Multi-field load applying method and device for aero-engine structure

Abstract

The invention discloses a multi-field load applying method and device for an aero-engine structure. The method comprises the following steps: acquiring multi-field load data of an aero-engine structure, and establishing a K-dimensional tree based on coordinate points of the multi-field load data; the method further comprises the steps that multiple coordinate points closest to the selected finite element grid nodes in the finite element model are searched in the K-dimensional tree, the data values of the selected finite element grid nodes are determined based on the data values corresponding to the coordinate points, and therefore finite element multi-field load application is completed.

Description

Technical Field

[0001] The present invention relates to the field of aeroengines, and particularly to a method and device for applying multi-field loads to the structure of an aeroengine. Background Art

[0002] During the operation of an aeroengine, the loads borne by the core components are extremely harsh. Taking a high-pressure turbine blade as an example, the blade is affected by centrifugal force, gas impact aerodynamic force, thermal stress, random load, etc. The interaction of multiple loads makes the actual working condition of the blade quite complex, and its strength analysis is a typical multi-field coupling problem. To accurately evaluate the strength of core components of an aeroengine such as blades, determine their failure probability, and ensure the safe operation of the aeroengine, it is necessary to perform multi-disciplinary analysis and calculation, apply multi-field load data, and carry out structural strength assessment.

[0003] In this process, accurately and efficiently applying multi-field loads is a problem. Since the calculation models and principles used in different disciplines are completely different, there are significant differences in the data form, mapping relationship, application position, etc. of the multi-field loads obtained. Especially, the meshes of different calculation models are different, resulting in a lack of effective load data at the nodes for strength calculation, and it is difficult to ensure the calculation accuracy.

[0004] Currently, the main method for applying multi-field loads is to manually process the load data, unify the format, take values near the nodes lacking effective data, and apply multi-field loads such as temperature field and pressure field. This method has two problems. One is that as the number of finite element meshes increases, the processing time grows exponentially, lacking efficiency. The other is that the practice of replacing missing data with nearby values introduces multi-field load errors, and this error may have a greater impact on the structural strength under specific working conditions.

[0005] Therefore, there is a need in the art for an improved method and device for applying multi-field loads to the structure of an aeroengine. Summary of the Invention

[0006] The present invention provides an improved method and device for applying multi-field loads to the structure of an aeroengine. Aiming at the multi-field coupling strength analysis problem of the core components of an aeroengine, the present invention proposes a new method for applying multi-field loads. First, it organizes the external load data points according to a specific structure, then queries the multi-field load data points adjacent to each finite element node for strength analysis, and realizes high-precision prediction of the loads at the nodes with missing data according to a specific algorithm. After traversing all nodes, the application of multi-field loads in the finite element is finally completed. This method realizes the efficient and accurate application of multi-field loads to the engine structure, with low time cost and high precision, and improves the design efficiency of engine components.

[0007] In one embodiment of the present invention, a method for applying multi-field loads to an aero-engine structure is provided, which includes: obtaining multi-field load data of the aero-engine structure, the multi-field load data including coordinate points and corresponding data values; establishing a K-dimensional tree based on the coordinate points of the multi-field load data; obtaining a finite element model of the aero-engine structure, the finite element model including a plurality of finite element mesh nodes; searching for N coordinate points closest to a selected finite element mesh node in the finite element model in the K-dimensional tree, where K and N are positive integers; and determining the data value of the selected finite element mesh node based on the data values corresponding to the N coordinate points.

[0008] In one aspect, searching for the N coordinate points includes: searching for N coordinate points closest to the selected finite element mesh node based on the Euclidean distance between the selected finite element mesh node and the coordinate points in the K-dimensional tree.

[0009] In one aspect, searching for the N coordinate points includes: performing a recursive query in the K-dimensional tree and backtracking the search path to find the coordinate point closest to the selected finite element mesh node; and cyclically querying the next closest coordinate point while ignoring the found closest coordinate point until the closest N coordinate points are obtained.

