A method, device, equipment and medium for predicting aerodynamic response of a long-span bridge

Abstract

The present invention provides a method, device, equipment and medium for predicting the aerodynamic response of a long-span bridge, and relates to the field of aerodynamic response of a long-span bridge. The method includes: obtaining bridge model data and wind tunnel test conditions; inputting the bridge model data and wind tunnel test conditions into a preset wind tunnel test model to obtain an aerodynamic response time history; constructing a model according to the aerodynamic shape of the bridge model in the bridge model data and the wind tunnel test conditions to obtain an initial numerical model; when the initial numerical model does not meet the preset convergence conditions, updating the initial numerical model according to the aerodynamic response time history to obtain an updated numerical model; until the updated numerical model meets the preset convergence conditions, a digital twin is obtained, and the aerodynamic response of the long-span bridge is predicted according to the digital twin. The method further accurately predicts the aerodynamic response and characteristics of the long-span bridge while ensuring the reliability of the test results.

Description

Technical Field

[0001] The present invention relates to the field of aerodynamic response of long-span bridges, and in particular to a method, device, equipment and medium for predicting aerodynamic response of long-span bridges. Background Art

[0002] In the construction of long-span bridges, as the span of bridges continues to increase, bridges are increasingly sensitive to wind-induced vibrations. Under normal wind speeds, long-span bridges are prone to vortex-induced resonance; when the wind speed approaches the critical value, long-span bridges may experience destructive soft flutter, and the bridge response caused by vortex-induced resonance and soft flutter is collectively referred to as the aerodynamic response of long-span bridges. In order to clarify the impact of the aerodynamic response of long-span bridges on bridges, in the prior art, wind tunnel tests or numerical simulations are usually used to predict the aerodynamic response and characteristics of long-span bridges. However, in wind tunnel tests, there are many limitations; such as scale ratio, blockage ratio, and model manufacturing errors, which make the wind tunnel test results unable to fully reproduce the vortex-induced vibration response and characteristics of actual bridges; in addition, the large-amplitude vibration of the bridge deck caused by soft flutter inevitably introduces damping nonlinearity and stiffness nonlinearity into the test model, reducing the reliability of the test results; in numerical simulations, due to the uncertainty in the algorithm and turbulence model of wind-bridge coupled vibration, the numerical simulation results are unreliable. Therefore, there is an urgent need for a prediction method for the aerodynamic response of long-span bridges. While ensuring the reliability of the test results, the aerodynamic response and characteristics of long-span bridges need to be further accurately predicted. Summary of the invention

[0003] The purpose of the present invention is to provide a method, device, equipment and medium for predicting the aerodynamic response of a long-span bridge to improve the above-mentioned problem. In order to achieve the above-mentioned purpose, the technical solution adopted by the present invention is as follows:

[0004] In a first aspect, the present application provides a method for predicting aerodynamic response of a long-span bridge, the method comprising:

[0005] Acquiring bridge model data and wind tunnel test conditions, wherein the bridge model data includes the mass of the bridge model, the nominal stiffness of the bridge model, the nominal damping of the bridge model, and the aerodynamic shape of the bridge model, and the wind tunnel test conditions include boundary conditions and wind field environment;

[0006] Inputting the bridge model data and the wind tunnel test conditions into a preset wind tunnel test model to obtain an aerodynamic response time history;

[0007] constructing a model according to the aerodynamic shape of the bridge model and the wind tunnel test conditions to obtain an initial numerical model, wherein the mass of the bridge model, the nominal stiffness of the bridge model and the nominal damping of the bridge model are set as initial calculation parameters;

[0008] When the initial numerical model does not meet a preset convergence condition, updating the initial numerical model according to the aerodynamic response time history to obtain an updated numerical model;

[0009] When the updated numerical model satisfies a preset convergence condition, a digital twin is obtained, and the aerodynamic response of the long-span bridge is predicted based on the digital twin.

