CN110750928A

CN110750928A - Finite element model optimization method and device and electronic equipment

Info

Publication number: CN110750928A
Application number: CN201910956879.XA
Authority: CN
Inventors: 温嘉琦; 殷秀海
Original assignee: Capital Engineering & Research Inc Ltd
Current assignee: Capital Engineering & Research Inc Ltd
Priority date: 2019-10-10
Filing date: 2019-10-10
Publication date: 2020-02-04

Abstract

The embodiment of the application provides a finite element model optimization method, a finite element model optimization device and electronic equipment, wherein the method comprises the following steps: determining a response surface model of a target engineering structure by using pre-acquired parameters and frequency samples of the target engineering structure, wherein the parameters and the frequency samples are used for storing all global parameter groups and all order modal frequencies of the target engineering structure; and performing parameter level inversion on the response surface model by taking the measured vibration frequency of the target engineering structure as a response target, and determining a target parameter group of the finite element model according to a result of the parameter level inversion. According to the method and the device, the optimal model parameters of the finite element model can be accurately obtained, and the accuracy and the reliability of applying the optimal model parameters to the finite element model can be effectively improved.

Description

Finite element model optimization method and device and electronic equipment

Technical Field

The application relates to the technical field of structural simulation, in particular to a finite element model optimization method and device and electronic equipment.

Background

The finite element simulation technology of the structure is an important means for engineering structure design research, and a finite element model corresponding to the entity engineering structure is a model established by using a finite element analysis method and is a group of unit combinations which are only connected at nodes, only transfer force by the nodes and are only restrained at the nodes.

At present, in some finite element models with large and complex structures, due to the insufficiency of design data or the lack of test research of local structures (parts), the parameter values of parts in the finite element models are not easy to determine, such as the rigidity of an aqueduct rubber support and the like. If special experimental studies are performed to determine certain parameter values, significant time, expense, and human resources are required, which is obviously impractical and does not ensure that the obtained parameters can be effectively applied to the finite element model. In real practice, it is often the practice to take fuzzy values empirically or to perform simulation trial calculations.

However, the above fuzzy evaluation method can further expand the uncertainty of the finite element model, and cannot ensure the accuracy of the subsequent model calculation, which is a problem to be solved in the field of structural engineering simulation research.

Disclosure of Invention

Aiming at the problems in the prior art, the application provides a finite element model optimization method, a finite element model optimization device and electronic equipment, which can accurately acquire the optimal model parameters of a finite element model, and further can effectively improve the accuracy and reliability of applying the optimal model parameters to the finite element model.

In order to solve the technical problem, the application provides the following technical scheme:

in a first aspect, the present application provides a finite element model optimization method, comprising:

determining a response surface model of a target engineering structure by using pre-acquired parameters and frequency samples of the target engineering structure, wherein the parameters and the frequency samples are used for storing all global parameter groups and all order modal frequencies of the target engineering structure;

and performing parameter level inversion on the response surface model by taking the measured vibration frequency of the target engineering structure as a response target, and determining a target parameter group of the finite element model according to a result of the parameter level inversion.

Further, before the determining the response surface model of the target engineering structure, the method further includes:

performing modal calculation in a preset finite element model of the target engineering structure by using a plurality of pre-acquired global parameter combinations of the target engineering structure and a plurality of orders of characteristic modal frequencies of the target vibration direction of the target engineering structure to obtain each order of modal frequency corresponding to the target engineering structure;

and generating parameter and frequency samples according to various global parameter combinations and respective corresponding modal frequencies of various orders.

performing prototype vibration test on a target engineering structure, and collecting a plurality of vibration response signals of the target engineering structure;

and carrying out fusion processing on the plurality of vibration response signals to obtain the actual measurement vibration frequency of the whole target engineering structure.

Further, the performing a prototype vibration test on the target engineering structure and collecting a plurality of vibration response signals of the target engineering structure includes:

taking a preset normal operation working condition of the target engineering structure as a test working condition, and arranging a plurality of test points on the target engineering structure;

and respectively collecting the vibration response signals of the test points.

Further, the performing fusion processing on the plurality of vibration response signals to obtain the measured vibration frequency of the whole target engineering structure includes:

and performing fusion processing on the plurality of vibration response signals with the same test direction by using a preset variance contribution rate algorithm to obtain the actual measurement vibration frequency of the whole target engineering structure.

Further, before the fusion processing is performed on the plurality of vibration response signals to obtain the measured vibration frequency of the whole target engineering structure, the method further includes:

and carrying out noise reduction processing on each vibration response signal, and filtering out water flow noise of which the frequency value is lower than a water flow noise threshold value and white noise of which the frequency value is higher than a white noise threshold value in each vibration response signal.

Further, the determining a response surface model of the target engineering structure by applying the pre-acquired parameters and frequency samples of the target engineering structure includes:

fitting a preset quadratic polynomial based on the parameters and the frequency samples of the target engineering structure obtained in advance, and taking the fitting result as a response surface model of the target engineering structure.

Further, the performing parameter level inversion on the response surface model by using the measured vibration frequency of the target engineering structure as a response target, and determining a target parameter group of the finite element model according to a result of the parameter level inversion includes:

performing parameter level inversion on the response surface model by taking the measured vibration frequency as a response target to obtain a plurality of groups of corresponding candidate parameters;

and applying a preset selection rule, and selecting one group of candidate parameters from the plurality of groups of candidate parameters as a target parameter group of the finite element model.