[0010] In one aspect, determining the data value of the selected finite element mesh node includes: calculating the data value of the selected finite element mesh node by the inverse distance weighting method based on the data values corresponding to the N coordinate points.

[0011] In one aspect, for the method for applying multi-field loads to the aero-engine structure, determining the data value of the selected finite element mesh node includes: calculating the load field data of the selected finite element mesh node based on N and an initial value of a power parameter p for the inverse distance weighting method; adjusting the value of N and obtaining an updated N value that makes the resolution of the load field data meet the requirements; adjusting the value of p and obtaining an updated p value that makes the smoothness of the load field data meet the requirements; and calculating the data value of the selected finite element mesh node based on the updated N value and the updated p value.

[0012] In one aspect, the method for applying multi-field loads to the aero-engine structure further includes: traversing all finite element mesh nodes in the finite element model to obtain the data value of each finite element mesh node.

[0013] In one aspect, establishing a K-dimensional tree includes: iteratively dividing each dimension of the multi-field load data until all coordinate points of the multi-field load data are traversed to establish the K-dimensional tree.

[0014] In one aspect, the multi-field load data includes one or more of the following: temperature field load data; pressure field load data; centrifugal force load data; aerodynamic force load data.

[0015] In one embodiment of the present invention, there is provided an apparatus for applying multi-field loads to an aero-engine structure, including: a memory for storing processor-executable instructions; and a processor coupled to the memory, which is configured to implement the method for applying multi-field loads to an aero-engine structure as described in any one of the above when executing the processor-executable instructions.

[0016] In one embodiment of the present invention, there is provided an apparatus for applying multi-field loads to an aero-engine structure, including: a load data acquisition module configured to acquire multi-field load data of the aero-engine structure, the multi-field load data including coordinate points and corresponding data values; a data organization module configured to build a K-dimensional tree based on the coordinate points of the multi-field load data; a finite element module configured to acquire a finite element model of the aero-engine structure, the finite element model including a plurality of finite element mesh nodes; a search module configured to search for N coordinate points closest to a selected finite element mesh node in the finite element model in the K-dimensional tree, where K and N are positive integers; and a calculation module configured to determine the data value of the selected finite element mesh node based on the data values corresponding to the N coordinate points. Description of the Drawings

[0017] Figure 1 is a flowchart of a method for applying multi-field loads to an aero-engine structure according to an embodiment of the present invention.

[0018] Figure 2 is a schematic diagram of a finite element multi-field load transfer process according to an embodiment of the present invention.

[0019] Figure 3 is a schematic diagram of the process of building a KDTree according to an embodiment of the present invention.

[0020] Figure 4 is a schematic diagram of calculating field data by the inverse distance weighting method according to an embodiment of the present invention.

[0021] Figure 5 is a schematic diagram of a finite element model of a certain turbine blade according to an embodiment of the present invention.

[0022] Figure 6 is a schematic diagram of the result of temperature field data transfer according to an embodiment of the present invention.

[0023] Figure 7 is a block diagram of an apparatus for applying multi-field loads to an aero-engine structure according to an embodiment of the present invention. Detailed Description of the Invention

[0024] The present invention will be further described below in conjunction with specific embodiments and the accompanying drawings, but the protection scope of the present invention should not be limited thereby.

[0025] The service load conditions of aero-engine components are typical multi-field coupling problems. When analyzing, it is necessary to accurately and efficiently apply multi-field load data. During the process of applying multi-field load data, the existing technology is to manually process the load data, unify the format, and take values near the nodes lacking effective data to apply multi-fields such as temperature fields and pressure fields. This method has a high computational complexity and a low execution efficiency. Especially for grids with more than one million, the program processing will take several hours.

