CN116502484B - Deformation calculation method and device for asymmetric section force transmission structure - Google Patents
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Abstract
The application provides a deformation calculation method and device of an asymmetric section force transmission structure, and relates to the technical field of engine structural strength design, wherein the method comprises the following steps: obtaining geometric parameters of an asymmetric section force transmission structure; the asymmetric section force transfer structure is a square-round transition structure, a rectangular variable section structure or a rectangular bending structure; processing geometric dimension parameters of the asymmetric section force transmission structure by using a pre-trained deformation calculation model corresponding to the asymmetric section force transmission structure to obtain a maximum deformation value; the deformation calculation model comprises a square-round transition structure deformation calculation model, a rectangular variable cross-section structure deformation calculation model and a rectangular bending structure deformation calculation model; the training sample set of the deformation calculation model is obtained through finite element analysis. The deformation calculation speed and precision of the asymmetric section force transmission structure can be improved, the operation is simple, and the influence caused by human errors is reduced.
Description
Technical Field
The application relates to the technical field of engine structural strength design, in particular to a deformation calculation method and device of an asymmetric section force transmission structure.
Background
The parallel turbine combined cycle engine (TBCC) is a power device combining a gas turbine engine (turbojet/turbofan) and other types of engines (ramjet engine), generally two airflow channels are arranged in parallel and are provided with a plurality of branch force transmission paths, meanwhile, in order to meet the design requirement of the integrated flying engine, an air inlet channel and a unilateral expansion spray pipe are of a binary structure, the structural form of the air inlet channel and the unilateral expansion spray pipe are closely related to the aircraft layout, and a turbine base, a jet pre-cooling section and a ramjet combustion chamber are of axisymmetric structures. The structure of the inlet duct, the outlet nozzle, the transition section, etc. therefore usually takes the form of an irregular geometry with an asymmetric cross-section or a curved force transmission path. The deformation of the asymmetric section force transmission structures under the working condition is obviously different from the axisymmetric structures of the conventional turbojet and turbofan engines. In the development of the design process of the asymmetric cross-section structures, the structure size needs to be adjusted according to the deformation condition of the asymmetric cross-section structures under the complex working conditions.
At present, a finite element analysis method is generally adopted to analyze the deformation of the asymmetric section force transmission structure, the method is complex to operate, and a great deal of time, energy and resources are consumed to obtain a relatively accurate calculation result.
Disclosure of Invention
In view of the above, the present application provides a deformation calculation method and apparatus for an asymmetric section force transmission structure, so as to solve the above technical problems.
In a first aspect, an embodiment of the present application provides a method for calculating deformation of an asymmetric section force transmission structure, including:
obtaining geometric parameters of an asymmetric section force transmission structure; the asymmetric section force transfer structure is a square-round transition structure, a rectangular variable section structure or a rectangular bending structure;
processing geometric dimension parameters of the asymmetric section force transmission structure by using a pre-trained deformation calculation model corresponding to the asymmetric section force transmission structure to obtain a maximum deformation value; the deformation calculation model comprises a square-round transition structure deformation calculation model, a rectangular variable cross-section structure deformation calculation model and a rectangular bending structure deformation calculation model; the training sample set of the deformation calculation model is obtained through finite element analysis.
Further, the input of the square-round transition structure deformation calculation model includes five geometric dimension parameters of the square-round transition structure: the output of the square-round transition structure deformation calculation model is the maximum deformation value of the square-round transition structure, and the square-round transition structure deformation calculation model adopts RBF neural network.
Further, the training step of the square-round transition structure deformation calculation model comprises the following steps:
establishing a first training sample set comprising a plurality of training samples, each training sample comprising: obtaining a square-round transition structure sample corresponding to five geometric dimension parameters by an optimal Latin hypercube sampling method, and obtaining a true maximum displacement value of the square-round transition structure sample by finite element analysis;
processing five geometric parameters of the square-round transition structure sample by utilizing the RBF neural network to obtain a predicted maximum displacement value;
calculating the root mean square error of the true displacement of the true maximum displacement value and the predicted maximum displacement value of the square-round transition structure sample as a first gradient function;
and updating the parameters of the RBF neural network by using the first gradient function.
Further, the input of the rectangular variable cross-section structure deformation calculation model includes six geometric parameters of the rectangular variable cross-section structure: the length H and the width W of the rectangular inlet end, a first sweeping path parameter L1, a second sweeping path parameter L2, a third sweeping path parameter L3 and a central angle alpha corresponding to the circular arc; the output of the rectangular variable cross-section structure deformation calculation model is the maximum deformation value of the rectangular variable cross-section structure, and the rectangular variable cross-section structure deformation calculation model adopts a first Kriging model.