In a second aspect, the present application provides a finite element model optimization apparatus comprising:

the parameter and frequency sample application module is used for applying pre-acquired parameters and frequency samples of the target engineering structure and determining a response surface model of the target engineering structure, wherein the parameters and frequency samples are used for storing all global parameter sets and all orders of modal frequencies of the target engineering structure;

and the target parameter group acquisition module is used for performing parameter level inversion on the response surface model by taking the measured vibration frequency of the target engineering structure as a response target, and determining the target parameter group of the finite element model according to the result of the parameter level inversion.

Further, the finite element model optimization device further comprises:

the modal frequency acquisition module is used for applying a plurality of pre-acquired global parameter combinations of the target engineering structure and a plurality of orders of characteristic modal frequencies of the target vibration direction of the target engineering structure to perform modal calculation in a preset finite element model of the target engineering structure to obtain each order of modal frequency corresponding to the target engineering structure;

and the parameter and frequency sample generation module is used for generating parameter and frequency samples according to various global parameter combinations and respective corresponding modal frequencies of various orders.

Further, the finite element model optimization device further comprises:

the vibration response signal acquisition module is used for performing prototype vibration test on the target engineering structure and acquiring a plurality of vibration response signals of the target engineering structure;

and the signal fusion model signal fusion module is used for carrying out fusion processing on the plurality of vibration response signals to obtain the actual measurement vibration frequency of the whole target engineering structure.

Further, the vibration response signal acquisition module includes:

the test point setting unit is used for arranging a plurality of test points on the target engineering structure by taking a preset normal operation working condition of the target engineering structure as a test working condition;

and the vibration response signal acquisition unit is used for respectively acquiring the vibration response signals of the test points.

Further, the signal fusion model signal fusion module comprises:

and the variance contribution rate application unit is used for applying a preset variance contribution rate algorithm to perform fusion processing on the plurality of vibration response signals with the same test direction to obtain the actual measurement vibration frequency of the whole target engineering structure.

Further, the finite element model optimization device further comprises:

and the noise reduction module is used for carrying out noise reduction processing on each vibration response signal and filtering water flow noise of which the frequency value is lower than a water flow noise threshold value and white noise of which the frequency value is higher than a white noise threshold value in each vibration response signal.

Further, the parameter and frequency sample application module includes:

and the fitting unit is used for fitting a preset quadratic polynomial based on the parameters and the frequency samples of the target engineering structure obtained in advance by application, and taking the fitting result as a response surface model of the target engineering structure.

Further, the target parameter group obtaining module includes:

the candidate parameter acquisition unit is used for performing parameter level inversion on the response surface model by taking the actually measured vibration frequency as a response target to obtain a plurality of groups of corresponding candidate parameters;

and the target parameter group selecting unit is used for applying a preset selecting rule and selecting one group of target parameter groups as the finite element model from the candidate parameters.

In a third aspect, the present application provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the finite element model optimization method when executing the program.

In a fourth aspect, the present application provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the finite element model optimization method.

According to the technical scheme, the finite element model optimization method, the finite element model optimization device and the electronic equipment provided by the application comprise the following steps: determining a response surface model of a target engineering structure by using pre-acquired parameters and frequency samples of the target engineering structure, wherein the parameters and the frequency samples are used for storing all global parameter groups and all order modal frequencies of the target engineering structure; taking the measured vibration frequency of the target engineering structure as a response target, performing parameter level inversion on the response surface model, and determining the target parameter group of the finite element model according to the result of the parameter level inversion, so as to accurately obtain the optimal model parameters of the finite element model, thereby effectively improving the accuracy and reliability of applying the optimal model parameters to the finite element model, the calculation result of the optimized finite element model can be more consistent with the measured data, the thought limit determined by a single parameter is skipped, not only the global parameter can be determined, meanwhile, the precision of the finite element model is improved, the thought is clear and novel, the result is good, the method is suitable for being popularized to structural simulation research workers, and further, the accuracy and reliability of the application of the optimized finite element model to the target engineering structure in specific applications such as damage identification can be effectively improved.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

FIG. 1 is a schematic flow chart of a finite element model optimization method of the present application.

Fig. 2 is a schematic flow chart of a specific acquisition process of parameter and frequency samples in the finite element model optimization method of the present application.

Fig. 3 is a schematic flow chart of a specific acquisition process of the measured vibration frequency in the finite element model optimization method of the present application.

Fig. 4 is a schematic flow chart of a specific process for acquiring a vibration response signal in the finite element model optimization method of the present application.

FIG. 5 is a flowchart illustrating a finite element model optimization method including a signal denoising process according to an embodiment of the present invention.

Fig. 6 is a schematic flow chart illustrating a specific obtaining manner of the target parameter set in the finite element model optimization method in the embodiment of the present application.

FIG. 7 is a schematic overall flow chart of a finite element model optimization method in a specific application example of the present application.

Fig. 8 is a noise reduction diagram of actual measurement frequency processing in a specific application example of the present application, and "parameter x" in fig. 8 represents a part of existing parameters which are uncertain or difficult to determine, and is not unique.

