Whole-stage multi-objective optimization method and device for chassis of railway vehicle and electronic equipment

Abstract

The invention discloses a rail vehicle chassis full-stage multi-objective optimization method and device and electronic equipment, and relates to the field of vehicle structure optimization. The method comprises the following steps: determining performance parameters of the section of the vehicle body chassis according to geometrical shape data and material property data, wherein the performance parameters comprise section area, section centroid coordinates, section bending inertia moment and section torsion inertia moment; in the conceptual design stage, based on the performance parameters and the load working condition parameters, topological optimization is conducted on the section of the vehicle body chassis through a variable density method, and a topological structure distribution result is obtained; in the basic design stage and the detailed design stage, on the basis of the topological structure distribution result, with the minimization of the section area and the maximization of the section bending inertia moment as multiple targets, collaborative optimization is conducted on shape parameters and size parameters of the section of the vehicle body chassis in combination with a non-dominated sorting genetic algorithm II, and the optimization result of the section of the vehicle body chassis is obtained; according to the method, the comprehensiveness of the optimization process can be ensured, the optimization effect is effectively improved, and the optimization efficiency is improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of vehicle structure optimization, and particularly relates to a full-stage multi-objective optimization method, device and electronic equipment for a rail vehicle underframe. BACKGROUND

[0002] In a rail vehicle body structure, the underframe is the part that bears the most force, and not only bears the mass of passengers and seats and other equipment in the vehicle, but also bears the mass of suspended equipment under the vehicle. The cross-sectional structure design of an aluminum alloy underframe has an important influence on the strength, stiffness and stability of the whole vehicle, and reasonable cross-sectional design can greatly reduce the mass of the vehicle body and improve the overall stiffness and strength. In this century, rail vehicles have developed rapidly at home and abroad, and the optimization design of the cross section of the rail vehicle body has become a research hotspot for scholars at home and abroad.

[0003] Cross-sectional design belongs to a part of product design, and internationally, product design is usually divided into three stages of "conceptual design", "basic design" and "detailed design". In the conceptual design stage, preliminary analysis and research are carried out, and a preliminary design direction and solution are formulated; the basic design stage is a further deepening stage after the conceptual design stage, which further refines the design scheme and technical parameters; the detailed design stage is the final determination of the design scheme, which further perfects and refines the preliminary design scheme to ensure the completeness of the details of the design scheme. The three stages are interdependent, and the design results of each stage will provide a basis for the next stage, gradually deepening and perfecting the design scheme to ensure the comprehensiveness, feasibility and quality of product design. Therefore, for the optimization of the vehicle body underframe, the whole stage of product design needs to be considered.

[0004] However, the existing optimization design for product structure is mostly optimized for one or two stages of product design, lacking systematic multi-objective optimization research on the structure in the whole stage (conceptual, basic and detailed design stages) of product design, and there is no method of using mechanical performance parameter theoretical calculation method to replace the real model of the cross section of the rail vehicle underframe for evaluation in the optimization process.

[0005] In view of the problems of the product design stage and the structure optimization level not being considered comprehensively, the correspondence between the two not being clear, and the challenges of the traditional finite element model optimization method being large in calculation amount, long in time consumption and difficult to meet the actual programming requirements in the optimization process of the rail vehicle body underframe. In order to consider continuous optimization of each design stage of the rail vehicle body underframe in the optimization design process, while reducing the evaluation difficulty and the research and development cycle, an efficient product design full-stage multi-objective optimization method is urgently needed. SUMMARY

[0006] In order to solve the above problems, the present application provides a rail vehicle underframe full-stage multi-objective optimization method, device and electronic equipment, which can consider continuous optimization of each design stage, ensure the comprehensiveness of the optimization process, effectively improve the optimization effect and improve the optimization efficiency.

[0007] In a first aspect, the present application provides a rail vehicle underframe full-stage multi-objective optimization method, comprising:

[0008] Obtaining geometric shape data, material property data and load working condition parameters of a rail vehicle body underframe section;

[0009] Determining performance parameters of the body underframe section based on a thin plate approximation method according to the geometric shape data and the material property data; wherein the performance parameters include section area, section centroid coordinates, section bending inertia moment and section torsional inertia moment;

[0010] In the conceptual design stage, based on the performance parameters and the load working condition parameters, a topology optimization is performed on the body underframe section by using a variable density method to obtain a topology structure distribution result;

[0011] In the basic design stage and the detailed design stage, based on the topology structure distribution result, a shape parameter and a size parameter of the body underframe section are cooperatively optimized by combining a non-dominated sorting genetic algorithm II with the minimization of the section area and the maximization of the section bending inertia moment to obtain a body underframe section optimization result.

[0012] In an optional implementation, the geometric shape data includes length and thickness, wherein the length and the thickness are the length and the thickness of a first rectangle approximated to the body underframe section; and the material property data includes the density of the body underframe section.

[0013] In an optional implementation, the determining of the performance parameters of the body underframe section based on the thin plate approximation method according to the geometric shape data and the material property data comprises:

[0014] Determining the section area A according to the geometric shape data and the following formula (1):

[0015]

[0016] In formula (1), l and t respectively represent the length and the thickness of the first rectangle; the body underframe section is divided into n layers of thin plates, each layer of thin plates has m second rectangles, A ij , l ij and t ij respectively represent the area, the length and the thickness of the jth second rectangle of the ith layer of thin plates of the body underframe section;

[0017] determining sectional centroid coordinates (x c ,y c ) according to the geometry data, the material property data, the following formula (2) and the following formula (3):

[0018]

[0019] In the formula (2) and the formula (3), ρ i represents the density of the i-th layer sheet of the vehicle body frame section; respectively represent the centroid coordinates of the j-th second rectangle of the i-th layer sheet of the vehicle body frame section;

[0020] determining sectional bending inertia moments I x and I y according to the geometry data, the following formula (4) and the following formula (5):

[0021]

[0022] In the formula (4) and the formula (5), the vehicle body frame section is placed in a global coordinate system xoy, θ ij represents the angle between the j-th second rectangle of the i-th layer sheet of the vehicle body frame section and the positive direction of the x-axis of the global coordinate system xoy;

[0023] determining the sectional torsional inertia moment according to the geometry data, and the sectional cavity area, the length of each layer sheet, the thickness of each layer sheet and the sectional shape of the vehicle body frame section.

[0024] In an optional embodiment, the determining the sectional torsional inertia moment according to the geometry data, and the sectional cavity area, the length of each layer sheet, the thickness of each layer sheet and the sectional shape of the vehicle body frame section comprises:

[0025] determining the sectional torsional inertia moment J according to the following formula (6):

[0026]

[0027] In the formula (6), S1 and S2 are the areas of the upper chamber and the lower chamber of the vehicle body frame section respectively; L1, L2 and L3 are the lengths of the upper sheet, the lower sheet and the rib of the vehicle body frame section respectively; t1, t2 and t3 are the thicknesses of the upper sheet, the lower sheet and the rib of the vehicle body frame section respectively.

[0028] In an optional embodiment, the load working condition parameters comprise an external load matrix borne by the vehicle body frame and a volume constraint coefficient.

[0029] In an optional embodiment, in the conceptual design stage, topology optimization is performed on the vehicle body frame section based on the performance parameters and the load condition parameters by using a variable density method to obtain a topology structure distribution result, including:

[0030] In step S310, a current topology structure of the vehicle body frame section, a current topology structure parameter, and a first design variable are obtained; the current topology structure parameter includes the performance parameters and the load condition parameters; and the first design variable includes a density value of material distribution.

[0031] In step S320, based on the current topology structure, the current topology structure parameter, and the first design variable, a pre-established multi-condition profile section topology optimization model is solved with minimum flexibility as an optimization objective to obtain a topology optimization model solution result; the multi-condition profile section topology optimization model is shown in the following formula (7); a weighted flexibility value C(ρ w ) in formula (7) is solved by using a compromise programming method, as shown in formula (8).

