are quickly determined by combining calculation coefficients from the associated database, thereby obtaining the cooling gas demand and cooling effect value. Since this process replaces the complete 3D flow-thermal coupling numerical simulation with a parameterized and networked approach, it can quickly evaluate and iteratively adjust different structural types and geometric parameters in a short time, thereby avoiding the repetitive calculation overhead caused by frequent modeling and mesh generation, and significantly improving the design efficiency of the turbine blade internal cooling structure. Furthermore, closed-loop optimization based on the demand and cooling effect value can make the target type and target geometric parameters of the final determined internal cooling structure more closely match the actual airflow and temperature characteristics, achieving better heat transfer performance and cooling effect under limited cooling gas conditions. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic flowchart of a turbine blade design method provided in an embodiment of this application; Figure 2 This is a schematic diagram of the honeycomb-type internal cavity cooling structure provided in the embodiments of this application; Figure 3 This is a schematic diagram of the tree-shaped internal cavity cooling structure provided in the embodiments of this application; Figure 4 This is another schematic flowchart of the design method for turbine blades provided in the embodiments of this application; Figure 5 This is a feature region distribution map provided in the embodiments of this application; Figure 6 This is a curve comparison of cooling gas demand versus cooling efficiency values ​​for different solutions provided in the embodiments of this application; Figure 7 This is a schematic diagram of the structure of the turbine blade design device provided in the embodiments of this application; Figure 8 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0017] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0018] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0019] The design method, apparatus, electronic equipment, and medium for turbine blades provided in this application will be described in detail below with reference to the accompanying drawings and through specific embodiments and application scenarios.

[0020] Figure 1 This is a schematic flowchart of a turbine blade design method provided in an embodiment of this application, as shown below. Figure 1 As shown, the first aspect of this application provides a design method for turbine blades, including the following steps S100-S600: Step S100: Obtain multiple relevant information about the turbine blade, wherein the multiple relevant information are used to characterize the structural features of the turbine blade and the airflow and temperature characteristics of the environment in which the turbine blade is located.

[0021] In this step, the processor first acquires several relevant information about the turbine blade. This information is used to characterize the structural features of the turbine blade and the airflow and temperature characteristics of the environment in which the turbine blade is located. These relevant information can be understood as file data related to the blade profile coordinate points and aerodynamic boundaries. By importing this information, the processor obtains basic data on the blade's geometry and external thermal environment, providing input conditions for the selection and parameter determination of the subsequent internal cooling structure. In this way, the processor first clarifies "what the blade looks like and how hot it gets outside," thereby avoiding subsequent designs from deviating from actual operating conditions.

[0022] For example, after the processor imports the profile coordinate point file and aerodynamic boundary file of a certain type of turbine blade, it identifies that the blade has a large leading edge curvature and a thin trailing edge. At the same time, it finds that the external gas has a high temperature and strong heat transfer in the leading edge region, and the airflow scouring conditions are different in the blade head and blade back regions. In this way, the processor obtains the basic information required for subsequent regional design.

[0023] Step S200: Based on multiple relevant information, determine the structural type and geometric parameters of the internal cavity cooling structure of the turbine blade, wherein the internal cavity cooling structure is located in the blade cavity of the turbine blade and is used to conduct cooling gas.

[0024] In this step, the processor determines the structural type and geometric parameters of the internal cavity cooling structure of the turbine blade based on multiple relevant information obtained in step S100. The internal cavity cooling structure is located in the inner cavity of the turbine blade (the wall surface of the inner cavity is the inner wall surface of the blade) and is used to conduct cooling gas. Figure 2 This is a schematic diagram of the honeycomb-type internal cavity cooling structure provided in the embodiments of this application. Figure 3 This is a schematic diagram of the tree-shaped internal cavity cooling structure provided in the embodiments of this application, as shown below. Figures 2-3 As shown ( Figures 2-3 The red arrows in the diagram indicate the direction of cold air flow. The internal cooling structure can be a biomimetic cooling structure such as a honeycomb or tree-shaped structure, and its geometric parameters can include unit size, branch size, channel dimensions, etc. (e.g., Figures 2-3 (D1, D2, D3, γ, W1, W2, θ); In this way, the thermal environment of the blade is matched with the internal cooling structure, so that the cooling structure is not set arbitrarily based on experience, but is customized according to the characteristics of the blade structure and the airflow temperature characteristics.

[0025] For example, the processor selects a tree-shaped cooling structure for the blade based on the blade's heating characteristics and sets the trunk diameter and branch size.

[0026] Step S300: Based on the internal flow network model and associated database of the turbine blades, determine the demand for cooling gas and the cooling effect value according to the structure type and geometric parameters. The internal flow network model is used to convert the internal cavity cooling structure into multiple units and determine the heat transfer performance parameters of the internal cavity cooling structure based on the structure type and geometric parameters. The associated database is used to store the calculation coefficients associated with the structure type and geometric parameters. The calculation coefficients are used to determine the heat transfer performance parameters.

[0027] In this step, the processor determines the required amount of cooling gas and the cooling efficiency value based on the internal flow network model of the turbine blades and the associated database, according to the structural type and geometric parameters selected in step S200. The internal flow network model is used to represent the internal cavity cooling structure as multiple units, and based on the selected structure type and geometric parameters, it calls the associated calculation coefficients from the associated database to calculate and determine the heat transfer performance parameters of the internal cavity cooling structure, so as to determine the cooling gas demand and cooling effect value based on the obtained heat transfer performance parameters. Among them, the internal flow network model (or one-dimensional fluid / cooling pipe network model) is an engineering model used in the design of aero-engine / gas turbine blades to simplify the complex hollow cooling channels, film cooling holes, turbulence columns, swirling cavities, etc. inside the blade into a one-dimensional fluid network composed of nodes and flow branches. It is used to quickly calculate the flow distribution, pressure loss, and temperature distribution of cooling gas. Specifically, the internal flow network model simplifies the complex biomimetic channel into a flow network composed of flow resistance units and heat exchange units. Then, it searches the associated database for calculation coefficients related to the selected structural type and geometric parameters to quickly calculate the heat transfer performance parameters (such as convective heat transfer coefficient) of the internal cavity cooling structure. Based on the determined heat transfer performance parameters, the processor then obtains the cooling gas demand and cooling effect value. In this way, rapid calculation replaces repeated full three-dimensional CFD simulation, thereby significantly improving the efficiency of scheme evaluation. As will be understood by those skilled in the art, the internal flow network model is a unified computational model that can be configured for different structural types and geometric parameters. It can generate corresponding flow networks and perform heat transfer performance calculations based on structural types and geometric parameters. For example, honeycomb structures and tree structures can be placed in the same computational framework of the internal flow network model, but their channel topology, flow resistance distribution, and heat transfer unit configuration are different, so the calculated heat transfer performance parameters will also be different.