Fig. 9 is a power spectral density map after measured frequency fusion in the specific application example of the present application.

Fig. 10 is a basic schematic diagram of parameter optimization in a specific application example of the present application.

Fig. 11 is a response surface model diagram of a certain response frequency established in the specific application example of the present application.

Fig. 12 is a comparison graph of the results of the specific application example of the present application.

FIG. 13 is a schematic structural diagram of a finite element model optimization apparatus in an embodiment of the present application.

Fig. 14 is a schematic structural diagram of an electronic device in an embodiment of the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in 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 obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

Considering the problem that the optimal model parameters of a finite element model cannot be accurately obtained and the finite element model cannot be accurately optimized in the prior art, the applicant of the present application carries out long-time research, design and practice in the direction of skipping the limitation of thought determined by a single parameter, finally confirms that the actual measurement frequency obtained based on the structural prototype test can be used as an optimization target, extends the uncertainty of a few uncertain parameters into the global parameters, utilizes a response surface method to carry out optimization solution in the constructed parameters and frequency samples, and finally outputs a group of model parameters which enable the model calculation frequency to be well matched with the actual measurement frequency, based on the above, the present application provides a finite element model optimization method, a finite element model optimization device, electronic equipment and a computer readable storage medium, through applying the parameters and frequency samples of the target engineering structure which are obtained in advance, determining a response surface model of the target engineering structure, wherein the parameter and frequency samples are used for storing each global parameter group and each order modal frequency of the target engineering structure; taking the measured vibration frequency of the target engineering structure as a response target, performing parameter level inversion on the response surface model, and determining a target parameter group of the finite element model according to the result of the parameter level inversion; substituting the target parameter group into the finite element model to further complete the optimization of the finite element model, accurately obtaining the optimal model parameter of the finite element model, and further effectively improving the accuracy and reliability of applying the optimal model parameter to the finite element model, so that the calculation result of the optimized finite element model can be more consistent with the measured data, and the thought limit determined by a single parameter is skipped, thereby not only determining the global parameter, but also improving the precision of the finite element model.

In view of the above, the present application provides a finite element model optimization apparatus, which may be a server or a client device, the finite element model optimization apparatus may be communicatively connected to at least one database, at least one client device equipped with simulation software, and at least one client device held by a user, and the finite element model optimization apparatus may be locally provided with the corresponding at least one database and at least one simulation software. The finite element model optimization device may receive a finite element model optimization instruction sent by a client device held by a user on line, and then the finite element model optimization device may obtain a predetermined parameter and frequency sample of a target engineering structure from the database and/or the simulation software, or may determine the parameter and frequency sample of the target engineering structure by applying a plurality of global parameter combinations of the target engineering structure pre-obtained from the database and/or the simulation software before performing the finite element model optimization. Then, the finite element model optimization device determines a response surface model of the target engineering structure by using pre-acquired parameters and frequency samples of the target engineering structure, wherein the parameters and the frequency samples are used for storing all global parameter sets and all orders of modal frequencies of the target engineering structure; and performing parameter level inversion on the response surface model by taking the measured vibration frequency of the target engineering structure as a response target, and determining a target parameter group of the finite element model according to a result of the parameter level inversion. Then, the finite element model optimizing device can send the optimizing result of the finite element model to client equipment held by a user so that the user can check the optimizing result of the finite element model; or, the finite element model optimizing device may further apply the optimized finite element model to perform applications such as damage identification on the target engineering structure.

It is understood that the client devices may include smart phones, tablet electronic devices, network set-top boxes, portable computers, desktop computers, Personal Digital Assistants (PDAs), in-vehicle devices, smart wearable devices, and the like. Wherein, intelligence wearing equipment can include intelligent glasses, intelligent wrist-watch, intelligent bracelet etc..

In practical applications, part of the finite element model optimization may be performed on the server side as described above, or all operations may be performed in the client device. The selection may be specifically performed according to the processing capability of the client device, the limitation of the user usage scenario, and the like. This is not a limitation of the present application. The client device may further include a processor if all operations are performed in the client device.

The client device may have a communication module (i.e., a communication unit), and may be communicatively connected to a remote server to implement data transmission with the server. The server may include a server on the task scheduling center side, and in other implementation scenarios, the server may also include a server on an intermediate platform, for example, a server on a third-party server platform that is communicatively linked to the task scheduling center server. The server may include a single computer device, or may include a server cluster formed by a plurality of servers, or a server structure of a distributed apparatus.

The server and the client device may communicate using any suitable network protocol, including network protocols not yet developed at the filing date of this application. The network protocol may include, for example, a TCP/IP protocol, a UDP/IP protocol, an HTTP protocol, an HTTPS protocol, or the like. Of course, the network Protocol may also include, for example, an RPC Protocol (Remote Procedure Call Protocol), a REST Protocol (Representational State Transfer Protocol), and the like used above the above Protocol.

In order to accurately obtain the optimal model parameters of the finite element model and further effectively improve the accuracy and reliability of applying the optimal model parameters to the finite element model, the application provides an embodiment of a finite element model optimization method, which specifically includes the following contents, with reference to fig. 1:

step 100: and determining a response surface model of the target engineering structure by using pre-acquired parameters and frequency samples of the target engineering structure, wherein the parameters and the frequency samples are used for storing all global parameter groups and all orders of modal frequencies of the target engineering structure.

