A Structural Strength Performance Evaluation Method Based on Multi-Level Virtual-Reality Fusion

Abstract

The present invention discloses a method for evaluating the structural strength performance based on multi-level virtual-real fusion, including: obtaining digital simulation data, displacement measurement data, load measurement data, and strain measurement data; establishing a displacement field twin evaluation model based on the displacement measurement data, load measurement data, and digital simulation data; establishing a strain field twin evaluation model based on the load measurement data, strain measurement data, and digital simulation data; obtaining a strain-displacement synchronous twin evaluation model based on the displacement field twin evaluation model and the strain field twin evaluation model; establishing an early warning analysis and evaluation model based on the strain-displacement synchronous twin evaluation model; and analyzing and determining the load early warning threshold in real time according to the real-time strain and real-time displacement in combination with the early warning analysis and evaluation model. The present invention solves the problems that in the existing strength test process, the states of parameters such as the strain and displacement of the test piece cannot be evaluated in real time, and the abnormal test process cannot be analyzed and warned in real time.

Description

Technical Field

[0001] The present invention belongs to the technical field of bearing structure strength tests, and particularly relates to a structural strength performance evaluation method based on multi-level virtual-real fusion. Background Art

[0002] The bearing structure strength test is an important mechanical environment test in many industrial fields such as aviation, aerospace, military, vehicles, ships, and architecture to test the load-bearing / pressure-bearing capacity of structural products, verify the structural strength / stiffness, and evaluate the reliability of structures under various mechanical working conditions and their service quality. During the test process, it is usually necessary to apply a certain magnitude of mechanical load to the product according to the product application working conditions to simulate its load-bearing state, and simultaneously measure various parameters such as stress, strain, and displacement of the loaded product to obtain the performance of each parameter data during the force-bearing process of the test piece. The changes in various performance parameters of the structure under the action of mechanical loads directly determine the service reliability of the structure. Since the existing strength test detection data evaluation methods mainly rely on analyzing parameters such as the order of magnitude and trend of the measured data after the test, abnormal data results are often only discovered after the test, and it is impossible to evaluate the state of each area of the test piece in real time. Moreover, since the existing measurement methods mainly rely on the sensor array for collection, the data of some areas of the test piece cannot be effectively collected, the global coverage of test parameters is limited, and the abnormal states in some blind areas during the test process cannot be warned in time, resulting in the problem of incomplete evaluation of the performance of the test piece. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art and provide a structural strength performance evaluation method based on multi-level virtual-real fusion, aiming to solve the problems that in the existing strength test process, it is impossible to evaluate the states of parameters such as strain and displacement of the test piece in real time, and the abnormal conditions during the test process cannot be analyzed and warned in real time.

[0004] To solve the above technical problems, the present invention discloses a structural strength performance evaluation method based on multi-level virtual-real fusion, including:

[0005] According to the digital model and load conditions of the test piece, perform digital simulation analysis to obtain the digital simulation data of the test piece;

[0006] Obtain the real physical measurement data during the strength test of the test piece; wherein, the real physical measurement data includes: displacement measurement data, load measurement data, and strain measurement data;

[0007] Based on the displacement measurement data and load measurement data, synchronously correct the digital simulation data to establish a displacement field twin evaluation model;

[0008] Based on the load measurement data and strain measurement data, synchronously correct the digital simulation data to establish a strain field twin evaluation model;

[0009] Couple the displacement field twin evaluation model with the strain field twin evaluation model to obtain a strain-displacement synchronous twin evaluation model;

[0010] According to the preset strain warning threshold and displacement warning threshold in the test, combined with the strain-displacement synchronous twin evaluation model, establish an early warning analysis and evaluation model;

[0011] According to the real-time strain and real-time displacement, combined with the early warning analysis and evaluation model, analyze and determine the load warning threshold in real time.

[0012] In the above structural strength performance evaluation method based on multi-level virtual-real fusion, according to the digital model and load conditions of the test piece, perform digital simulation analysis to obtain the digital simulation data of the test piece, including:

[0013] Establish a three-dimensional digital model of the test piece;

[0014] According to the three-dimensional digital model of the test piece, combined with the boundary requirements of the test load conditions, establish a digital simulation model of the test piece;

[0015] Based on the digital simulation model, perform digital simulation analysis to obtain the digital simulation data of the test piece.

