A train modal optimization method
By constructing a general model of the train coupling system, determining modal variables and performing topology optimization, the train structure and materials are optimized, solving the problem of increased weight or cost in existing methods, and achieving train modal optimization that balances lightweight and high performance.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CRRC TANGSHAN CO LTD
- Filing Date
- 2026-01-21
- Publication Date
- 2026-06-02
Smart Images
Figure CN122133249A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of transportation technology, and more specifically, to a train modal optimization method. Background Technology
[0002] With the rapid development of high-speed railways and urban rail transit, train operating speeds are constantly increasing, placing higher demands on vehicle dynamics, safety, and comfort. The modal characteristics of a train (including natural frequencies, mode shapes, and damping) directly affect its vibration response, fatigue life, and passenger experience. During train operation, the vehicle system is subjected to various excitations from track irregularities, wheel-rail contact, and aerodynamics. When the excitation frequency approaches or equals a certain natural frequency of the train, resonance will occur, leading to intensified vibration, increased structural stress, and potentially even safety hazards. For example, if the bending or torsional modes of the car body are too low, it is susceptible to large vibrations due to track excitation during high-speed operation, affecting comfort and accelerating structural fatigue. An unreasonable suspension system modal design can lead to an increased wheel load reduction rate, affecting driving safety. When the modes of undercarriage equipment are coupled with the modes of the car body or frame, it may cause abnormal vibration and damage to the equipment.
[0003] Traditional train modal optimization methods either significantly increase the weight of the vehicle body, which is not conducive to energy consumption control and may change the center of gravity distribution of the vehicle; or they require the replacement of high-cost materials, which involve complex processing technology and difficult maintenance.
[0004] Therefore, there is an urgent need for a train modal optimization method that can reduce costs without increasing the weight of the car body. Summary of the Invention
[0005] This application provides a train modal optimization method.
[0006] A first aspect of this application provides a train modal optimization method, comprising: A general model of the train coupling system is constructed based on the train's structural and operational parameters; wherein, the general model of the train coupling system includes a multibody dynamics model for characterizing global vibration and a finite element coupling model for local stress deformation; Based on the overall model of the train coupling system, determine the modal variables that affect each mode; the modal variables refer to the various factors that affect the train modes. The modal optimization objective and modal lightweighting constraints are determined based on the largest variable among the modal variables. Based on the modal optimization objective and the modal lightweighting constraint, the train structure topology is optimized to obtain the optimized modal variables. The structure and materials of the train are optimized based on the optimized modal variables.
[0007] In one optional embodiment of this application, the step of constructing a general model of the train coupled system based on the train's structural parameters and operating parameters includes: The multibody dynamics model of the train system is constructed based on the train's structural parameters and operating parameters; A finite element modal reduction model of the coupling interface between the rigid body and the flexible body of the train is constructed based on preset coupling constraints, the structural parameters, and the operating parameters. The finite element modal reduction model is fused with the multibody dynamics model to obtain the overall model of the train coupling system.
[0008] In one optional embodiment of this application, the multibody dynamics model includes:
[0009] Where q represents the generalized coordinate vector of the train system in the operating parameters. This represents the generalized velocity vector of the train system in the operating parameters. M(q) represents the generalized acceleration vector of the train system in the operating parameters; M(q) represents the mass matrix in the structural parameters; C(q, K(q) represents the Coriolis force and centrifugal force matrix in the structural parameters; K(q) represents the stiffness matrix in the structural parameters; F ext This refers to the external excitation of the train system.
[0010] In one optional embodiment of this application, the finite element modal reduction model includes:
[0011] Among them, F mb [K] represents the balancing force of the train system. fe [C] fe ]、[M fe ] respectively represent the stiffness matrix, damping matrix, and mass matrix of the finite element component in the structural parameters; {u}, { }、{ } represent the nodal displacement, velocity vector, and acceleration vector of the finite element component in the operating parameters, respectively.
[0012] In one optional embodiment of this application, the overall model of the train coupling system includes:
[0013] Where q represents the generalized coordinate vector of the train system in the operating parameters. This represents the generalized velocity vector of the train system in the operating parameters. M(q) represents the generalized acceleration vector of the train system in the operating parameters; M(q) represents the mass matrix in the structural parameters; C(q, K(q) represents the Coriolis force and centrifugal force matrix in the structural parameters; K(q) represents the stiffness matrix in the structural parameters. These represent the mass matrix, damping matrix, and stiffness matrix of the flexible component relative to the rigid component at the coupling interface, respectively, in the structural parameters. These represent the mass matrix, damping matrix, and stiffness matrix of the rigid body component relative to the flexible body component at the coupling interface, respectively, in the structural parameters. Let F represent the reduced finite element mass matrix, the reduced damping matrix, and the reduced stiffness matrix, respectively; ext This refers to the external excitation of the train system.
[0014] In an optional embodiment of this application, before determining the modal optimization objective and modal lightweighting constraints based on the largest variable among the modal variables, the method further includes: For each mode, the sensitivity of the modal frequency to the modal variables within each component of the train is calculated; For each train component, the sum of the sensitivities of all modal variables is calculated to obtain the modal contribution of the train component; The train component with the largest modal contribution is identified as the largest variable among the modal variables.