In the above description, the global parameters may include uncertain parameters and certain parameters, and specifically, the global parameters generally include uncertain and uncertain material parameters which are the main parameters of the finite element model of the engineering structure, such as the elastic modulus, the density, and the stiffness of the material at different parts of the engineering structure.

In step 100, each global parameter set and each order of modal frequency are stored in the parameter and frequency sample, a response surface model of the target engineering structure is obtained by fitting the parameter and frequency sample to a preset quadratic polynomial, and the response surface model of the target engineering structure is used for representing a corresponding relationship between each global parameter set and each order of modal frequency.

The response surface refers to a functional relation between the response variable and a set of input variables, and in the present application, the functional relation may be preset.

It will be appreciated that each object has its own resonant frequency, but that there is more than one resonant frequency. It may resonate at tens of Hz and at hundreds of Hz. If modal analysis is carried out, the resonance frequencies of the object are found out and are arranged according to the natural frequency, and modal frequencies of various orders such as first order, second order, third order and the like are formed in sequence.

Step 200: and performing parameter level inversion on the response surface model by taking the measured vibration frequency of the target engineering structure as a response target, and determining a target parameter group of the finite element model according to a result of the parameter level inversion.

In step 200, the finite element model optimization device takes the actual measurement frequency obtained based on the structural prototype test as an optimization target, extends uncertainty of a few uncertain parameters into global parameters, performs optimization solution in the constructed parameter and frequency sample by using a response surface method, and finally outputs a group of parameters which make the model calculation frequency well coincide with the actual measurement frequency.

In addition, the finite element model optimization device may perform damage identification on the target engineering structure by applying the optimized finite element model, for example, the finite element model optimization device may perform environmental excitation load simulation on the optimized finite element model; and determining a response surface model of the target engineering structure based on multiple preset damage working conditions of the target engineering structure and the finite element model simulated by the environmental excitation load, and obtaining damage behavior data of the target engineering structure based on the response surface model.

In order to effectively improve the accuracy, pertinence, and reliability of obtaining the parameters and the frequency samples, so as to further improve the reliability and accuracy of applying the finite element model, the present application further provides an embodiment of a specific obtaining process of the parameters and the frequency samples in the finite element model optimization method, referring to fig. 2, before step 100 of the finite element model optimization method, the finite element model optimization method further includes the following contents:

step 010: and carrying out modal calculation in a preset finite element model of the target engineering structure by using a plurality of pre-acquired global parameter combinations of the target engineering structure and a plurality of orders of characteristic modal frequencies of the target vibration direction of the target engineering structure to obtain each order of modal frequency corresponding to the target engineering structure.

In step 010, three points need to be predetermined: determining global parameters as experimental factors, correctly selecting parameter level values and determining proper level intervals. Meanwhile, a plurality of orders of characteristic modal frequencies of the main vibration direction of the structure are selected. Wherein, selecting a plurality of orders of characteristic modal frequencies of the main vibration direction of the structure requires attention: the actually measured vibration frequency used in the final calculation in step 200 should correspond to the actual measured vibration frequency, and for example, the main vibration direction of the structures such as the aqueduct and the bridge with small span is horizontal and transverse, and the first 4-order or the first 5-order modal frequency can be generally selected.

Step 020: and generating parameter and frequency samples according to various global parameter combinations and respective corresponding modal frequencies of various orders.

In step 020, the finite element model optimization device may adopt a center review design experiment method, design the parameter level into a plurality of groups of experiment conditions, sequentially perform modal calculation in the finite element software, extract modal frequencies of each order, fill in a table of parameter and frequency samples, and obtain sample data for fitting the response surface model.

The present application further provides an embodiment of a specific acquisition process of measured vibration frequency in a finite element model optimization method, referring to fig. 3, before step 100 of the finite element model optimization method, the finite element model optimization method further specifically includes the following steps:

step 030: and carrying out prototype vibration test on the target engineering structure, and collecting a plurality of vibration response signals of the target engineering structure.

It can be understood that the prototype vibration test is an important means for researching the dynamic characteristics of the engineering structure, and the prototype test is combined with the model test and the numerical analysis means, and is very important for deeply researching the dynamic characteristics of the engineering structure. Especially engineering structures such as dams and the like.

Step 040: and carrying out fusion processing on the plurality of vibration response signals to obtain the actual measurement vibration frequency of the whole target engineering structure.

It can be understood that, step 030 and step 040 in this embodiment, and the aforementioned step 010 and step 020 do not have a fixed execution order, and the two embodiments are not executed sequentially or simultaneously, which may be specifically set according to an actual application situation, and the present application does not limit this.

From the above description, it can be known that the finite element model optimization method provided in the embodiment of the present application obtains the actually measured vibration frequency by applying the prototype vibration test, and can effectively improve the accuracy and reliability of the actually measured vibration frequency, and further can further improve the reliability and accuracy of applying the finite element model.

The present application further provides an embodiment of a specific process for acquiring a vibration response signal in a finite element model optimization method, and referring to fig. 4, the step 030 in the finite element model optimization method specifically includes the following steps:

step 031: and taking a preset normal operation working condition of the target engineering structure as a test working condition, and arranging a plurality of test points on the target engineering structure.

