Active control of wind tunnel physical simulation method and system for any fluctuating wind field

Abstract

The application discloses a kind of arbitrary pulsating wind field active control wind tunnel physical simulation method and system, comprising: establishing generalized pulsating wind field spectrum model, it has 6 controllable pulsating wind spectrum in the energy distribution model parameter of frequency domain;Based on generalized pulsating wind field spectrum model, inter-spectrum matrix is calculated using coherence function;Based on harmonic superposition method, inter-spectrum matrix is converted into pulsating wind field space-time information;Pulsating wind field space-time information is introduced into the control program of active control wind tunnel, through real-time monitoring and iterative adjustment model parameter, until the target wind field generated reaches expectation.The application can simulate wind field with arbitrary frequency energy distribution by generalized pulsating wind field spectrum model with 6 model parameters, combined with iterative closed-loop control, significantly improve the simulation accuracy and adaptability of complex wind field such as mountain valley wind, typhoon.

Description

Technical Field

[0001] This invention belongs to the field of wind tunnel testing technology, specifically relating to a method and system for active control of wind tunnel physical simulation of arbitrary pulsating wind fields. Background Technology

[0002] In the field of civil engineering, the safety of wind-sensitive structures such as long-span bridges and high-rise buildings largely depends on the accurate assessment of the wind loads they experience. Wind tunnel testing is the primary method for studying the wind-induced effects on structures, and its core function is to realistically reproduce the characteristics of the incoming wind field at the structure's location within the wind tunnel.

[0003] Wind in nature, especially strong winds, consists of two parts: mean wind and fluctuating wind. The frequency domain energy distribution of the fluctuating wind (i.e., the fluctuating wind power spectrum) is a key parameter describing the characteristics of the wind field. Traditional wind tunnel tests often employ passive simulation methods, generating wind fields with specific turbulent characteristics by arranging devices such as wedges, grids, and rough elements. This method is advantageous due to its low cost and ease of implementation, but the resulting fluctuating wind power spectrum has a relatively fixed shape. For example, after passing through grids and wedges, the fluctuating wind spectrum typically exhibits a von Kármán spectrum form, characterized by a slope approaching 1 in the low-frequency range and a slope approaching -5 / 3 in the high-frequency range, all within a double logarithmic coordinate system.

[0004] However, extensive field measurements show that wind fields under many special topographical and climatic conditions do not conform to traditional spectral models. For example, mountain canyon winds, due to severe topographic disturbances, exhibit high wind speeds and turbulence intensity, resulting in a frequency domain energy distribution that differs significantly from that of pulsating winds in flat terrain. Similarly, the power spectrum of pulsating winds in the eyewall region of typhoons, due to their complex vortex structures, also does not agree well with traditional models. These wind fields, with their "arbitrary" frequency domain energy distribution characteristics, can generate aerodynamic loads on structures that differ significantly from those estimated using traditional spectral models. Directly applying traditional methods for evaluation will lead to structural designs that are either overly risky or overly conservative.

[0005] To address this challenge, active wind tunnel technology has emerged. These wind tunnels, by arranging independently controllable multi-fan arrays, can theoretically generate more complex wind fields. Existing technologies, such as Chinese patent CN119935481B, disclose an active wind tunnel that separately simulates average wind and pulsating wind, generating complex wind fields by independently controlling the array fans and the main fan. However, the core of such methods lies in the wind tunnel hardware control and the generation of drive signals; they lack a universal mathematical model capable of flexibly describing and accurately generating "arbitrary" wind fields (especially for wind fields with non-standard spectral characteristics such as mountain canyon winds and typhoons) at the frequency domain level. How to actively and accurately generate the spatiotemporal information of pulsating wind speed required to drive the fan array based on the frequency domain characteristics of the actual wind field, and how to perform closed-loop verification and correction of the generation effect, are key problems that urgently need to be solved in current active wind tunnel simulation technology.