[0026] Aiming at the problems of low execution efficiency and insufficient accuracy of the existing multi-field load data application method, the present invention is based on a specific data structure. By organizing multi-field load data, querying the neighbor data of finite element nodes and predicting node loads, it realizes the rapid and accurate transfer of finite element multi-field load data. The problems to be solved in this process include one or more of the following:

[0027] (1) How to quickly match the finite element grid nodes with the multi-field load field data;

[0028] (2) How to accurately predict the missing data for the finite element grid nodes according to the matched load data points.

[0029] Figure 1 It is a flowchart of a multi-field load application method 100 for an aero-engine structure according to an embodiment of the present invention. The method 100 can be executed by devices such as a computer, a processor, a server, a controller, and a cloud service.

[0030] In step 102, multi-field load data of the aero-engine structure can be obtained. The multi-field load data can include coordinate points and corresponding data values. The multi-field load data can include one or more of the following: temperature field load data, pressure field load data, centrifugal force load data, aerodynamic force load data, prestress field load data, electric field load data, magnetic field load data, sound field load data, mass flow field load data, heat flow field load data, etc.

[0031] In step 104, a K-dimensional tree can be established based on the coordinate points of the multi-field load data. Establishing a K-dimensional tree can include iteratively dividing each dimension of the multi-field load data until all coordinate points of the multi-field load data are traversed to establish a K-dimensional tree. A corresponding K-dimensional tree can be established for each field. The K-dimensional tree is also called a KDTree, which is a structure for organizing data points in a K-dimensional Euclidean space and is often used for high-dimensional space search. K can be a positive integer. The value of K is related to the analysis of the engine structure. For example, if a two-dimensional analysis is performed on the component, then K = 2; if a three-dimensional analysis is performed, then K = 3, and so on.

[0032] In step 106, a finite element model of the aero-engine structure can be obtained. The finite element model may include a plurality of finite element mesh nodes. Step 106 can be performed sequentially or in parallel with step 102 or step 104.

[0033] In step 108, N coordinate points closest to the selected finite element mesh node in the finite element model can be searched for in the K-d tree. N can be a positive integer. The larger the value of N, the smoother the obtained load field, but the spatial resolution of the load field decreases accordingly. In one embodiment, the value of N can be between 5 and 20. In a preferred embodiment, N can be taken as 5 to 8. Searching for the N coordinate points may include: searching for the N coordinate points closest to the selected finite element mesh node based on the Euclidean distance between the selected finite element mesh node and the coordinate points in the K-d tree. Searching for the N coordinate points may further include: performing a recursive query in the K-d tree and backtracking the search path to find the coordinate point closest to the selected finite element mesh node; and cyclically querying the next closest coordinate point while ignoring the found closest coordinate point until the N closest coordinate points are obtained.

[0034] In step 110, the data value of the selected finite element mesh node can be determined based on the data values corresponding to the N coordinate points. Determining the data value of the selected finite element mesh node may include: calculating the data value of the selected finite element mesh node by the inverse distance weighting method based on the data values corresponding to the N coordinate points. In one embodiment, determining the data value of the selected finite element mesh node may further include: calculating the data value of the selected finite element mesh node by adjusting N and the power parameter p used in the inverse distance weighting method.

[0035] In one embodiment, method 100 may include traversing all the finite element mesh nodes in the finite element model to obtain the data value of each finite element mesh node. For example, steps 108 and 110 can be performed for each finite element mesh node to complete the application of the finite element field load. By establishing a K-d tree for each field and transferring / applying the field data to the finite element model, each finite element node can include the load data of multiple fields, thereby realizing the application of multi-field loads.

[0036] According to an embodiment of the present invention, when both the number of finite element mesh nodes and the number of external multi-field load data points are very large, using the KDTree for matching retrieval can greatly reduce the time consumption. Therefore, by utilizing the KDTree data structure characteristics to organize the multi-field load data points, the finite element mesh nodes can be quickly matched with the multi-field load field data. In addition, according to the matched load data points, the corresponding data value of the finite element mesh node can be calculated, thereby accurately predicting the missing data of the finite element mesh node.