[0015] In one optional embodiment of this application, optimizing the train's structure and materials based on the optimized modal variables includes: The target optimized components of the train are determined based on the optimized modal variables. An optimization operation is performed on the target optimized component; the optimization operation includes at least one of the following: replacing redundant materials in the train, optimizing the component layout, optimizing the shape, and adding a reinforcing structure.
[0016] A second aspect of this application provides a train mode optimization device, comprising: A construction module is used to construct a general model of the train coupling system based on the train's structural and operational parameters; wherein, the general model of the train coupling system includes a multibody dynamics model for characterizing global vibration and a finite element coupling model for local stress deformation; The first determining module is used to determine the modal variables that affect each order mode based on the overall model of the train coupling system; the modal variables refer to the various influencing factors that affect the train modes; The second determining module is used to determine the modal optimization objective and modal lightweighting constraints based on the largest variable among the modal variables. The first optimization module is used to perform topology optimization on the train structure based on the modal optimization objective and the modal lightweighting constraint to obtain the optimized modal variables. The second optimization module is used to optimize the structure and materials of the train based on the optimized modal variables.
[0017] A third aspect of this application provides a computer device, including: a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of any of the above methods.
[0018] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any of the preceding claims.
[0019] In the first aspect, the train modal optimization method provided in this application optimizes the train structure by performing topology optimization based on the modal optimization objective and the modal lightweighting constraint, and then optimizes the train structure based on the optimized modal variables. The optimization object is the local key components corresponding to the optimized modal variables, rather than all structures. Without significantly increasing the weight and cost of the train, it effectively improves the natural frequency of the car body and key components, avoids resonance risk, and improves the smoothness and safety of operation. Secondly, the overall train coupling system model proposed in this application includes a multibody dynamics model for characterizing global vibration and a finite element coupling model for local stress deformation. This application establishes a multibody dynamics model, considers various actual operating factors, and performs collaborative optimization of the overall structure of the car body, giving full play to the synergistic effect between various components. Through the collaborative optimization of the overall train structure using this train coupling system model, the vibration response of various car body components becomes more coordinated, reducing additional vibration and noise, improving the stability and comfort of train operation, and significantly reducing the vibration and noise felt by passengers during train operation, thus enhancing the riding experience.
[0020] Thirdly, the embodiments of this application perform topology optimization on the train structure based on the modal optimization objective and the modal lightweighting constraint. By optimizing the topology, the amount of material used is reduced, and the weight is avoided from increasing significantly. It can be applied to various types of trains such as high-speed trains, subway cars, and intercity trains, achieving a balance between lightweighting and high performance, and has stronger applicability.
[0021] In summary, the embodiments of this application provide a train optimization modal optimization method that can simultaneously reduce train weight and cost. Attached Figure Description
[0022] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 A flowchart of a train modal optimization method provided in one embodiment of this application; Figure 2 A flowchart of a train modal optimization method provided in one embodiment of this application; Figure 3 A flowchart of a train modal optimization method provided in one embodiment of this application; Figure 4 A flowchart of a train modal optimization method provided in one embodiment of this application; Figure 5 This is a quadratic function fitting curve for an embodiment of this application; Figure 6 This application's embodiment shows a structure where the flexible connection is changed to a rigid connection. Figure 7 This is a force cloud diagram of various parts of the train body in an embodiment of this application; Figure 8 This is a simulation diagram of the modal influence law at the top and root of the train sidewall in an embodiment of this application; Figure 9 To illustrate this application embodiment, a ring beam structure is added inside the vehicle; Figure 10 This is the modal array of the vehicle body without a ring beam structure according to an embodiment of this application; Figure 11 The following are the 3mm and 6mm no-ring beam structure vehicle body modal arrays in the embodiments of this application; Figure 12 This is a schematic diagram of the structure of a train modal optimization device provided in one embodiment of this application; Figure 13 This is a schematic diagram of a computer device structure provided in one embodiment of this application. Detailed Implementation
[0023] In the process of developing this application, the inventors discovered that current train modal optimization methods all increase the weight or cost of the train.
[0024] To address the aforementioned issues, this application provides a train modal optimization method.
[0025] The solutions in this application embodiment can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.
[0026] To make the technical solutions and advantages of the embodiments of this application clearer, the exemplary embodiments of this application will be described in further detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not an exhaustive list of all embodiments. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other.
[0027] Please see Figure 1 The train modal optimization method provided in this application includes the following steps 101-105: Step 101: Construct a general model of the train coupling system based on the train's structural and operational parameters; The structural parameters of the train include, but are not limited to: the structural parameters of the car body, the structural parameters of each component, such as length, width, thickness, material type and density, shape, etc. Thickness includes the thickness of the plates of each component of the car body, such as the thickness of the skin of the roof, side walls, and underframe, as well as the thickness of the internal stiffening plates, etc., which are not exhaustive here and can be flexibly set according to the actual situation; correspondingly, the operating parameters of the train include, but are not limited to: the speed, inertia, acceleration, displacement, etc. of the car body when it is running, as well as the speed, inertia, acceleration, displacement, etc. of each component and load in the train when it is running. The overall model of the train coupling system includes a multibody dynamics model for characterizing global vibration and a finite element coupled model for local stress deformation. The multibody dynamics model is used to determine the overall motion (large displacement, large rotation) of the system under load and the interaction forces between various train components, i.e., the relationship between force, mass, acceleration, and motion in train dynamics. The finite element method is used to discretize the continuum into a large number of tiny elements, and to study the continuous deformation and internal stress of the structure by calculating the deformation and stress of each element. The finite element coupled model in this embodiment is used to obtain details such as elastic deformation and local stress / strain of specific components with high precision while analyzing the overall motion of the system, including but not limited to each rigid body component, each flexible body component, and the coupling interface between rigid body components and flexible body components. The multibody dynamics model can accurately obtain displacement, velocity, acceleration, and joint forces, but cannot obtain the stress and strain distribution inside the structure. For flexible components, modal flexible bodies are used, and the deformation is based on the superposition of a few modes, which has limited accuracy. Finite element coupled models focus on the microscopic and local aspects, providing continuous stress / strain fields, detailed vibration modes, and local plastic deformation in the region of interest. The combined train coupled system overall model can simultaneously achieve the integration of microscopic and macroscopic, local and global aspects, improving the determination of the overall state of the train system.
