Method and system for screening most dangerous falling attitude of launching device

Abstract

The invention provides a screening method and system for the most dangerous falling posture of a launching device, and the method comprises the steps: S1, defining the falling posture of the launching device through a parameterized language, and determining a posture parameter space according to geometric features; s2, sampling the falling attitude in the attitude parameter space; s3, establishing a finite element model of the launching device, and solving dynamic responses of all sampling falling postures; and S4, based on a dynamic response result, screening out the most dangerous attitude. According to the method, the most dangerous attitude screening accuracy, standardization and stylization can be realized, so that the most dangerous attitude screening is more convenient and quicker, the reliability of subsequent drop verification test design is ensured, meanwhile, relevant professional designers are helped to shorten the design process, unnecessary tests are reduced, and the test efficiency is improved. The test precision is greatly improved, the design process digitization is realized, and the method has important engineering value.

Description

Technical Field

[0001] This invention relates to the field of aircraft strength analysis technology, specifically to a method and system for screening the most dangerous attitude of a launch device during a fall. Background Technology

[0002] For shipborne launchers, a catapult launch could affect the safety of equipment on and around the ship.

[0003] Drop attitude is a critical parameter in testing. The test should employ the attitude that inflicts the most severe damage on the launch device (i.e., the most dangerous attitude) to fully encompass possible operating conditions. The most dangerous attitude is influenced by factors such as materials, structure, and contents; different launch devices often have different most dangerous attitudes, therefore, the most dangerous attitude needs to be determined for each model. Considering that launch devices are typically large, complex in manufacturing, and expensive, coupled with the long preparation period and destructive nature of drop tests, the actual number of drop tests that can be conducted is limited due to time and economic constraints. If a conservative and comprehensive most dangerous attitude can be identified before testing, unnecessary testing can be reduced, helping to shorten the testing cycle and improve economic efficiency.

[0004] To address the aforementioned issues, this invention aims to propose a method for screening the most dangerous drop attitude of a launch device. This method enables precise, standardized, and procedural screening of the most dangerous attitude, making the screening process more convenient and efficient. It ensures the reliability of subsequent drop verification test designs, while also helping relevant professional designers shorten the design process, reduce unnecessary tests, greatly improve test accuracy, and digitize the design process. This method has significant engineering value. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the purpose of this invention is to provide a method and system for screening the most dangerous attitudes of aircraft during crashes.

[0006] A method for screening the most dangerous drop posture of a launching device according to the present invention includes:

[0007] Step S1: Define the drop attitude of the launching device using a parametric language, and determine the attitude parameter space based on geometric features;

[0008] Step S2: Sample the fall posture within the attitude parameter space;

[0009] Step S3: Establish a finite element model of the launching device and solve for the dynamic response of all sampled drop attitudes;

[0010] Step S4: Based on the dynamic response results, select the most dangerous posture.

[0011] Preferably, step S1 includes:

[0012] Using the virtual ground method, the fixed launching device no longer translates or rotates, and the virtual ground rotates around the center of gravity of the launching device, with the direction of gravity pointing from the center of gravity to the ground; when the drop height is fixed, the virtual ground is described by two parameters, longitude and latitude, and the virtual ground is the drop attitude.

[0013] Preferably, the QTM sampling method is used to sample the fall posture. An equilateral triangle is used to represent 1 / 8 of the sphere. Each triangle is divided into four smaller equilateral triangles. Each triangle is further subdivided until the desired density is reached. When expanded j times, 3j sampling points will be obtained.

[0014] Preferably, a multi-criteria decision-making method is used to select the most dangerous posture from the responses output by the sampling point scheme generated by QTM.

[0015] Preferably, the multi-criteria decision-making method employs grey relational analysis, selecting the optimal solution by comparing the correlation between different alternative solutions and the ideal optimal solution. The process includes:

[0016] Dimensionless data:

[0017] To eliminate the influence of range and unit between different variables, the raw data from the experimental design are dimensionless and the data distribution is between 0 and 1.