[0006] Therefore, there is an urgent need to develop a simulation method that can accurately model pulsating wind fields with arbitrary frequency domain energy distribution, efficiently generate their spatiotemporal information, and ultimately reproduce them with high precision in an active control wind tunnel. Summary of the Invention

[0007] In view of this, the purpose of this invention is to provide a method and system for physical simulation of wind tunnels with active control of arbitrary pulsating wind fields. By establishing a generalized pulsating wind field spectrum model, high-precision modeling and spatiotemporal information generation of wind fields with arbitrary frequency domain energy distribution can be performed. Furthermore, the method utilizes active control of wind tunnels for iterative reproduction to accurately simulate complex wind fields such as mountain canyon winds and typhoons.

[0008] To achieve the above objectives, the present invention provides the following technical solution: This invention first proposes a method for actively controlling the physical simulation of wind tunnels in arbitrary pulsating wind fields, comprising the following steps: Step 1: Establish a generalized pulsating wind field spectrum model, represented as: in: The power spectrum of pulsating wind; For frequency; , , , , and These are model parameters, and the energy distribution of the pulsating wind spectrum in the frequency domain is controlled by adjusting these model parameters; Step 2: Based on the generalized pulsating wind field spectrum model, calculate the cross spectrum of pulsating wind at multiple points in space using the Davenport coherence function model to form the cross spectrum matrix of pulsating wind; Step 3: Based on the harmonic superposition method, the cross-spectral matrix of the pulsating wind is converted into the spatiotemporal information of the pulsating wind field; Step 4: Import the spatiotemporal information of the pulsating wind field into the control program of the active control wind tunnel to generate the target wind field, and monitor the generated pulsating wind speed in real time. Adjust the model parameters according to the difference between the monitoring results and the expected target, and iterate until the generated pulsating wind speed spectrum reaches the expected value.

[0009] Furthermore, in step one, the method for determining the pulsating wind spectrum by adjusting the model parameters is as follows: Adjust parameters Determine the slope of the pulsating wind spectrum at low frequencies; Adjust parameters and Determine the slope of the pulsating wind spectrum at high frequencies. And slope The value is less than 0; Adjust parameters and Determine the boundary between low and high frequencies. ; Adjust parameters Determine the overall energy of the pulsating wind spectrum .

[0010] Furthermore, the dividing point between low and high frequencies. Represented as: Overall energy of the pulsating wind spectrum Represented as: .

[0011] Furthermore, in step one, when the model parameters take the following specific values, the generalized fluctuating wind field spectrum model degenerates into a traditional fluctuating wind spectrum model: when , , , , and At that time, the generalized pulsating wind field spectrum model is consistent with the von Kármán model; when , , , , and At that time, the generalized pulsating wind field spectrum model is consistent with the Simiu model; when , , , , and At that time, the generalized pulsating wind field spectrum model is consistent with the Panofsky model; in: The standard deviation of the downstream pulsating wind; The longitudinal integral scale; Average wind speed; For height; The frictional speed is denoted as .

[0012] Furthermore, in step two, the Davenport coherence function model is expressed as: in: It is a coherence function; The attenuation coefficient; The distance between two points in space; Average wind speed; Based on the Davenport coherence function model and the generalized fluctuating wind field spectrum model, the cross spectrum of fluctuating wind is calculated: in: The distance is spatial point and spatial points The pulsating wind spectrum; and For spatial points and spatial points The pulsating wind spectrum; For spatial points and spatial points The coherence function; When space has When there are points, space is formed. Point-to-point pulsating wind cross-spectral matrix : in: For spatial points and spatial points The pulsating wind spectrum.

[0013] Furthermore, in step three, the pulsating wind cross-spectral matrix is ​​transformed based on the harmonic superposition method. The method for obtaining spatiotemporal information of pulsating wind fields is as follows: Discretize the pulsating wind field into space Point, cutoff frequency is The number of discrete frequency points is Then the first Pulsating wind time history at each point Simulate using the following formula: Where: angular frequency , To distribute evenly in random phase, Let be the frequency step size, and ; Here are the component indices after Cholesky decomposition, and ; It is a frequency discrete index, and ; For time; for Point space pulsating wind cross spectrum matrix Cholesky decomposition matrix: in: for The Cholesky decomposition matrix; for The conjugate matrix; It is the angular frequency; for The phase, and: in: To extract the imaginary part; To take the real part.