[0028] For example, when the processor performs calculations on the tree-shaped cooling structure, the internal flow network model equates it to multiple flow resistance units and heat exchange units. After calling the calculation coefficients in the associated database, it quickly obtains the heat exchange performance parameters of the structure and further calculates the cooling gas flow rate and corresponding cooling efficiency value required to meet the current operating conditions. Assuming that the calculation results show that the cooling gas demand corresponding to the structure is 0.012 kg / s and the cooling efficiency value is high, it indicates that the structure has good cooling capacity under the current operating conditions.

[0029] Step S400: If the demand for cooling gas and the cooling effect value do not meet the preset conditions, adjust the structure type and geometric parameters according to the demand and cooling effect value to obtain the adjusted structure type and geometric parameters.

[0030] When the cooling gas demand and cooling efficiency value obtained in step S300 do not meet the preset conditions, the processor adjusts the structure type and geometric parameters selected in step S200 according to the demand and cooling efficiency value to obtain the adjusted structure type and geometric parameters. The preset conditions can be understood as design goals, such as the cooling gas demand cannot be too large, the cooling efficiency value cannot be too low, or both must meet a certain performance threshold at the same time. In this way, the design modification is driven by the result, forming a closed-loop optimization, rather than fixing the solution after one calculation.

[0031] For example, if the internal cooling structure of the blade adopts a honeycomb structure, and the calculated cooling gas demand is too high, but the cooling efficiency value still does not meet the requirements, the processor can reduce the size of the honeycomb cells in that area, or change it to a tree structure that is more suitable for the heat load distribution of the blade, and then recalculate.

[0032] Step S500: Using the adjusted structure type and geometric parameters as the structure type and geometric parameters, jump to the internal flow network model and associated database based on the turbine blade, and execute the step of determining the cooling gas demand and cooling effect value according to the structure type and geometric parameters until the cooling gas demand and cooling effect value meet the preset conditions.

[0033] In this step, the processor takes the adjusted structure type and geometric parameters obtained in step S400 as the current structure type and geometric parameters, jumps back to step S300 and performs the calculation based on the internal flow network model and the associated database again until the demand for cooling gas and the cooling effect value meet the preset conditions. In this way, parameter iterative convergence is achieved, and the optimal design is gradually approached through multiple rounds of rapid calculation, without having to perform full 3D modeling and CFD solution in each round.

[0034] For example, the processor re-substitutes the adjusted tree structure parameters from the previous step into the internal flow network model and recalculates that the cooling gas demand has decreased to 0.010 kg / s, while the cooling efficiency value reaches the preset target. Therefore, the processor no longer makes any further modifications and considers that the set of parameters is close to meeting the design requirements.

[0035] Step S600: Determine the structural type and target geometric parameters corresponding to the required amount of cooling gas and the cooling effect value that meet the preset conditions as the target type and target geometric parameters of the internal cavity cooling structure.

[0036] When the result after step S500 iteration meets the preset conditions, the processor determines the structure type and geometric parameters corresponding to the cooling gas demand and cooling effect value that meet the preset conditions as the target type and target geometric parameters of the internal cavity cooling structure, and outputs the final design result, that is, the internal cavity cooling structure scheme suitable for the current blade operating conditions is obtained, providing finalized parameters for subsequent manufacturing or further verification.

[0037] For example, after multiple iterations, the processor finally determines that the blade adopts a certain set of tree structure parameters, and the cooling gas demand and cooling efficiency corresponding to these parameters meet the preset conditions. Thus, these structure types and geometric parameters are determined as the target type and target geometric parameters of the turbine blade internal cavity cooling structure.

[0038] Through steps S100-S600, by acquiring multiple relevant information about the turbine blades, the structural type and geometric parameters of the internal cooling structure are first determined. Then, the complex internal cooling structure is equivalent to multiple units using an internal flow network model, and the heat transfer performance parameters are quickly determined by combining the calculation coefficients in the associated database, thereby obtaining the demand for cooling gas and the cooling effect value. Since this process replaces the complete three-dimensional flow-thermal coupling numerical simulation with a parameterized and networked approach, it can complete the rapid evaluation and iterative adjustment of different structural types and geometric parameters in a short time, thereby avoiding the repetitive calculation overhead caused by frequent modeling and mesh generation, and significantly improving the design efficiency of the turbine blade internal cooling structure. Furthermore, closed-loop optimization based on the demand and cooling effect value can make the target type and target geometric parameters of the final determined internal cooling structure more closely match the actual airflow and temperature characteristics, achieving better heat transfer performance and cooling effect under limited cooling gas conditions.

[0039] Figure 4 This is another schematic flowchart illustrating the design method for turbine blades provided in this application embodiment; please refer to it as well. Figures 1-4 .

[0040] In some implementations, the structural type and geometric parameters of the turbine blade's internal cooling structure are determined based on multiple relevant information, including: Based on multiple relevant information, the turbine blade is divided into regions to obtain multiple characteristic regions. The external thermal boundary information of each characteristic region is different. The external thermal boundary information of the characteristic region is used to characterize the boundary heat exchange conditions of the blade outer wall surface included in the characteristic region and the heat flux density of the blade outer wall surface in the characteristic region. For each feature region, determine the structural type and geometric parameters of the internal cavity cooling structure of that feature region.