[0010] In a second aspect, the present application provides a device for predicting aerodynamic response of a long-span bridge, the device comprising:

[0011] An acquisition module, used to acquire bridge model data and wind tunnel test conditions, wherein the bridge model data includes the mass of the bridge model, the nominal stiffness of the bridge model, the nominal damping of the bridge model, and the aerodynamic shape of the bridge model, and the wind tunnel test conditions include boundary conditions and wind field environment;

[0012] A first processing module, used for inputting the bridge model data and the wind tunnel test conditions into a preset wind tunnel test model to obtain an aerodynamic response time history;

[0013] A second processing module is used to construct a model according to the aerodynamic shape of the bridge model and the wind tunnel test conditions to obtain an initial numerical model, wherein the mass of the bridge model, the nominal stiffness of the bridge model and the nominal damping of the bridge model are set as initial calculation parameters;

[0014] a third processing module, configured to update the initial numerical model according to the aerodynamic response time history to obtain an updated numerical model when the initial numerical model does not meet a preset convergence condition;

[0015] The fourth processing module is used to obtain a digital twin when the updated numerical model meets a preset convergence condition, and predict the aerodynamic response of the long-span bridge according to the digital twin.

[0016] In a third aspect, the present application also provides a device for predicting the aerodynamic response of a long-span bridge, comprising:

[0017] Memory for storing computer programs;

[0018] A processor is used to implement the steps of the method for predicting the aerodynamic response of the long-span bridge when executing the computer program.

[0019] In a fourth aspect, the present application further provides a medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned prediction method based on the aerodynamic response of a long-span bridge are implemented.

[0020] The beneficial effects of the present invention are:

[0021] The present invention proposes a high-precision prediction method for aerodynamic response of a long-span bridge based on digital twin technology. In the digital twin, since the uncertainty in the wind-bridge coupled vibration algorithm and the turbulence model has been eliminated, the aerodynamic response of the long-span bridge can be predicted based on the digital twin. In terms of ensuring the reliability of the test results, it is convenient to determine the influence of different conditions (such as wind tunnel test blockage ratio) and various parameters (such as structural stiffness and structural damping ratio) on the vortex-induced vibration or soft flutter of the bridge, so as to obtain an accurate prediction of the aerodynamic response of the long-span bridge and improve the quality of the wind-resistant design of the long-span bridge. In addition, since the digital twin can vividly and meticulously reproduce the complex fluid-solid coupled vibration and fluid flow, the mechanism of vortex-induced vibration or soft flutter of the bridge can also be accurately studied with the help of the digital twin.

[0022] Other features and advantages of the present invention will be set forth in the following description, and in part will become apparent from the description, or may be understood by practicing embodiments of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 It is a schematic flow chart of a method for predicting aerodynamic response of a long-span bridge according to an embodiment of the present invention;

[0025] Figure 2 A schematic diagram of a process of mapping vibration-fitness judgment-update in an embodiment of the present invention;

[0026] Figure 3 Schematic diagram of the structure of the prediction device for aerodynamic response of a long-span bridge described in an embodiment of the present invention;

[0027] Figure 4 It is a schematic diagram of the structure of the prediction device for the aerodynamic response of a long-span bridge described in an embodiment of the present invention;

[0028] Markings in the figure:

[0029] 1. Acquisition module; 2. First processing module; 3. Second processing module; 4. Third processing module; 5. Fourth processing module; 800. Prediction device for aerodynamic response of large-span bridge; 801. Processor; 802. Memory; 803. Multimedia component; 804. I / O interface; 805. Communication component. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0031] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0032] Embodiment 1:

[0033] This embodiment provides a method for predicting the aerodynamic response of a long-span bridge.

[0034] See also Figure 1 , the figure shows that the method includes steps S1 to S5, specifically:

[0035] S1: acquiring bridge model data and wind tunnel test conditions, wherein the bridge model data includes the mass of the bridge model, the nominal stiffness of the bridge model, the nominal damping of the bridge model and the aerodynamic shape of the bridge model, and the wind tunnel test conditions include boundary conditions and wind field environment;

[0036] In step S1, the bridge model is designed and manufactured according to the existing wind tunnel test criteria to obtain a model that can be installed with vortex-induced resonance and soft flutter bridge wind tunnel segment tests, and the mass of the model that can be installed with vortex-induced resonance and soft flutter bridge wind tunnel segment tests is suspended and measured, and used as the mass of the bridge model. Under windless conditions in the wind tunnel, the bridge model is suspended to perform free decay vibration, and the nominal stiffness and nominal damping of the bridge model are analyzed from the recorded free decay vibration time history. Among them, the nominal stiffness of the bridge model includes the actual stiffness change caused by the introduction of additional mass; the nominal damping of the bridge model includes structural damping and additional damping, and the additional damping is additional damping caused by air, etc. The aerodynamic shape of the bridge model is determined according to the outer contour of the suspended bridge model.