Further, the training step of the rectangular variable cross-section structure deformation calculation model comprises the following steps:
establishing a second training sample set comprising a plurality of training samples, each training sample comprising: obtaining a rectangular variable cross-section structure sample corresponding to six geometric dimension parameters by an optimal Latin hypercube sampling method, and obtaining a real maximum displacement value of the rectangular variable cross-section structure sample by finite element analysis;
processing six geometric parameters of a rectangular variable cross-section structure sample by using a first Kriging model to obtain a predicted maximum displacement value;
calculating the root mean square error of the true maximum displacement value and the predicted maximum displacement value of the rectangular variable cross-section structure sample as a second gradient function;
parameters of the first Kriging model are updated using the second gradient function.
Further, the input of the rectangular bending structure deformation calculation model includes six geometric parameters of the rectangular bending structure: the length H and the width W of the rectangular inlet end, a first sweeping path parameter L1, a second sweeping path parameter L2, a third sweeping path parameter L3 and a central angle alpha corresponding to the circular arc; the output of the rectangular bending structure deformation calculation model is the maximum deformation value of the rectangular bending structure, and the rectangular bending structure deformation calculation model adopts a second Kriging model.
Further, the training step of the rectangular bending structure deformation calculation model comprises the following steps:
establishing a third training sample set comprising a plurality of training samples, each training sample comprising: obtaining a rectangular bending structure sample corresponding to six geometric dimension parameters by an optimal Latin hypercube sampling method, and obtaining a real maximum displacement value of the rectangular bending structure sample by finite element analysis;
processing six geometric parameters of the rectangular bending structure sample by using a second Kriging model to obtain a predicted maximum displacement value;
calculating the root mean square error of the true maximum displacement value and the predicted maximum displacement value of the rectangular bending structure sample as a third gradient function;
and updating parameters of the second Kriging model by using a third gradient function.
In a second aspect, embodiments of the present application provide a deformation calculation device of an asymmetric section force transmission structure, including:
the acquisition unit is used for acquiring the geometric dimension parameters of the asymmetric section force transmission structure; the asymmetric section force transfer structure is a square-round transition structure, a rectangular variable section structure or a rectangular bending structure;
the deformation calculation unit is used for processing the geometric dimension parameters of the asymmetric section force transmission structure by utilizing a pre-trained deformation calculation model corresponding to the asymmetric section force transmission structure to obtain a maximum deformation value; the deformation calculation model comprises a square-round transition structure deformation calculation model, a rectangular variable cross-section structure deformation calculation model and a rectangular bending structure deformation calculation model; the training sample set of the deformation calculation model is obtained through finite element analysis.
In a third aspect, an embodiment of the present application provides an electronic device, including: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the methods of the embodiments of the present application when executing the computer program.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium storing computer instructions that, when executed by a processor, implement a method of embodiments of the present application.
The deformation calculation speed and precision of the asymmetric section force transmission structure can be improved, the operation is simple, and the influence caused by human errors is reduced.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a deformation calculation method of an asymmetric section force transmission structure provided in an embodiment of the present application;
fig. 2 is a schematic diagram of a square-round transition structure, a rectangular variable cross-section structure and a rectangular bending structure provided in an embodiment of the present application;
fig. 3 is a schematic diagram of geometric parameters of a rectangular variable cross-section structure according to an embodiment of the present application;
FIG. 4 is a schematic diagram of geometric parameters of a rectangular curved structure according to an embodiment of the present application;
FIG. 5 is a functional block diagram of a deformation computing device of an asymmetric cross-section force transfer structure provided in an embodiment of the present application;
fig. 6 is a functional block diagram of an electronic device according to an embodiment of the present application.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, which are generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, as provided in the accompanying drawings, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
First, the design concept of the embodiment of the present application will be briefly described.
At present, a finite element analysis method is generally adopted to analyze the deformation of the asymmetric section force transmission structure, the method is complex in operation, a great deal of time, energy and resources are consumed to obtain a relatively accurate calculation result, and in addition, different operators can generate different calculation results due to human factors.