[0018] Calculate the deviation sequence:

[0019] Calculate the ideal optimal solution Each point on the curve and the candidate solution The difference between each point on the curve yields the deviation sequence Δ. i :

[0020]

[0021] Calculate the grey relational coefficient:

[0022]

[0023] In the formula, ζ is the resolution coefficient, which is selected between 0 and 1; and These represent the maximum and minimum distances between the ideal optimal solution and the candidate solution at the k-th response, respectively. and These represent the maximum and minimum distances between the ideal optimal solution and the candidate solutions across all responses, respectively.

[0024] Calculate the grey relational degree:

[0025] The grey relational degree Γ is obtained by averaging the grey relational coefficients. i ;

[0026]

[0027] Where N represents the number of grey relational coefficients, i represents the design sample number, and k represents the performance response number;

[0028] Sort:

[0029] The input schemes are sorted according to their grey relational degree to obtain the optimal value among the input schemes.

[0030] A system for screening the most dangerous fall posture of a launching device according to the present invention includes:

[0031] Module M1: Defines the drop attitude of the launch device using a parametric language and determines the attitude parameter space based on geometric features;

[0032] Module M2: Samples the fall posture within the attitude parameter space;

[0033] Module M3: Establish a finite element model of the launching device and solve for the dynamic response of all sampled drop attitudes;

[0034] Module M4: Based on the dynamic response results, the most dangerous posture is selected.

[0035] Preferably, the module M1 includes:

[0036] Using the virtual ground method, the fixed launching device no longer translates or rotates, and the virtual ground rotates around the center of gravity of the launching device, with the direction of gravity pointing from the center of gravity to the ground; when the drop height is fixed, the virtual ground is described by two parameters, longitude and latitude, and the virtual ground is the drop attitude.

[0037] Preferably, the QTM sampling method is used to sample the fall posture. An equilateral triangle is used to represent 1 / 8 of the sphere. Each triangle is divided into four smaller equilateral triangles. Each triangle is further subdivided until the desired density is reached. When expanded j times, 3j sampling points will be obtained.

[0038] Preferably, a multi-criteria decision-making method is used to select the most dangerous posture from the responses output by the sampling point scheme generated by QTM.

[0039] Preferably, the multi-criteria decision-making method employs grey relational analysis, selecting the optimal solution by comparing the correlation between different alternative solutions and the ideal optimal solution. The process includes:

[0040] Dimensionless data:

[0041] To eliminate the influence of range and unit between different variables, the raw data from the experimental design are dimensionless and the data distribution is between 0 and 1.

[0042] Calculate the deviation sequence:

[0043] Calculate the ideal optimal solution Each point on the curve and the candidate solution The difference between each point on the curve yields the deviation sequence Δ. i :

[0044]

[0045] Calculate the grey relational coefficient:

[0046]

[0047] In the formula, ζ is the resolution coefficient, which is selected between 0 and 1; and These represent the maximum and minimum distances between the ideal optimal solution and the candidate solution at the k-th response, respectively. and These represent the maximum and minimum distances between the ideal optimal solution and the candidate solutions across all responses, respectively.

[0048] Calculate the grey relational degree:

[0049] The grey relational degree Γ is obtained by averaging the grey relational coefficients. i ;

[0050]

[0051] Where N represents the number of grey relational coefficients, i represents the design sample number, and k represents the performance response number;

[0052] Sort:

[0053] The input schemes are sorted according to their grey relational degree to obtain the optimal value among the input schemes.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] 1. This invention enables the precise, standardized, and programmed screening of the most dangerous postures, making the screening of the most dangerous postures more convenient and faster, and ensuring the credibility of the subsequent drop verification test design.

[0056] 2. This invention helps relevant professional designers shorten the design process, reduce unnecessary experiments, greatly reduce experimental costs, improve experimental accuracy, and realize the digitalization of the design process, which has important engineering value.

[0057] 3. The present invention employs a multi-criteria decision-making method to select the most dangerous posture from the response output of the sampling point scheme generated by QTM, which greatly improves the accuracy and ensures the scientific nature and reliability of the determination of the most dangerous posture. Attached Figure Description

[0058] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0059] Figure 1 This is a flowchart of the method of the present invention.