[0032]

[0033] In formula (7) and formula (8), C(ρ w ) is a weighted flexibility value; F is an external load matrix of the vehicle body frame; U is a displacement matrix of the vehicle body frame; V is an optimized volume of the profile section; V0 is an initial volume of the profile section; f is a volume constraint coefficient; ρ w is a unit w density value; C k (ρ w ) is a flexibility target value of the kth condition; is a minimum value of the kth condition flexibility, is a maximum value of the kth condition flexibility;

[0034] In step S330, it is determined whether the topology optimization model solution result satisfies a pre-determined first termination condition; if yes, iteration is stopped, the current topology structure is taken as a topology structure distribution result, and is output; otherwise, the current topology structure is adjusted according to the variable density method to generate a new topology structure, the new topology structure is taken as input, and the step S310 is returned to continue execution until the first termination condition is satisfied.

[0035] In an optional embodiment, in the basic design stage and the detailed design stage, based on the topology structure distribution result, a profile section area minimization and a profile section bending inertia moment maximization are taken as multi-objects, a non-dominated sorting genetic algorithm II is combined to perform collaborative optimization on shape parameters and size parameters of the vehicle body frame section to obtain a vehicle body frame section optimization result, including:

[0036] Step S410: Obtain the topology distribution result and the second design variable; wherein, the second design variable includes shape optimization design variable and size optimization design variable; the shape optimization design variable includes the x-coordinate of the endpoint of the stiffening plate of the vehicle body chassis section. i-1 The size optimization design variables include the thickness t of each layer of thin plates in the cross-section of the vehicle body chassis. i-2 ;

[0037] Step S420: Based on the topological distribution results and the second design variable, the pre-established collaborative optimization model is solved using the non-dominated sorting genetic algorithm II to obtain the Pareto solution set; wherein, the collaborative optimization model minimizes the cross-sectional area A and the cross-sectional bending moment of inertia I. x Maximize multiple objectives, with the bending moment of inertia of the cross section I as the primary objective. y Using the cross-sectional torsional moment of inertia J as a constraint, the collaborative optimization model is shown in equation (9) below:

[0038]

[0039] In equation (9), A is the cross-sectional area, and I x I y G is the bending moment of inertia of the cross section, and J is the torsional moment of inertia of the cross section; g i As the second design variable;

[0040] Step S430: Determine whether the Pareto solution set satisfies the predetermined second termination condition. If yes, stop the iteration, select the optimal solution from the Pareto solution set as the vehicle body chassis section optimization result and output it; otherwise, continue to adjust the second design variable according to the non-dominated sorting genetic algorithm II to obtain the adjusted second design variable. Use the adjusted second design variable as input and return to step S410 to continue execution until the second termination condition is met. The vehicle body chassis section optimization result includes the optimized section area, the optimized section bending moment of inertia, and the optimized section torsional moment of inertia.

[0041] In an optional implementation, after optimizing the shape and size parameters of the vehicle chassis section based on the topology distribution results during the basic design and detailed design stages, with the minimization of the cross-sectional area and the maximization of the cross-sectional bending moment of inertia as multiple objectives, and combining the non-dominated sorting genetic algorithm II to obtain the optimized vehicle chassis section, the method further includes:

[0042] Determine whether the optimized cross-sectional area meets a preset area threshold, and / or whether the optimized cross-sectional bending moment of inertia meets a preset bending moment of inertia threshold, and / or whether the optimized cross-sectional torsional moment of inertia meets a preset torsional moment of inertia threshold; if yes, it is determined to be a valid optimization; otherwise, it is determined to be an invalid optimization.

[0043] Secondly, the present invention provides a multi-objective optimization device for the entire stage of a rail vehicle chassis, comprising:

[0044] The data acquisition module is used to acquire geometric shape data, material property data, and load condition parameters of the cross-section of the rail vehicle body underframe.

[0045] The parameter determination module is used to determine the performance parameters of the vehicle body chassis section based on the geometric shape data and the material property data, using the thin plate approximation method; wherein, the performance parameters include the section area, the section centroid coordinates, the section bending moment of inertia, and the section torsional moment of inertia;

[0046] The concept design module is used to perform topology optimization on the cross-section of the vehicle body chassis using the variable density method based on the performance parameters and the load condition parameters during the concept design phase, so as to obtain the topology distribution result.

[0047] The basic design and detailed design modules are used in the basic design and detailed design stages to optimize the shape and size parameters of the vehicle body chassis section based on the topological distribution results, with the minimization of the cross-sectional area and the maximization of the cross-sectional bending moment of inertia as multiple objectives, and combined with the non-dominated sorting genetic algorithm II to obtain the optimized results of the vehicle body chassis section.

[0048] Thirdly, the present invention provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described in any of the foregoing embodiments.

[0049] Fourthly, the present invention provides a computer-readable medium having processor-executable non-volatile program code, the program code causing the processor to perform the method described in any of the foregoing embodiments.

[0050] The beneficial effects of the technical solution provided by the embodiments of the present invention are as follows: The multi-objective optimization method, device and electronic equipment for the entire stage of rail vehicle underframe of the present invention first calculates performance parameters based on the geometric shape data and material property data of the cross-section of the rail vehicle underframe. In the conceptual design stage, based on the performance parameters and load condition parameters, the cross-section of the underframe is optimized by combining the variable density method to obtain the topological distribution result. In the basic design stage and detailed design stage, based on the topological distribution result, the shape parameters and size parameters of the underframe cross-section are optimized by combining the non-dominated sorting genetic algorithm II with the minimization of cross-sectional area and the maximization of cross-sectional bending moment of inertia as multiple objectives to obtain the optimized result of the underframe cross-section. The present invention considers optimization in the entire stage of product design, ensuring the comprehensiveness of the optimization process, effectively improving the optimization effect, and ensuring that the final product reaches the best state. The use of the cross-sectional mechanical performance parameter calculation method to replace the original real objective function evaluation greatly reduces the required evaluation difficulty and time, and improves design efficiency. This invention can effectively reduce the number of iterations during the optimization process, improve optimization efficiency, significantly improve the static stiffness of the rail vehicle body frame section while achieving the goal of lightweighting, and has guiding significance for the systematic optimization design of products. Attached Figure Description

[0051] Figure 1 A flowchart illustrating the multi-objective optimization method for the entire stage of a rail vehicle underframe provided in an embodiment of the present invention;

[0052] Figure 2 This is a schematic diagram of a typical underframe cross-section structure provided in an embodiment of the present invention;

[0053] Figure 3 This is a schematic diagram of the structure of the vehicle chassis topology optimization object provided in an embodiment of the present invention;

[0054] Figure 4 This is a schematic diagram of the planar topology optimization model of the vehicle chassis provided in an embodiment of the present invention;

[0055] Figure 5 An iterative diagram of the objective function of profile 1 provided in an embodiment of the present invention;

[0056] Figure 6 This is a diagram showing the distribution of stiffeners after topology optimization of the underframe section provided in an embodiment of the present invention.

[0057] Figure 7 This is a schematic diagram of the Pareto front of profile 1 provided in an embodiment of the present invention;

[0058] Figure 8 Distribution diagram of the underframe stiffeners before and after full-stage optimization provided in this embodiment of the invention;

[0059] Figure 9This is a system schematic diagram of the multi-objective optimization device for the entire stage of a rail vehicle chassis provided in an embodiment of the present invention;

[0060] Figure 10 A system schematic diagram of an electronic device provided in an embodiment of the present invention.