Therefore, the application provides a deformation calculation method of an asymmetric section force transmission structure, which applies a machine learning method to structural design analysis, firstly, single simulation analysis is carried out based on finite element analysis software, grid convergence analysis is carried out, parametric scripts for parametric modeling, finite element analysis and result extraction are established, and in addition, a visual plug-in is developed for convenient use of structural designers. And then, extracting sample points in a parameter design space, establishing a training sample set and a verification sample set based on the script, and finally, establishing a proxy model based on the training sample set and verifying model accuracy based on the verification sample set. Finally, the user can develop deformation prediction work of the batched parameter combination based on the agent model.
The method and the device have the advantages that the structural design efficiency can be greatly improved while high-precision prediction is guaranteed, meanwhile, the operation is simple, and the influence caused by human errors is reduced.
After the application scenario and the design idea of the embodiment of the present application are introduced, the technical solution provided by the embodiment of the present application is described below.
As shown in fig. 1, an embodiment of the present application provides a deformation calculation method of an asymmetric section force transmission structure, including:
step 101: obtaining geometric parameters of an asymmetric section force transmission structure; the asymmetric section force transfer structure is a square-round transition structure, a rectangular variable section structure or a rectangular bending structure;
the square-round transition structure, the rectangular variable cross-section structure or the rectangular bending structure is shown in figure 2, the calculation working condition is that one end is fixed and supported, one end is free, and the load is the combined working condition of the shaft, the bending, the torsion, the external pressure and the internal pressure.
Step 102: processing geometric dimension parameters of the asymmetric section force transmission structure by using a pre-trained deformation calculation model corresponding to the asymmetric section force transmission structure to obtain a maximum deformation value;
the deformation calculation model comprises a square-round transition structure deformation calculation model, a rectangular variable cross-section structure deformation calculation model and a rectangular bending structure deformation calculation model.
The input of the square-round transition structure deformation calculation model comprises five geometric dimension parameters of the square-round transition structure: the method comprises the steps of obtaining the length H and the width W of a rectangular inlet end, the diameter R of a circular outlet end, the axial length L (the distance from the rectangular inlet end to the circular outlet end) and the wall thickness D, wherein the output of a square-round transition structure deformation calculation model is the maximum deformation value of a square-round transition structure, and the square-round transition structure deformation calculation model adopts an RBF neural network.
The training step of the square-round transition structure deformation calculation model comprises the following steps:
establishing a first training sample set comprising a plurality of training samples, each training sample comprising: obtaining a square-round transition structure sample corresponding to five geometric dimension parameters by an optimal Latin hypercube sampling method, and obtaining a true maximum displacement value of the square-round transition structure sample by finite element analysis; the displacement value refers to the distance between the initial position of any point of the square-round transition structure and the deformation position after load application.
Processing five geometric parameters of the square-round transition structure sample by utilizing the RBF neural network to obtain a predicted maximum displacement value;
calculating the root mean square error of the true displacement of the true maximum displacement value and the predicted maximum displacement value of the square-round transition structure sample as a first gradient function;
and updating the parameters of the RBF neural network by using the first gradient function.
As shown in fig. 3, the input of the deformation calculation model of the rectangular variable cross-section structure includes six geometric parameters of the rectangular variable cross-section structure: the length H and the width W (not shown) of the rectangular inlet end, a first sweeping path parameter L1, a second sweeping path parameter L2, a third sweeping path parameter L3 and a central angle alpha corresponding to the circular arc, wherein the width of the rectangular outlet end is unchanged, and the length can be calculated according to the first sweeping path parameter L1, the second sweeping path parameter L2, the third sweeping path parameter L3 and the central angle alpha corresponding to the circular arc; the output of the rectangular variable cross-section structure deformation calculation model is the maximum deformation value of the rectangular variable cross-section structure, and the rectangular variable cross-section structure deformation calculation model adopts a first Kriging model.
The training step of the rectangular variable cross-section structure deformation calculation model comprises the following steps:
establishing a second training sample set comprising a plurality of training samples, each training sample comprising: obtaining a rectangular variable cross-section structure sample corresponding to six geometric dimension parameters by an optimal Latin hypercube sampling method, and obtaining a real maximum displacement value of the rectangular variable cross-section structure sample by finite element analysis;
processing six geometric parameters of a rectangular variable cross-section structure sample by using a first Kriging model to obtain a predicted maximum displacement value;
calculating the root mean square error of the true maximum displacement value and the predicted maximum displacement value of the rectangular variable cross-section structure sample as a second gradient function;
parameters of the first Kriging model are updated using the second gradient function.