[0016] In the above structural strength performance evaluation method based on multi-level virtual-real fusion, the digital simulation data includes: displacement simulation data Y0 and strain simulation data S0.

[0017] In the above structural strength performance evaluation method based on multi-level virtual-real fusion,

[0018]

[0019] Among them, F represents the digital simulation load condition force value, K represents the function relationship between displacement and load condition force value, J represents the function relationship between strain and load condition force value, G represents the structural stiffness, and E represents the elastic modulus of the material.

[0020] In the above structural strength performance evaluation method based on multi-level virtual-real fusion, obtain the real physical measurement data during the strength test of the test piece, including:

[0021] According to the test requirements, build a strength test system and install a displacement measurement system, a load measurement system, and a strain measurement system;

[0022] Based on the strength test system, conduct a strength test on the test piece, and measure the displacement measurement data Y during the strength test through the displacement measurement system, the load measurement system, and the strain measurement system z , load measurement data F zand strain measurement data S z 。

[0023] In the above method for evaluating the structural strength performance based on multi-level virtual-real fusion,

[0024] Y z = L(F z ), S z = M(F z )

[0025] where L represents the functional relationship between F z and Y z , and M represents the functional relationship between F z and S z .

[0026] In the above method for evaluating the structural strength performance based on multi-level virtual-real fusion, the digital simulation data is synchronously corrected based on the displacement measurement data and the load measurement data, and a displacement field twin evaluation model is established, including:

[0027] Determine the actual position P of each displacement measurement sensor in the displacement measurement system;

[0028] According to the load measurement data F z and the actual position P of each displacement measurement sensor, the displacement simulation data Y0 corresponding to F z and P is queried from the digital simulation data;

[0029] Synchronously correct the displacement simulation data Y0 with the displacement measurement data Y z , and determine the displacement parameter correction function Δy according to the deviation of the load condition force value corresponding to when each displacement measurement data is equal to the corresponding displacement simulation data:

[0030] Y0 = K(F z ), Δy =

[0031] where, σ represents the weight of the measurement result of each displacement measurement point, and n represents the number of measurement points;

[0032] Take the displacement parameter correction function Δy as the correction amount, and perform real-time correction and update on the digital simulation model to ensure that the displacement simulation data is consistent with the displacement measurement data, and obtain the displacement field twin evaluation model Y1:

[0033] Y1 ∈ ∑[K(Y z - Y0) + Δy] × α

[0034] where α represents the twin evaluation displacement correction coefficient.

[0035] In the above method for evaluating the structural strength performance based on multi-level virtual-real fusion, the digital simulation data is synchronously corrected based on the load measurement data and the strain measurement data, and a strain field twin evaluation model is established, including:

[0036] Determine the actual position Q of each strain measurement sensor in the strain measurement system;

[0037] According to the load measurement data F z and the actual position Q of each strain measurement sensor, query the strain simulation data S0 corresponding to F z and Q from the digital simulation data;

[0038] Synchronously correct the strain simulation data S0 with the strain measurement data S z According to the deviation of the load condition force value corresponding to when each strain measurement data is equal to the corresponding strain simulation data, determine the strain parameter correction function Δs:

[0039] S0 = J(F z ), Δs =

[0040] where γ represents the weight of the measurement results of each strain measurement point;

[0041] Take the strain parameter correction function Δs as the correction amount, and perform real-time correction and update on the digital simulation model to ensure that the strain simulation data is consistent with the strain measurement data, and obtain the strain field twin evaluation model S1:

[0042] S1 ∈ ∑[J(S z - S0) + Δs]×β

[0043] where β represents the twin evaluation strain correction coefficient.