[0041] In this embodiment, the processor first acquires multiple relevant information about the turbine blade, which may include blade profile coordinate points, aerodynamic boundaries, matrix materials, and cooling gas inlet temperature, etc., to simultaneously characterize the structural features of the turbine blade and the airflow and temperature characteristics of the environment in which it is located. Subsequently, the processor divides the turbine blades into regions based on this information, obtaining multiple feature regions, and assigns different external thermal boundary information to each feature region. The external thermal boundary information is used to characterize the boundary heat exchange conditions of the blade outer wall surface included in the feature region (which may include the convective heat transfer coefficient of the blade outer wall surface, the adiabatic wall temperature, and the blade outer wall surface temperature) and the blade outer wall surface heat flux density in that region. Specifically, the processor can calculate the convective heat transfer coefficient of the external gas combustion side of each characteristic region (i.e., the convective heat transfer coefficient of the outer wall of the blade) based on the data (i.e., multiple relevant information) in the file containing the blade profile coordinate points and aerodynamic boundaries, combined with the built-in aerothermodynamic algorithm or database. ( (The serial number of the characteristic region) and the temperature of the adiabatic wall. Then, the heat flux density of the blade outer wall surface in each characteristic region is estimated using the following formula. And use it as an input parameter for subsequent design:

[0042] in, The outer wall temperature of the feature region, for each feature region The maximum allowable temperature of the blade matrix material can be used as the initial value for engineering design. Based on this, the processor determines the structural type and geometric parameters of the internal cavity cooling structure for each feature region, so that regions with different heat-affected characteristics can be matched with different internal cavity cooling structures, realizing regionalized and customized blade cooling design. In this way, the processor can first accurately identify the differences in heat load in each region based on the external thermal boundary information of different characteristic regions of the turbine blade, and then match the structural type and geometric parameters of the corresponding internal cavity cooling structure for each characteristic region. This avoids the problem of uneven cooling or low design efficiency caused by treating different thermal boundary regions with a uniform structure in traditional design. Since the heat flux density of the outer wall of the blade is explicitly calculated and used as the input for subsequent design, the entire design process is more targeted and easier to iterate and optimize quickly.

[0043] For example, Figure 5 This is a feature region distribution map provided in the embodiments of this application. Please refer to it. Figure 5 Technicians import the blade profile coordinates and aerodynamic boundary files of a certain type of turbine blade through the operation interface, select a suitable matrix material, and input the cooling gas inlet temperature. Based on the built-in aerodynamic thermodynamic algorithm or database, the processor automatically divides the cross section of the blade into at least 6 feature regions with different external thermal boundary information along the circumference, namely leading edge 1, blade basin I 2, blade basin II 3, blade back I 4, blade back II 5, and trailing edge 6. Subsequently, the processor records the calculated external thermal boundary information for each feature region: For example, the convective heat transfer coefficient of the outer wall in region 1 of the leading edge. Larger, insulation wall temperature The higher density results in a larger heat flux density on the outer surface of the blade. Leaf basin I2 and leaf underside I4 regions and Relatively low corresponds to a smaller ; The processor will handle these regions , and As input for subsequent design, the tree-shaped internal cavity cooling structure and corresponding geometric parameters can be determined for the leading edge region 1, the honeycomb-shaped internal cavity cooling structure and its geometric parameters can be determined for the blade basin region I2 and the blade back region I4, and the tail edge region 6 is selected based on its corresponding thermal boundary information to select a more suitable structural type and size parameters, thereby completing the customized design of each feature region.

[0044] In some implementations, based on the internal flow network model of the turbine blades and the associated database, the cooling gas demand and cooling efficiency are determined according to the structural type and geometric parameters, including: For each feature region, based on the internal flow network model of the turbine blade and the associated database, the heat exchange between the internal cavity of the feature region and the outside of the turbine blade is determined according to the structural type and geometric parameters of the internal cooling structure of the feature region. Based on the heat exchange between the inner cavity of the characteristic region and the outside of the turbine blades, determine the required amount of cooling gas and the cooling efficiency value of the characteristic region. Based on the demand and cooling efficiency, the structural type and geometric parameters are adjusted to obtain the adjusted structural type and geometric parameters, including: For each characteristic region, the structural type and geometric parameters of the characteristic region are adjusted according to the cooling gas demand and cooling efficiency value of the characteristic region, so as to obtain the adjusted structural type and geometric parameters of the characteristic region.

[0045] In this embodiment, corresponding to the previous embodiment, for each feature region, the processor no longer relies on full 3D CFD for complete fluid-thermal coupling solution. Instead, based on the internal flow network model of the turbine blade and the associated database, combined with the structural type and geometric parameters of the internal cooling structure of the feature region, it quickly determines the heat transfer between the internal cavity and the outside of the turbine blade of the feature region. Here, the internal flow network model is used to represent the internal cooling structure as an equivalent flow network composed of multiple units, and the heat transfer performance parameters are determined accordingly based on the structural type and geometric parameters. The associated database is used to store the calculation coefficients associated with the structural type and geometric parameters, so as to quickly calculate the corresponding heat transfer performance parameters, thereby further obtaining the heat transfer of the feature region. The processor then determines the cooling gas demand and cooling efficiency value of the feature region based on the heat exchange between the inner cavity of the feature region and the outside of the turbine blades. Subsequently, when the demand and cooling efficiency value do not meet the preset conditions, the processor adjusts the structural type and geometric parameters of each feature region according to its cooling gas demand and cooling efficiency value to obtain the adjusted structural type and geometric parameters, and continues to calculate and judge the heat exchange, demand and cooling efficiency value accordingly, forming a closed-loop iterative optimization process. In this way, by parametrically representing the internal cooling structure of different characteristic regions and quickly solving for heat transfer, cooling gas demand, and cooling efficiency using an internal flow network model and associated database, rapid iterative optimization of each characteristic region can be achieved without repeated full-3D CFD reconstruction and mesh generation. This not only improves design efficiency but also enables the structural type and geometric parameters of different characteristic regions to more accurately match their corresponding heat load and cooling requirements, thereby improving the adaptability and cooling effect of the turbine blade internal cooling structure.