[0061] In the diagram: 21-Upper base plate; 22-Lower base plate; 23-Base frame section; 24-Stiffener plate; K1-Upper chamber; K2-Lower chamber; X1-Profile 1; X2-Profile 2; X3-Profile 3; S1-Design domain; S2-Non-Design domain; G1-Working condition 1; G2-Working condition 2; 100-Data acquisition module; 200-Parameter determination module; 300-Conceptual design module; 400-Basic design and detailed design module; 1000-Electronic equipment; 1001-Communication interface; 1002-Processor; 1003-Memory; 1004-Bus. Detailed Implementation

[0062] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0063] See Figure 1 The present invention provides a multi-objective optimization method for the entire stage of a rail vehicle underframe, comprising the following steps S100 to S400.

[0064] Step S100: Obtain the geometric shape data, material property data, and load condition parameters of the cross-section of the rail vehicle body underframe.

[0065] Here, geometric data includes length and thickness, material property data includes density, and load condition parameters include the external load matrix and volume constraint coefficient of the vehicle chassis.

[0066] Step S200: Based on geometric shape data and material property data, determine the performance parameters of the vehicle body chassis section using the thin plate approximation method; wherein, the performance parameters include the cross-sectional area, cross-sectional centroid coordinates, cross-sectional bending moment of inertia, and cross-sectional torsional moment of inertia.

[0067] Specifically, bending moment of inertia is a geometric quantity used to describe the resistance of a cross section to bending, reflecting an object's ability to resist shape changes when subjected to bending forces. Torsional moment of inertia is an important parameter used to describe the torsional performance of a component, reflecting the component's ability to resist shape changes when torsion.

[0068] This embodiment derives performance parameters for the mechanical response and performance evaluation of the underframe structure based on the geometric shape and material property data of the rail vehicle body underframe. The mass and manufacturing cost of the underframe are closely related to the cross-sectional area; the location of the center of gravity is crucial for the balance and stability of the structure; the bending moment of inertia and torsional moment of inertia are key performance indicators for evaluating the stiffness and strength of the underframe structure, directly affecting its performance under external loads. Therefore, when designing the underframe structure, it is necessary to comprehensively consider the cross-sectional area, center of gravity location, bending moment of inertia, and torsional moment of inertia parameters to ensure that the underframe has good mechanical properties and reliability.

[0069] According to the definition of cross-sectional mechanical property parameters in mechanics of materials, the cross-sectional area A, the cross-sectional bending moment of inertia I about the x-axis and y-axis... x and I y The cross-sectional torsional moment of inertia J can be calculated using the following formulas (10) to (13).

[0070] A=∫ s ds (10)

[0071] I x =∫ A y 2 dA (11)

[0072] I y =∫ A x 2 dA (12)

[0073]

[0074] In equations (10) to (13), r represents the distance from the cross-sectional element to the torsion center, and t represents the thickness.

[0075] Since actual programming optimization of the cross-section is required, the calculation method defined above is difficult to meet the needs. Therefore, this embodiment adopts the thin plate approximation method to approximate the thin plate in the cross-section as... Figure 2 The rectangular thin plate with length l and thickness t is shown in the calculation of its cross-sectional mechanical properties. Commonly used cross-sectional structures in the underframe of rail vehicles include... Figure 2 As shown, a series of theoretical calculation formulas for the mechanical performance parameters of the chassis section are derived based on its characteristics. The geometric data includes the length and thickness of a first rectangle that approximates the chassis section; the material property data includes the density of the chassis section. Step S200 specifically includes the following steps S210 to S240. In this embodiment, the performance parameters are calculated through the following steps S210 to S240.

[0076] Step S210, determine the cross-sectional area A based on the geometric data and the following formula (1):

[0077]

[0078] In equation (1), l and t represent the length and thickness of the first rectangle, respectively; the cross-section of the vehicle chassis is divided into n thin plates, each thin plate having m second rectangles, A ij l ij and t ij Let represent the area, length, and thickness of the j-th second rectangle of the i-th thin plate in the cross-section of the vehicle body chassis, respectively.

[0079] Here, for Figure 2 The typical frame cross-section shown is divided into n layers, each layer having m approximately rectangular thin plates. The area of ​​each approximately rectangular thin plate is calculated, and the areas of the m rectangles in each of the n layers are superimposed to obtain the formula (1) for calculating the area A of the entire cross-section.

[0080] Step S220: Determine the centroid coordinates (x, y) of the cross section based on the geometric shape data, material property data, and the following equations (2) and (3). c ,y c ):

[0081]

[0082] In equations (2) and (3), ρ i This represents the density of the i-th thin plate in the cross-section of the vehicle body chassis. These represent the centroid coordinates of the j-th second rectangle of the i-th thin plate in the cross-section of the vehicle body chassis.

[0083] Here, based on the centroids of the n×m second rectangles in the underframe cross-section and their corresponding areas, the coordinates (x, y, c) of the centroid of the entire underframe cross-section can be derived. c ,y c The calculation formulas (2) and (3) are given.

[0084] Step S230: Determine the section bending moment of inertia I based on the geometric data, equation (4) and equation (5). x and I y :

[0085]

[0086]

[0087] In equations (4) and (5), the cross-section of the vehicle chassis is placed in the global coordinate system xoy, θ ij It represents the angle between the j-th second rectangle of the i-th thin plate of the vehicle body chassis section and the positive x-axis in the global coordinate system xoy.

[0088] Here, the bending moment of inertia of a section is a physical quantity that describes the ability of a section to resist bending deformation. It reflects the influence of the section shape on the bending stiffness under bending load and directly affects the structural stiffness performance. The bending moment of inertia of the entire section can be obtained by coordinate transformation and superposition of the moments of inertia of the sections with respect to their own centroids by n×m approximate rectangles. The formulas for calculating the bending moment of inertia of the section with respect to the overall coordinate system xoy are Equations (4) to (5).

[0089] Step S240: Determine the cross-sectional torsional moment of inertia based on the geometric data, the cross-sectional cavity area of ​​the vehicle body underframe, the length of each thin plate, the thickness of each thin plate, and the cross-sectional shape.

[0090] Specifically, see Figure 2 The cross-sectional cavity area is the sum of the areas of the upper chamber K1 and the lower chamber K2. The thin plate includes the upper bottom plate 21, the lower bottom plate 22, and the stiffening plate 24 of the base frame cross-section 23.

[0091] The torsional moment of inertia J of the cross section is determined according to the following formula (6):

[0092]

[0093] In equation (6), S1 and S2 are the areas of the upper chamber K1 and lower chamber K2 in the cross-section of the car body underframe, respectively; L1, L2 and L3 are the areas of the cross-section 23 of the car body underframe (see Figure 2 The lengths of the upper base plate 21, the lower base plate 22, and the stiffening plate 24 are: t1, t2, and t3 are the thicknesses of the upper base plate 21, the lower base plate 22, and the stiffening plate 24 in the cross-section of the vehicle body underframe, respectively.

[0094] Here, by combining the formula for calculating the torsional moment of inertia of a cross-section in mechanics of materials with typical underframe cross-sectional shapes, a theoretical calculation method for the torsional moment of inertia of the underframe cross-section can be derived. The magnitude of the torsional moment of inertia depends on the area of ​​the cross-sectional cavity, the length and thickness of each thin plate, and the cross-sectional shape. Figure 2 The theoretical formula for calculating the torsional moment of inertia of the three-layer thin plate section shown is Equation (6).

[0095] by Figure 2 Taking the typical cross-section of the underframe shown as an example, the results calculated using Matlab programming based on the theoretical calculation formula for cross-sectional mechanical properties derived in this embodiment are compared with the results calculated using the built-in module of Hypermesh (a high-performance finite element preprocessing software). The results are shown in Table 1, with the cross-sectional area A and the cross-sectional bending moment of inertia I... x I yThe derivation of the cross-sectional torsional moment J from the definition in mechanics of materials based on the actual structure has almost no error. In the derivation process, the warping torsion (referring to the phenomenon of warping deformation of the cross-section under the action of torque, which can be divided into free torsion and constrained torsion) is ignored according to the actual structural characteristics. Only free torsion is considered, and the error is also very small. This proves that it is feasible to apply the theoretical calculation method of the cross-sectional performance parameters of the underframe to the optimization design of the cross-section.