[0037] In the wind tunnel test conditions, the boundary conditions mainly refer to the wind tunnel blockage ratio, and the wind field environment mainly refers to the incoming flow wind speed, incoming flow turbulence, etc.

[0038] S2: inputting the bridge model data and the wind tunnel test conditions into a preset wind tunnel test model to obtain an aerodynamic response time history;

[0039] S3: constructing a model according to the aerodynamic shape of the bridge model and the wind tunnel test conditions to obtain an initial numerical model, wherein the mass of the bridge model, the nominal stiffness of the bridge model and the nominal damping of the bridge model are set as initial calculation parameters;

[0040] In step S3, space independence and time independence verification can be performed in the initial numerical model, specifically: by setting different space lengths and time lengths, the simulation results of the initial numerical model at different space and time scales are obtained to verify that the calculation results of the initial numerical model are not affected by the set space and time lengths, and to ensure that the grid scale and time step can match the requirements of the turbulence model calculation accuracy.

[0041] In order to clarify the specific calculation process of the initial calculation parameters, step S3 includes steps S31 to S35, specifically:

[0042] S31: Obtaining convergence parameters, stability parameters and step length of numerical simulation;

[0043] S32: solving the convergence parameter of the numerical simulation, the stability parameter and the step size of the numerical simulation by using a preset first parameter solving model to obtain a first parameter data set;

[0044] In step S32, the first parameter data set includes a first characteristic parameter, a second characteristic parameter and a third characteristic parameter, and the first parameter solution model includes:

[0045]

[0046] In the above formula (1), p1 represents the first characteristic parameter, p2 represents the second characteristic parameter, p3 represents the third characteristic parameter, β represents the convergence parameter of the numerical simulation, γ represents the stability parameter, and t represents the step size of the numerical simulation.

[0047] S33: solving the convergence parameter of the numerical simulation, the stability parameter and the step size of the numerical simulation by using a preset second parameter solving model to obtain a second parameter data set;

[0048] In step S33, the second parameter data set includes a fourth characteristic parameter, a fifth characteristic parameter and a sixth characteristic parameter, and the second parameter solution model includes:

[0049]

[0050] In the above formula (2), p4 represents the fourth characteristic parameter, p5 represents the fifth characteristic parameter, p6 represents the sixth characteristic parameter, β represents the convergence parameter of the numerical simulation, γ represents the stability parameter, and t represents the step size of the numerical simulation.

[0051] S34: solving the initial calculation parameters, the first parameter data set and the second parameter data set through a preset parameter model to obtain parameter information of the current time step, wherein the parameter information of the current time step includes a model displacement of the current time step, a model velocity of the current time step and a model acceleration of the current time step;

[0052] In step S34, the preset parameter model includes:

[0053]

[0054] In the above formula (3), x_cur represents the model displacement at the current time step, v_cur represents the model velocity at the current time step, and a cur represents the model acceleration at the current time step, L represents the intermediate parameter, f represents the preset aerodynamic force, m represents the mass of the bridge model, and x p represents the preset initial model displacement, v p Indicates the preset initial model speed, a p represents the preset initial model acceleration, p1 represents the first characteristic parameter, p2 represents the second characteristic parameter, p3 represents the third characteristic parameter, p4 represents the fourth characteristic parameter, p5 represents the fifth characteristic parameter, p6 represents the sixth characteristic parameter, k represents the nominal stiffness of the bridge model, c represents the nominal damping of the bridge model, β represents the convergence parameter of the numerical simulation, γ represents the stability parameter, and t represents the step size of the numerical simulation.

[0055] S35: When the parameter information of the current time step meets the preset parameter stability judgment condition, stop the iterative calculation of the next time step.

[0056] In step S35, the preset parameter stability judgment condition is: the model displacement of the current time step, the model velocity of the current time step and the model acceleration of the current time step all meet the preset parameter stability range, at which time the iterative calculation of the next time step is stopped.