[0014] Furthermore, the first Pulsating wind time history at each point In complex exponential form: make: in: and To simplify the time history of pulsating wind Use intermediate parameters in complex exponential form; Then the first Pulsating wind time history at each point The complex exponential form can be rewritten as: in: For time, and satisfy , For time step, Index for discrete time points; It is the imaginary unit.

[0015] Furthermore, in step four, the method for iteratively generating the fluctuating wind speed spectrum is as follows: After the generated spatiotemporal information of the pulsating wind field is imported into the active control wind tunnel control program, the wind speed is monitored and fed back in real time by placing an anemometer in the wind tunnel. The actual wind speed time history is obtained and spectral analysis is performed. The actual wind field characteristics are compared with the target wind field characteristics. The model parameters of the generalized pulsating wind field spectrum model established in step one are fine-tuned according to the degree of difference. Steps one to three are then re-executed to generate a new pulsating wind speed time history and import it into the active control wind tunnel control program again until the generated wind field reaches the expected target, thus achieving accurate simulation of the target wind field.

[0016] This invention also proposes a system for implementing the above-described method for active control of wind tunnel physical simulation of arbitrary fluctuating wind fields, comprising: The modeling module is used to build a generalized pulsating wind field spectrum model; The matrix calculation module is used to calculate the cross spectrum of pulsating wind at multiple points in space based on the generalized pulsating wind field spectrum model and using the Davenport coherence function model to form the cross spectrum matrix of pulsating wind. The time history generation module is used to convert the cross-spectral matrix of the pulsating wind into the spatiotemporal information of the pulsating wind field based on the harmonic superposition method. The active control wind tunnel execution module is used to import the spatiotemporal information of the pulsating wind field into its control program to generate the target wind field, and includes a wind speed monitoring and feedback unit, which is used to adjust the model parameters in the modeling module according to the difference between the actual generated wind field and the expected target, forming an iterative control loop.

[0017] The beneficial effects of this invention are as follows: The wind tunnel physical simulation method for active control of arbitrary pulsating wind fields of the present invention has the following technical effects.

[0018] (1) High-precision modeling and spatiotemporal information generation of arbitrary wind fields with different frequency domain energy distributions have been achieved. Due to the complexity of mountain canyon winds and typhoons, their frequency domain characteristics are difficult to describe using traditional pulsating wind spectrum models, resulting in inaccurate assessments of aerodynamic characteristics and aerodynamic loads. In addition, wind fields inconsistent with traditional pulsating wind spectrum models can be observed in wind tunnel tests. This invention, by reviewing the characteristics of traditional pulsating wind spectrum models, innovatively proposes a generalized pulsating wind field frequency domain model and test generation method, which can realize the generation and simulation of arbitrary pulsating wind fields. This provides a strong guarantee for in-depth analysis of the aerodynamic characteristics of long-span bridges under extreme winds such as mountain canyon winds and typhoons, and for correcting the results of wind tunnel tests.

[0019] (2) It has wide applicability and strong versatility. The arbitrary wind field frequency domain model and test generation method proposed in this invention can generate various pulsating wind fields with different characteristics by adjusting the parameters in the model, which solves the industry problem of insufficient adaptability of existing models, so as to carry out parameter analysis in wind tunnel tests; when the parameters in the model take specific values, the model can also be transformed into a form consistent with the traditional pulsating wind spectrum model, so as to be able to compare and analyze with the traditional wind spectrum model.

[0020] (3) It has both important theoretical and engineering value. In theory, this invention provides a generalized pulsating wind spectrum model to simulate and generate various pulsating wind spectra, thus laying a solid foundation for subsequent parametric analysis. In engineering, it provides key technical support for the structural safety and performance optimization of long-span bridges in mountain valleys, long-span bridges along the coast, and high-rise buildings, with outstanding economic and safety benefits.