[0046] For example, the processor has divided the turbine blade into multiple feature regions such as leading edge 1, blade basin I 2, blade basin II 3, blade back I 4, blade back II 5 and trailing edge 6, and determined a tree-shaped internal cavity cooling structure and corresponding geometric parameters for the leading edge 1 region, and a honeycomb-shaped internal cavity cooling structure and corresponding geometric parameters for the other regions. At this point, for the leading edge region 1, the processor, based on the internal flow network model of the turbine blade and the associated database, equates the tree-shaped internal cavity cooling structure to multiple units. Combining the corresponding structural type and geometric parameters, it quickly calculates the heat transfer between the internal cavity of the leading edge region and the outside of the turbine blade. Assuming that the heat transfer in this region is high, the processor further combines this heat transfer to calculate the required cooling gas quantity and cooling efficiency value of the leading edge region. If it is found that the cooling gas quantity is too large but the cooling efficiency value still does not meet the preset conditions, the processor adjusts the structural type and geometric parameters of the leading edge region, such as reducing the branch size, optimizing the flow unit size, or changing the tree hierarchy. It then recalculates the heat transfer based on the internal flow network model and the associated database, and redetermines the required cooling gas quantity and cooling efficiency value. For regions I2, II3, I4, II5, and 6 of the blade base, the processor repeats the calculation and adjustment process according to their respective structural types and geometric parameters until the cooling gas demand and cooling efficiency of each feature region meet the preset conditions.

[0047] In some implementations, the heat transfer performance parameters of the internal cavity cooling structure include the convective heat transfer coefficient of the inner wall of the turbine blades. Based on the internal flow network model and associated database of turbine blades, the cooling gas demand and cooling efficiency are determined according to the structural type and geometric parameters, including: Based on the internal flow network model and associated database of turbine blades, the convective heat transfer coefficient and heat transfer area of ​​the inner wall of the turbine blades are determined according to the structural type and geometric parameters. The heat exchange between the inner cavity and the outer surface of the turbine blade is determined based on the convective heat transfer coefficient and heat transfer area of ​​the inner wall. Based on the heat exchange, determine the required amount of cooling gas and the cooling efficiency.

[0048] In this embodiment, in conjunction with the aforementioned embodiments, after the structure type and initial geometric dimensions of each feature region have been set, the processor does not start full three-dimensional CFD calculation. Instead, it calls the built-in internal flow network model and associated database to quickly interpolate or calculate the core index parameters such as the convective heat transfer coefficient, heat transfer area, heat transfer capacity, cooling efficiency value and required cooling volume of the inner wall of each feature region under the current design. Specifically, for each characteristic region, the internal flow network model simplifies the complex internal cavity cooling structure into a flow network composed of flow resistance units and heat exchange units, and automatically calculates the hydraulic diameter of each flow channel based on the selected structural type and geometric parameters. Circulation area and heat exchange area The associated database stores the calculation coefficients of the Nusselt number correlation formulas corresponding to different structure types and geometric parameters. The processor quickly determines the convective heat transfer coefficient of the inner wall of the characteristic region by calling the calculation coefficients associated with the selected structure type and geometric parameters in the database according to the following formula. :

[0049] in, The thermal conductivity of the cooling gas. For Prandtl numbers, For Nusselt numbers, Let Reynolds number be 1. , , All are coefficients. , , , , , Each structural type can be calibrated using high-fidelity simulation or experimental data and then stored in the associated database; Subsequently, the processor combines the heat exchange area of ​​this feature region. Temperature of the inner wall surface of the blade air conditioning temperature (Preset) The heat exchange in this characteristic region is calculated using the following formula:

[0050] Those skilled in the art will understand that the inner wall temperature of this feature region Instead of a full three-dimensional solution, it is based on the principle of continuous heat flow and uses a one-dimensional steady-state heat conduction formula, combined with the heat flux density of the blade outer wall, the blade wall thickness, and the thermal conductivity of the material in the aforementioned implementation method to quickly calculate the temperature. This satisfies the efficiency requirements for rapid evaluation (the inner wall temperature needs to be calculated in each iteration for the structural type and geometric parameters of the feature region). Finally, the processor combines the obtained heat exchange data to output the calculated cooling efficiency value and required cooling volume for the characteristic region, providing a basis for subsequent adjustments to the structural type and geometric parameters.

[0051] Thus, by equating the complex biomimetic channel to a flow network and using a heat transfer correlation database to quickly solve for the convective heat transfer coefficient, heat transfer area, and heat transfer of the inner wall, the dependence on full 3D CFD can be significantly reduced, avoiding the high computational cost caused by frequent modeling and mesh generation. Since the inner wall temperature can be obtained quickly through one-dimensional steady-state heat conduction, the processor can output the cooling efficiency value and the required cooling air volume in a short time, realizing rapid evaluation and iterative optimization of the internal cavity cooling structure.

[0052] For example, the processor has already set the tree-shaped biomimetic structure type and initial geometry for the leading edge of a turbine blade. After calling the internal flow network model, the processor simplifies the biomimetic channel into a flow network composed of flow resistance units and heat exchange units, and calculates the hydraulic diameter of the region based on the input geometric parameters. Circulation area and heat exchange area ; Subsequently, the processor retrieves the corresponding data for the tree structure from the associated database. , , Calculate the coefficients and combine them. , (Also obtainable from a related database), thermal conductivity of cold air ,pass The convective heat transfer coefficient of the inner wall was calculated. ; Next, the processor rapidly iterates to obtain the inner wall temperature based on the outer wall heat flux density, the corresponding blade wall thickness, and the material's thermal conductivity, according to the one-dimensional steady-state heat conduction formula. According to the preset air conditioning temperature Then based on The heat exchange in this region was calculated. ; Based on this, the processor combines the calculated heat exchange to output the cooling efficiency value and the required cooling volume for the area; if the result does not meet the preset conditions, it returns to the structure type and geometric parameters for adjustment, such as changing the branch size or heat exchange area and recalculating (the recalculation process includes recalculating the inner wall temperature) until a solution that meets the requirements is obtained. Once the target type and target geometry parameters of the leading edge region are determined, the processor can begin a multi-iteration process for the next feature region.