[0057] S4: when the initial numerical model does not meet the preset convergence condition, updating the initial numerical model according to the aerodynamic response time history to obtain an updated numerical model;

[0058] In the wind tunnel test, the aerodynamic response time history is the result of the wind tunnel test, which is collected by sensors and recorded through an acquisition program installed on the computer. However, the existing Fluent has the problem of low computational efficiency. At the same time, in the initial stage of the calculation, there is an unstable flow field, which leads to a lot of time spent on stabilizing the flow field during the numerical model calculation process, increasing the computational cost. In addition, for large-amplitude aerodynamic responses, such as flutter, there are serious nonlinear problems, which poses a challenge to the computational accuracy of the numerical model. Therefore, in order to improve the computational efficiency and accuracy, such as Figure 2 As shown, this method adopts the mapping vibration-fitness judgment-update method.

[0059] In order to clarify the specific process of mapping the vibration of the initial numerical model according to the aerodynamic response time history, step S4 includes steps S41 to S43, specifically:

[0060] S41: inputting the aerodynamic response time history into the initial numerical model to obtain mapping vibration data of the initial numerical model;

[0061] In step S41, in order to clarify the specific calculation process of mapping vibration data, step S41 includes S411 to S413, specifically:

[0062] S411: Obtaining a time history array and a speed time history;

[0063] S412: inputting the aerodynamic response time history into the initial numerical model, and determining the mapping vibration stage according to the time history array and the velocity time history;

[0064] S413: After the mapping vibration is completed, the mapping vibration data of the initial numerical model is stored.

[0065] S42: judging the flow field stability of the mapped vibration data;

[0066] In step S42, since the aerodynamic response time series obtained by the wind tunnel test is input into the initial numerical model in the above step, the initial numerical model is vibrated according to the aerodynamic response time series obtained by the wind tunnel test to obtain the mapping vibration data of the initial numerical model, so when judging the flow field stability, the stability can be judged by monitoring the surface lift of the initial numerical model. When performing specific operations, the aerodynamic response time series obtained by the wind tunnel test can be mapped for multiple cycles according to its vibration frequency, and the mapping vibration can be ended. When the surface lift of the initial numerical model meets the preset surface lift threshold, at this time, the aerodynamic response of the initial numerical model and the surrounding flow field enter a relatively stable state close to the true value.

[0067] S43: When the flow field meets the preset stability judgment, the initial numerical model is subjected to fluid-solid coupling calculation to obtain an updated numerical model.

[0068] In step S43, the fluid-structure coupling calculation adopts the existing Newmark-β model.

[0069] In step S4, in order to clarify the specific content of fitness judgment in this method, that is, the preset convergence condition is fitness judgment, step S4 includes S44 to S47, specifically:

[0070] S44: Obtaining the vibration frequency of the wind tunnel test and the aerodynamic response amplitude of the wind tunnel test;

[0071] S45: Calculating the vibration frequency of the wind tunnel test by using a preset first fitness model to obtain a first fitness value;

[0072] In step S45, the first fitness model is:

[0073]

[0074] In the above formula (4), R1 represents the first fitness value, f m represents the preset vibration frequency of the numerical model, and f0 represents the vibration frequency of the wind tunnel test.

[0075] S46: Calculating the aerodynamic response amplitude of the wind tunnel test by using a preset second fitness model to obtain a second fitness value;

[0076] In step S46, the second fitness model is:

[0077]

[0078] In the above formula (5), R2 represents the second fitness value, Amp m represents the preset aerodynamic response amplitude of the numerical model, and Amp represents the aerodynamic response amplitude of the wind tunnel test.

[0079] S47: Calculate according to the first fitness value and the second fitness value to obtain a fitness parameter, and the fitness parameter is used to make a fitness judgment with a preset threshold.

[0080] In step S47, the calculation formula of the fitness parameter is:

[0081] R r =R1+R2; (6)

[0082] In the above formula (6), R r Represents the fitness parameter, R1 represents the first fitness value, and R2 represents the second fitness value.

[0083] When the fitness parameter and the preset threshold do not meet the preset convergence condition, the update is performed after step S47. The method adopts the particle swarm algorithm as the update algorithm. The particle swarm algorithm simulates the birds in the flock by designing a massless particle. The particle has only two attributes: speed and position. The speed represents the speed of movement and the position represents the direction of movement. Each particle searches for the optimal solution in the search space separately and records it as the current individual extreme value. The individual extreme value is shared with other particles in the entire particle swarm. The optimal individual extreme value is found as the current global optimal solution of the entire particle swarm. All particles in the particle swarm adjust their speed and position according to the current individual extreme value found by themselves and the current global optimal solution shared by the entire particle swarm. When optimizing the parameters to be optimized, the particle swarm algorithm analyzes the fitness parameter according to the calculation results of the previous numerical simulation, and on this basis, uses the particle swarm algorithm to find more suitable parameters and update the parameters to be optimized. The parameters to be optimized in this method are the nominal stiffness of the bridge model and the nominal damping of the bridge model.