[0044] In the above method for evaluating the structural strength performance based on multi-level virtual-real fusion, couple the displacement field twin evaluation model with the strain field twin evaluation model to obtain a strain-displacement synchronous twin evaluation model, including:

[0045] According to the displacement field twin evaluation model, analyze the displacement trend of each region of the test piece in real time, and identify and determine the key region A; where the key region A refers to: the region with the maximum displacement data, and / or the region where the change of displacement and the load condition force value is non-linear;

[0046] Obtain the strain analysis data S A corresponding to the key region A in the strain field twin evaluation model;

[0047] Take the strain analysis data S AAs the input of the displacement field twin evaluation model, the displacement field twin evaluation model is corrected and updated to obtain the strain-displacement synchronous twin evaluation model YS2:

[0048] YS2 ∈ [∑(∑(S A -pY1) × T) - Y z × δ

[0049] Wherein, p represents the mapping function of strain and displacement determined by digital simulation, T represents the correlation coefficient between the strain change gradient and the displacement change gradient, and δ represents the strain-displacement correction coefficient.

[0050] In the above structural strength performance evaluation method based on multi-level virtual-real fusion,

[0051] The identification and determination basis of the key area A is as follows:

[0052] MAX Y1||ΔY1 / ΔF ≠ C

[0053] Wherein, ΔY1 represents the displacement change amount, ΔF represents the load condition force value change amount, and C represents a constant;

[0054]

[0055] Wherein, (x i , y i , z i ) represents the coordinate of the feature point within the range of the typical feature radius near the key area A, and S Ai represents the strain analysis data at the position of (x i , y i , z i ).

[0056] The present invention has the following advantages:

[0057] (1) The present invention discloses a structural strength performance evaluation method based on multi-level virtual-real fusion. Through multi-level twin evaluation, it can effectively and dynamically associate various measured data and digital simulation data in real time during the test process, realize synchronous evaluation and analysis, improve the accuracy of the performance evaluation of the tested parts during the strength test process, and solve the problems that in the existing strength test process, it is impossible to evaluate the states of various parameters such as strain and displacement of the tested parts in real time, and it is impossible to give a real-time warning and analysis for abnormal test processes.

[0058] (2) The present invention discloses a method for evaluating the structural strength performance based on multi-level virtual-real fusion, which correlates the measured data of strain-load and displacement-load with the digital simulation data, achieving a preliminary correction of the digital simulation model; further, by identifying key regions, the displacement field twin evaluation model and the strain field twin evaluation model are coupled to establish a strain-displacement synchronous twin evaluation model, refining the evaluation model and achieving a high consistency between the measured results and the digital simulation results.

[0059] (3) The present invention discloses a method for evaluating the structural strength performance based on multi-level virtual-real fusion. According to the strain warning threshold and displacement warning threshold preset in the experiment, combined with the strain-displacement synchronous twin evaluation model, a warning analysis and evaluation model is established, and the dynamic warning evaluation of the test process is realized based on the warning analysis and evaluation model, solving the problem that the traditional fixed warning threshold cannot adapt to the dynamic changes of the test piece in real time during the test process.

[0060] (4) The present invention discloses a method for evaluating the structural strength performance based on multi-level virtual-real fusion, which is applicable to the real-time high-precision real-time twin evaluation of the structural strength performance during the strength test of load-bearing structures such as large cabins, spacecraft load platforms, load-bearing structures, and pressure vessels. Description of the Drawings

[0061] Figure 1 It is a flowchart of a method for evaluating the structural strength performance based on multi-level virtual-real fusion in an embodiment of the present invention. Detailed Embodiments

[0062] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will further describe in detail the disclosed embodiments of the present invention with reference to the drawings.

[0063] Refer to Figure 1 In this embodiment, the method for evaluating the structural strength performance based on multi-level virtual-real fusion includes:

[0064] Step 1: According to the digital model and load conditions of the test piece, perform digital simulation analysis to obtain the digital simulation data of the test piece.

[0065] In this embodiment, the digital simulation data of the test piece can be obtained in the following manner:

[0066] Establish a three-dimensional digital model of the test piece.

[0067] According to the three-dimensional digital model of the test piece and combined with the boundary requirements of the test load conditions, establish a digital simulation model of the test piece.

[0068] Based on the digital simulation model, digital simulation analysis is carried out to obtain the digital simulation data of the test piece. Among them, the digital simulation data includes but is not limited to: displacement simulation data Y0 and strain simulation data S0:

[0069]

[0070] Among them, F represents the force value of the digital simulation load condition; K represents the functional relationship between displacement and the force value of the load condition; J represents the functional relationship between strain and the force value of the load condition; G represents the structural stiffness, which is related to the material properties and structural configuration of the test piece; E represents the elastic modulus of the material.