[0096] Table 1 Comparison of Calculation Methods for Cross-Sectional Performance Parameters

[0097]

[0098] The theoretical calculation method for the performance parameters of the rail vehicle body underframe section derived in the above-mentioned specific step S200 is used as the evaluation criterion for the performance of the optimization process in the following steps S300 and S400. The rail vehicle body underframe section is then optimized in all stages using a combination of steps S300 and S400. First, topology optimization of the rail vehicle body underframe section is performed in the conceptual design stage using step S300. Then, shape-free dimension co-optimization is performed in the basic and detailed design stages using step S400 based on the optimization results of step S300.

[0099] Step S300: In the conceptual design phase, based on performance parameters and load condition parameters, the variable density method is used to perform topology optimization on the cross-section of the vehicle body chassis to obtain the topology distribution results. The load condition parameters include the external load matrix and volume constraint coefficients acting on the vehicle body chassis.

[0100] Specifically, topology optimization is a structural design method whose core objective is to achieve lightweight structural design by optimizing the spatial distribution of materials while meeting mechanical performance requirements (such as stiffness and strength). In the conceptual design phase, the overall form and layout of the structure are initially determined. Topology optimization at this stage aims to minimize material usage and reduce structural weight by optimizing the overall layout and connection methods, while ensuring that the overall stability and load-bearing capacity of the structure are not affected. For the topology optimization of the vehicle body underframe section, this embodiment uses the variable density method to quickly and efficiently obtain the distribution scheme of material stiffeners. Finally, by verifying the mechanical properties of the section, it is ensured that the optimization results meet the design requirements, thus completing the topology optimization of the section. Step S300 specifically includes the following steps S310 to S330.

[0101] Step S310: Obtain the current topology, current topology parameters, and first design variable of the vehicle body chassis section; wherein, the current topology parameters include performance parameters and load condition parameters; the first design variable includes the density value of the material distribution.

[0102] Typically, the underframe floor of a rail vehicle has a symmetrical structure, welded together from five profiles of three different types. Therefore, this embodiment selects a three-profile structure. Figure 3 As shown, Figure 3 (a) is a schematic diagram of the subway car body structure. Figure 3 (b) is a schematic diagram of the AA cross-sectional structure of the subway car body in (a), and (c) is a schematic diagram of the cross-sectional structures of the three profiles of the subway car body underframe in (b). The main technical parameters of the underframe profile cross-sections are shown in Table 1. The cross-sectional structure of the rail vehicle body is as follows: Figure 3 As shown in (b), the profile selection is as follows: Figure 3 As shown in (c). Figure 3 (c) In this case, X1 represents profile 1, X2 represents profile 2, and X3 represents profile 3.

[0103] Table 2 Main Technical Parameters of Base Frame Profile Section

[0104] Name Profile 1 Profile 2 Profile 3 Length / mm 568 424 438 Height / mm 58 58 58 Upper and lower floor thickness / mm 2.8 2.8 2.8 Left and right web thickness / mm 3.5 3.5 3.5 Middle rib thickness / mm 2.5 2.5 2.5

[0105] To optimize the arrangement of reinforcing ribs, the cross-sectional structure is simplified. Taking profile 1 as an example, the cross-section is simplified to a rectangle, and a topology optimization model is established as follows: Figure 4 As shown, it is divided into a design domain S1 and a non-design domain S2. The non-design domain S2 simulates the upper and lower base plates, while the design domain S1 simulates the distribution of stiffeners.

[0106] Because the load conditions on rail vehicle bodies are complex and variable during actual service, the working conditions for underframe topology optimization are defined by combining static and dynamic loads to improve reliability during topology optimization. Regarding the weight of passengers and interior facilities on the upper part of the underframe, according to EN 12663 standard, its mass is M1 = 9.6t under the vertical preparation condition (AW0), corresponding to a vertical static load F on the upper part of the underframe. a For the suspension equipment under the underframe, based on the vehicle body structure, the main considerations are four devices: the high-voltage box, the braking resistor, the traction inverter, and the filter reactor. Their mass is M2 = 2.005t, corresponding to a vertical static load F on the underframe. b Considering the vertical dynamic load F during vehicle operation c The load is determined by the dynamic load factor, which is generally taken as 0.3. The magnitude of the load on the base frame is shown in equations (14) to (16), and the direction is vertically downward.

[0107] F a =M1×g=9.6t×9810N / t=94176N (14)F b =M2×g=2.005t×9810N / t=19669N (15)

[0108] F c =0.3×Fa =28253N (16)

[0109] Based on the load conditions of the aforementioned underframe, topology optimization is performed under two working conditions, considering both underframe suspension equipment and other factors. Figure 4 In working condition G1, the load of the equipment suspended by the underframe is not considered. Therefore, its load F1 is the load F on the upper part of the underframe. a Apply vertical dynamic load F c ; Figure 4 In working condition G2, considering only the load of the equipment suspended from the underframe, its load F2 is the load F of the lower part of the underframe. b Load F1 in Condition 1 (G1) and load F2 in Condition 2 (G2) are converted onto the topology-optimized cross-sectional model based on area (cross-sectional thickness is set to 10mm) to obtain the topology-optimized loads for each profile under the two conditions. For both conditions, fixed constraints are applied to the four ends of the base frame profile cross-section. The topology-optimized conditions for the base frame cross-section are shown in Table 3, and the load constraint application is as follows: Figure 4 As shown.

[0110] Table 3 Topology Optimization Conditions for Underframe Section

[0111]

[0112] Step S320: Based on the current topology, current topology parameters, and the first design variable, with the optimization objective of minimizing compliance, solve the pre-established profile cross-section topology optimization model based on multiple working conditions to obtain the topology optimization model solution result; wherein, the profile cross-section topology optimization model for multiple working conditions is shown in Equation (7); the weighted compliance value C(ρ) in Equation (7) is shown in Equation (7). w The solution is obtained using the compromise programming method, as shown in equation (8):

[0113]

[0114] In equations (7) and (8), C(ρ) w ) represents the weighted compliance value; F represents the external load matrix of the vehicle body underframe; U represents the displacement matrix of the vehicle body underframe; V represents the volume of the profile section after optimization; V0 represents the initial volume of the profile section; f represents the volume constraint coefficient; ρ w The density value of element w (the cross-section of the vehicle chassis is divided into N elements; when w = 1, ρ1 represents the density value of element 1, and so on; when w = N, ρ...) N Represents the density value of element N, where the density value of each element ranges from 0 to 1; C k (ρ w ) represents the compliance target value for the k-th working condition; The minimum compliance value for the k-th operating condition. This represents the maximum flexibility value for the k-th operating condition.

[0115] Specifically, the external load matrix F on the vehicle chassis is combined with Figure 4 Based on the data in Table 3, the volume constraint coefficient f is determined (pre-determined according to actual engineering needs and experience). The variable density method introduces a hypothetical material with a relative density varying between 0 and 1 within the design domain, and artificially assumes a certain correspondence between the properties of this material (such as elastic modulus) and density. Through this correspondence, the topology optimization problem of the structure can be transformed into the optimal distribution problem of the material.

[0116] For the chassis section, the reasonable distribution and effective utilization of materials in the cross-sectional space has always been the design goal. The maximum stiffness commonly used in engineering is adopted as the optimization target. The maximum stiffness is transformed into the minimum flexibility. The multi-condition weighted flexibility topology optimization design of the chassis section is carried out in the conceptual design stage. The mathematical model is shown in Equation (7).