[0084] S5: Until the updated numerical model meets the preset convergence condition, a digital twin is obtained, and the aerodynamic response of the long-span bridge is predicted based on the digital twin.

[0085] Since the uncertainty in the wind-bridge coupled vibration algorithm and turbulence model has been eliminated, the aerodynamic response of the long-span bridge can be predicted according to the digital twin in step S5 to determine the influence of different conditions (such as wind tunnel test blockage ratio) and various parameters on the vortex-induced vibration or soft flutter of the bridge, so as to obtain an accurate prediction of the aerodynamic response of the long-span bridge and improve the quality of the wind-resistant design of the long-span bridge. In addition, since the digital twin can vividly and meticulously reproduce the complex fluid-solid coupled vibration and fluid flow, the mechanism of the vortex-induced vibration or soft flutter of the bridge can also be accurately studied with the help of the digital twin.

[0086] Embodiment 2:

[0087] like Figure 3 As shown, this embodiment provides a device for predicting aerodynamic response of a long-span bridge, the device comprising:

[0088] An acquisition module 1 is used to acquire bridge model data and wind tunnel test conditions, wherein the bridge model data includes the mass of the bridge model, the nominal stiffness of the bridge model, the nominal damping of the bridge model, and the aerodynamic shape of the bridge model, and the wind tunnel test conditions include boundary conditions and wind field environment;

[0089] A first processing module 2, used for inputting the bridge model data and the wind tunnel test conditions into a preset wind tunnel test model to obtain an aerodynamic response time history;

[0090] The second processing module 3 is used to construct a model according to the aerodynamic shape of the bridge model and the wind tunnel test conditions to obtain an initial numerical model, wherein the mass of the bridge model, the nominal stiffness of the bridge model and the nominal damping of the bridge model are set as initial calculation parameters;

[0091] A third processing module 4 is used for updating the initial numerical model according to the aerodynamic response time history to obtain an updated numerical model when the initial numerical model does not meet the preset convergence condition;

[0092] The fourth processing module 5 is used to obtain a digital twin when the updated numerical model meets a preset convergence condition, and predict the aerodynamic response of the long-span bridge according to the digital twin.

[0093] In an implementation method disclosed in the present invention, the second processing module 3 includes:

[0094] An acquisition unit, used for acquiring convergence parameters, stability parameters and step length of numerical simulation;

[0095] A first processing unit, configured to solve the convergence parameter of the numerical simulation, the stability parameter and the step size of the numerical simulation by using a preset first parameter solving model to obtain a first parameter data set;

[0096] A second processing unit, configured to solve the convergence parameter of the numerical simulation, the stability parameter and the step size of the numerical simulation by using a preset second parameter solving model to obtain a second parameter data set;

[0097] a third processing unit, configured to solve the initial calculation parameters, the first parameter data set, and the second parameter data set through a preset parameter model to obtain parameter information of a current time step, wherein the parameter information of the current time step includes a model displacement of the current time step, a model velocity of the current time step, and a model acceleration of the current time step;

[0098] The fourth processing unit is used to stop the iterative calculation of the next time step when the parameter information of the current time step meets the preset parameter stability judgment condition.

[0099] It should be noted that, regarding the device in the above embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment of the method, and will not be elaborated here.

[0100] Embodiment 3:

[0101] Corresponding to the above method embodiment, this embodiment also provides a prediction device for the aerodynamic response of a large-span bridge. The prediction device for the aerodynamic response of a large-span bridge described below and the prediction method for the aerodynamic response of a large-span bridge described above can refer to each other.