As shown in fig. 4, the input of the rectangular bending structure deformation calculation model includes six geometric parameters of the rectangular bending structure: the length H and the width W (not shown) of the rectangular inlet end, the first sweeping path parameter L1, the second sweeping path parameter L2, the third sweeping path parameter L3 and the central angle alpha corresponding to the circular arc, and the rectangular shape of the outlet end and the rectangular shape of the inlet end have the same size; the output of the rectangular bending structure deformation calculation model is the maximum deformation value of the rectangular bending structure, and the rectangular bending structure deformation calculation model adopts a second Kriging model.
The training step of the rectangular bending structure deformation calculation model comprises the following steps:
establishing a third training sample set comprising a plurality of training samples, each training sample comprising: obtaining a rectangular bending structure sample corresponding to six geometric dimension parameters by an optimal Latin hypercube sampling method, and obtaining a real maximum displacement value of the rectangular bending structure sample by finite element analysis;
processing six geometric parameters of the rectangular bending structure sample by using a second Kriging model to obtain a predicted maximum displacement value;
calculating the root mean square error of the true maximum displacement value and the predicted maximum displacement value of the rectangular bending structure sample as a third gradient function;
and updating parameters of the second Kriging model by using a third gradient function.
Furthermore, the method comprises the following steps: and selecting the optimal geometric parameters of the force transmission structure of each asymmetric section according to the maximum deformation value.
The method is used for carrying out rapid deformation calculation, the deformation calculation error is within 1%, the structural analysis duration of 100 groups of geometric parameters is about 1 minute, and the method is simple to operate and easy to operate.
Based on the above embodiments, the present embodiment provides a deformation calculation device of an asymmetric section force transmission structure, and referring to fig. 5, the deformation calculation device 200 of an asymmetric section force transmission structure provided in the present embodiment at least includes:
an acquisition unit 201, configured to acquire a geometric parameter of the asymmetric section force transmission structure; the asymmetric section force transfer structure is a square-round transition structure, a rectangular variable section structure or a rectangular bending structure;
the deformation calculation unit 202 is configured to process the geometric dimension parameter of the asymmetric section force transmission structure by using a pre-trained deformation calculation model corresponding to the asymmetric section force transmission structure, so as to obtain a maximum deformation value; the deformation calculation model comprises a square-round transition structure deformation calculation model, a rectangular variable cross-section structure deformation calculation model and a rectangular bending structure deformation calculation model; the training sample set of the deformation calculation model is obtained through finite element analysis.
It should be noted that, the principle of solving the technical problem of the deformation computing device 200 of the asymmetric section force transmission structure provided in the embodiment of the present application is similar to that of the method provided in the embodiment of the present application, so that the implementation of the deformation computing device 200 of the asymmetric section force transmission structure provided in the embodiment of the present application can refer to the implementation of the method provided in the embodiment of the present application, and the repetition is omitted.
As shown in fig. 6, the electronic device 300 provided in the embodiment of the present application at least includes: processor 301, memory 302, and a computer program stored on memory 302 and executable on processor 301, processor 301 when executing the computer program implements the deformation calculation method of the asymmetric cross-section force transmission structure provided in the embodiments of the present application.
The electronic device 300 provided by the embodiments of the present application may also include a bus 303 that connects the different components, including the processor 301 and the memory 302. Bus 303 represents one or more of several types of bus structures, including a memory bus, a peripheral bus, a local bus, and so forth.
The Memory 302 may include readable media in the form of volatile Memory, such as random access Memory (Random Access Memory, RAM) 3021 and/or cache Memory 3022, and may further include Read Only Memory (ROM) 3023.
The memory 302 may also include a program tool 3024 having a set (at least one) of program modules 3025, the program modules 3025 including, but not limited to: an operating subsystem, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
The electronic device 300 may also communicate with one or more external devices 304 (e.g., keyboard, remote control, etc.), one or more devices that enable a user to interact with the electronic device 300 (e.g., cell phone, computer, etc.), and/or any device that enables the electronic device 300 to communicate with one or more other electronic devices 300 (e.g., router, modem, etc.). Such communication may occur through an Input/Output (I/O) interface 305. Also, electronic device 300 may communicate with one or more networks such as a local area network (Local Area Network, LAN), a wide area network (Wide Area Network, WAN), and/or a public network such as the internet via network adapter 306. As shown in fig. 6, the network adapter 306 communicates with other modules of the electronic device 300 over the bus 303. It should be appreciated that although not shown in fig. 6, other hardware and/or software modules may be used in connection with electronic device 300, including, but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, disk array (Redundant Arrays of Independent Disks, RAID) subsystems, tape drives, data backup storage subsystems, and the like.