[0028] Step 102: Determine the modal variables affecting each mode based on the overall model of the train coupling system; the modal variables refer to the various influencing factors affecting the train modes; It's important to explain that "modal" is a relative concept. For example, for a complete train system, the corresponding modal variables include, but are not limited to: displacement (representing the degree of participation or amplification factor of a particular mode shape in the overall deformation at a given moment), velocity (the first derivative of the modal displacement with respect to time, used to describe the rate of change of that mode shape's participation in the system's motion), and acceleration (the second derivative of the modal displacement with respect to time, used to describe the inertial force effect of that mode's motion). These modal variables—displacement, velocity, and acceleration—constitute a dimensionally reduced "state space" describing the elastic deformation of the train system. For instance, if a train body is described using the first 10 elastic modes, then its elastic deformation state is defined by these 10 modal displacements and 10 modal velocities (a total of 20 state variables).
[0029] For specific train components, such as the car body underframe, gearbox housing, bogie frame, axles, traction converter, and battery, an exhaustive list is not provided here. Modal variables of a specific component refer to the component's inherent properties and their contribution to the response. These modal variables include, but are not limited to, modal attribute variables and modal response variables. Modal attribute variables include, but are not limited to: natural frequency (the frequency of free vibration of that mode), mode shape (the spatial deformation shape of that mode during vibration, a vector field used to describe the displacement ratio and direction of each point on the structure relative to other points), mass (equivalent mass related to the mode shape), stiffness (equivalent stiffness related to the mode shape; the square of the natural frequency = modal stiffness / modal mass), and modal damping ratio (a dimensionless parameter describing the rate of energy dissipation in that mode, playing a decisive role in the resonance peak). Modal response variables include, but are not limited to: modal participation factor (similar to "modal displacement", representing the degree to which a certain mode is excited under a specific load (such as track irregularity excitation; different load types will excite different modes) and modal contribution (in frequency response analysis, used to evaluate the percentage contribution of a certain mode to the total response (displacement, stress, acceleration) at a certain frequency point).
[0030] Step 103: Determine the modal optimization objective and modal lightweighting constraints based on the largest variable among the modal variables; The constraints in this embodiment are based on meeting the modal design standards for rail vehicles, such as ensuring that the first-order vertical bending mode frequency and the first-order rhomboid mode frequency are within a certain range, while also considering that the increase in vehicle weight should not be too large. For example, the roof skin thickness has the greatest impact on the overall modality, so the roof skin thickness is determined as the modal optimization target, and the standard range of the roof skin thickness is determined as the modal lightweighting constraint. Of course, this is only an example and does not constitute a specific limitation on the maximum variable, modal optimization target, and modal lightweighting constraint.
[0031] The constraints in this application embodiment are modal lightweighting constraints. Under the lightweighting constraints, the specific parameters are the thicknesses of the main components of the vehicle body, such as the thickness of the roof skin and the thickness of the chassis profile. Multiple modal optimization objectives must be satisfied simultaneously, including "lightweighting (minimizing mass)," "modal performance (maximizing key frequencies)," and "stiffness / strength (meeting constraint thresholds)."
[0032] Step 104: Perform topology optimization on the train structure based on the modal optimization objective and the modal lightweighting constraints to obtain the optimized modal variables; In this embodiment, topology optimization of the train structure can be achieved using algorithms such as NSGA-II (Non-dominated sorting genetic algorithm II). Commonly used algorithms include gradient-free algorithms and convolutional neural network surrogate models combined with covariance matrix adaptive evolutionary optimization algorithms (CMA-ES). The NSGA-II algorithm achieves Pareto optimal solution sets through non-dominated sorting and congestion calculation, making it suitable for multi-constraint optimization scenarios in engineering. This embodiment takes minimizing the car body mass as the overall objective, combining the modal optimization objective and the modal lightweighting constraints to achieve overall modal optimization of the train.
[0033] For example, using the first-order vertical bending mode natural frequency as a constraint, the modal variables (thickness parameters) of each component are optimized through multiple iterations to achieve weight reduction while ensuring the modal performance of the vehicle body. For instance, by optimizing the thickness of the vehicle body profile section using the CMA-ES algorithm, the frame mass was reduced by 680 kg, and the first-order rhomboid frequency was increased by 1.66 Hz. The multi-objective algorithm efficiently searches on the surrogate model, simultaneously satisfying objectives such as "lightweight (minimum mass), high stiffness (maximum K / m), and optimal modes (target frequency)". The analysis results show that the stiffness of the bottom plate in the middle and ends of the vehicle body has the greatest impact on the first-order bending mode, while the suspension stiffness has a significant impact on the vehicle body's buoyancy and pitching modes.