It can be understood that the normal operation condition is taken as the test condition, and the subsequent modal calculation of the finite element model is consistent with the condition; the test point can adopt an impact-resistant DP type earthquake type low-frequency vibration sensor to collect vibration response signals; meanwhile, the test direction of each test point may include both horizontal (H) and vertical (V) directions.

Step 032: and respectively collecting the vibration response signals of the test points.

Specifically, the finite element model optimization device may be in communication connection with each of the test points, and receive the vibration response signal acquired by each of the test points in real time after the prototype vibration test is started.

From the above description, it can be known that, in the finite element model optimization method provided in the embodiment of the present application, the prototype vibration test is performed through the plurality of test points, so that the accuracy and reliability of the prototype vibration test process can be effectively improved, and further, the reliability and accuracy of obtaining the vibration response signal can be further improved.

The present application further provides an embodiment of a specific process of signal fusion in the finite element model optimization method, wherein step 040 of the finite element model optimization method specifically includes the following steps:

and fusing a plurality of vibration response signals with the same testing direction by using a preset variance contribution rate algorithm to obtain the overall actual measurement vibration frequency of the target engineering structure, and further effectively utilizing the correlation and complementarity of different channel signals through the variance contribution rate to enable the fused information to reflect the overall vibration characteristic of the structure, thereby further improving the reliability and accuracy of obtaining the actual measurement vibration frequency.

The present application further provides an embodiment of a signal denoising process in a finite element model optimization method, and referring to fig. 5, the following is further specifically included between step 030 and step 040 in the finite element model optimization method:

step 033: and carrying out noise reduction processing on each vibration response signal, and filtering out water flow noise of which the frequency value is lower than a water flow noise threshold value and white noise of which the frequency value is higher than a white noise threshold value in each vibration response signal.

Based on this, the finite element model optimization method provided by the embodiment of the application can effectively improve the accuracy of the vibration response signals, and further can improve the accuracy and reliability of fusing a plurality of vibration response signals, so as to further improve the reliability and accuracy of the finite element model application.

In order to effectively apply the parameters and the frequency samples to obtain the response surface model of the target engineering structure, effectively improve the obtaining reliability and the accuracy of the response surface model, and further improve the application accuracy and the reliability of the finite element model, the application further provides an embodiment of a specific obtaining mode of the response surface model in the finite element model optimization method, wherein the step 100 of the finite element model optimization method specifically comprises the following contents:

Wherein, the quadratic polynomial is shown as formula (1):

in formula (1): x is the number of_i∈[x_i ^l,x_i ^u]，x_i ^l、x_i ^uIs the level interval of the test factor β₀、β_i、β_ij、β_iiAre regression coefficients.

In order to select an optimal target parameter set from a plurality of sets of candidate parameters by applying a preset selection rule, and further effectively improve the accuracy and reliability of the target parameter set, so as to further improve the accuracy and reliability of the finite element model application, referring to fig. 6, the present application further provides an embodiment of a specific obtaining method of the target parameter set in the finite element model optimization method, and step 200 of the finite element model optimization method specifically includes the following contents:

step 201: and performing parameter level inversion on the response surface model by taking the measured vibration frequency as a response target to obtain a plurality of groups of corresponding candidate parameters.

Step 202: and applying a preset selection rule, and selecting one group of candidate parameters from the plurality of groups of candidate parameters as a target parameter group of the finite element model.

To further explain the present solution, in order to solve the problem that the parameters of a part of finite element models cannot (are not easy to) be determined, the present application proposes a method for determining global parameters of a finite element model, and more specifically, based on the measured frequency obtained by a structural prototype test as an optimization target, the uncertainty of a few uncertain parameters is extended into the global parameters, a response surface method is used to perform an optimization solution in a constructed "parameter-frequency" sample, and finally a set of parameters is output to make the model calculation frequency and the measured frequency well coincide with each other, the present application also provides a specific application example of the finite element model optimization method, see fig. 7, and the method for optimizing the finite element model specifically includes the following contents:

1. prototype vibration testing

And performing prototype vibration test on the actual engineering structure, and collecting vibration response data of the structure.

Specifically, the method comprises the following steps: firstly, prototype vibration test is carried out on the actual engineering structure. Taking a normal operation working condition as a test working condition, and the finite element modal calculation is consistent with the working condition; in the field vibration measurement, an impact-resistant DP type earthquake type low-frequency vibration sensor is adopted to collect vibration response signals, the sampling frequency is 51.2Hz, and the sampling time is 1200 s; test points are arranged on the structure, such as a bridge structure, the test points are respectively arranged on two sides of a span, the distance of longitudinal test points is not more than 12 meters, and the number of test points in one test is not less than 8 (covering three spans); each test point contains both horizontal (H) and vertical (V) directions.

2. Processing test data

Firstly, carrying out noise reduction treatment on an original test signal, filtering the influence of low-frequency water flow noise and high-frequency white noise, and extracting the natural vibration frequency of the structure. Secondly, the multi-channel vibration test signals are fused to obtain the complete vibration frequency. The variance contribution rate can effectively utilize the correlation and complementarity of different channel signals, so that the fused information reflects the overall vibration characteristic of the structure. The algorithm can fuse a large amount of original information on the basis of using the same sensor to acquire data, so that the content is rich and detailed and the accuracy is higher.