[0053] In some implementations, the required amount of cooling gas and the cooling efficiency are determined based on the heat exchange, including: The external thermal boundary information of the turbine blade and the internal thermal boundary information of the turbine blade corresponding to the structural type and geometric parameters are determined. The external thermal boundary information of the turbine blade is used to characterize the boundary heat exchange conditions and the heat flux density of the outer wall surface of the turbine blade. The internal thermal boundary information of the turbine blade is used to characterize the boundary heat exchange conditions and the heat flux density of the inner wall surface of the turbine blade. Based on the heat exchange, external thermal boundary information, and internal thermal boundary information of the turbine blades corresponding to the structural type and geometric parameters, the required amount of cooling gas and the cooling efficiency value are determined.

[0054] In this embodiment, in conjunction with the aforementioned embodiments, for each feature region, the processor does not determine whether the cooling is sufficient based on a single parameter, but rather incorporates the heat exchange, external thermal boundary information, and internal thermal boundary information into the same set of heat flow coupling judgment logic, thereby simultaneously solving the cooling gas demand and cooling efficiency value. The external thermal boundary information is obtained in the external heat transfer analysis for region division performed in the aforementioned embodiments: the processor calculates the adiabatic wall temperature of the blade outer wall surface in this characteristic region based on the blade profile coordinate points and aerodynamic boundaries, combined with the built-in aerodynamic thermodynamic algorithm or database. convective heat transfer coefficient of the outer wall and heat flux density of the outer wall surface These quantities collectively characterize the boundary heat exchange conditions of the outer wall surface of the turbine blade; The internal thermal boundary information is obtained during the heat transfer calculation process using an internal flow network model and a related database. The processor treats the internal cavity cooling structure of this feature region as multiple units and calculates the inner wall temperature by combining the corresponding structure type and geometric parameters. convective heat transfer coefficient of inner wall Preset air conditioning temperature and the heat flux density of the inner wall surface These quantities collectively characterize the boundary heat exchange conditions of the inner wall surface of the turbine blade. Subsequently, the processor calculates the heat transfer in this characteristic region. External thermal boundary information and internal thermal boundary information are defined, on the one hand, through cooling effect values: Calculate the cooling efficiency value of this feature region (e.g., and All are used to determine , here Instead of using the maximum allowable temperature of the blade matrix material as the initial engineering value in formula (1), it is a value that needs to be determined based on... and (The value to be calculated); On the other hand, based on the internal flow network model, given gas supply pressure boundary, the flow channel pressure drop calculation results of the internal cavity cooling structure, and the matching iteration of the gas supply pressure difference, the cooling gas demand of this characteristic region is solved simultaneously. Therefore, the cooling effect value reflects the actual cooling effect under the current external thermal boundary and internal thermal boundary coupling conditions, and the cooling gas demand reflects the gas supply scale required to meet the cooling effect. The pressure drop of the internal cooling structure in this characteristic region can be calculated based on the following formula:

[0055] in, Let f be the flow channel pressure drop of the internal cooling structure in this characteristic region, f be the friction factor, which is a function of the Reynolds number Re and the relative roughness, and L be the characteristic length of the flow channel of the internal cooling structure in this characteristic region. and u are the density and average velocity of the cooling air, respectively; In this way, external and internal thermal boundary information is obtained from external heat transfer analysis and rapid evaluation of the internal flow network, respectively. This information, along with the heat transfer, participates in the coupled solution of cooling efficiency and cooling demand. This allows the processor to quickly output cooling performance results and complete design screening without relying on repeated full-3D CFD modeling and mesh generation. This improves the efficiency of turbine blade cooling design and ensures that the determined cooling structure parameters better match actual airflow and temperature characteristics, thereby enhancing the targeting and accuracy of the cooling design.

[0056] In some implementations, the target type and the number of target geometric parameters of the internal cavity cooling structure are multiple sets; After determining the structural type and geometric parameters corresponding to the required amount of cooling gas and the cooling effect value that meet the preset conditions as the target type and target geometric parameters of the cavity cooling structure, the method further includes: For each set of target types and target geometric parameters, determine the curve of the cooling effect value of the cooling gas corresponding to the target type and target geometric parameters as a function of the cooling gas demand. Based on the variation curves of each target type and target geometric parameters, the optimal type and optimal geometric parameters are determined among multiple sets of target types and target geometric parameters.

[0057] In this embodiment, when there are multiple sets of target types and target geometric parameters of the internal cavity cooling structure, the processor does not retain only one set of results that meet the preset conditions, but for each set of target types and target geometric parameters, it further determines the curve of the cooling effect value of the cooling gas corresponding to the set of target types and target geometric parameters as a function of the required amount of cooling gas. That is, the processor can expand multiple feasible schemes obtained by the same design method under different cooling gas demand conditions to obtain the corresponding "cooling gas demand - cooling effect value" change relationship, thereby forming comparable scheme curves; Subsequently, the processor determines the optimal type and optimal geometric parameters from multiple sets of target types and target geometric parameters based on the variation curves of each set of target types and target geometric parameters; In this way, by further expanding multiple sets of target types and target geometric parameters that meet preset conditions into "cooling gas demand - cooling efficiency value" variation curves, the processor can make quantitative comparisons among multiple feasible solutions, thereby avoiding the one-sidedness of making judgments based solely on a single point of cooling efficiency value. This is not only conducive to selecting the optimal type and optimal geometric parameters from multiple solutions, but also provides a clear, intuitive and quantitative decision basis for the multi-objective optimization of the turbine blade internal cavity cooling structure.