[0021] In summary, the wind tunnel physical simulation method for active control of arbitrary pulsating wind fields of the present invention, by constructing a generalized pulsating wind field spectrum model with six parameters, can flexibly adjust the low-frequency / high-frequency slope and energy distribution. It can not only simulate arbitrarily complex wind fields such as mountain canyon winds and typhoons, but also degenerate into various traditional spectrum models, solving the problem of insufficient adaptability of existing models. By using coherent functions and harmonic superposition methods to generate spatiotemporal information, and by using a closed-loop feedback mechanism of "generation, monitoring, and iteration" to correct model parameters in real time, it ensures that the simulated wind field is highly consistent with the target wind field. Attached Figure Description

[0022] To make the objectives, technical solutions, and beneficial effects of this invention clearer, the following figures are provided for illustration: Figure 1 This is a flowchart of the wind tunnel physical simulation method for active control of arbitrary pulsating wind fields according to the present invention; Figure 2 Comparison of mountain canyon wind spectrum, typhoon measured spectrum, wind tunnel test pulsating wind field spectrum with traditional pulsating wind spectrum model; (a) measured frequency domain characteristics of mountain canyon wind; (b) measured frequency domain characteristics of typhoon; (c) wind tunnel test pulsating wind field spectrum; Figure 3 The effect of parameter variations on the function graph in the frequency domain model of pulsating wind field; (a) parameters The effect on the function graph; (b) parameters The effect on the function graph; (c) parameters The effect on the function graph; (d) parameters The effect on the function graph; (e) parameters The effect on the graph of the function; (f) parameters The effect on the function graph; Figure 4A schematic diagram illustrating the process of generating the target pulsating wind field; Figure 5 This is the active control wind tunnel diagram used in this embodiment; Figure 6 The time histories of fluctuating wind generated using the harmonic superposition method are shown in (a) and (b). (a) is the time histories of fluctuating wind at spatial point 1. Figure 7 The following are the power spectra of two wind fields with different low-frequency slopes and different high-frequency slopes generated by the wind tunnel in this embodiment; (a) are three pulsating wind fields (WF1~WF3) with different low-frequency slopes; (b) are three pulsating wind fields (WF4~WF6) with different high-frequency slopes. Figure 8 The following graphs represent the coherence functions of the wind fields generated by the wind tunnel in this embodiment: (a) is the coherence function graph of the fluctuating wind of wind field WF1; (b) is the coherence function graph of the fluctuating wind of wind field WF4; (c) is the coherence function graph of the fluctuating wind of wind field WF2; (d) is the coherence function graph of the fluctuating wind of wind field WF5; (e) is the coherence function graph of the fluctuating wind of wind field WF3; and (f) is the coherence function graph of the fluctuating wind of wind field WF6. Figure 9 This is the 4:1 rectangular model used in the wind tunnel test in this embodiment; Figure 10 (a) shows the cross-sectional buffeting force spectrum of the rectangular model in this embodiment; (b) shows the comparison of the buffeting force spectrum of the rectangular model in turbulent fields with different low-frequency slopes; (c) shows the comparison of the buffeting force spectrum of the rectangular model in turbulent fields with different high-frequency slopes. Detailed Implementation

[0023] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0024] like Figure 1 As shown in the figure, the wind tunnel physical simulation method for active control of arbitrary pulsating wind fields in this embodiment includes the following steps.

[0025] Step 1: Establish a generalized pulsating wind field spectrum model.

[0026] Specifically, based on the measured frequency domain characteristics of mountain canyon winds, typhoons, some wind tunnel measured pulsating winds, and common pulsating wind spectral models, a generalized pulsating wind field spectral model is established. The measured frequency domain characteristics of mountain canyon winds are shown in [reference needed]. Figure 2 (a) Measured frequency domain characteristics of typhoons are shown in [reference needed]. Figure 2 (b) Some wind tunnel test results are shown in Figure 2 (c). Based on the traditional pulsating wind spectrum theory model and wind tunnel test results, the generalized pulsating wind field spectrum model established in this embodiment is expressed as follows: in: The power spectrum of pulsating wind; For frequency; , , , , and These are model parameters, and adjusting these parameters controls the energy distribution of the fluctuating wind spectrum in the frequency domain, such as... Figure 3 As shown in (a)-(f), these are the parameters. , , , , and The effect on the graph of the function.