[0071] Step 2: Obtain the true physical measurement data during the strength test of the test piece.

[0072] In this embodiment, the true physical measurement data includes but is not limited to: displacement measurement data, load measurement data, and strain measurement data.

[0073] The true physical measurement data during the strength test of the test piece can be obtained in the following way:

[0074] According to the test requirements, build a strength test system and install a displacement measurement system, a load measurement system, and a strain measurement system; based on the strength test system, conduct a strength test on the test piece, and measure the displacement measurement data Y z , load measurement data F z and strain measurement data S z :

[0075] Y z =L(F z ), S z =M(F z )

[0076] Among them, L represents the functional relationship between F z and Y z , and M represents the functional relationship between F z and S z .

[0077] Step 3: Based on the displacement measurement data and the load measurement data, synchronously correct the digital simulation data to establish a displacement field twin evaluation model.

[0078] In this embodiment, the displacement field twin evaluation model can be established in the following way:

[0079] Determine the actual positions P of the displacement measurement sensors in the displacement measurement system.

[0080] According to the load measurement data Fz and the actual positions P of each displacement measurement sensor, query the corresponding displacement simulation data Y0 of F z and P from the digital simulation data.

[0081] Use the displacement measurement data Y z to synchronously correct the displacement simulation data Y0. According to the deviation of the load condition force value corresponding to when each displacement measurement data is equal to the corresponding displacement simulation data, determine the displacement parameter correction function Δy:

[0082] Y0 = K(F z ), Δy =

[0083] wherein, σ represents the weight of the measurement results of each displacement measurement point, which is related to the structural configuration of the test piece, material properties, and the magnitude of the displacement data corresponding to the measurement point; n represents the number of measurement points.

[0084] Use the displacement parameter correction function Δy as the correction amount to perform real-time correction and update on the digital simulation model to ensure that the displacement simulation data is consistent with the displacement measurement data, and obtain the displacement field twin evaluation model Y1:

[0085] Y1 ∈ ∑[K(Y z - Y0) + Δy] × α

[0086] wherein, α represents the twin evaluation displacement correction coefficient, which is related to the change gradient of the displacement and local force value at the corresponding measurement position.

[0087] Step 4, synchronously correct the digital simulation data based on the load measurement data and strain measurement data, and establish a strain field twin evaluation model.

[0088] In this embodiment, the strain field twin evaluation model can be established in the following manner:

[0089] Determine the actual positions Q of each strain measurement sensor in the strain measurement system.

[0090] According to the load measurement data F z and the actual positions Q of each strain measurement sensor, query the corresponding strain simulation data S0 of F z and Q from the digital simulation data.

[0091] Use the strain measurement data S z to synchronously correct the strain simulation data S0. According to the deviation of the load condition force value corresponding to when each strain measurement data is equal to the corresponding strain simulation data, determine the strain parameter correction function Δs:

[0092] S0 = J(F z ), Δs =

[0093] Among them, γ represents the weight of the measurement results of each strain measurement point, which is related to the distribution of the structural residual stress, the material properties, and the magnitude of the corresponding strain data of the measurement point.

[0094] Taking the strain parameter correction function Δs as the correction amount, the digital simulation model is corrected and updated in real time to ensure that the strain simulation data is consistent with the strain measurement data, and the strain field twin evaluation model S1 is obtained:

[0095] S1 ∈ ∑[J(S z - S0) + Δs] × β

[0096] Among them, β represents the twin evaluation strain correction coefficient, which is related to the change gradient of the strain and the local force value at the corresponding measurement position.

[0097] Step 5: Couple the displacement field twin evaluation model with the strain field twin evaluation model to obtain the strain-displacement synchronous twin evaluation model.