[0058] For example, Figure 6 This is a curve comparing the cooling gas demand versus cooling efficiency of different solutions provided in the embodiments of this application. Please refer to [link / reference]. Figure 6 After the aforementioned iterations, the processor obtains three sets of target types and target geometric parameters of the internal cavity cooling structure that meet the preset conditions, corresponding to Scheme 1, Scheme 2 and Scheme 3 respectively; At this point, the user loads at least three different scheme files through the user interface. The processor automatically parses the cooling efficiency data under different cooling gas volume conditions from Scheme 1, Scheme 2, and Scheme 3, respectively, such as the cooling gas demand ( Figure 6 When the cooling air volume (represented as cooling capacity) is 0.010 kg / s, 0.012 kg / s, and 0.014 kg / s, the corresponding cooling efficiency value of Scheme 1 is ( Figure 6 The cooling efficiency values ​​(represented as cooling efficiency) for Scheme 1 are 0.36, 0.40, and 0.43, respectively; for Scheme 2, the corresponding cooling efficiency values ​​are 0.38, 0.43, and 0.45, respectively; and for Scheme 3, the corresponding cooling efficiency values ​​are 0.35, 0.39, and 0.41, respectively. Based on this, the processor generates three curves showing the change in "cooling gas demand - cooling efficiency". By comparing the overall trend of the curves, the growth rate of cooling efficiency, and the cooling efficiency level under the same cooling gas volume conditions, the processor ultimately determines that the target type and target geometric parameters corresponding to Scheme 2 are the optimal type and optimal geometric parameters. This is because Scheme 2 can achieve a higher cooling efficiency value with a lower cooling gas demand and still has good cooling performance under medium to high cooling gas volume conditions. In some embodiments, after determining the structural type and geometric parameters corresponding to the required amount of cooling gas and the cooling effect value that meet preset conditions as the target type and target geometric parameters of the cavity cooling structure, the method further includes: The external thermal boundary information of the turbine blade and the internal thermal boundary information of the turbine blade corresponding to the target type and target geometric parameters are determined. The external thermal boundary information of the turbine blade is used to characterize the boundary heat exchange conditions and the heat flux density of the outer wall surface of the turbine blade. The internal thermal boundary information of the turbine blade is used to characterize the boundary heat exchange conditions and the heat flux density of the inner wall surface of the turbine blade. Based on the external thermal boundary information and the internal thermal boundary information of the turbine blade corresponding to the target type and target geometric parameters, the two-dimensional steady-state heat conduction control equation of the turbine blade is solved to obtain the solution result. Based on the solution results, determine the temperature field cloud map of the turbine blade, the maximum temperature of the turbine blade, and the average temperature of the turbine blade; Based on the temperature field cloud map, the highest temperature, and the average temperature, the target type and target geometric parameters are verified to obtain the verification results. The verification results are used to characterize whether the target type and target geometric parameters meet the temperature control requirements of the turbine blade.

[0059] In this embodiment, after the processor determines the structural type and target geometric parameters corresponding to the required amount of cooling gas and the cooling efficiency value that meet the preset conditions as the target type and target geometric parameters of the internal cavity cooling structure, the processor enters the final design verification stage. Instead of relying solely on the iteration results in the iteration process, it couples the external thermal boundary information with the internal thermal boundary information corresponding to the target type and target geometric parameters to solve the two-dimensional steady-state heat conduction control equation of the turbine blade, so as to obtain a more accurate full-section temperature field distribution result. Here, the external thermal boundary information is used to characterize the boundary heat exchange conditions and the heat flux density of the outer wall surface of the turbine blade, and the internal thermal boundary information is used to characterize the boundary heat exchange conditions and the heat flux density of the inner wall surface of the turbine blade. During the solution process, the processor relies on the thermal conductivity of the material due to its anisotropy. Through the two-dimensional steady-state heat conduction control equation:

[0060] The blade matrix is ​​solved using a full-section coupled solution, with boundary conditions applied to the outer wall surfaces:

[0061] And the boundary conditions of the inner wall surface:

[0062] This allows us to obtain temperature field cloud maps of the blade cross section, as well as results such as maximum temperature and average temperature. Based on these results, we can verify the target type and target geometric parameters to determine whether they meet the temperature control requirements of turbine blades. in, For the leaf temperature, The x-axis is... The vertical axis is , The thermal conductivity of the material. The adiabatic temperature of the turbine blades. This refers to the temperature of the outer wall surface of the turbine blade. The convective heat transfer coefficient of the outer wall of the turbine blade. This refers to the temperature of the inner wall surface of the turbine blades. The convective heat transfer coefficient of the inner wall of the turbine blade is denoted as . The preset air conditioning temperature The normal direction of the boundary is characterized, which is the direction perpendicular to the outer wall surface of the turbine blade; Those skilled in the art will understand that the outer wall temperature and inner wall temperature in this embodiment are not at the same level as the outer wall temperature and inner wall temperature used in the aforementioned rapid iteration process: The outer wall temperature in the aforementioned rapid iteration process is usually an engineering preset initial value used for rapid iteration, while the inner wall temperature is an approximation obtained under one-dimensional steady-state heat conduction rapid estimation, used to support rapid evaluation and parameter selection; however, the outer wall temperature and inner wall temperature in this embodiment are the final verification results obtained by solving the two-dimensional steady-state heat conduction control equation and the internal and external heat transfer boundary conditions through the finite difference method or finite element method. They directly reflect the true temperature distribution state of the entire cross section of the turbine blade under the target type and target geometric parameters. In other words, the temperature in the aforementioned rapid iteration process is more of an "approximate quantity used for rapid iteration," while the temperature in this section is the "precise result after full-section coupled solution."

[0063] In conjunction with the aforementioned implementation methods, when the turbine blade is divided into multiple characteristic regions, the steps of solving the two-dimensional steady-state heat conduction control equation in this implementation method can be performed separately for each characteristic region. Finally, the results are combined to obtain the temperature field cloud map, maximum temperature, and average temperature of the entire turbine blade, and based on this, it is determined whether the target type and target parameters of the turbine blade meet the load temperature control requirements.