[0117] Step S330: Determine whether the solution result of the topology optimization model meets the predetermined first termination condition; if yes, stop the iteration, take the current topology as the topology distribution result and output it; otherwise, continue to adjust the current topology according to the variable density method, generate a new topology, take the new topology as input, and return to step S310 to continue execution until the first termination condition is met.

[0118] Specifically, in the optimization design of the underframe cross-section, the variable density method is used with the profile cross-sectional volume as a constraint and the weighted compliance of the profile cross-section as the objective to perform topology optimization on the three types of underframe profiles. The relationship between the objective function (minimum weighted compliance) and the number of iterations is obtained as follows: Figure 5 As shown, taking profile 1 as an example (the hardware environment is an Intel(R) Core(TM) i7-10700F CPU with a main frequency of 2.90GHz, and 65 iterations require 15-30 minutes).

[0119] In the post-processing of topology optimization results, the density value of the topology optimization elements is adjusted to a suitable threshold. The material distribution of each profile in the topology optimization results is as follows: Figure 6 As shown.

[0120] In step S400, during the basic design and detailed design phases, based on the topology distribution results, the shape and size parameters of the vehicle chassis section are optimized in conjunction with the non-dominated sorting genetic algorithm II (NSGA-II algorithm) with multiple objectives of minimizing the cross-sectional area and maximizing the cross-sectional bending moment of inertia, to obtain the optimized results of the vehicle chassis section.

[0121] Currently, shape optimization and size optimization are widely used in the optimization design of various equipment, but they are mostly considered independently and sequentially. In actual engineering, to reduce the number of iterations and improve optimization efficiency, it is a better choice to optimize them collaboratively.

[0122] Based on the collaborative optimization design concept, and building upon the topology optimization in the conceptual design phase, a non-dominated sorting genetic algorithm II is further employed to collaboratively optimize the shape and free dimensions of the underframe cross-section in both the basic and detailed design phases. The basic design phase primarily focuses on improving the structural performance, reducing stress concentration, and enhancing load-bearing capacity and stiffness by adjusting the shape and cross-sectional form of components. In the detailed design phase, the focus shifts to optimizing specific dimensions, thicknesses, spacing, and other detailed parameters of components to meet actual engineering requirements, reduce structural weight and material costs, and ensure structural safety and reliability. Both phases complement each other throughout the design process, aiming to ultimately achieve the design goals and requirements. The shape-free dimension collaborative optimization in both the basic and detailed design phases primarily utilizes the coordinates of the underframe cross-section stiffeners to control shape changes, while simultaneously adjusting the plate thickness to control dimensional changes. The non-dominated sorting genetic algorithm II is used to iteratively calculate the mechanical performance parameters of the body beam cross-section under different shapes and plate thicknesses, thereby achieving shape-free dimension collaborative optimization between the basic and detailed design phases. Step S400 specifically includes steps S410 to S430.

[0123] Step S410: Obtain the topology distribution result and the second design variable; wherein, the second design variable includes shape optimization design variable and size optimization design variable; the shape optimization design variable includes the x-coordinate of the endpoint of the stiffening plate of the vehicle body chassis section. i-1 The size optimization design variables include the thickness t of each layer of thin plates in the body chassis section. i-2 .

[0124] Step S420: Based on the topological distribution results and the second design variable, the pre-established collaborative optimization model is solved using the non-dominated sorting genetic algorithm II to obtain the Pareto set; wherein, the collaborative optimization model minimizes the cross-sectional area A and the cross-sectional bending moment of inertia I. x Maximize multiple objectives, with the bending moment of inertia of the cross section I as the primary objective. y With the cross-sectional torsional moment of inertia J as a constraint, the collaborative optimization model is shown in equation (9):

[0125]

[0126] In equation (9), A is the cross-sectional area, and I x I y G is the bending moment of inertia of the cross section, and J is the torsional moment of inertia of the cross section; g iThe second design variable is g. i Including the x-coordinate of the endpoint of the stiffening plate of the car body underframe section i-1 The thickness t of each layer of thin plates in the cross-section of the vehicle body underframe i-2 .

[0127] Specifically, in the chassis section optimization design, a shape-free dimension collaborative optimization model is constructed based on the non-dominated sorting genetic algorithm II. The chassis structure mass has a significant impact on the overall vehicle mass and production cost, while body stiffness is a crucial performance indicator. Furthermore, the cross-sectional length (x-direction) has a large span and is a stress-bearing component, while the height (y-direction) has a small span and minimal deformation. Therefore, minimizing the body mass and maximizing the cross-sectional bending moment of inertia (I) are key considerations. x Maximize as the optimization objective; sectional bending moment of inertia I y The torsional moment of inertia J of the cross-section is used as a constraint condition, requiring that the optimized mechanical properties are not less than the performance level after topology optimization, so as to ensure the static stiffness of the chassis. For the cross-sectional model of the vehicle chassis, the shape optimization in the basic design stage is based on the x-coordinate of the endpoint of the stiffening plate of the chassis cross-section. i-1 As a design variable, the free dimension optimization in the detailed design phase is based on the thickness t of each layer of the plate in the cross-section. i-2 As a design variable, optimization design is carried out simultaneously. In the cross-sectional design of the underframe, only the cross-sectional performance parameters can be used for calculation, so the cross-sectional area is used to represent the profile quality. The established mathematical model is shown in equation (9).

[0128] Step S430: Determine whether the Pareto solution set satisfies the predetermined second termination condition. If so, stop the iteration, select the optimal solution from the Pareto solution set as the optimized result of the car body chassis section, and output it. Otherwise, continue to adjust the second design variable according to the non-dominated sorting genetic algorithm II to obtain the adjusted second design variable. Use the adjusted second design variable as input and return to step S410 to continue execution until the second termination condition is met. The optimized car body chassis section result includes the optimized section area, the optimized section bending moment of inertia, and the optimized section torsional moment of inertia.

[0129] Specifically, a multi-objective genetic algorithm, Non-dominated sorting genetic algorithm II, is used for shape-free size co-optimization. The population size is set to 50, and the resulting Pareto solution set is as follows: Figure 7 As shown, taking profile 1 as an example.

[0130] The Pareto solution set obtained from the non-dominated sorting genetic algorithm II shows that the overall cross-sectional bending moment of inertia I... xThe trend is that it decreases as the area A (mass) increases. Since this optimization is based on the topology optimization performed during the conceptual design phase, the results (Tables 4-6) show that after topology optimization, the cross-sectional areas of the three profiles of the base frame are reduced, meeting the lightweight design requirements, and the torsional moment of inertia J is also increased. However, the bending moment of inertia of the three profiles fluctuates slightly from the original structure. Therefore, a balance must be struck between area A (mass) and bending moment of inertia I. x The optimization effect is selected based on the cross-sectional area and the bending moment of inertia of the cross-section. Figure 7 The solution shown (e.g.) Figure 7 (The solution indicated by the middle arrow). Similarly, shape-free dimension co-optimization is performed sequentially for profiles 2 and 3. The optimal cross-sectional shape is finally obtained through topology optimization and shape-free dimension co-optimization based on a non-dominated sorting genetic algorithm II, as shown below. Figure 8 As shown.

[0131] Finally, the mechanical performance parameters of the underframe profile section were calculated using the theoretical calculation method of cross-sectional performance parameters. The results of the optimized model and the original model were compared, as shown in Tables 4 to 6. The underframe of the rail vehicle body is welded from hollow extruded aluminum profiles. Both the fully optimized structure and the original structure require the design of corresponding extrusion dies during processing. The fully optimized structure is slightly more difficult to design dies than the regular original structure, but it is worthwhile due to the superior performance of the fully optimized structure.