[0034] Step 105: Optimize the structure and materials of the train based on the optimized modal variables.
[0035] Optimizing the train structure refers to optimizing the structure of the local components of the train corresponding to the optimized modal variables, such as removing some redundant structures or adjusting the shape, thickness, position, etc. of some structures. Optimizing the train materials refers to replacing the materials of the local components of the train corresponding to the optimized modal variables, such as replacing flexible materials with rigid materials or composite materials.
[0036] In the first aspect, the train modal optimization method provided in this application optimizes the train structure by performing topology optimization based on the modal optimization objective and the modal lightweighting constraint, and then optimizes the train structure based on the optimized modal variables. The optimization object is the local key components corresponding to the optimized modal variables, rather than all structures. Without significantly increasing the weight and cost of the train, it effectively improves the natural frequency of the car body and key components, avoids resonance risk, and improves the smoothness and safety of operation. Secondly, the overall train coupling system model proposed in this application includes a multibody dynamics model for characterizing global vibration and a finite element coupling model for local stress deformation. This application establishes a multibody dynamics model, considers various actual operating factors, and performs collaborative optimization of the overall structure of the car body, giving full play to the synergistic effect between various components. Through the collaborative optimization of the overall train structure using this train coupling system model, the vibration response of various car body components becomes more coordinated, reducing additional vibration and noise, improving the stability and comfort of train operation, and significantly reducing the vibration and noise felt by passengers during train operation, thus enhancing the riding experience.
[0037] Thirdly, the embodiments of this application perform topology optimization on the train structure based on the modal optimization objective and the modal lightweighting constraint. By optimizing the topology, the amount of material used is reduced, and the weight is avoided from increasing significantly. It can be applied to various types of trains such as high-speed trains, subway cars, and intercity trains, achieving a balance between lightweighting and high performance, and has stronger applicability.
[0038] In summary, the embodiments of this application provide a train optimization modal optimization method that can simultaneously reduce train weight and cost.
[0039] Please see Figure 2 In an optional embodiment of this application, step 101 above, the construction of the overall model of the train coupling system based on the train's structural parameters and operating parameters, includes the following steps 201-203: The overall model of the train coupling system is established by comprehensively considering the mass, stiffness, and damping characteristics of key components such as the car body, frame, suspension system, and undercarriage equipment, thereby constructing a model that accurately reflects the actual vibration characteristics. The overall model construction process of the train coupling system in this embodiment can be executed step-by-step as follows: "component modeling → separate construction of multibody dynamics model and finite element coupling model → interface coupling → application of constraint loads → verification and correction." By achieving displacement coordination and force transmission, the multibody dynamics model (global vibration) and the finite element coupling model (local stress and deformation) are combined to ensure accurate mapping of vibration characteristics.
[0040] For component modeling: In this embodiment of the application, the components of the train can be divided into rigid body parts and flexible body parts and modeled separately; Rigid body parts: non-critical equipment under the vehicle, wheelsets (simplified as rigid bodies, retaining mass and inertia); Flexible body parts: car body, frame (elastic deformation can be considered, and imported into a multibody system after finite element modal reduction).
[0041] Step 201: Construct the multibody dynamics model of the train system based on the train's structural parameters and operating parameters; The multibody dynamics model is a system dynamics equation constructed based on the Lagrange equations. In one optional embodiment of this application, the multibody dynamics model includes:
[0042] Where q represents the generalized coordinate vector (translation + rotation) of the train system in the operating parameters. , This represents the generalized velocity vector of the train system in the operating parameters. M(q) represents the generalized acceleration vector of the train system in the operating parameters; M(q) represents the mass matrix in the structural parameters; C(q, K(q) represents the Coriolis force and centrifugal force matrix in the structural parameters; K(q) represents the stiffness matrix in the structural parameters; F ext This represents the external excitation of the train system, which includes the mechanical vectors of all active forces, such as gravity, spring force, damping force, wheel-rail force, and action force.
[0043] Step 202: Based on the preset coupling constraints, the structural parameters, and the operating parameters, construct a finite element modal reduction model of the coupling interface between the rigid body and the flexible body of the train. It should be explained that the preset coupling constraint in this embodiment is different from the modal lightweighting constraint in the above embodiments. The modal lightweighting constraint is for the overall train structure or various local components in the train, such as rigid components or flexible components. The preset coupling constraint in this embodiment refers to the constraint of the coupling interface between flexible components and rigid components, or the constraint of the coupling interface between different train structure components.
[0044] In one optional embodiment of this application, the finite element modal reduction model includes:
[0045] Among them, F mb [K] represents the balancing force of the train system. fe [C] fe ]、[M fe] respectively represent the stiffness matrix, damping matrix, and mass matrix of the finite element component in the structural parameters; {u}, { }、{ } represent the nodal displacement, velocity vector, and acceleration vector of the finite element component in the operating parameters, respectively. Wherein, the balancing force F of the train system... mb This includes, but is not limited to, the total suspension force of the suspension system and the interaction forces between other components. The total suspension force consists of the superposition of forces in the x, y, and z directions.
[0046] F susp F represents the total suspension force. k F represents the linear spring force. c Indicates viscous damping force. Indicates the spring deformation. This represents the relative velocity of deformation.