[0060] Figure 2 This is a schematic diagram of the QTM sampling method in an embodiment of the present invention.

[0061] Figure 3 This is a distribution diagram of the grayscale calculation results in an embodiment of the present invention.

[0062] Figure 4 This is a schematic diagram of the two most dangerous fall postures output by the most dangerous fall screening method of the launching device in an embodiment of the present invention. Detailed Implementation

[0063] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0064] Reference Figure 1 As shown, a method for screening the most dangerous drop posture of a launching device according to the present invention is characterized by comprising:

[0065] Step S1: Parameterization and spatial determination of fall attitude;

[0066] Step S2: Drop attitude sampling test design;

[0067] Step S3: Finite element modeling, solving, and outputting calculation results;

[0068] Step S4: Screening for the most dangerous pose.

[0069] Specifically: Step S1, the parameterization and spatial determination of the fall posture, refers to describing the fall posture using a parameterized language and determining the range of the fall posture based on geometric features.

[0070] The parameterization employs a virtual ground method. The fixed launching device no longer translates or rotates, while the virtual ground rotates around the launching device's center of gravity, with the direction of gravity pointing from the center of gravity towards the ground. Therefore, changing the drop attitude is achieved by rotating the ground. When the drop height is fixed, the trajectory of the virtual ground always lies on the same sphere, requiring only two parameters—longitude (Log) and latitude (Lat)—to describe the virtual ground (i.e., the drop attitude).

[0071] Step S2, drop posture sampling test design, refers to using test design methods to test drop postures within a parameter range.

[0072] Reference Figure 2 As shown, the experimental design method employs the QTM (Spherical Quadruple Triangle) sampling method to sample the drop parameter space. An equilateral triangle represents 1 / 8 of the sphere, and each triangle is then divided into four smaller equilateral triangles (4-fold expansion). Each triangle is further subdivided until the desired density is achieved. When expanded j times, 3j sampling points are obtained. (The number of expansions j is determined by considering the sampling density, finite element calculation accuracy, and computational cost; typically j = 3 to 4.)

[0073] Step S3, finite element modeling, solving, and outputting calculation results, refers to performing finite element modeling on the launching device model, submitting explicit dynamic solutions, and outputting drop response results such as acceleration and stress.

[0074] Step S4, the selection of the most dangerous posture, refers to using a multi-criteria decision-making method to select the most dangerous posture from the response output of the sampling point scheme generated by QTM.

[0075] Reference Figure 3 and Figure 4 As shown, the multi-criteria decision-making method employs grey relational analysis to select the optimal solution by comparing the correlation between different alternative solutions and the ideal optimal solution. The process is as follows:

[0076] (1) Dimensionless data

[0077] To eliminate the influence of range and unit between different variables, the raw data from the experimental design are dimensionless, so that the data distribution is between 0 and 1.

[0078] (2) Calculate the deviation sequence

[0079] Calculate the ideal optimal solution (i.e., the expected data). Each point on the curve corresponds to a candidate solution (i.e., the actual data). The difference between each point on the curve yields the deviation sequence Δ. i .

[0080]

[0081] (3) Calculate the grey relational coefficient

[0082]

[0083] In the formula, ζ is the resolution coefficient, which is selected between 0 and 1, and is usually taken as 0.5; and These represent the maximum and minimum distances between the ideal optimal solution and the candidate solution at the k-th response, respectively. and These represent the maximum and minimum distances between the ideal optimal solution and the candidate solution across all responses, respectively.

[0084] (3) Calculate the grey relational degree

[0085] The grey relational degree Γ is obtained by averaging the grey relational coefficients. i .

[0086]

[0087] (4) Sorting

[0088] The input schemes are sorted according to their grey relational degree to obtain the optimal value among the input schemes.

[0089] The present invention also provides a system for screening the most dangerous drop posture of a launch device. The system for screening the most dangerous drop posture of a launch device can be implemented by executing the process steps of the method for screening the most dangerous drop posture of a launch device. That is, those skilled in the art can understand the method for screening the most dangerous drop posture of a launch device as a preferred embodiment of the system for screening the most dangerous drop posture of a launch device.