[0102] Figure 4 8 is a block diagram of a device 800 for predicting aerodynamic response of a long-span bridge according to an exemplary embodiment. Figure 4 As shown, the long-span bridge aerodynamic response prediction device 800 may include: a processor 801 and a memory 802. The long-span bridge aerodynamic response prediction device 800 may also include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0103] The processor 801 is used to control the overall operation of the long-span bridge aerodynamic response prediction device 800 to complete all or part of the steps in the above-mentioned long-span bridge aerodynamic response prediction method. The memory 802 is used to store various types of data to support the operation of the long-span bridge aerodynamic response prediction device 800, and these data may include, for example, instructions for any application or method operating on the long-span bridge aerodynamic response prediction device 800, and application-related data, such as contact data, sent and received messages, pictures, audio, video, etc. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, disk or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 802 or sent via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules, and the above-mentioned other interface modules can be keyboards, mice, buttons, etc. These buttons can be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the prediction device 800 of the aerodynamic response of the long-span bridge and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 805 can include: Wi-Fi module, Bluetooth module, NFC module.

[0104] In an exemplary embodiment, the prediction device 800 for the aerodynamic response of a large-span bridge can be implemented by one or more application-specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned prediction method for the aerodynamic response of a large-span bridge.

[0105] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the above-mentioned method for predicting the aerodynamic response of a long-span bridge are implemented. For example, the computer-readable storage medium may be the above-mentioned memory 802 including program instructions, and the above-mentioned program instructions may be executed by the processor 801 of the device 800 for predicting the aerodynamic response of a long-span bridge to complete the above-mentioned method for predicting the aerodynamic response of a long-span bridge.

[0106] Embodiment 4:

[0107] Corresponding to the above method embodiment, a medium is also provided in this embodiment. The medium described below and the method for predicting the aerodynamic response of a long-span bridge described above can refer to each other.

[0108] A medium stores a computer program, which, when executed by a processor, implements the steps of the method for predicting the aerodynamic response of a long-span bridge in the above method embodiment.

[0109] The medium may specifically be a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other readable storage media that can store program codes.

[0110] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0111] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for predicting the aerodynamic response of a long-span bridge, characterized in that: include: Acquiring bridge model data and wind tunnel test conditions, wherein the bridge model data includes the mass of the bridge model, the nominal stiffness of the bridge model, the nominal damping of the bridge model, and the aerodynamic shape of the bridge model, and the wind tunnel test conditions include boundary conditions and wind field environment; Inputting the bridge model data and the wind tunnel test conditions into a preset wind tunnel test model to obtain an aerodynamic response time history; constructing a model according to the aerodynamic shape of the bridge model and the wind tunnel test conditions to obtain an initial numerical model, wherein the mass of the bridge model, the nominal stiffness of the bridge model and the nominal damping of the bridge model are set as initial calculation parameters; When the initial numerical model does not meet a preset convergence condition, updating the initial numerical model according to the aerodynamic response time history to obtain an updated numerical model; When the updated numerical model satisfies a preset convergence condition, a digital twin is obtained, and the aerodynamic response of the long-span bridge is predicted according to the digital twin; wherein the initial numerical model includes the aerodynamic shape of the bridge model, the wind tunnel test conditions and the parameter model; The parameter model is: x_cur represents the model displacement at the current time step, v_cur represents the model velocity at the current time step, and a cur represents the model acceleration at the current time step, L represents the intermediate parameter, f represents the preset aerodynamic force, m represents the mass of the bridge model, and x p represents the preset initial model displacement, v p Indicates the preset initial model speed, a p represents the preset initial model acceleration, p1 represents the first characteristic parameter, p2 represents the second characteristic parameter, p3 represents the third characteristic parameter, p4 represents the fourth characteristic parameter, p5 represents the fifth characteristic parameter, p6 represents the sixth characteristic parameter, k represents the nominal stiffness of the bridge model, c represents the nominal damping of the bridge model, β represents the convergence parameter of the numerical simulation, γ represents the stability parameter, and t represents the step size of the numerical simulation; The convergence parameter of the numerical simulation, the stability parameter and the step size of the numerical simulation are solved by a preset first parameter solving model to obtain a first parameter data set, wherein the first parameter data set includes a first characteristic parameter, a second characteristic parameter and a third characteristic parameter, and the first parameter solving model includes: In the above formula, p1 represents the first characteristic parameter, p2 represents the second characteristic parameter, p3 represents the third characteristic parameter, β represents the convergence parameter of the numerical simulation, γ represents the stability parameter, and t represents the step size of the numerical simulation; The convergence parameter of the numerical simulation, the stability parameter and the step size of the numerical simulation are solved by a preset second parameter solution model to obtain a second parameter data set, wherein the second parameter data set includes a fourth characteristic parameter, a fifth characteristic parameter and a sixth characteristic parameter, and the second parameter solution model includes: In the above formula, p4 represents the fourth characteristic parameter, p5 represents the fifth characteristic parameter, p6 represents the sixth characteristic parameter, β represents the convergence parameter of the numerical simulation, γ represents the stability parameter, and t represents the step size of the numerical simulation; The specific process of mapping the vibration of the initial numerical model by the aerodynamic response time history includes: Inputting the aerodynamic response time history into the initial numerical model to obtain mapping vibration data of the initial numerical model; Performing flow field stability judgment on the mapped vibration data; When the flow field meets the preset stability judgment, the initial numerical model is subjected to fluid-solid coupling calculation to obtain an updated numerical model; The preset convergence conditions are fitness judgment, including: Obtain the vibration frequency and aerodynamic response amplitude of the wind tunnel test; The vibration frequency of the wind tunnel test is calculated by using a preset first fitness model to obtain a first fitness value; The first fitness model is: In the above formula, R1 represents the first fitness value, f m represents the preset vibration frequency of the numerical model, and f0 represents the vibration frequency of the wind tunnel test; The aerodynamic response amplitude of the wind tunnel test is calculated by using a preset second fitness model to obtain a second fitness value; The second fitness model is: In the above formula, R2 represents the second fitness value, Amp m represents the preset aerodynamic response amplitude of the numerical model, and Amp represents the aerodynamic response amplitude of the wind tunnel test; A fitness parameter is obtained by calculation based on the first fitness value and the second fitness value, and the fitness parameter is used to perform fitness judgment with a preset threshold.