It should be noted that the electronic device 300 shown in fig. 6 is only an example, and should not impose any limitation on the functions and application scope of the embodiments of the present application.
The embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores computer instructions, and the computer instructions realize the deformation calculation method of the asymmetric section force transmission structure provided by the embodiment of the application when the computer instructions are executed by a processor.
Furthermore, although the operations of the methods of the present application are depicted in the drawings in a particular order, this is not required to or suggested that these operations must be performed in this particular order or that all of the illustrated operations must be performed in order to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.
Claims (4)
1. The deformation calculation method of the asymmetric section force transmission structure is characterized by comprising the following steps of:
obtaining geometric parameters of an asymmetric section force transmission structure; the asymmetric section force transfer structure is a square-round transition structure, a rectangular variable section structure or a rectangular bending structure;
processing geometric dimension parameters of the asymmetric section force transmission structure by using a pre-trained deformation calculation model corresponding to the asymmetric section force transmission structure to obtain a maximum deformation value; the deformation calculation model comprises a square-round transition structure deformation calculation model, a rectangular variable cross-section structure deformation calculation model and a rectangular bending structure deformation calculation model; the training sample set of the deformation calculation model is obtained through finite element analysis;
the input of the square-round transition structure deformation calculation model comprises five geometric dimension parameters of the square-round transition structure: the output of the square-round transition structure deformation calculation model is the maximum deformation value of the square-round transition structure, and the square-round transition structure deformation calculation model adopts an RBF neural network;
the training step of the square-round transition structure deformation calculation model comprises the following steps:
establishing a first training sample set comprising a plurality of training samples, each training sample comprising: obtaining a square-round transition structure sample corresponding to five geometric dimension parameters by an optimal Latin hypercube sampling method, and obtaining a true maximum displacement value of the square-round transition structure sample by finite element analysis;
processing five geometric parameters of the square-round transition structure sample by utilizing the RBF neural network to obtain a predicted maximum displacement value;
calculating the root mean square error of the true maximum displacement value and the predicted maximum displacement value of the square-round transition structure sample as a first gradient function;
updating parameters of the RBF neural network by using a first gradient function;
the input of the rectangular variable cross-section structure deformation calculation model comprises six geometric dimension parameters of the rectangular variable cross-section structure: the length H and the width W of the rectangular inlet end, a first sweeping path parameter L1, a second sweeping path parameter L2, a third sweeping path parameter L3 and a central angle alpha corresponding to the circular arc; the output of the rectangular variable cross-section structure deformation calculation model is the maximum deformation value of the rectangular variable cross-section structure, and the rectangular variable cross-section structure deformation calculation model adopts a first Kriging model;
the training step of the rectangular variable cross-section structure deformation calculation model comprises the following steps:
establishing a second training sample set comprising a plurality of training samples, each training sample comprising: obtaining a rectangular variable cross-section structure sample corresponding to six geometric dimension parameters by an optimal Latin hypercube sampling method, and obtaining a real maximum displacement value of the rectangular variable cross-section structure sample by finite element analysis;
processing six geometric parameters of a rectangular variable cross-section structure sample by using a first Kriging model to obtain a predicted maximum displacement value;
calculating the root mean square error of the true maximum displacement value and the predicted maximum displacement value of the rectangular variable cross-section structure sample as a second gradient function;
updating parameters of the first Kriging model by using a second gradient function;
the input of the rectangular bending structure deformation calculation model comprises six geometric dimension parameters of the rectangular bending structure: the length H and the width W of the rectangular inlet end, a first sweeping path parameter L1, a second sweeping path parameter L2, a third sweeping path parameter L3 and a central angle alpha corresponding to the circular arc; the output of the rectangular bending structure deformation calculation model is the maximum deformation value of the rectangular bending structure, and the rectangular bending structure deformation calculation model adopts a second Kriging model;
the training step of the rectangular bending structure deformation calculation model comprises the following steps:
establishing a third training sample set comprising a plurality of training samples, each training sample comprising: obtaining a rectangular bending structure sample corresponding to six geometric dimension parameters by an optimal Latin hypercube sampling method, and obtaining a real maximum displacement value of the rectangular bending structure sample by finite element analysis;
processing six geometric parameters of the rectangular bending structure sample by using a second Kriging model to obtain a predicted maximum displacement value;
calculating the root mean square error of the true maximum displacement value and the predicted maximum displacement value of the rectangular bending structure sample as a third gradient function;
and updating parameters of the second Kriging model by using a third gradient function.