[0132] As shown in Tables 4-6, based on the topological cross-section, shape-free dimension co-optimization was performed in the basic and detailed design stages. The area A (i.e., mass) of the three profiles was further reduced. The area of ​​profile 1 was reduced from 2.26% to 4.06% after topological optimization, the area of ​​profile 2 was reduced from 0.79% to 1.31%, and the area of ​​profile 3 was reduced from 5.02% to 5.17%. Simultaneously, the section bending moment of inertia I... x Compared to the topology optimization, which resulted in some floating changes in each section of the original structure, all sections showed significant improvements in bending moment of inertia I of profiles 1, 2, and 3. x These improvements increased the original structure by 9.5%, 5.21%, and 9.00%, respectively; and the section bending moment of inertia I y The torsional moment of inertia J of the cross-section did not decrease relative to the original structure. (Comparison) Figure 6 and Figure 8 Compared to the stiffener distribution results after topology optimization, the material distribution within the profile is more uniform after full-stage optimization, with stiffeners filling the entire cross-sectional space. This indicates that full-stage multi-objective optimization of the product design for the base frame profile cross-section is feasible and effective.

[0133] Table 4 Comparison of Original and Optimized Values ​​for Profile 1

[0134]

[0135] Table 5 Comparison of Original and Optimized Values ​​for Profile 2

[0136]

[0137]

[0138] Table 6 Comparison of Original and Optimized Values ​​for Profile 3

[0139]

[0140] Furthermore, after step S430, the method of this embodiment also includes the following step S500, which determines whether the optimization result is effective.

[0141] Step S500: Determine whether the optimized cross-sectional area meets the preset area threshold, and / or whether the optimized cross-sectional bending moment of inertia meets the preset bending moment of inertia threshold, and / or whether the optimized cross-sectional torsional moment of inertia meets the preset torsional moment of inertia threshold; if yes, it is determined to be a valid optimization; otherwise, it is determined to be an invalid optimization.

[0142] Specifically, it is determined whether the optimized cross-sectional area meets a preset area threshold, or whether the optimized cross-sectional bending moment of inertia meets a preset bending moment of inertia threshold, or whether the optimized cross-sectional torsional moment of inertia meets a preset torsional moment of inertia threshold. If any one or more of the above conditions are met, it is considered a valid optimization; otherwise, it is considered an invalid optimization. The multiple conditions judged in this step can be freely combined according to the optimization objective.

[0143] This embodiment addresses the problems of incomplete consideration of product design stages and structural optimization levels, unclear correspondence between the two, and challenges of traditional finite element model optimization methods such as high computational load, long time consumption, and difficulty in meeting practical programming requirements during the optimization of rail vehicle body underframes. It proposes a multi-objective optimization design method based on cross-sectional performance parameterization for the entire product design process. First, theoretical calculation formulas for cross-sectional performance parameters affecting the quality and stiffness of the underframe are derived. Then, for aluminum alloy rail vehicle body underframes, multi-objective optimization is implemented throughout the entire product design process. In this optimization process, the minimum weighted compliance (i.e., maximum stiffness) is used as the optimization objective. In the conceptual design stage of the underframe cross-section, a variable density method is used for topology optimization; while in the basic design and detailed design stages, the derived theoretical calculation formulas for the underframe cross-sectional performance parameters are combined with a non-dominated sorting genetic algorithm II for shape-free dimension co-optimization.

[0144] The technical solution of this embodiment is a multi-objective optimization design method for the entire product design stage of a rail vehicle chassis. Adopting a full-stage product design optimization method allows for continuous optimization at each design stage, ensuring the comprehensiveness of the optimization process and effectively improving optimization results. Using cross-sectional mechanical performance parameter calculation methods to replace the original real objective function evaluation can significantly reduce the required evaluation difficulty and time, improving design efficiency. Collaborative design of shape optimization in the basic design stage and free dimension optimization in the detailed design stage can effectively reduce the number of iterations during the optimization process, improving optimization efficiency.

[0145] The multi-objective optimization method for the rail vehicle underframe proposed in this embodiment considers optimization throughout the entire product design process, continuously seeking and solving problems to ensure the comprehensiveness of the optimization process, effectively improving the optimization effect, and ensuring that the final product reaches the optimal state. At the same time, the method of calculating cross-sectional mechanical performance parameters replaces the original real objective function evaluation, which can greatly reduce the required evaluation difficulty and time, and improve design efficiency.

[0146] Compared to existing methods, the method in this embodiment comprehensively considers the correspondence between structural optimization levels and the entire product design stage for the first time. It derives theoretical calculation formulas for the cross-sectional performance parameters affecting the chassis mass and stiffness, replacing the traditional finite element model optimization model. This fundamentally solves the problems of existing optimization methods' incomplete consideration of the product design stage and structural optimization levels, unclear correspondence between the two, and the challenges of traditional finite element model optimization methods such as high computational load, long processing time, and difficulty in meeting practical programming requirements. The method involved in this embodiment can consider continuous optimization at each design stage, ensuring the comprehensiveness of the optimization process, greatly reducing the required evaluation difficulty and time, and improving design efficiency. This achieves a significant improvement in the static stiffness of the rail vehicle chassis cross-section while achieving lightweighting, providing guidance and reference for the systematic optimization design of products.

[0147] See Figure 9 This invention provides a multi-objective optimization device method for the entire stage of a rail vehicle underframe, comprising a data acquisition module 100, a parameter determination module 200, a conceptual design module 300, and a basic design and detailed design module 400.

[0148] The data acquisition module 100 is used to acquire geometric shape data, material property data, and load condition parameters of the cross-section of the rail vehicle body underframe. The parameter determination module 200 is used to determine the performance parameters of the body underframe cross-section based on the geometric shape data and material property data, using the thin-plate approximation method; these performance parameters include cross-sectional area, cross-sectional centroid coordinates, cross-sectional bending moment of inertia, and cross-sectional torsional moment of inertia. The conceptual design module 300 is used in the conceptual design stage to perform topology optimization of the body underframe cross-section using the variable density method based on the performance parameters and load condition parameters, obtaining the topology distribution results. The basic design and detailed design module 400 is used in the basic design and detailed design stages to perform collaborative optimization of the shape and size parameters of the body underframe cross-section based on the topology distribution results, with multiple objectives of minimizing the cross-sectional area and maximizing the cross-sectional bending moment of inertia, combined with a non-dominated sorting genetic algorithm II, to obtain the optimized body underframe cross-section results.

[0149] In an optional embodiment, the geometric data includes length and thickness, wherein the length and thickness are respectively the length and thickness of a first rectangle that approximates the cross-section of the vehicle body chassis; the material property data includes the density of the cross-section of the vehicle body chassis.

[0150] In an optional embodiment, the parameter determination module 200 includes a cross-sectional area determination module, a cross-sectional centroid determination module, a cross-sectional bending moment of inertia determination module, and a cross-sectional torsional moment of inertia determination module.

[0151] The cross-sectional area determination module is used to determine the cross-sectional area A based on the geometric data and the following formula (1):

[0152]

[0153] In equation (1), l and t represent the length and thickness of the first rectangle, respectively; the cross-section of the vehicle chassis is divided into n thin plates, each thin plate having m second rectangles, A ij l ij and t ij Let represent the area, length, and thickness of the j-th second rectangle of the i-th thin plate in the cross-section of the vehicle body chassis, respectively.

[0154] The cross-sectional centroid determination module is used to determine the cross-sectional centroid coordinates (x) based on geometric shape data, material property data, and the following equations (2) and (3). c ,y c ):

[0155]

[0156] In equations (2) and (3), ρ i This represents the density of the i-th thin plate in the cross-section of the vehicle body chassis. These represent the centroid coordinates of the j-th second rectangle of the i-th thin plate in the cross-section of the vehicle body chassis.

[0157] The section bending moment of inertia determination module is used to determine the section bending moment of inertia I based on geometric data, the following equations (4) and (5). x and I y :

[0158]

[0159] In equations (4) and (5), the cross-section of the vehicle chassis is placed in the global coordinate system xoy, θ ij It represents the angle between the j-th second rectangle of the i-th thin plate of the vehicle body chassis section and the positive x-axis in the global coordinate system xoy.