[0098] In this embodiment, the displacement field twin evaluation model is coupled with the strain field twin evaluation model. According to the mapping analysis result of the displacement field twin evaluation model, the key area A with abnormal displacement change is identified, and further, the double-model synchronous correction is carried out through the strain field twin evaluation model, so as to realize the zero-deviation accurate prediction of the actual test state and the twin analysis result, improve the evaluation accuracy, and form the strain-displacement synchronous twin evaluation model. The specific implementation method is as follows:

[0099] According to the displacement field twin evaluation model, the displacement trend of each area of the test piece is analyzed in real time, and the key area A is identified and determined. Among them, the key area A refers to: the area with the maximum displacement data, and / or the area where the displacement and the load condition force value change non-linearly; the identification and determination basis of the key area A are as follows:

[0100] MAX Y1 || ΔY1 / ΔF ≠ C

[0101] Among them, ΔY1 represents the displacement change amount, ΔF represents the load condition force value change amount, and C represents a constant.

[0102] Obtain the strain analysis data S corresponding to the key area A in the strain field twin evaluation model A :

[0103]

[0104] Among them, (x i , y i , z i) represents the coordinates of feature points within the range of the typical feature radius near the key area A (usually 3% of the geometric size of the test piece or 10 times the size of the strain measurement sensor), S Ai represents the strain analysis data at the position of (x i , y i , z i ).

[0105] Take the strain analysis data S A as the input of the displacement field twin evaluation model (i.e., directly reference S A during the digital simulation process of the key area A to further refine the digital simulation model of the key area A), and correct and update the displacement field twin evaluation model to obtain the strain-displacement synchronous twin evaluation model YS2:

[0106] YS2 ∈ [∑(∑(S A - pY1) × T) - Y z × δ

[0107] where p represents the mapping function of strain and displacement determined by digital simulation, T represents the correlation coefficient between the strain change gradient and the displacement change gradient; δ represents the strain-displacement correction coefficient, which is related to the distribution of strain and displacement measurement data of the test piece and the configuration and material of the test piece.

[0108] Step 6, according to the preset strain warning threshold and displacement warning threshold in the experiment, combined with the strain-displacement synchronous twin evaluation model, establish a warning analysis and evaluation model.

[0109] Step 7, according to the real-time strain and real-time displacement, combined with the warning analysis and evaluation model, analyze and determine the load warning threshold in real time.

[0110] Although the present invention has been disclosed above with preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make possible changes and modifications to the technical solution of the present invention without departing from the spirit and scope of the present invention. Therefore, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall fall within the protection scope of the technical solution of the present invention.

[0111] The content not described in detail in the specification of the present invention belongs to the well-known technology of those skilled in the art.

Claims

1. A structural strength performance evaluation method based on multi-level virtual-real fusion, characterized in that Including: Conduct digital simulation analysis according to the digital model and load conditions of the test piece to obtain digital simulation data of the test piece; Obtain the real physical measurement data during the strength test of the test piece; among them, the real physical measurement data includes: displacement measurement data, load measurement data, and strain measurement data; Synchronously correct the digital simulation data based on the displacement measurement data and the load measurement data, and establish a displacement field twin evaluation model, including: determining the actual position P of each displacement measurement sensor in the displacement measurement system; according to the load measurement data F z and the actual position P of each displacement measurement sensor, query the displacement simulation data Y0 corresponding to F z and P from the digital simulation data; use the displacement measurement data Y z to synchronously correct the displacement simulation data Y0, and determine the displacement parameter correction function Δy according to the deviation of the load condition force value corresponding to when each displacement measurement data is equal to the corresponding displacement simulation data; use the displacement parameter correction function Δy as the correction amount to perform real-time correction and update on the digital simulation model to ensure that the displacement simulation data is consistent with the displacement measurement data, and obtain the displacement field twin evaluation model Y1; Synchronously correct the digital simulation data based on the load measurement data and the strain measurement data, and establish a strain field twin evaluation model, including: determining the actual positions Q of the strain measurement sensors in the strain measurement system; according to the load measurement data F z and the actual positions Q of the strain measurement sensors, query the corresponding strain simulation data S0 of F z and Q from the digital simulation data; synchronously correct the strain simulation data S0 with the strain measurement data S z Determine the strain parameter correction function Δs according to the deviation of the load condition force value corresponding to when each strain measurement data is equal to the corresponding strain simulation data; use the strain parameter correction function Δs as the correction amount to perform real-time correction and update on the digital simulation model to ensure that the strain simulation data is consistent with the strain measurement data, and obtain the strain field twin evaluation model S1; Couple the displacement field twin evaluation model and the strain field twin evaluation model to obtain a strain-displacement synchronous twin evaluation model; According to the preset strain warning threshold and displacement warning threshold of the test, and in combination with the strain-displacement synchronous twin evaluation model, establish a warning analysis and evaluation model; According to the real-time strain and real-time displacement, and in combination with the warning analysis and evaluation model, analyze and determine the load warning threshold in real time.