[0064] Thus, by coupling the external and internal thermal boundary information into a two-dimensional steady-state heat conduction solution, the system can obtain a more accurate full-section temperature field distribution in the final verification stage, thereby accurately determining whether the target type and target geometric parameters truly meet the temperature control requirements. Compared to the rapid iterative estimation in step two, the solution results in this part are closer to the actual working conditions, which can significantly improve the verification accuracy and reliability of the turbine blade cooling design.

[0065] Please see Figure 7This is a schematic diagram of the structure of a turbine blade design device provided in an embodiment of this application. A second aspect of this application provides a turbine blade design device 10, which includes: The acquisition module 11 is used to acquire multiple related information of the turbine blade, wherein the multiple related information are used to characterize the structural features of the turbine blade and the airflow and temperature characteristics of the environment in which the turbine blade is located. The first determining module 12 is used to determine the structural type and geometric parameters of the internal cavity cooling structure of the turbine blade based on multiple relevant information, wherein the internal cavity cooling structure is located in the blade cavity of the turbine blade and is used to conduct cooling gas. The second determining module 13 is used to determine the cooling gas demand and cooling efficiency value based on the internal flow network model and associated database of the turbine blade, according to the structural type and geometric parameters. The internal flow network model is used to convert the internal cavity cooling structure into multiple units and to determine the heat transfer performance parameters of the internal cavity cooling structure based on the structural type and geometric parameters. The associated database is used to store the calculation coefficients associated with the structural type and geometric parameters. The calculation coefficients are used to determine the heat transfer performance parameters. The adjustment module 14 is used to adjust the structure type and geometric parameters according to the demand and cooling effect value when the demand and cooling effect value of the cooling gas do not meet the preset conditions, so as to obtain the adjusted structure type and geometric parameters. The iteration module 15 is used to take the adjusted structure type and geometric parameters as the structure type and geometric parameters, jump to the internal flow network model and associated database based on the turbine blade, and execute the step of determining the cooling gas demand and cooling effect value according to the structure type and geometric parameters until the cooling gas demand and cooling effect value meet the preset conditions. The third determining module 16 is used to determine the structural type and target geometric parameters corresponding to the required amount of cooling gas and the cooling effect value that meet the preset conditions as the target type and target geometric parameters of the internal cavity cooling structure.

[0066] The turbine blade design apparatus 10 provided in the second aspect of this application can realize the various processes implemented in the above method embodiments and achieve the same beneficial effects. To avoid repetition, it will not be described again here.

[0067] Please see Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. A third aspect of this application provides an electronic device 800, including a processor 810 and a memory 820. The memory 820 stores machine-executable instructions that can be executed by the processor 810. The processor 810 can execute the machine-executable instructions to implement the above-described design method for turbine blades.

[0068] A fourth aspect of this application provides a machine-readable storage medium storing instructions that, when executed by a processor, cause the processor to implement the aforementioned design method for turbine blades.

[0069] In one embodiment of this application, a computer program product is also provided, including a computer program that, when executed by a processor, implements the design method for turbine blades according to the above embodiments.

[0070] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied 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.

[0071] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0072] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0073] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0074] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0075] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0076] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

[0077] Furthermore, various different embodiments of the present invention can be combined in any way, as long as they do not violate the spirit of the present invention, they should also be regarded as the content disclosed by the present invention.

Claims

1. A design method for turbine blades, characterized in that, The method includes: Multiple relevant information of the turbine blade are obtained, wherein the multiple relevant information are used to characterize the structural features of the turbine blade and the airflow and temperature characteristics of the environment in which the turbine blade is located; Based on the aforementioned relevant information, the structural type and geometric parameters of the internal cavity cooling structure of the turbine blade are determined, wherein the internal cavity cooling structure is located inside the blade cavity of the turbine blade and is used to conduct cooling gas. Based on the internal flow network model and associated database of the turbine blades, the demand for cooling gas and the cooling efficiency value are determined according to the structure type and geometric parameters. The internal flow network model is used to convert the internal cavity cooling structure into multiple units and to determine the heat transfer performance parameters of the internal cavity cooling structure based on the structure type and geometric parameters. The associated database is used to store calculation coefficients associated with the structure type and geometric parameters, and the calculation coefficients are used to determine the heat transfer performance parameters. If the required amount and cooling efficiency of the cooling gas do not meet the preset conditions, the structure type and geometric parameters are adjusted according to the required amount and cooling efficiency to obtain the adjusted structure type and geometric parameters. The adjusted structural type and geometric parameters are used as the structural type and geometric parameters. The process jumps to the internal flow network model and associated database based on the turbine blade. The step of determining the cooling gas demand and cooling effect value according to the structural type and geometric parameters is executed until the cooling gas demand and cooling effect value meet the preset conditions. The structural type and geometric parameters corresponding to the required amount and cooling efficiency value of the cooling gas that meet the preset conditions are determined as the target type and target geometric parameters of the internal cavity cooling structure.

2. The method according to claim 1, characterized in that, The step of determining the structural type and geometric parameters of the internal cooling structure of the turbine blade based on the multiple relevant information includes: Based on the aforementioned relevant information, the turbine blade is divided into regions to obtain multiple characteristic regions. The external thermal boundary information of each characteristic region is different. The external thermal boundary information of the characteristic region is used to characterize the boundary heat exchange conditions of the blade outer wall surface included in the characteristic region and the heat flux density of the blade outer wall surface of the characteristic region. For each of the aforementioned feature regions, the structural type and geometric parameters of the internal cavity cooling structure of the feature region are determined.