2. The method for predicting aerodynamic response of a long-span bridge according to claim 1, characterized in that: A model is constructed according to the aerodynamic shape of the bridge model and the wind tunnel test conditions to obtain an initial numerical model, wherein the mass of the bridge model, the nominal stiffness of the bridge model and the nominal damping of the bridge model are set as initial calculation parameters, including: Obtain the convergence parameters, stability parameters and step size of numerical simulation; The convergence parameter of the numerical simulation, the stability parameter and the step size of the numerical simulation are solved by a preset first parameter solution model to obtain a first parameter data set; Solving the convergence parameter of the numerical simulation, the stability parameter and the step size of the numerical simulation by using a preset second parameter solving model to obtain a second parameter data set; Solving the initial calculation parameters, the first parameter data set, and the second parameter data set through a preset parameter model to obtain parameter information of a current time step, wherein the parameter information of the current time step includes a model displacement of the current time step, a model velocity of the current time step, and a model acceleration of the current time step; When the parameter information of the current time step meets the preset parameter stability judgment condition, the iterative calculation of the next time step is stopped.

3. A device for predicting the aerodynamic response of a long-span bridge, characterized in that: include: An acquisition module, used to acquire bridge model data and wind tunnel test conditions, wherein the bridge model data includes the mass of the bridge model, the nominal stiffness of the bridge model, the nominal damping of the bridge model, and the aerodynamic shape of the bridge model, and the wind tunnel test conditions include boundary conditions and wind field environment; A first processing module, used for inputting the bridge model data and the wind tunnel test conditions into a preset wind tunnel test model to obtain an aerodynamic response time history; A second processing module is used to construct a model according to the aerodynamic shape of the bridge model and the wind tunnel test conditions to obtain an initial numerical model, wherein the mass of the bridge model, the nominal stiffness of the bridge model and the nominal damping of the bridge model are set as initial calculation parameters; a third processing module, configured to update the initial numerical model according to the aerodynamic response time history to obtain an updated numerical model when the initial numerical model does not meet a preset convergence condition; a fourth processing module, configured to obtain a digital twin when the updated numerical model satisfies a preset convergence condition, and predict the aerodynamic response of the long-span bridge according to the digital twin; The initial numerical model includes the aerodynamic shape of the bridge model, the wind tunnel test conditions and a preset parameter model; The preset parameter model is: x_cur represents the model displacement at the current time step, v_cur represents the model velocity at the current time step, and a cur represents the model acceleration at the current time step, L represents the intermediate parameter, f represents the preset aerodynamic force, m represents the mass of the bridge model, and x p represents the preset initial model displacement, v p Indicates the preset initial model speed, a p represents the preset initial model acceleration, p1 represents the first characteristic parameter, p2 represents the second characteristic parameter, p3 represents the third characteristic parameter, p4 represents the fourth characteristic parameter, p5 represents the fifth characteristic parameter, p6 represents the sixth characteristic parameter, k represents the nominal stiffness of the bridge model, c represents the nominal damping of the bridge model, β represents the convergence parameter of the numerical simulation, γ represents the stability parameter, and t represents the step size of the numerical simulation; The convergence parameter of the numerical simulation, the stability parameter and the step size of the numerical simulation are solved by a preset first parameter solving model to obtain a first parameter data set, wherein the first parameter data set includes a first characteristic parameter, a second characteristic parameter and a third characteristic parameter, and the first parameter solving model includes: In the above formula, p1 represents the first characteristic parameter, p2 represents the second characteristic parameter, p3 represents the third characteristic parameter, β represents