[0090] Specifically, a system for screening the most dangerous attitude of a launch device during a fall includes:

[0091] Module M1: Defines the drop attitude of the launch device using a parametric language and determines the attitude parameter space based on geometric features;

[0092] Module M2: Samples the fall posture within the attitude parameter space;

[0093] Module M3: Establish a finite element model of the launching device and solve for the dynamic response of all sampled drop attitudes;

[0094] Module M4: Based on the dynamic response results, the most dangerous posture is selected.

[0095] The module M1 includes:

[0096] Using the virtual ground method, the fixed launching device no longer translates or rotates, and the virtual ground rotates around the center of gravity of the launching device, with the direction of gravity pointing from the center of gravity to the ground; when the drop height is fixed, the virtual ground is described by two parameters, longitude and latitude, and the virtual ground is the drop attitude.

[0097] The QTM sampling method is used to sample the fall posture. An equilateral triangle is used to represent 1 / 8 of the sphere. Each triangle is divided into four smaller equilateral triangles. Each triangle is further subdivided until the desired density is reached. When it is expanded j times, 3j sampling points will be obtained.

[0098] A multi-criteria decision-making method is used to select the most dangerous posture from the responses output by the sampling point schemes generated by QTM.

[0099] The multi-criteria decision-making method employs grey relational analysis to select the optimal solution by comparing the correlation between different alternative solutions and the ideal optimal solution. The process includes:

[0100] Dimensionless data:

[0101] To eliminate the influence of range and unit between different variables, the raw data from the experimental design are dimensionless and the data distribution is between 0 and 1.

[0102] Calculate the deviation sequence:

[0103] Calculate the ideal optimal solution Each point on the curve and the candidate solution The difference between each point on the curve yields the deviation sequence Δ. i :

[0104]

[0105] Calculate the grey relational coefficient:

[0106]

[0107] In the formula, ζ is the resolution coefficient, which is selected between 0 and 1; and These represent the maximum and minimum distances between the ideal optimal solution and the candidate solution at the k-th response, respectively. and These represent the maximum and minimum distances between the ideal optimal solution and the candidate solutions across all responses, respectively.

[0108] Calculate the grey relational degree:

[0109] The grey relational degree Γ is obtained by averaging the grey relational coefficients. i ;

[0110]

[0111] Sort:

[0112] The input schemes are sorted according to their grey relational degree to obtain the optimal value among the input schemes.

[0113] Those skilled in the art will understand that, besides implementing the system and its various devices, modules, and units provided by this invention in the form of purely computer-readable program code, the same functions can be achieved entirely through logical programming of the method steps, making the system and its various devices, modules, and units of this invention function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, the system and its various devices, modules, and units provided by this invention can be considered as a hardware component, and the devices, modules, and units included therein for implementing various functions can also be considered as structures within the hardware component; alternatively, the devices, modules, and units for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0114] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A method for selecting the most dangerous drop posture of a launching device, characterized in that, include: Step S1: Define the drop attitude of the launching device using a parametric language, and determine the attitude parameter space based on geometric features; Step S2: Sample the fall posture within the attitude parameter space; Step S3: Establish a finite element model of the launching device and solve for the dynamic response of all sampled drop attitudes; Step S4: Based on the dynamic response results, select the most dangerous posture.

2. The method for screening the most dangerous drop posture of a launching device according to claim 1, characterized in that, Step S1 includes: Using the virtual ground method, the fixed launching device no longer translates or rotates, and the virtual ground rotates around the center of gravity of the launching device, with the direction of gravity pointing from the center of gravity to the ground; when the drop height is fixed, the virtual ground is described by two parameters, longitude and latitude, and the virtual ground is the drop attitude.

3. The method for screening the most dangerous drop posture of a launching device according to claim 2, characterized in that, The QTM sampling method is used to sample the fall posture. An equilateral triangle is used to represent 1 / 8 of the sphere. Each triangle is divided into four smaller equilateral triangles. Each triangle is further subdivided until the desired density is reached. When it is expanded j times, 3j sampling points will be obtained.