[0160] The section torsional moment of inertia determination module is used to determine the section torsional moment of inertia based on geometric data, as well as the cross-sectional cavity area of ​​the vehicle body underframe, the length of each thin plate, the thickness of each thin plate, and the cross-sectional shape.

[0161] In an optional embodiment, the section torsional moment of inertia determination module is specifically used for:

[0162] The torsional moment of inertia J of the cross section is determined according to the following formula (6):

[0163]

[0164] In equation (6), S1 and S2 are the areas of the upper and lower chambers in the cross section of the car body underframe, respectively; L1, L2 and L3 are the lengths of the upper bottom plate, the lower bottom plate and the stiffening plate in the cross section of the car body underframe, respectively; t1, t2 and t3 are the thicknesses of the upper bottom plate, the lower bottom plate and the stiffening plate in the cross section of the car body underframe, respectively.

[0165] In an optional embodiment, the load condition parameters include the external load matrix and volume constraint coefficient of the vehicle chassis.

[0166] In an optional embodiment, the conceptual design module 300 includes a first parameter acquisition module, a first iteration module, and a first judgment module.

[0167] The first parameter acquisition module is used to acquire the current topology, current topology parameters, and first design variables of the vehicle body chassis section; wherein, the current topology parameters include performance parameters and load condition parameters; and the first design variables include the density value of the material distribution.

[0168] The first iteration module is used to solve the pre-established profile section topology optimization model based on the current topology, current topology parameters, and the first design variable, with the optimization objective of minimizing compliance, to obtain the topology optimization model solution result; wherein, the profile section topology optimization model for multiple working conditions is shown in Equation (7) below; the weighted compliance value C(ρ) in Equation (7) is as follows.w The solution is obtained by using the compromise programming method, as shown in equation (8).

[0169]

[0170] In equations (7) and (8), C(ρ) w ) represents the weighted compliance value; F represents the external load matrix of the vehicle body underframe; U represents the displacement matrix of the vehicle body underframe; V represents the volume of the profile section after optimization; V0 represents the initial volume of the profile section; f represents the volume constraint coefficient; ρ w C represents the density value of the element w; k (ρ w ) represents the compliance target value for the k-th working condition; The minimum compliance value for the k-th operating condition. This represents the maximum flexibility value for the k-th operating condition.

[0171] The first judgment module is used to determine whether the solution result of the topology optimization model meets the predetermined first termination condition. If it does, the iteration stops, the current topology is used as the topology distribution result and output. Otherwise, the current topology is adjusted according to the variable density method to generate a new topology. The new topology is used as input and the first parameter acquisition module is returned to continue execution until the first termination condition is met.

[0172] In an optional embodiment, the basic design and detailed design module 400 includes a second parameter acquisition module, a second iteration module, and a second judgment module.

[0173] The second parameter acquisition module is used to obtain the topology distribution results and the second design variables; wherein, the second design variables include shape optimization design variables and size optimization design variables; the shape optimization design variables include the x-coordinate of the endpoint of the stiffening plate of the base frame section. i-1 The size optimization design variables include the thickness t of each layer of thin plates in the body chassis section. i-2 .

[0174] The second iteration module is used to solve the pre-established collaborative optimization model based on the topological distribution results and the second design variable, combined with the non-dominated sorting genetic algorithm II, to obtain the Pareto solution set; wherein, the collaborative optimization model minimizes the cross-sectional area A and the cross-sectional bending moment of inertia I. x Maximize multiple objectives, with the bending moment of inertia of the cross section I as the primary objective. y With the cross-sectional torsional moment of inertia J as a constraint, the collaborative optimization model is shown in equation (9):

[0175]

[0176] In equation (9), A is the cross-sectional area, and I x I yG is the bending moment of inertia of the cross section, and J is the torsional moment of inertia of the cross section; g i This is the second design variable.

[0177] The second judgment module is used to determine whether the Pareto solution set meets the predetermined second termination condition. If it does, the iteration stops, the optimal solution is selected from the Pareto solution set as the optimization result of the car body chassis section and output; otherwise, the second design variable is adjusted according to the non-dominated sorting genetic algorithm II to obtain the adjusted second design variable. The adjusted second design variable is used as input and returned to the second parameter acquisition module to continue execution until the second termination condition is met. The optimization result of the car body chassis section includes the optimized section area, the optimized section bending moment of inertia, and the optimized section torsional moment of inertia.

[0178] In an optional embodiment, the device further includes a third judgment module, used to determine whether the optimized cross-sectional area meets a preset area threshold, and / or whether the optimized cross-sectional bending moment of inertia meets a preset bending moment of inertia threshold, and / or whether the optimized cross-sectional torsional moment of inertia meets a preset torsional moment of inertia threshold; if yes, it is determined to be a valid optimization; otherwise, it is determined to be an invalid optimization.

[0179] The apparatus provided in the embodiments of this application has the same inventive concept as the method provided in the embodiments of this application. As long as the method can solve the technical problem, the apparatus can also solve the technical problem. This will not be elaborated here.

[0180] Reference Figure 10 The present invention also provides an electronic device 1000, including a communication interface 1001, a processor 1002, a memory 1003, and a bus 1004. The processor 1002, the communication interface 1001, and the memory 1003 are connected via the bus 1004. The memory 1003 is used to store a computer program that supports the processor 1002 in executing the above-mentioned multi-objective optimization method for the entire stage of the rail vehicle chassis. The processor 1002 is configured to execute the program stored in the memory 1003.

[0181] Optionally, embodiments of the present invention also provide a computer-readable medium having non-volatile program code executable by a processor 1002, the program code causing the processor 1002 to execute the multi-objective optimization method for the entire stage of the rail vehicle underframe as described in the above embodiments.

[0182] As is known from common technical knowledge, this invention can be implemented through other embodiments that do not depart from its spirit or essential characteristics. Therefore, the disclosed embodiments described above are merely illustrative in all respects and are not the only ones. All modifications within the scope of this invention or its equivalents are included in this invention.

Claims

1. A multi-objective optimization method for the entire stage of a rail vehicle underframe, characterized in that, include: Obtain geometric shape data, material property data, and load condition parameters of the cross-section of the rail vehicle body underframe; Based on the geometric shape data and the material property data, the performance parameters of the vehicle body chassis section are determined using the thin plate approximation method; wherein, the performance parameters include the cross-sectional area, the cross-sectional centroid coordinates, the cross-sectional bending moment of inertia, and the cross-sectional torsional moment of inertia. During the conceptual design phase, based on the performance parameters and load condition parameters, the variable density method is used to perform topology optimization on the cross-section of the vehicle body chassis to obtain the topology distribution results. In the basic design and detailed design phases, based on the topological distribution results, with the minimization of the cross-sectional area and the maximization of the cross-sectional bending moment of inertia as multiple objectives, the shape and size parameters of the vehicle body chassis cross-section are optimized in conjunction with the non-dominated sorting genetic algorithm II to obtain the optimized results of the vehicle body chassis cross-section.

2. The multi-objective optimization method for the entire stage of a rail vehicle underframe according to claim 1, characterized in that, The geometric data includes length and thickness, wherein the length and thickness are respectively the length and thickness of a first rectangle that approximates the cross-section of the vehicle body chassis; the material property data includes the density of the cross-section of the vehicle body chassis.