[0037] Figure 2 is a schematic diagram of a finite element multi-field load transfer process 200 according to an embodiment of the present invention.

[0038] The process may start with obtaining a finite element calculation file (e.g., which contains a finite element model of an aeroengine structure) and an in-service load data file (e.g., which contains multi-field load data of an aeroengine structure). The finite element model may include a plurality of finite element mesh nodes P i , which may be indexed by an ID and have coordinates (X, Y, Z). The mesh nodes P in the initially established finite element model i may not have corresponding load values. The multi-field load data may include multiple fields, each field may have multiple data points, each data point may have an n-dimensional feature, and the features in each dimension have coordinate points T j (represented by X, Y, Z) and corresponding data values O j (represented by V). For example, the multi-field load data may include temperature field load data, pressure field load data, centrifugal force load data, aerodynamic force load data, prestress field load data, electric field load data, magnetic field load data, sound field load data, mass flow field load data, heat flow field load data, etc. The coordinate points in different dimensions of each field load data may be the same or different, or have some same coordinate points and other different coordinate points. The coordinates of the mesh nodes P i may coincide or not coincide with the coordinate points T of the multi-field load data j . For example, the coordinates of some mesh nodes P i may coincide with some coordinate points T j , while the coordinates of other mesh nodes P i do not coincide with some coordinate points T j .

[0039] According to an embodiment of the present invention, for each type of field load data, a K-dimensional tree may be built based on the coordinates T j . Figure 3 is a schematic diagram 300 of the KDTree building process according to an embodiment of the present invention. Taking two-dimensional random data points as an example, the process is as follows: Select the dimension with a larger coordinate variance as the splitting axis, find the median in the splitting dimension, and use this point as the splitting point to divide the left and right subtrees; Rotate to the next dimension, select the median in the left and right subtrees respectively as the splitting point, and further divide; Repeat this process until all T j are traversed, and the building of the tree for T j is completed j .

[0040] Return to Figure 2 , after building the KDTree, the nearest T i to the finite element node P may be searched in the KDTree j : For the finite element node P i, first start from the root node of the KDTree and calculate P i and the Euclidean distance d j between this root node T ij . Temporarily take this distance as the minimum distance between P i and T j , and take the root node as the nearest neighbor of P i . According to the current splitting dimension, compare the numerical values of P i and the splitting dimension of the root node to determine whether P i is located in the left subtree or the right subtree; taking P i in the left subtree as an example, calculate the Euclidean distance d i between P ij and the splitting node of the left subtree. If this distance is less than the current minimum distance, then take it as the new minimum distance, and take the splitting node of the left subtree as the new nearest neighbor of P i . Recursively query until the query subtree of the current nearest neighbor is empty, or the distance between the subtree and P i is greater than the current minimum distance; according to the nearest neighbor search path in the KDTree, backtrack the Euclidean distances between P i and each splitting axis on the search path. If P i is greater than the current minimum distance from all splitting axes, then the search ends, and the query of the nearest neighbor of P i is completed. However, if P i is less than the current minimum distance from a certain splitting axis, then it is necessary to start from this splitting point and query the distance between the other branch node and P i until no new minimum distance is found through recursion and backtracking, and the search for the nearest point T i of P j is completed.

[0041] Search for the N nearest T i to P j : Similar to the above method for querying the nearest neighbor of P i , save the nearest neighbor T j and the nearest distance d ij during each query process, and then, in the case of deleting the found nearest neighbor T j node, loop N times to complete the search for the N nearest neighbors of P i . As shown in Figure 2 , k = 0 in the first round of search. One nearest neighbor T j can be obtained in each round of search, and k is incremented by 1 until k is no longer less than N, and N nearest neighbors are obtained. N can be a positive integer, and its specific value can be set according to needs or experience.