[0047] When constructing the finite element modal reduction model to characterize the local elastic response, the constraints can be determined first: the coupling interface between the finite element model and the multibody system adopts a "master node-slave node" setting, where the master node corresponds to the centroid or mounting point of the multibody component. Then, the core governing equations of the finite element model are set, which are the dynamic equilibrium equations used to characterize the time domain:
[0048] Among them, [K fe [C] fe ]、[M fe ] respectively represent the stiffness matrix, damping matrix, and mass matrix of the finite element component in the structural parameters; {u}, { }、{ } represent the nodal displacement, velocity vector, and acceleration vector of the finite element component in the operating parameters, respectively, {F fe (t)} represents the coupling interface force, which can also be understood as the coupling load.
[0049] Step 203: Merge the finite element modal reduction model with the multibody dynamics model to obtain the overall model of the train coupling system.
[0050] Displacement at coupling point of multibody system = displacement of principal node in finite element method, i.e., q mb =Ф master ·q fe , where q mb (qmb represents the generalized coordinates of the multibody coupling point, Ф) master The principal node modal matrix is represented; force balance: the force exerted by the multibody system on the finite element equals the reaction force at the principal node of the finite element, thus yielding the above formula. The physical meaning of each letter in the formula has been explained in detail in the above embodiments and will not be repeated here.
[0051] In an optional embodiment of this application, the finite element modal reduction equations are substituted into the multibody dynamics equations to form the overall model expression of the train coupling system:
[0052] Where q represents the generalized coordinate vector of the train system in the operating parameters. This represents the generalized velocity vector of the train system in the operating parameters. M(q) represents the generalized acceleration vector of the train system in the operating parameters; M(q) represents the mass matrix in the structural parameters; C(q, K(q) represents the Coriolis force and centrifugal force matrix in the structural parameters; K(q) represents the stiffness matrix in the structural parameters. These represent the mass matrix, damping matrix, and stiffness matrix of the flexible component relative to the rigid component at the coupling interface, respectively, in the structural parameters. These represent the mass matrix, damping matrix, and stiffness matrix of the rigid body component relative to the flexible body component at the coupling interface, respectively, in the structural parameters. Let F represent the reduced finite element mass matrix, the reduced damping matrix, and the reduced stiffness matrix, respectively; ext This refers to the external excitation of the train system.
[0053] Please see Figure 3 In an optional embodiment of this application, before step 103 above, and before determining the modal optimization objective and modal lightweighting constraints based on the largest variable among the modal variables, the method further includes the following steps 301-303: Step 301: For each mode, calculate the sensitivity of the modal frequency to the modal variables within each component of the train; Step 302: For each train component, calculate the sum of the sensitivities of all modal variables to obtain the modal contribution of the train component; Step 303: Determine the train component with the largest modal contribution as the largest variable among the modal variables.
[0054] This application embodiment calculates the sensitivity of modal variables within each component of the train. Modal sensitivity analysis identifies components and parameters (the largest variables among modal variables) that have the greatest impact on each order of modes, such as plate thickness, local stiffness, and connection method. This facilitates the determination of optimization objectives and constraints through the largest variable.
[0055] In one optional embodiment of this application, the structure with the greatest influence on each modal is identified by calculating the modal contribution of each component. First, a finite element model of the vehicle body can be established, dividing the vehicle body into several parts or integral components, such as the roof, side walls, and chassis. Then, the sensitivity of a certain modal frequency of the vehicle body to the design variables within each component is calculated, and the sensitivity of a certain modal frequency of the vehicle body to a certain modal variable x is calculated. k The formula for calculating sensitivity is:
[0056] Where K and M are the stiffness matrix and mass matrix of the vehicle body structure, respectively, and λ i Let {φ} be the i-th modal frequency of the vehicle body structure. i Let} be the vector of the i-th mode shape of the vehicle body structure. The modal contribution of a component can be obtained by summing the sensitivities of all design variables within that component. The larger the modal contribution, the greater the influence of that component on the modal frequencies.
[0057] By identifying the optimization parameters (topological volume fraction, shape and size, and material type) that have the most significant impact on the "first-order vertical bending frequency of the vehicle body," the effectiveness of parameter adjustments is verified (e.g., whether the modal frequency becomes more sensitive to a certain parameter after lightweight material replacement), and the parameter adjustment boundaries are clarified (e.g., how much the volume fraction can be reduced to without causing the modal frequency to fall below the target value). This provides a basis for modifying the modal lightweighting constraints of the multibody coupled overall model of the train structure for topological optimization, such as enhancing the constraint accuracy of highly sensitive parameters.
[0058] Please see Figure 4 In an optional embodiment of this application, the above-described train modal optimization method, wherein optimizing the train's structure and materials based on the optimized modal variables, includes the following steps 401-402: Step 401: Determine the target optimized components of the train based on the optimized modal variables; Step 402: Perform optimization operations on the target optimized component; The optimization operations include at least one of the following: replacing redundant materials in the train, optimizing component layout, optimizing shape, and adding reinforcing structures.