3. The multi-objective optimization method for the entire stage of a rail vehicle underframe according to claim 2, characterized in that, The step of determining the performance parameters of the vehicle body underframe section based on the geometric shape data and the material property data using the thin plate approximation method includes: The cross-sectional area A is determined based on the geometric data and the following formula (1): In equation (1), l and t represent the length and thickness of the first rectangle, respectively; the cross-section of the vehicle chassis is divided into n thin plates, each thin plate having m second rectangles, A ij l ij and t ij These represent the area, length, and thickness of the j-th second rectangle of the i-th thin plate in the cross-section of the vehicle body chassis, respectively. The centroid coordinates (x, y) of the cross section are determined based on the geometric shape data, the material property data, and the following formulas (2) and (3). c ,y c ): In equations (2) and (3), ρ i This represents the density of the i-th thin plate in the cross-section of the vehicle body chassis; These represent the centroid coordinates of the j-th second rectangle of the i-th thin plate in the cross-section of the vehicle body chassis; The section bending moment of inertia I is determined based on the geometric data, the following equations (4) and (5). x and I y : In equations (4) and (5), the cross-section of the vehicle chassis is placed in the global coordinate system xoy, θ ij This represents the angle between the j-th second rectangle of the i-th thin plate of the vehicle body chassis section and the positive x-axis in the overall coordinate system xoy; The torsional moment of inertia of the cross section is determined based on the geometric data, the cross-sectional cavity area of ​​the vehicle body underframe, the length of each thin plate, the thickness of each thin plate, and the cross-sectional shape.

4. The multi-objective optimization method for the entire stage of a rail vehicle underframe according to claim 3, characterized in that, The determination of the cross-sectional torsional moment of inertia based on the geometric data, the cross-sectional cavity area of ​​the vehicle body underframe, the length of each thin plate, the thickness of each thin plate, and the cross-sectional shape includes: The torsional moment of inertia J of the cross section is determined according to the following formula (6): In formula (6), S1 and S2 are the areas of the upper and lower chambers in the cross section of the vehicle body underframe, respectively; L1, L2 and L3 are the lengths of the upper bottom plate, the lower bottom plate and the stiffening plate in the cross section of the vehicle body underframe, respectively; t1, t2 and t3 are the thicknesses of the upper bottom plate, the lower bottom plate and the stiffening plate in the cross section of the vehicle body underframe, respectively.

5. The multi-objective optimization method for the entire stage of a rail vehicle underframe according to claim 1, characterized in that, The load condition parameters include the external load matrix and volume constraint coefficient of the vehicle chassis.

6. The multi-objective optimization method for the entire stage of a rail vehicle underframe according to claim 5, characterized in that, During the conceptual design phase, based on the performance parameters and load condition parameters, a variable density method is used to perform topology optimization on the cross-section of the vehicle body chassis, resulting in a topology distribution, including: Step S310: Obtain the current topology, current topology parameters, and first design variable of the vehicle body underframe section; wherein, the current topology parameters include the performance parameters and the load condition parameters; the first design variable includes the density value of the material distribution; Step S320: Based on the current topology, the current topology parameters, and the first design variable, with the optimization objective of minimizing compliance, solve the pre-established profile cross-section topology optimization model based on multiple working conditions to obtain the topology optimization model solution result; wherein, the profile cross-section topology optimization model based on multiple working conditions is shown in Equation (7); the weighted compliance value C(ρ) in Equation (7) is shown in Equation (7). w The solution is obtained by using the compromise programming method, as shown in equation (8). In equations (7) and (8), C(ρ) w ) represents the weighted compliance value; F represents the external load matrix of the vehicle body underframe; U represents the displacement matrix of the vehicle body underframe; V represents the volume of the profile section after optimization; V0 represents the initial volume of the profile section; f represents the volume constraint coefficient; ρ w C represents the density value of the element w; k (ρ w ) represents the compliance target value for the k-th working condition; The minimum compliance value for the k-th operating condition. This represents the maximum compliance value for the k-th operating condition. Step S330: Determine whether the solution result of the topology optimization model meets the predetermined first termination condition; if yes, stop the iteration, take the current topology as the topology distribution result and output it; otherwise, continue to adjust the current topology according to the variable density method, generate a new topology, take the new topology as input, and return to step S310 to continue execution until the first termination condition is met.

7. The multi-objective optimization method for the entire stage of a rail vehicle underframe according to claim 1, characterized in that, In the basic design and detailed design stages, based on the topological distribution results, with the minimization of the cross-sectional area and the maximization of the cross-sectional bending moment of inertia as multiple objectives, a non-dominated sorting genetic algorithm II is used to collaboratively optimize the shape and size parameters of the vehicle body chassis cross-section, resulting in optimized cross-sectional results, including: Step S410: Obtain the topology distribution result and the second design variable; wherein, the second design variable includes shape optimization design variable and size optimization design variable; the shape optimization design variable includes the x-coordinate of the endpoint of the stiffening plate of the vehicle body chassis section. i-1 The size optimization design variables include the thickness t of each layer of thin plates in the cross-section of the vehicle body chassis. i-2 ; Step S420: Based on the topological distribution results and the second design variable, the pre-established collaborative optimization model is solved using the non-dominated sorting genetic algorithm II to obtain the Pareto solution set; wherein, the collaborative optimization model minimizes the cross-sectional area A and the cross-sectional bending moment of inertia I. x Maximize multiple objectives, with the bending moment of inertia of the cross section I as the primary objective. y Using the cross-sectional torsional moment of inertia J as a constraint, the collaborative optimization model is shown in equation (9) below: In equation (9), A is the cross-sectional area, and I x I y G is the bending moment of inertia of the cross section, and J is the torsional moment of inertia of the cross section; g i As the second design variable; Step S430: Determine whether the Pareto solution set satisfies the predetermined second termination condition. If yes, stop the iteration, select the optimal solution from the Pareto solution set as the vehicle body chassis section optimization result and output it; otherwise, continue to adjust the second design variable according to the non-dominated sorting genetic algorithm II to obtain the adjusted second design variable. Use the adjusted second design variable as input and return to step S410 to continue execution until the second termination condition is met. The vehicle body chassis section optimization result includes the optimized section area, the optimized section bending moment of inertia, and the optimized section torsional moment of inertia.

8. The multi-objective optimization method for the entire stage of a rail vehicle underframe according to claim 7, characterized in that, In the basic design and detailed design stages, based on the topological distribution results, and with the minimization of the cross-sectional area and the maximization of the cross-sectional bending moment of inertia as multiple objectives, the shape and size parameters of the vehicle chassis cross-section are jointly optimized using a non-dominated sorting genetic algorithm II. After obtaining the optimized vehicle chassis cross-section results, the process further includes: Determine whether the optimized cross-sectional area meets a preset area threshold, and / or whether the optimized cross-sectional bending moment of inertia meets a preset bending moment of inertia threshold, and / or whether the optimized cross-sectional torsional moment of inertia meets a preset torsional moment of inertia threshold; if yes, it is determined to be a valid optimization; otherwise, it is determined to be an invalid optimization.

9. A method for multi-objective optimization of a rail vehicle underframe throughout all stages, characterized in that, include: The data acquisition module is used to acquire geometric shape data, material property data, and load condition parameters of the cross-section of the rail vehicle body underframe. The parameter determination module is used to determine the performance parameters of the vehicle body chassis section based on the geometric shape data and the material property data, using the thin plate approximation method; wherein, the performance parameters include the section area, the section centroid coordinates, the section bending moment of inertia, and the section torsional moment of inertia; The concept design module is used to perform topology optimization on the cross-section of the vehicle body chassis using the variable density method based on the performance parameters and the load condition parameters during the concept design phase, so as to obtain the topology distribution result. The basic design and detailed design modules are used in the basic design and detailed design stages to optimize the shape and size parameters of the vehicle body chassis section based on the topological distribution results, with the minimization of the cross-sectional area and the maximization of the cross-sectional bending moment of inertia as multiple objectives, and combined with the non-dominated sorting genetic algorithm II to obtain the optimized results of the vehicle body chassis section.

10. An electronic device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method according to any one of claims 1-8.

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