[0042] Obtain the N nearest T i to P jAfter that, according to the N external field data O corresponding to the j index j , from O j Interpolate to obtain R i . To obtain a more accurate R i , the external multi-field load can be regarded as a local influence, that is, the closer to the load point, the greater the influence, and the farther the influence is smaller. Through the nearest N Ts j and the P between them i distance d ij , determine the weight of O j . This interpolation method with higher weight for smaller distance and lower weight for larger distance between data points and the target point is called the inverse distance weighting method.

[0043] Figure 4 is a schematic diagram 400 of calculating field data by the inverse distance weighting method according to an embodiment of the present invention.

[0044] The weight function of the inverse distance weighting method is as follows:

[0045]

[0046] where p is the power parameter of the inverse distance weighting interpolation, and d ij is the distance between P i and T j . The weight of each data point is a function of the reciprocal of the distance from P i . By way of example and not limitation, the value range of the power parameter p of the inverse distance weighting is 0.5 to 3, generally taking 1 or 2. The larger the power parameter, the closer the interpolation result is to the data of the nearest neighbor point. The smaller the power parameter, the weight of relatively far points increases and the result is smoother. For finite element analysis, to ensure the accuracy and continuity of the transfer of the external field load data, in one embodiment, the power parameter p can be taken as 1.

[0047] Back to Figure 2 , according to the weight function, the data R i result of point P i can be calculated, and the formula is as follows:

[0048]

[0049] In one embodiment, the data value accuracy of the finite element mesh nodes can be improved by adjusting N and the power parameter p, as described below.

[0050] First, the load field data of the finite element mesh nodes can be calculated based on the initial N and p, and observe whether the spatial resolution and smoothness of the load field meet the needs of problem analysis.

[0051] The value of N is related to the spatial resolution of the load field. If a higher spatial resolution of the load field is required for analysis, the value of N can be gradually decreased; if a lower spatial resolution of the load field is required for analysis, the value of N can be gradually increased. Subsequently, a new load field is calculated based on the updated value of N. When the resolution of the load field meets the analysis requirements, this updated value is the final value of N.

[0052] The value of p is related to the smoothness of the load field. If a smoother load field is required for analysis, the value of p can be gradually increased; if more original information of the load field needs to be retained for analysis, the value of p can be gradually decreased. For example, the minimum value of p can be 0.5. Subsequently, a new load field is calculated based on the updated value of p. When the smoothness of the load field meets the analysis requirements, this updated value is the final value of p.

[0053] According to an embodiment of the present invention, by adjusting N and the power parameter p to obtain the load field data required for problem analysis, the accuracy of the analysis results can be improved.

[0054] All nodes P of the cyclic finite element model can be traversed similarly i to obtain the corresponding load value R for each node i , that is, the data transfer of the finite element field load is completed.

[0055] By establishing a K-dimensional tree for each type of field load data and transferring / applying the field data to the finite element model, each finite element node can include the load data of multiple fields, thereby realizing the data transfer / application of the finite element multi-field load.

[0056] It can be found from the KDTree construction process that the KDTree is similar to a K-dimensional binary tree. All splitting nodes can be regarded as using a hyperplane to divide the K-dimensional space into two half-spaces; querying finite element nodes is also similar to binary search, which can quickly approximate the nearest data point from the entire space and is very efficient when dealing with large data. For example, for 1 million external multi-field data points, the Euclidean distance only needs to be calculated at least 20 times, greatly improving the search efficiency.

[0057] Compared with the traditional finite element multi-field load transfer method, the present method has the following innovative points:

[0058] (1) It can achieve the rapid and accurate application of finite element multi-field loads;

[0059] (2) Organizing multi-field load data based on the KDTree structure significantly improves the matching efficiency between multi-field load field data and finite element nodes;

[0060] (3) Using the inverse distance weighting method to calculate the load of data missing points, considering the influence of multiple data points in the node neighborhood, and improving the load prediction accuracy;

[0061] (4) It can directly read the information of finite element nodes and multi-field load data points, without being restricted by software versions and file formats, and has high compatibility.