4. The method for screening the most dangerous drop posture of a launching device according to claim 3, characterized in that, A multi-criteria decision-making method is used to select the most dangerous posture from the responses output by the sampling point schemes generated by QTM.

5. The method for screening the most dangerous drop posture of a launching device according to claim 4, characterized in that, The multi-criteria decision-making method employs grey relational analysis to select the optimal solution by comparing the correlation between different alternative solutions and the ideal optimal solution. The process includes: Dimensionless data: To eliminate the influence of range and unit between different variables, the raw data from the experimental design are dimensionless and the data distribution is between 0 and 1. Calculate the deviation sequence: Calculate the ideal optimal solution Each point on the curve and the candidate solution The difference between each point on the curve yields the deviation sequence Δ. i : Calculate the grey relational coefficient: In the formula, ζ is the resolution coefficient, which is selected between 0 and 1; and These represent the maximum and minimum distances between the ideal optimal solution and the candidate solution at the k-th response, respectively. and These represent the maximum and minimum distances between the ideal optimal solution and candidate solutions across all responses, respectively. Calculate the grey relational degree: The grey relational degree Γ is obtained by averaging the grey relational coefficients. i ; Where N represents the number of grey relational coefficients, i represents the design sample number, and k represents the performance response number; Sort: The input schemes are sorted according to their grey relational degree to obtain the optimal value among the input schemes.

6. A system for screening the most dangerous drop posture of a launching device, characterized in that, include: Module M1: Defines the drop attitude of the launch device using a parametric language and determines the attitude parameter space based on geometric features; Module M2: Samples the fall posture within the attitude parameter space; Module M3: Establish a finite element model of the launching device and solve for the dynamic response of all sampled drop attitudes; Module M4: Based on the dynamic response results, the most dangerous posture is selected.

7. The launch device drop most dangerous attitude screening system according to claim 6, characterized in that, The module M1 includes: Using the virtual ground method, the fixed launching device no longer translates or rotates, and the virtual ground rotates around the center of gravity of the launching device, with the direction of gravity pointing from the center of gravity to the ground; when the drop height is fixed, the virtual ground is described by two parameters, longitude and latitude, and the virtual ground is the drop attitude.

8. The launch device drop most dangerous attitude screening system according to claim 7, characterized in that, The QTM sampling method is used to sample the fall posture. An equilateral triangle is used to represent 1 / 8 of the sphere. Each triangle is divided into four smaller equilateral triangles. Each triangle is further subdivided until the desired density is reached. When it is expanded j times, 3j sampling points will be obtained.

9. The launch device drop most dangerous attitude screening system according to claim 8, characterized in that, A multi-criteria decision-making method is used to select the most dangerous posture from the responses output by the sampling point schemes generated by QTM.

10. The launch device drop most dangerous attitude screening system according to claim 9, characterized in that, The multi-criteria decision-making method employs grey relational analysis to select the optimal solution by comparing the correlation between different alternative solutions and the ideal optimal solution. The process includes: Dimensionless data: To eliminate the influence of range and unit between different variables, the raw data from the experimental design are dimensionless and the data distribution is between 0 and 1. Calculate the deviation sequence: Calculate the ideal optimal solution Each point on the curve and the candidate solution The difference between each point on the curve yields the deviation sequence Δ. i : Calculate the grey relational coefficient: In the formula, ζ is the resolution coefficient, which is selected between 0 and 1; and These represent the maximum and minimum distances between the ideal optimal solution and the candidate solution at the k-th response, respectively. and These represent the maximum and minimum distances between the ideal optimal solution and candidate solutions across all responses, respectively. Calculate the grey relational degree: The grey relational degree Γ is obtained by averaging the grey relational coefficients. i ; Where N represents the number of grey relational coefficients, i represents the design sample number, and k represents the performance response number; Sort: The input schemes are sorted according to their grey relational degree to obtain the optimal value among the input schemes.

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