2. A deformation calculation device of an asymmetric cross-section force transmission structure, comprising:
the acquisition unit is used for acquiring the geometric dimension parameters of the asymmetric section force transmission structure; the asymmetric section force transfer structure is a square-round transition structure, a rectangular variable section structure or a rectangular bending structure;
the deformation calculation unit is used for processing the geometric dimension parameters of the asymmetric section force transmission structure by utilizing a pre-trained deformation calculation model corresponding to the asymmetric section force transmission structure to obtain a maximum deformation value; the deformation calculation model comprises a square-round transition structure deformation calculation model, a rectangular variable cross-section structure deformation calculation model and a rectangular bending structure deformation calculation model; the training sample set of the deformation calculation model is obtained through finite element analysis;
the input of the square-round transition structure deformation calculation model comprises five geometric dimension parameters of the square-round transition structure: the output of the square-round transition structure deformation calculation model is the maximum deformation value of the square-round transition structure, and the square-round transition structure deformation calculation model adopts an RBF neural network;
the training step of the square-round transition structure deformation calculation model comprises the following steps:
establishing a first training sample set comprising a plurality of training samples, each training sample comprising: obtaining a square-round transition structure sample corresponding to five geometric dimension parameters by an optimal Latin hypercube sampling method, and obtaining a true maximum displacement value of the square-round transition structure sample by finite element analysis;
processing five geometric parameters of the square-round transition structure sample by utilizing the RBF neural network to obtain a predicted maximum displacement value;
calculating the root mean square error of the true maximum displacement value and the predicted maximum displacement value of the square-round transition structure sample as a first gradient function;
updating parameters of the RBF neural network by using a first gradient function;
the input of the rectangular variable cross-section structure deformation calculation model comprises six geometric dimension parameters of the rectangular variable cross-section structure: the length H and the width W of the rectangular inlet end, a first sweeping path parameter L1, a second sweeping path parameter L2, a third sweeping path parameter L3 and a central angle alpha corresponding to the circular arc; the output of the rectangular variable cross-section structure deformation calculation model is the maximum deformation value of the rectangular variable cross-section structure, and the rectangular variable cross-section structure deformation calculation model adopts a first Kriging model;
the training step of the rectangular variable cross-section structure deformation calculation model comprises the following steps:
establishing a second training sample set comprising a plurality of training samples, each training sample comprising: obtaining a rectangular variable cross-section structure sample corresponding to six geometric dimension parameters by an optimal Latin hypercube sampling method, and obtaining a real maximum displacement value of the rectangular variable cross-section structure sample by finite element analysis;
processing six geometric parameters of a rectangular variable cross-section structure sample by using a first Kriging model to obtain a predicted maximum displacement value;
calculating the root mean square error of the true maximum displacement value and the predicted maximum displacement value of the rectangular variable cross-section structure sample as a second gradient function;
updating parameters of the first Kriging model by using a second gradient function;
the input of the rectangular bending structure deformation calculation model comprises six geometric dimension parameters of the rectangular bending structure: the length H and the width W of the rectangular inlet end, a first sweeping path parameter L1, a second sweeping path parameter L2, a third sweeping path parameter L3 and a central angle alpha corresponding to the circular arc; the output of the rectangular bending structure deformation calculation model is the maximum deformation value of the rectangular bending structure, and the rectangular bending structure deformation calculation model adopts a second Kriging model;
the training step of the rectangular bending structure deformation calculation model comprises the following steps:
establishing a third training sample set comprising a plurality of training samples, each training sample comprising: obtaining a rectangular bending structure sample corresponding to six geometric dimension parameters by an optimal Latin hypercube sampling method, and obtaining a real maximum displacement value of the rectangular bending structure sample by finite element analysis;
processing six geometric parameters of the rectangular bending structure sample by using a second Kriging model to obtain a predicted maximum displacement value;
calculating the root mean square error of the true maximum displacement value and the predicted maximum displacement value of the rectangular bending structure sample as a third gradient function;
and updating parameters of the second Kriging model by using a third gradient function.
3. An electronic device, comprising: memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method of claim 1 when executing the computer program.
4. A computer readable storage medium storing computer instructions which, when executed by a processor, implement the method of claim 1.
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