[0062] Figure 5 It is a schematic diagram of a finite element model of a certain turbine blade according to an embodiment of the present invention. The finite element model 500 may include a plurality of finite element mesh nodes P i , for simulating the structure of the turbine blade.

[0063] According to Figure 2 the process shown, the implementation manner of the multi-field load application method for the turbine blade structure can be as follows:

[0064] (1) Input the finite element calculation file and the external load data file;

[0065] (2) Read the coordinates P of the finite element nodes i , read the coordinates T of the multi-field load data points j , and the O of the field data j ;

[0066] (3) Use the form of the KDTree binary search tree to build a tree for the whole of T j ;

[0067] (4) For the finite element node P i , recursively query on the KDTree and backtrack the search path to obtain its nearest neighbor point T j ;

[0068] (5) According to the index j of the nearest neighbor point T j , obtain the field data O j and the distance d i from P ij ;

[0069] (6) According to the weight function, circularly query and save the O i of the N nearest neighbor points of P j and d ij ;

[0070] (7) Use the inverse distance weighting method to adjust N and p, and calculate the field data R j and d ij of P i ; i ;

[0071] (8) Traverse all the finite element mesh nodes to complete the transfer of the finite element field data.

[0072] See Figure 6, which shows that after the external load data (temperature field data) is transferred to the finite element nodes, the finite element model 600 contains the temperature field data of each grid node. In addition, by building a K-d tree for each type of field load data and transferring / applying the field data to the finite element model, the finite element model 600 can include the load data of multiple fields, thus realizing the transfer of multi-field data in the finite element model.

[0073] Figure 7 is a block diagram of a multi-field load application device 700 for an aero-engine structure according to an embodiment of the present invention.

[0074] The device 700 may include a load data acquisition module 702, which is configured to acquire the multi-field load data of the aero-engine structure. The multi-field load data includes coordinate points and corresponding data values. The multi-field load data includes one or more of the following: temperature field load data, pressure field load data, centrifugal force load data, aerodynamic force load data, etc.

[0075] The device 700 may further include a data organization module 704, which is configured to build a K-d tree based on the coordinate points of the multi-field load data. Building the K-d tree may include iteratively dividing each dimension of the multi-field load data until all the coordinate points of the multi-field load data are traversed to build the K-d tree.

[0076] The device 700 may further include a finite element module 706, which is configured to acquire the finite element model of the aero-engine structure. The finite element model includes a plurality of finite element grid nodes.

[0077] The device 700 may further include a search module 708, which is configured to search for the N coordinate points closest to the selected finite element grid nodes in the finite element model in the K-d tree, where K and N are positive integers. Searching for the N coordinate points may include: searching for the N coordinate points closest to the selected finite element grid nodes based on the Euclidean distance between the selected finite element grid nodes and the coordinate points in the K-d tree. Searching for the N coordinate points may further include: performing a recursive query in the K-d tree and backtracking the search path to find the coordinate points closest to the selected finite element grid nodes; and cyclically querying the next closest coordinate points while ignoring the found closest coordinate points until the closest N coordinate points are obtained.

[0078] The device 700 may further include a calculation module 710, which is configured to determine the data values of the selected finite element grid nodes based on the data values corresponding to the N coordinate points. Determining the data values of the selected finite element grid nodes may include: calculating the data values of the selected finite element grid nodes by the inverse distance weighting method based on the data values corresponding to the N coordinate points. In one embodiment, determining the data values of the selected finite element grid nodes may further include: calculating the data values of the selected finite element grid nodes by adjusting N and the power parameter p for the inverse distance weighting method.

[0079] By traversing all the finite element grid nodes in the finite element model, the data values of each finite element grid node can be obtained, thereby completing the application of the finite element field load.