Specifically, the method comprises the following steps: and (5) carrying out noise reduction and fusion processing on the test data. Selecting test signal channel data with good state, performing noise reduction processing on each signal channel data by adopting a CEEMDAN-SVD method, filtering the influence of low-frequency environment noise and high-frequency white noise, and extracting the natural vibration frequency of the structure, wherein a comparison graph before and after noise reduction of a certain channel signal is shown in FIG. 8; the multi-channel (from one acquisition direction) vibration test signal is then fused using a variance contribution rate data-level fusion algorithm to obtain a complete vibration frequency. Finally, performing spectrum analysis, and making a spectrogram of the fused signal as shown in fig. 9.

The main steps of the variance contribution rate algorithm are as follows:

(1) firstly, calculating the variance contribution rate K of each data point in each sensor to the whole data sequence_pq；

(2) Calculating the fusion coefficient a of each data point in each sensor_pq；

(3) And multiplying the data by corresponding data points according to the proportion of the different sensor data to obtain fused data, thereby realizing multi-channel data fusion.

3. Design of experiments

The experimental design includes three points: determining global parameters as experimental factors, correctly selecting parameter level values and determining proper level intervals. Meanwhile, a plurality of orders of characteristic modal frequencies of the main vibration direction of the structure are selected.

Specifically, global parameters are selected, wherein the global parameters comprise uncertain parameters and definite parameters, and the global parameters are generally main material parameters of the finite element model of the engineering structure, such as the elastic modulus, the density, the rigidity and the like of materials at different parts of the structure;

selecting a plurality of orders of characteristic modal frequencies of the main vibration direction of the structure, and paying attention to: and finally, the measured frequency corresponds to the measured frequency, for example, the main vibration direction of structures such as a small-span aqueduct, a bridge and the like is horizontal and transverse, and the modal frequency of the first 4 orders or the first 5 orders is generally selected.

4. Construction of test specimens

Through a test design method, a plurality of groups of finite element model modal frequency calculation conditions are designed, and calculation is sequentially carried out to obtain a 'parameter (factor) -frequency (response)' test sample.

By adopting a center recheck design experiment method, the parameter level is designed into a plurality of groups of experiment working conditions, modal calculation is sequentially carried out in finite element software, modal frequencies of all orders are extracted and filled in a parameter-frequency table, and sample data of a fitting response surface model is obtained, as shown in fig. 10.

A response surface model, such as the response surface model corresponding to a certain order frequency shown in fig. 11, is fitted, and the abscissa is the two model parameters that most significantly affect the order frequency.

5. Building response surface model

And fitting a response surface model according to the obtained test sample. The essence is to replace the true, but not explicitly expressed, limit state function by assuming an analytical expression between the limit state function containing the unknowns and the basic variables. The response surface method is a statistical comprehensive optimization method and is used for processing the problem of the effect of several factors on a system, namely the problem of the conversion relation between the input (factors) and the output (response) of the system. The application adopts a quadratic polynomial response surface model, and the formula is as follows:

6. Outputting globally determined parameters

And outputting a group of deterministic parameters by taking the corresponding measured frequency as an optimization target and through a parameter-frequency implicit relation contained in the response surface model. The set of parameters can enable the modal frequency calculated by the finite element model to be fitted with the actual structure to the maximum extent.

Taking the measured frequency as a response target, inverting the model parameter level based on the established response surface model, outputting a plurality of groups of parameter levels, sorting according to precision, and selecting a group with the highest precision as a final finite element model parameter, wherein the following table 1 is the comparison between an original parameter and an output parameter;

fig. 12 is a comparison graph of the modal frequency calculated by the parameter output by the present application, the modal frequency calculated by the initial parameter value, and the actual measurement frequency, and it can be seen from fig. 12 that the modal frequency calculated by the parameter output by the present application is closer to the actual measurement frequency.

TABLE 1 comparison of parameter inputs with determined values

Parameter(s)

K_V

K_H

E₁

M₁

E₂

M₂

Initial value

8*

2*

4.16

2525

3.48

2475

Determining a value

10

4.78

3.33

2554.79

3.94

2520.09

Rate/value of change

+25％

+139％

-20％

+20

+13％

+45

It should be noted that the finite element model should reach a certain modeling level, and have a certain modeling fineness in terms of size, detail structure, material assignment, etc. to optimize the parameter determination effect.

In terms of software, in order to accurately obtain the optimal model parameters of the finite element model and further effectively improve the accuracy and reliability of applying the optimal model parameters to the finite element model, the present application provides an embodiment of a finite element model optimization apparatus capable of implementing all or part of the contents in the finite element model optimization method, referring to fig. 13, the finite element model optimization apparatus specifically includes the following contents:

the parameter and frequency sample application module 10 is configured to apply pre-acquired parameters and frequency samples of a target engineering structure to determine a response surface model of the target engineering structure, where the parameters and frequency samples are used to store global parameter sets and modal frequencies of orders of the target engineering structure.