2. The method for evaluating the structural strength performance based on multi-level virtual-real fusion according to claim 1, wherein Conduct digital simulation analysis according to the digital model and load conditions of the test piece to obtain digital simulation data of the test piece, including: Establish a three-dimensional digital model of the test piece; According to the three-dimensional digital model of the test piece and in combination with the boundary requirements of the test load conditions, establish a digital simulation model of the test piece; Based on the digital simulation model, conduct digital simulation analysis to obtain digital simulation data of the test piece.

3. The structural strength performance evaluation method based on multi-level virtual-real fusion according to claim 2, characterized in that, The digital simulation data includes: displacement simulation data Y0 and strain simulation data S0.

4. The method for evaluating the structural strength performance based on multi-level virtual-real fusion according to claim 3, characterized in that Among them, F represents the digital simulation load condition force value, K represents the function relationship between displacement and load condition force value, J represents the function relationship between strain and load condition force value, G represents the structural stiffness, and E represents the elastic modulus of the material.

5. The method for evaluating the structural strength performance based on multi-level virtual-real fusion according to claim 4, wherein Obtain the real physical measurement data during the strength test of the test piece, including: According to the test requirements, build a strength test system and install a displacement measurement system, a load measurement system, and a strain measurement system; Based on the strength test system, a strength test is carried out on the test piece, and during the strength test, displacement measurement data Y is measured through a displacement measurement system, a load measurement system, and a strain measurement system z , load measurement data F z and strain measurement data S z .

6. The method for evaluating the structural strength performance based on multi-level virtual-real fusion according to claim 5, characterized in that Y z = L(F z ),S z = M(F z ) Among them, L represents the functional relationship between F z and Y z and M represents the functional relationship between F z and S z .

7. The method for evaluating the structural strength performance based on multi-level virtual-real fusion according to claim 6, characterized in that Y0 = K(F z ) Δy= Y1∈∑[K(Y z -Y0)+Δy]×α Among them, σ represents the weight of the measurement results of each displacement measurement point, n represents the number of measurement points, and α represents the twin evaluation displacement correction coefficient.

8. The method for evaluating the structural strength performance based on multi-level virtual-real fusion according to claim 7, characterized in that S0 = J(F z ) Δs= S1∈∑[J(S z -S0)+Δs]×β Among them, γ represents the weight of the measurement results of each strain measurement point, and β represents the twin evaluation strain correction coefficient.

9. The method for evaluating structural strength performance based on multi-level virtual-real fusion according to claim 8, wherein, Couple the displacement field twin evaluation model and the strain field twin evaluation model to obtain a strain-displacement synchronous twin evaluation model, including: According to the displacement field twin evaluation model, analyze the displacement trend of each region of the test piece in real time, and identify and determine the key region A; among them, the key region A refers to: the region with the maximum displacement data, and / or the region where the change of displacement and load condition force value is non-linear; Obtain the strain analysis data S corresponding to the key area A in the strain field twin evaluation model A ; Taking the strain analysis data S A as the input of the displacement field twin evaluation model, correcting and updating the displacement field twin evaluation model to obtain the strain-displacement synchronous twin evaluation model YS2: YS2 ∈ [∑(∑(S A - pY1) × T) - Y z × δ Among them, p represents the mapping function of strain and displacement determined by digital simulation, T represents the correlation coefficient between the strain change gradient and the displacement change gradient, and δ represents the strain-displacement correction coefficient.

10. The method for evaluating the structural strength performance based on multi-level virtual-real fusion according to claim 8, characterized in that The identification and determination basis of the key region A is as follows: MAX Y1||ΔY1 / ΔF≠C Among them, ΔY1 represents the displacement change amount, ΔF represents the load condition force value change amount, and C represents a constant; Among them, (x i , y i , z i ) represents the coordinates of the feature points within the radius of the typical features near the key area A, and S Ai represents the strain analysis data at the position of (x i , y i , z i ).

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