[0080] Figure 7 Each module of the device 700 shown in the figure can be implemented in a processor or a memory. For example, processor-executable instructions can be stored in the memory, and these processor-executable instructions are configured to implement the functions realized by each module when executed by the processor.

[0081] The method of the present invention constructs a K-dimensional tree through multi-field load data points, queries the nearest neighbor data points of the finite element nodes, and calculates the load values of the data missing nodes by using the inverse distance weighting method, realizing the rapid and accurate application of the finite element multi-field load data, and having the following beneficial effects:

[0082] (1) It realizes the efficient and accurate application of the finite element multi-field load, and improves the analysis efficiency of the multi-field coupling strength of the engine structure (such as the strength analysis of turbine blades);

[0083] (2) The matching method of the finite element multi-field load data points given by the present invention can quickly process the matching problem of a large number of data points based on the KDTree. Based on the matching calculation of a large number of data points, it can handle the loading of complex multi-field loads such as sound field, temperature field, aerodynamic pressure field, electromagnetic field, and prestress field;

[0084] (3) Compared with the prior art, the load transfer time is significantly reduced. Especially for a large number of data points, the processing time is only 1% - 5% of the original, improving the data transfer speed; considering the influence of multiple data points in the neighborhood of the data missing nodes, the load prediction accuracy is improved;

[0085] (4) Compared with the built-in method of traditional finite element software, the parameter values of the weight function can be adjusted according to specific problems, improving the data transfer accuracy;

[0086] (5) It is compatible with a variety of calculation software and file formats, supports the engine structure design, and reduces the obstacles to the analysis of multi-disciplinary coupling problems.

[0087] The various steps and modules of the methods and apparatuses described above can be implemented in hardware, software, or a combination thereof. If implemented in hardware, the various illustrative steps, modules, and circuits described in connection with the present disclosure can be implemented or executed using a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic components, hardware components, or any combination thereof. The general-purpose processor can be a processor, a microprocessor, a controller, a microcontroller, or a state machine, etc. If implemented in software, the various illustrative steps and modules described in connection with the present disclosure can be stored on or transmitted as one or more instructions or codes on a computer-readable medium. The software modules for implementing the various operations of the present disclosure can reside in a storage medium such as RAM, flash memory, ROM, EPROM, EEPROM, registers, a hard disk, a removable disk, a CD-ROM, cloud storage, etc. The storage medium can be coupled to the processor such that the processor can read from / write to the storage medium and execute the corresponding program modules to implement the various steps of the present disclosure. Moreover, the software-based embodiments can be uploaded, downloaded, or remotely accessed via appropriate communication means. Such appropriate communication means include, for example, the Internet, the World Wide Web, an intranet, a software application, a cable (including an optical fiber cable), magnetic communication, electromagnetic communication (including RF, microwave, and infrared communication), electronic communication, or other such communication means.

[0088] The numerical values given in the embodiments are only examples and do not limit the scope of the present invention. According to specific practices, the specific parameters of each component can be appropriately set as needed, rather than being limited to the specific values given as examples in this document. In addition, as an overall technical solution, there are also other components or steps that are not listed in the claims or the specification of the present invention. Moreover, the single name of a component does not exclude other names of the component.

[0089] It should also be noted that these embodiments may be described as processes depicted as flowcharts, flow diagrams, structural diagrams, or block diagrams. Although a flowchart may describe the operations as a sequential process, many of these operations can be performed in parallel or concurrently. Additionally, the order of these operations can be rearranged.

[0090] The disclosed methods, apparatuses, and systems should not be limited in any way. On the contrary, the present disclosure encompasses all novel and non-obvious features and aspects of the various disclosed embodiments (separately and in various combinations and sub-combinations with each other). The disclosed methods, apparatuses, and systems are not limited to any specific aspect or feature or their combination, and any disclosed embodiment does not require the presence of any one or more specific advantages or the solution of specific or all technical problems.