And the target parameter group acquisition module 20 is configured to perform parameter level inversion on the response surface model by using the measured vibration frequency of the target engineering structure as a response target, and determine the target parameter group of the finite element model according to a result of the parameter level inversion.

In order to effectively improve the accuracy, pertinence and reliability of obtaining the parameter and frequency samples, so as to further improve the reliability and accuracy of applying the finite element model, the finite element model optimization device further includes the following contents:

the modal frequency acquisition module is used for applying a plurality of pre-acquired global parameter combinations of the target engineering structure and a plurality of orders of characteristic modal frequencies of the target vibration direction of the target engineering structure to perform modal calculation in a preset finite element model of the target engineering structure to obtain each order of modal frequency corresponding to the target engineering structure.

In order to obtain the actually measured vibration frequency by applying the prototype vibration test, the accuracy and reliability of the actually measured vibration frequency can be effectively improved, and further the reliability and accuracy of applying the finite element model can be further improved, the finite element model optimization device further comprises the following contents:

and the vibration response signal acquisition module is used for performing prototype vibration test on the target engineering structure and acquiring a plurality of vibration response signals of the target engineering structure.

And the signal fusion module is used for carrying out fusion processing on the plurality of vibration response signals to obtain the actual measurement vibration frequency of the whole target engineering structure.

In order to perform a prototype vibration test through a plurality of test points, which can effectively improve the accuracy and reliability of the prototype vibration test process, and further can further improve the reliability and accuracy of obtaining a vibration response signal, the vibration response signal obtaining module in the finite element model optimization device specifically includes the following contents:

and the test point setting unit is used for arranging a plurality of test points on the target engineering structure by taking the preset normal operation working condition of the target engineering structure as a test working condition.

In order to effectively utilize correlation and complementarity of signals of different channels through a variance contribution rate, so that the fused information reflects the overall vibration characteristic of the structure, and further, the reliability and accuracy of the actual measurement vibration frequency acquisition can be further improved, the signal fusion module in the finite element model optimization device specifically includes the following contents:

In order to effectively improve the accuracy of the vibration response signal and further improve the accuracy and reliability of the fusion of the plurality of vibration response signals, so as to further improve the reliability and accuracy of the application of the finite element model, the finite element model optimization device further comprises the following components:

In order to effectively apply the parameters and the frequency samples to obtain the response surface model of the target engineering structure, effectively improve the obtaining reliability and accuracy of the response surface model, and further improve the accuracy and reliability of the finite element model application, the parameter and frequency sample application module 10 in the finite element model optimization device specifically includes the following contents:

In order to select an optimal target parameter set from a plurality of sets of candidate parameters by applying a preset selection rule, and further effectively improve the accuracy and reliability of the target parameter set, so as to further improve the accuracy and reliability of the finite element model application, the present application further provides that the target parameter set obtaining module 20 in the finite element model optimizing apparatus specifically includes the following contents:

and the candidate parameter acquisition unit is used for performing parameter level inversion on the response surface model by taking the actually measured vibration frequency as a response target to obtain a plurality of groups of corresponding candidate parameters.

In terms of hardware, in order to accurately obtain the optimal model parameters of the finite element model and further effectively improve the accuracy and reliability of applying the optimal model parameters to the finite element model, the present application provides an embodiment of an electronic device for implementing all or part of the contents in the method for optimizing the finite element model, where the electronic device specifically includes the following contents:

a processor (processor), a memory (memory), a communication Interface (Communications Interface), and a bus; the processor, the memory and the communication interface complete mutual communication through the bus; the communication interface is used for realizing information transmission between the finite element model optimization device and relevant equipment such as a database, a test point, a user terminal and the like; the electronic device may be a desktop computer, a tablet computer, a mobile terminal, and the like, but the embodiment is not limited thereto. In this embodiment, the electronic device may be implemented with reference to the embodiments of the finite element model optimization method and the finite element model optimization apparatus in the embodiments, and the contents thereof are incorporated herein, and repeated descriptions are omitted.

Fig. 14 is a schematic block diagram of a system configuration of an electronic device 9600 according to an embodiment of the present application. As shown in fig. 14, the electronic device 9600 can include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. Notably, this FIG. 14 is exemplary; other types of structures may also be used in addition to or in place of the structure to implement telecommunications or other functions.

In one embodiment, the finite element model optimization function may be integrated into the central processor 9100. The central processor 9100 may be configured to control as follows:

From the above description, the electronic device provided in the embodiment of the present application can accurately obtain the optimal model parameters of the finite element model, and then can effectively improve the accuracy and reliability of applying the optimal model parameters to the finite element model, so that the calculation result of the optimized finite element model can be more consistent with the measured data, and the thought limitation determined by a single parameter is skipped, thereby not only determining the global parameters, but also improving the precision of the finite element model, and the thought is clear and novel, has a good result, is suitable for being popularized to structural simulation researchers, and then can effectively improve the accuracy and reliability of applying and optimizing the finite element model to the target engineering structure for specific applications such as damage identification.

In another embodiment, the finite element model optimization device may be configured separately from the central processor 9100, for example, the finite element model optimization device may be configured as a chip connected to the central processor 9100, and the finite element model optimization function is realized by the control of the central processor.

As shown in fig. 14, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is noted that the electronic device 9600 also does not necessarily include all of the components shown in fig. 14; further, the electronic device 9600 may further include components not shown in fig. 14, which can be referred to in the related art.