[0059] Optimize component layout: For example, rationally plan the installation positions of heavy objects such as traction converters and batteries to avoid local resonance caused by concentrated loads; Adding reinforced structures: For example, strengthening the connection between additional components and the vehicle body to reduce the interference of non-structural parts on the overall vehicle modal performance. In addition to using aluminum alloy reinforcing ribs, carbon fiber composite reinforcing ribs can be explored. Topology optimization technology can be used to achieve a reasonable layout of the reinforcing ribs, combined with the design of variable cross-section reinforcing ribs, to improve structural strength in key areas and enhance the overall modal performance of the vehicle body. Carbon fiber composites have a higher strength-to-weight ratio, which can further reduce the weight of the vehicle body and improve its modal performance while achieving the same reinforcement effect. Replacing some elastic connections with rigid connections without affecting vehicle dynamics can significantly increase the system's natural frequency. Using high-strength alloy materials to manufacture suspension brackets reduces deformation; localized reinforcement of suspension connection areas improves overall stiffness.
[0060] Replace redundant materials in the train: For example, use a new adhesive-riveting connection method instead of a hybrid riveting-welding connection. High-strength structural adhesive is applied to the joints between the underframe and sidewalls, and between the roof and sidewalls, before riveting. This method can further reduce the weight of the joints, and the adhesive bonding effectively isolates vibration and noise transmission.
[0061] Based on the optimized modal variables, the train structure is optimized. The optimization targets local components corresponding to the optimized modal variables, rather than the entire structure. This achieves a combination of global structural topology optimization and local stiffness enhancement, along with coordinated adjustments to equipment layout and suspension parameters, resulting in an overall improvement in the train's modal performance. In this application's embodiments, structural topology and shape optimization, along with material replacement methods, optimize the topology of load-bearing structures such as the car body and frame under lightweight constraints, removing redundant materials and optimizing component layout. Shape optimization or the addition of reinforcing structures is performed on locally weak areas. High-strength, high-modulus composite materials or lightweight alloys are used to replace traditional steel, improving structural stiffness while ensuring lightweighting. Finite element modal simulations of the white car body and equipment compartment show that weight reduction in the equipment compartment can improve the vertical bending mode of the car body. Test results demonstrate that by applying the train modal optimization method provided in this application to the target optimized components, the first-order vertical bending mode frequency of the car body can be increased by approximately 1.5-2.5 Hz, and the first-order torsional mode frequency can be increased by 1-1.5 Hz. This effectively avoids resonance caused by modal issues during high-speed train operation, improving the safety and stability of train operation. Simultaneously, the optimized new connection structure and optimized stiffener layout reduce stress concentration at connection points, decrease the generation of weld fatigue cracks, and improve the reliability and durability of the car body structure. Tests show that under the same operating conditions, the fatigue life of the car body structure is extended by 30-50%.
[0062] Please see Figure 5 , Figure 5 This is the quadratic function fitting curve in the embodiments of this application. Figure 6The structural diagram was changed from a flexible connection to a rigid connection. Figure 5 and Figure 6 As can be seen, the train modal optimization method provided in this application improves the system's natural frequency by strengthening the overall and local materials and structures, and changing some elastic connections to rigid connections without affecting the vehicle's dynamic performance. High-strength alloy materials are used to fabricate the suspension brackets to reduce deformation; local reinforcement of the suspension connection areas improves the overall stiffness of the train.
[0063] like Figure 7 This is a force cloud diagram of various parts of the train body in an embodiment of this application. Figure 8 To illustrate the modal influence patterns at the top and root of the train sidewalls in this embodiment, topology optimization technology is employed to rearrange constraints based on the force cloud diagrams of various parts of the vehicle body. Triangular constraints are set in the area where the underframe connects to the sidewalls; adjustments are made to the hoisting point positions in the roof area. Simultaneously, the constraint structure is improved by changing traditional constant-section constraints to variable-section constraints, all of which contribute to enhancing vehicle modal characteristics and improving the bending and torsional resistance of key components.
[0064] from Figure 7 and Figure 8 The simulation results show that: in the top area of the EMU, with the same thickness, the edge top has a better modal effect than the junction of the edge top and the middle top; in the underframe area, the higher the reinforcing plate is on the side wall, the better the modal effect; at the same position, different thicknesses have little effect on the modal effect; for reinforcing plates of different shapes, straight reinforcing plates have a better modal effect than curved ones.
[0065] This application establishes a multibody dynamics model of the high-speed train body, considering the elastic connections and damping characteristics between various components, as well as various loads during train operation, such as aerodynamics and track irregularities. Through simulation analysis, the material parameters, geometry, and connection methods of each component are adjusted to achieve coordinated optimization of the overall structure of the car body, ensuring that the vibration responses of each component are coordinated and improving the overall modal performance of the car body. Through three-dimensional optimization of "topology + shape + material," a weight reduction rate of 28.6% can be achieved for the entire vehicle, while ensuring that the modal frequencies, strength, and dynamic performance meet the standards. Topology optimization targets core load-bearing components such as the car body underframe and frame crossbeams / longitudinal beams, achieving "optimal material distribution" based on the variable density method (SIMP) to avoid blindly reducing weight and causing a decrease in stiffness. In the specific optimization content and structural adjustment scheme, redundant materials can be removed from the car body underframe, such as: 1. Non-load-bearing areas in the middle of the underframe (away from sleeper beams and equipment mounting seats); 2. Non-load-bearing webs on the inner side of the side beams (redundant parts with a thickness >12mm); 3. Blank areas between crossbeams (no equipment installation requirements). The core objective of structural shape optimization is to optimize the cross-section / profile of components and reduce stress concentration. Shape optimization is based on the results of topology optimization, and parametric optimization is performed on the cross-sectional dimensions, profile curves and chamfer radii of key components to further improve the lightweight effect and structural smoothness.