[0091] The present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative rather than restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can also make many forms without departing from the spirit of the present invention and the scope protected by the claims. All of these fall within the protection scope of the present invention.

Claims

1. A method for applying multi-field loads to an aero-engine structure, characterized in that, it includes: Obtain multi-field load data of the aero-engine structure, where the multi-field load data includes coordinate points and corresponding data values; Based on the coordinate points of the multi-field load data, establish a K-dimensional tree; Obtain the finite element model of the aero-engine structure, where the finite element model includes multiple finite element mesh nodes; Search for the N coordinate points closest to the selected finite element mesh node in the finite element model in the K-dimensional tree, where K and N are positive integers; and Determine the data value of the selected finite element mesh node based on the data values corresponding to the N coordinate points.

2. The method for applying multi-field loads to an aero-engine structure according to claim 1, characterized in that, Searching for the N coordinate points includes: Search for the N coordinate points closest to the selected finite element mesh node based on the Euclidean distance between the selected finite element mesh node and the coordinate points in the K-dimensional tree.

3. The method for applying multi-field loads to an aero-engine structure according to claim 2, characterized in that, Searching for the N coordinate points includes: Perform recursive queries and backtrack the search path in the K-dimensional tree to find the coordinate point closest to the selected finite element mesh node; and Without considering the closest coordinate point found, cyclically query the next closest coordinate point until the closest N coordinate points are obtained.

4. The method for applying multi-field loads to an aero-engine structure according to claim 1, characterized in that, Determining the data value of the selected finite element mesh node includes: Calculate the data value of the selected finite element mesh node by the inverse distance weighting method based on the data values corresponding to the N coordinate points.

5. The method for applying multi-field loads to an aero-engine structure according to claim 4, characterized in that, Determining the data value of the selected finite element mesh node includes: Calculate the load field data of the selected finite element mesh node based on N and the initial value of the power parameter p for the inverse distance weighting method; Adjust the value of N and obtain the updated N value that makes the resolution of the load field data meet the requirements; Adjust the value of p and obtain the updated p value that makes the smoothness of the load field data meet the requirements; and Calculate the data value of the selected finite element mesh node based on the updated N value and the updated p value.

6. The method for applying multi-field loads to an aero-engine structure according to claim 4, characterized in that, It further includes: Traverse all the finite element mesh nodes in the finite element model to obtain the data value of each finite element mesh node.

7. The method for applying multi-field loads to an aero-engine structure according to claim 1, characterized in that, Establishing the K-dimensional tree includes: Iteratively divide each dimension of the multi-field load data until all the coordinate points of the multi-field load data are traversed to establish the K-dimensional tree.

8. The method for applying multi-field loads to an aero-engine structure according to claim 1, characterized in that, The multi-field load data includes one or more of the following: Temperature field load data; Pressure field load data; Centrifugal force load data; Aerodynamic force load data.

9. A device for applying multi-field loads to an aero-engine structure, characterized in that, it includes: A memory for storing processor-executable instructions; and a processor coupled to the memory, the processor being configured to implement the method for applying multi-field loads to an aero-engine structure according to any one of claims 1-8 when executing the processor-executable instructions.

10. A device for applying multi-field loads to an aero-engine structure, characterized in that it comprises: a load data acquisition module configured to acquire multi-field load data of the aero-engine structure, the multi-field load data including coordinate points and corresponding data values; a data organization module configured to build a K-dimensional tree based on the coordinate points of the multi-field load data; a finite element module configured to acquire a finite element model of the aero-engine structure, the finite element model including a plurality of finite element mesh nodes; a search module configured to search for N coordinate points closest to a selected finite element mesh node in the finite element model in the K-dimensional tree, where K and N are positive integers; and a calculation module configured to determine the data value of the selected finite element mesh node based on the data values corresponding to the N coordinate points.

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