As shown in fig. 14, a central processor 9100, sometimes referred to as a controller or operational control, can include a microprocessor or other processor device and/or logic device, which central processor 9100 receives input and controls the operation of the various components of the electronic device 9600.

The memory 9140 can be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information relating to the failure may be stored, and a program for executing the information may be stored. And the central processing unit 9100 can execute the program stored in the memory 9140 to realize information storage or processing, or the like.

The input unit 9120 provides input to the central processor 9100. The input unit 9120 is, for example, a key or a touch input device. Power supply 9170 is used to provide power to electronic device 9600. The display 9160 is used for displaying display objects such as images and characters. The display may be, for example, an LCD display, but is not limited thereto.

The memory 9140 can be a solid state memory, e.g., Read Only Memory (ROM), Random Access Memory (RAM), a SIM card, or the like. There may also be a memory that holds information even when power is off, can be selectively erased, and is provided with more data, an example of which is sometimes called an EPROM or the like. The memory 9140 could also be some other type of device. Memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application/function storage portion 9142, the application/function storage portion 9142 being used for storing application programs and function programs or for executing a flow of operations of the electronic device 9600 by the central processor 9100.

The memory 9140 can also include a data store 9143, the data store 9143 being used to store data, such as contacts, digital data, pictures, sounds, and/or any other data used by an electronic device. The driver storage portion 9144 of the memory 9140 may include various drivers for the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging applications, contact book applications, etc.).

The communication module 9110 is a transmitter/receiver 9110 that transmits and receives signals via an antenna 9111. The communication module (transmitter/receiver) 9110 is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as in the case of a conventional mobile communication terminal.

Based on different communication technologies, a plurality of communication modules 9110, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, may be provided in the same electronic device. The communication module (transmitter/receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and receive audio input from the microphone 9132, thereby implementing ordinary telecommunications functions. The audio processor 9130 may include any suitable buffers, decoders, amplifiers and so forth. In addition, the audio processor 9130 is also coupled to the central processor 9100, thereby enabling recording locally through the microphone 9132 and enabling locally stored sounds to be played through the speaker 9131.

An embodiment of the present application further provides a computer-readable storage medium capable of implementing all the steps in the finite element model optimization method with the execution subject being the server or the client in the foregoing embodiments, where the computer-readable storage medium stores thereon a computer program, and when the computer program is executed by a processor, the computer program implements all the steps in the finite element model optimization method with the execution subject being the server or the client in the foregoing embodiments, for example, when the processor executes the computer program, the processor implements the following steps:

From the above description, the computer-readable storage medium provided in the embodiment of the present application can accurately obtain the optimal model parameters of the finite element model, and further can effectively improve the accuracy and reliability of applying the optimal model parameters to the finite element model, so that the calculation result of the optimized finite element model can be more consistent with the actually measured data, and the thought limit determined by a single parameter is skipped, thereby not only determining the global parameters, but also improving the precision of the finite element model, and the thought is clear and novel, has a good result, and is suitable for being popularized to structural simulation research workers, and further can effectively improve the accuracy and reliability of applying and optimizing the finite element model to the target engineering structure for specific applications such as damage identification.

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may 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, and the like) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may 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, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

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 such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.

Claims

1. A method of finite element model optimization, comprising:

2. A finite element model optimization method according to claim 1, further comprising, before the determining the response surface model of the target engineered structure:

3. A finite element model optimization method according to claim 1, further comprising, before the determining the response surface model of the target engineered structure:

4. A finite element model optimization method as claimed in claim 3, wherein the performing a prototype vibration test on the target engineered structure and collecting a plurality of vibration response signals of the target engineered structure comprises:

and respectively collecting the vibration response signals of the test points.

5. A finite element model optimization method according to claim 3, wherein the fusing the plurality of vibration response signals to obtain the measured vibration frequency of the entire target engineering structure comprises:

6. A finite element model optimization method according to claim 3, wherein before the fusing the plurality of vibration response signals to obtain the measured vibration frequency of the entire target engineering structure, the method further comprises:

7. A finite element model optimization method according to claim 1, wherein the determining the response surface model of the target engineering structure by applying pre-acquired parameters and frequency samples of the target engineering structure comprises:

8. The finite element model optimization method according to claim 1, wherein the performing a parameter level inversion on the response surface model with the measured vibration frequency of the target engineering structure as a response target and determining a target parameter set of the finite element model according to a result of the parameter level inversion comprises:

9. A finite element model optimization apparatus, comprising:

10. The finite element model optimization device of claim 9, further comprising:

11. The finite element model optimization device of claim 9, further comprising:

12. The finite element model optimization device of claim 11, wherein the vibrational response signal acquisition module comprises:

13. The finite element model optimization device of claim 11, wherein the signal fusion model signal fusion module comprises:

14. The finite element model optimization device of claim 11, further comprising:

15. The finite element model optimization device of claim 9, wherein the parameter and frequency sample application module comprises:

16. The finite element model optimization device of claim 9, wherein the target parameter set obtaining module comprises:

17. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the finite element model optimization method of any one of claims 1 to 8 when executing the program.

18. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the finite-element model optimization method of any one of claims 1 to 8.