[0066] The core objective of the material replacement method is to replace traditional materials with high-strength, lightweight materials. Material replacement focuses on materials with a higher strength-to-density ratio, directly reducing component weight while meeting stiffness and strength constraints. Simultaneously, the feasibility of the process must be verified. For example... Figure 9 As shown, a ring beam structure is added inside the vehicle. The gap between the aluminum alloy body and the interior wall panels is small. Under the condition of limited space, a ring beam structure is set between the side wall of the body and the interior side wall panels. Figure 10 The vehicle body has a modal array without a ring beam structure. Figure 11 Table 1 below shows the modal test results of the train body with added ring beam structure for the 3mm and 6mm non-ring beam structure: Table 1
[0067] Modal simulation analysis revealed that adding three ring beams with a 3mm thickness in the middle of the vehicle body increased the rhombic mode frequency by 0.22Hz; increasing the ring beam thickness to 6mm increased the rhombic mode frequency by 0.33Hz. Equipment layout and connection stiffness optimization avoided low-order mode coupling between the equipment and the vehicle body / frame by adjusting the distribution and connection stiffness of the under-vehicle equipment. Suspension system parameter adjustment optimized the suspension stiffness and damping parameters to stagger the suspension system modes from the vehicle body modes, reducing the risk of resonance. The optimization effect was verified through simulation and experimentation, and fine-tuning was performed based on the results until the modal performance met the design requirements.
[0068] It should be understood that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order constraint on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the diagram may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.
[0069] Please see Figure 12 One embodiment of this application provides a train mode optimization device 1200, comprising: The construction module 1210 is used to construct a general model of the train coupling system based on the train's structural parameters and operating parameters; wherein, the general model of the train coupling system includes a multibody dynamics model for characterizing global vibration and a finite element coupling model for local stress deformation; The first determining module 1220 is used to determine the modal variables affecting each order mode based on the overall model of the train coupling system; the modal variables refer to the various influencing factors affecting the train modes; The second determining module 1230 is used to determine the modal optimization objective and modal lightweighting constraints based on the largest variable among the modal variables; The first optimization module 1240 is used to perform topology optimization on the train structure based on the modal optimization objective and the modal lightweighting constraint to obtain optimized modal variables. The second optimization module 1250 is used to optimize the structure and materials of the train based on the optimized modal variables.
[0070] In an optional embodiment of this application, the construction module 1210 is further configured to: construct the multibody dynamics model of the train system based on the structural parameters and operating parameters of the train; construct a finite element modal reduction model of the coupling interface between the rigid body and the flexible body of the train based on preset coupling constraints, the structural parameters and the operating parameters; and fuse the finite element modal reduction model with the multibody dynamics model to obtain the overall model of the train coupling system.
[0071] In one optional embodiment of this application, the multibody dynamics model includes:
[0072] Where q represents the generalized coordinate vector of the train system in the operating parameters. This represents the generalized velocity vector of the train system in the operating parameters. M(q) represents the generalized acceleration vector of the train system in the operating parameters; M(q) represents the mass matrix in the structural parameters; C(q, K(q) represents the Coriolis force and centrifugal force matrix in the structural parameters; K(q) represents the stiffness matrix in the structural parameters; F ext This refers to the external excitation of the train system.
[0073] In one optional embodiment of this application, the finite element modal reduction model includes:
[0074] Among them, F mb [K] represents the balancing force of the train system. fe [C] fe ]、[M fe ] respectively represent the stiffness matrix, damping matrix, and mass matrix of the finite element component in the structural parameters; {u}, { }、{ } represent the nodal displacement, velocity vector, and acceleration vector of the finite element component in the operating parameters, respectively.
[0075] In one optional embodiment of this application, the overall model of the train coupling system includes:
[0076] Where q represents the generalized coordinate vector of the train system in the operating parameters. This represents the generalized velocity vector of the train system in the operating parameters. M(q) represents the generalized acceleration vector of the train system in the operating parameters; M(q) represents the mass matrix in the structural parameters; C(q, K(q) represents the Coriolis force and centrifugal force matrix in the structural parameters; K(q) represents the stiffness matrix in the structural parameters. These represent the mass matrix, damping matrix, and stiffness matrix of the flexible component relative to the rigid component at the coupling interface, respectively, in the structural parameters. These represent the mass matrix, damping matrix, and stiffness matrix of the rigid body component relative to the flexible body component at the coupling interface, respectively, in the structural parameters. Let F represent the reduced finite element mass matrix, the reduced damping matrix, and the reduced stiffness matrix, respectively; ext This refers to the external excitation of the train system.
[0077] In an optional embodiment of this application, the second determining module 1230 is further configured to: calculate the sensitivity of the modal frequency to the modal variables within each component of the train for each order of modes; calculate the sum of the sensitivities of all modal variables for each train component to obtain the modal contribution of the train component; and determine the train component with the largest modal contribution as the largest variable among the modal variables.
[0078] In an optional embodiment of this application, the second optimization module 1250 is further configured to: determine the target optimized component of the train based on the optimized modal variables; perform optimization operations on the target optimized component; the optimization operations include: replacing redundant materials of the train, optimizing component layout, optimizing shape, and adding at least one of the following: adding reinforcing structures.