the convergence parameter of the numerical simulation, γ represents the stability parameter, and t represents the step size of the numerical simulation; The convergence parameter of the numerical simulation, the stability parameter and the step size of the numerical simulation are solved by a preset second parameter solution model to obtain a second parameter data set, wherein the second parameter data set includes a fourth characteristic parameter, a fifth characteristic parameter and a sixth characteristic parameter, and the second parameter solution model includes: In the above formula, p4 represents the fourth characteristic parameter, p5 represents the fifth characteristic parameter, p6 represents the sixth characteristic parameter, β represents the convergence parameter of the numerical simulation, γ represents the stability parameter, and t represents the step size of the numerical simulation; The specific process of mapping the vibration of the initial numerical model by the aerodynamic response time history includes: A first construction unit is used to input the aerodynamic response time history into the initial numerical model to obtain mapping vibration data of the initial numerical model; A second construction unit is used to determine flow field stability of the mapped vibration data; The third construction unit is used to perform fluid-solid coupling calculation on the initial numerical model to obtain an updated numerical model when the flow field meets the preset stability judgment; The preset convergence conditions are fitness judgment, including: A first judgment unit is used to obtain the vibration frequency of the wind tunnel test and the aerodynamic response amplitude of the wind tunnel test; A second judgment unit is used to calculate the vibration frequency of the wind tunnel test through a preset first fitness model to obtain a first fitness value; The first fitness model is: In the above formula, R1 represents the first fitness value, f m represents the preset vibration frequency of the numerical model, and f0 represents the vibration frequency of the wind tunnel test; A third judgment unit is used to calculate the aerodynamic response amplitude of the wind tunnel test through a preset second fitness model to obtain a second fitness value; The second fitness model is: In the above formula, R2 represents the second fitness value, Amp m represents the preset aerodynamic response amplitude of the numerical model, and Amp represents the aerodynamic response amplitude of the wind tunnel test; The fourth judgment unit is used to perform calculation according to the first fitness value and the second fitness value to obtain a fitness parameter, and the fitness parameter is used to perform fitness judgment with a preset threshold.

4. The device for predicting aerodynamic response of a long-span bridge according to claim 3, characterized in that: The second processing module comprises: An acquisition unit, used for acquiring convergence parameters, stability parameters and step length of numerical simulation; A first processing unit, configured to solve the convergence parameter of the numerical simulation, the stability parameter and the step size of the numerical simulation by using a preset first parameter solving model to obtain a first parameter data set; A second processing unit, configured to solve the convergence parameter of the numerical simulation, the stability parameter and the step size of the numerical simulation by using a preset second parameter solving model to obtain a second parameter data set; a third processing unit, configured to solve the initial calculation parameters, the first parameter data set, and the second parameter data set through a preset parameter model to obtain parameter information of a current time step, wherein the parameter information of the current time step includes a model displacement of the current time step, a model velocity of the current time step, and a model acceleration of the current time step; The fourth processing unit is used to stop the iterative calculation of the next time step when the parameter information of the current time step meets the preset parameter stability judgment condition.

5. A device for predicting the aerodynamic response of a long-span bridge, characterized in that: include: Memory for storing computer programs; A processor is used to implement the steps of the method for predicting the aerodynamic response of a long-span bridge as described in any one of claims 1 to 2 when executing the computer program.

6. A medium, characterized in that: The medium stores a computer program, which, when executed by a processor, implements the steps of the method for predicting the aerodynamic response of a long-span bridge as claimed in any one of claims 1 to 2.

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