3. The method according to claim 2, characterized in that, The internal flow network model and associated database based on the turbine blades determine the cooling gas demand and cooling efficiency value according to the structural type and geometric parameters, including: For each of the aforementioned feature regions, based on the internal flow network model and associated database of the turbine blade, the heat exchange between the inner cavity of the feature region and the outside of the turbine blade is determined according to the structural type and geometric parameters of the internal cavity cooling structure of the feature region. Based on the heat exchange between the inner cavity of the characteristic region and the outside of the turbine blades, determine the required amount of cooling gas and the cooling efficiency value of the characteristic region; The step of adjusting the structural type and geometric parameters based on the demand and cooling efficiency value to obtain the adjusted structural type and geometric parameters includes: For each of the aforementioned characteristic regions, the structural type and geometric parameters of the characteristic region are adjusted according to the cooling gas demand and cooling efficiency value of the characteristic region, thereby obtaining the adjusted structural type and geometric parameters of the characteristic region.

4. The method according to claim 1, characterized in that, The heat transfer performance parameters of the internal cavity cooling structure include the convective heat transfer coefficient of the inner wall of the turbine blade; The internal flow network model and associated database based on the turbine blades determine the cooling gas demand and cooling efficiency value according to the structural type and geometric parameters, including: Based on the internal flow network model and associated database of the turbine blade, the convective heat transfer coefficient and heat transfer area of ​​the inner wall of the turbine blade are determined according to the structural type and geometric parameters. The amount of heat exchanged between the inner cavity of the turbine blade and the outside of the turbine blade is determined based on the convective heat transfer coefficient and heat transfer area of ​​the inner wall. Based on the heat exchange, the required amount and cooling efficiency of the cooling gas are determined.

5. The method according to claim 4, characterized in that, The step of determining the required amount and cooling efficiency of the cooling gas based on the heat exchange includes: The external thermal boundary information of the turbine blade and the internal thermal boundary information of the turbine blade corresponding to the structural type and geometric parameters are determined. The external thermal boundary information of the turbine blade is used to characterize the boundary heat exchange conditions of the outer wall surface of the turbine blade and the heat flux density of the outer wall surface of the turbine blade. The internal thermal boundary information of the turbine blade is used to characterize the boundary heat exchange conditions of the inner wall surface of the turbine blade and the heat flux density of the inner wall surface of the turbine blade. The required amount and cooling efficiency of the cooling gas are determined based on the heat exchange, the external thermal boundary information, and the internal thermal boundary information of the turbine blades corresponding to the structural type and geometric parameters.

6. The method according to claim 1, characterized in that, The number of target types and target geometric parameters of the internal cavity cooling structure are multiple sets; After determining the structural type and geometric parameters corresponding to the required amount and cooling efficiency value of the cooling gas that meet the preset conditions as the target type and target geometric parameters of the cavity cooling structure, the method further includes: For each set of target types and target geometric parameters, determine the curve of the cooling effect value of the cooling gas corresponding to the target type and target geometric parameters as a function of the required amount of cooling gas; Based on the variation curves of the target type and target geometric parameters in each group, the optimal type and optimal geometric parameters are determined among multiple groups of target types and target geometric parameters.

7. The method according to claim 1, characterized in that, After determining the structural type and geometric parameters corresponding to the required amount and cooling efficiency value of the cooling gas that meet the preset conditions as the target type and target geometric parameters of the cavity cooling structure, the method further includes: The external thermal boundary information of the turbine blade and the internal thermal boundary information of the turbine blade corresponding to the target type and target geometric parameters are determined. The external thermal boundary information of the turbine blade is used to characterize the boundary heat exchange conditions of the outer wall surface of the turbine blade and the heat flux density of the outer wall surface of the turbine blade. The internal thermal boundary information of the turbine blade is used to characterize the boundary heat exchange conditions of the inner wall surface of the turbine blade and the heat flux density of the inner wall surface of the turbine blade. Based on the external thermal boundary information and the internal thermal boundary information of the turbine blade corresponding to the target type and target geometric parameters, the two-dimensional steady-state heat conduction control equation of the turbine blade is solved to obtain the solution result; Based on the solution results, the temperature field cloud map of the turbine blade, the maximum temperature of the turbine blade, and the average temperature of the turbine blade are determined. Based on the temperature field cloud map, the highest temperature, and the average temperature, the target type and target geometric parameters are verified to obtain verification results. The verification results are used to characterize whether the target type and target geometric parameters meet the temperature control requirements of the turbine blade.

8. A design device for turbine blades, characterized in that, The device includes: The acquisition module is used to acquire multiple related information of the turbine blade, wherein the multiple related information are used to characterize the structural features of the turbine blade and the airflow and temperature characteristics of the environment in which the turbine blade is located; The first determining module is used to determine the structural type and geometric parameters of the internal cavity cooling structure of the turbine blade based on the multiple relevant information, wherein the internal cavity cooling structure is located in the blade cavity of the turbine blade and is used to conduct cooling gas; The second determining module is used to determine the demand for cooling gas and the cooling efficiency value based on the internal flow network model of the turbine blade and the associated database, according to the structure type and geometric parameters. The internal flow network model is used to convert the internal cavity cooling structure into multiple units and to determine the heat transfer performance parameters of the internal cavity cooling structure based on the structure type and geometric parameters. The associated database is used to store calculation coefficients associated with the structure type and geometric parameters, and the calculation coefficients are used to determine the heat transfer performance parameters. The adjustment module is used to adjust the structure type and geometric parameters according to the demand and cooling efficiency value when the demand and cooling efficiency value of the cooling gas do not meet the preset conditions, so as to obtain the adjusted structure type and geometric parameters. The iterative module is used to take the adjusted structure type and geometric parameters as the structure type and geometric parameters, jump to the internal flow network model and associated database based on the turbine blade, and execute the step of determining the demand amount and cooling effect value of the cooling gas according to the structure type and geometric parameters until the demand amount and cooling effect value of the cooling gas meet the preset conditions. The third determining module is used to determine the structural type and target geometric parameters corresponding to the required amount and cooling effect value of the cooling gas that meet the preset conditions as the target type and target geometric parameters of the internal cavity cooling structure.

9. An electronic device, characterized in that, The device includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When a processor executes a program stored in a memory, it implements the steps of the design method for turbine blades according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the design method for turbine blades as described in any one of claims 1-7.

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