[0079] Specific limitations regarding the aforementioned train modal optimization device 1200 can be found in the limitations of the train modal optimization method described above, and will not be repeated here. Each module in the aforementioned train modal optimization device 1200 can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in hardware or independently of the processor in a computer device, or stored in software in the memory of a computer device, so that the processor can call and execute the operations corresponding to each module.
[0080] In one embodiment, a computer device is provided, the internal structure of which can be as follows: Figure 13 As shown. The computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and the database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements the train mode optimization method described above. It includes: memory and a processor; the memory stores the computer program; and the processor executes the computer program to implement any step of the train mode optimization method described above.
[0081] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, can perform any step of the above-described train mode optimization method.
[0082] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0083] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0084] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0085] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0086] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.
[0087] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.
Claims
1. A train modal optimization method, characterized in that, include: A general model of the train coupling system is constructed based on the train's structural and operational parameters; wherein, the general model of the train coupling system includes a multibody dynamics model for characterizing global vibration and a finite element coupling model for local stress deformation; Based on the overall model of the train coupling system, determine the modal variables that affect each mode; the modal variables refer to the various factors that affect the train modes. The modal optimization objective and modal lightweighting constraints are determined based on the largest variable among the modal variables. Based on the modal optimization objective and the modal lightweighting constraint, the train structure topology is optimized to obtain the optimized modal variables. The structure and materials of the train are optimized based on the optimized modal variables.
2. The train modal optimization method according to claim 1, characterized in that, The overall model of the train coupled system, constructed based on the train's structural and operational parameters, includes: The multibody dynamics model of the train system is constructed based on the train's structural parameters and operating parameters; A finite element modal reduction model of the coupling interface between the rigid body and the flexible body of the train is constructed based on preset coupling constraints, the structural parameters, and the operating parameters. The finite element modal reduction model is fused with the multibody dynamics model to obtain the overall model of the train coupling system.
3. The train modal optimization method according to claim 2, characterized in that, The multibody dynamics model includes: ； Where q represents the generalized coordinate vector of the train system in the operating parameters. This represents the generalized velocity vector of the train system in the operating parameters. M(q) represents the generalized acceleration vector of the train system in the operating parameters; M(q) represents the mass matrix in the structural parameters; C(q, K(q) represents the Coriolis force and centrifugal force matrix in the structural parameters; K(q) represents the stiffness matrix in the structural parameters; F ext This refers to the external excitation of the train system.
4. The train modal optimization method according to claim 2, characterized in that, The finite element modal reduction model includes: ； Among them, F mb [K] represents the balancing force of the train system. fe [C] fe ]、[M fe ] respectively represent the stiffness matrix, damping matrix, and mass matrix of the finite element component in the structural parameters; {u}, { }、{ } represent the nodal displacement, velocity vector, and acceleration vector of the finite element component in the operating parameters, respectively.
5. The train modal optimization method according to claim 2, characterized in that, The overall model of the train coupling system includes: ； Where q represents the generalized coordinate vector of the train system in the operating parameters. This represents the generalized velocity vector of the train system in the operating parameters. M(q) represents the generalized acceleration vector of the train system in the operating parameters; M(q) represents the mass matrix in the structural parameters; C(q, K(q) represents the Coriolis force and centrifugal force matrix in the structural parameters; K(q) represents the stiffness matrix in the structural parameters. These represent the mass matrix, damping matrix, and stiffness matrix of the flexible component relative to the rigid component at the coupling interface, respectively, in the structural parameters. These represent the mass matrix, damping matrix, and stiffness matrix of the rigid body component relative to the flexible body component at the coupling interface, respectively, in the structural parameters. Let F represent the reduced finite element mass matrix, the reduced damping matrix, and the reduced stiffness matrix, respectively; ext This refers to the external excitation of the train system.
6. The train modal optimization method according to claim 2, characterized in that, Before determining the modal optimization objective and modal lightweighting constraints based on the largest variable among the modal variables, the method further includes: For each mode, the sensitivity of the modal frequency to the modal variables within each component of the train is calculated; For each train component, the sum of the sensitivities of all modal variables is calculated to obtain the modal contribution of the train component; The train component with the largest modal contribution is identified as the largest variable among the modal variables.
7. The train modal optimization method according to claim 2, characterized in that, The optimization of the train's structure and materials based on the optimized modal variables includes: The target optimized components of the train are determined based on the optimized modal variables. An optimization operation is performed on the target optimized component; the optimization operation includes at least one of the following: replacing redundant materials in the train, optimizing the component layout, optimizing the shape, and adding a reinforcing structure.
8. A train modal optimization device, characterized in that, include: A construction module is used to construct a general model of the train coupling system based on the train's structural and operational parameters; wherein, the general model of the train coupling system includes a multibody dynamics model for characterizing global vibration and a finite element coupling model for local stress deformation; The first determining module is used to determine the modal variables that affect each order mode based on the overall model of the train coupling system; the modal variables refer to the various influencing factors that affect the train modes; The second determining module is used to determine the modal optimization objective and modal lightweighting constraints based on the largest variable among the modal variables. The first optimization module is used to perform topology optimization on the train structure based on the modal optimization objective and the modal lightweighting constraint to obtain the optimized modal variables. The second optimization module is used to optimize the structure and materials of the train based on the optimized modal variables.
9. A computer device, comprising: A memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method according to any one of claims 1 to 7.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.