[0027] Specifically, the effects of the six parameters on the energy distribution of the pulsating wind spectrum are as follows.

[0028] The generalized pulsating wind field spectrum model was changed to This format is for subsequent analysis: The above equation is passed through a double logarithmic coordinate system ( coordinate transformation () We can obtain: In a log-log coordinate system, the slope of its left asymptote is: The slope of the right asymptote is (note) ): Extreme points : To further clarify the impact of changes in the six parameters on the model, we plot the function. The initial values ​​of the six parameters are: , , , , and . Figure 3 The six graphs in the image sequentially illustrate the effect of changes in the six parameters on the function's graph. This also confirms the above conclusion: It affects both low-frequency and high-frequency slopes; and The effect on high-frequency slope is the same; The overall upward and downward shifts in the influence function, i.e., the overall energy of the pulsating wind; It mainly affects the low-frequency energy of the function (but not the slope). It mainly affects the high-frequency energy of the function (but not the slope).

[0029] Therefore, a method for determining the fluctuating wind spectrum by adjusting model parameters can be obtained as follows: Adjust parameters Determine the slope of the pulsating wind spectrum at low frequencies; Adjust parameters and Determine the slope of the pulsating wind spectrum at high frequencies. And slope The value is less than 0 to match the actual wind spectrum characteristics; Adjust parameters and Determine the boundary between low and high frequencies. ; Adjust parameters Determine the overall energy of the pulsating wind spectrum .

[0030] Specifically, when the model parameters take the following specific values, the generalized fluctuating wind field spectrum model degenerates into the traditional fluctuating wind spectrum model, as follows: (1) When , , , , and At that time, the generalized pulsating wind field spectrum model is consistent with the von Kármán model; (2) When , , , , and At that time, the generalized pulsating wind field spectrum model is consistent with the Simiu model (i.e., the downstream pulsating wind power spectrum recommended in the "Specification for Wind Resistance Design of Highway Bridges JTG / T 3360-01-2018"); (3) When , , , , and At that time, the generalized pulsating wind field spectrum model was consistent with the Panofsky model (i.e., the vertical pulsating wind power spectrum recommended in the "Specification for Wind Resistance Design of Highway Bridges JTG / T 3360-01-2018").

[0031] in: The standard deviation of longitudinal pulsating wind; The longitudinal integral scale; Average wind speed; For height; The frictional speed is denoted as .

[0032] Step 2: Based on the generalized pulsating wind field spectrum model, calculate the cross spectrum of pulsating wind at multiple points in space using the Davenport coherence function model to form the cross spectrum matrix of pulsating wind.

[0033] Specifically, in this embodiment, the Davenport coherence function model is expressed as: in: It is a coherence function; The attenuation coefficient; The distance between two points in space; This represents the average wind speed.

[0034] Based on the Davenport coherence function model and the generalized fluctuating wind field spectrum model, the cross spectrum of fluctuating wind is calculated and expressed as: in: The distance is spatial point and spatial points The pulsating wind spectrum; and For spatial points and spatial points The pulsating wind spectrum; For spatial points and spatial points The coherence function.

[0035] Since a pulsating wind field contains multiple spatial points, therefore, when space has When there are a few points, space can be formed. Point-to-point pulsating wind cross-spectral matrix , is represented as: in: For spatial points and spatial points The pulsating wind spectrum.

[0036] It is important to note that the formula for calculating the cross spectrum of pulsating wind remains unchanged regardless of which Davenport coherence function model is used.

[0037] The cross-spectrum and cross-correlation function of pulsating wind form a Fourier transform pair. The cross-correlation function can be obtained by performing an inverse Fourier transform on the cross-spectrum of pulsating wind, as calculated below: in: The distance is spatial point and spatial points The cross-correlation function; It is the imaginary unit.

[0038] The integral scale is defined as the integral of the cross-correlation function; therefore, the theoretical integral scale of fluctuating wind can be calculated. in: Indicates the longitudinal integral scale; Indicates the three directions of the vortex.

[0039] Step 3: Based on the harmonic superposition method, the cross-spectral matrix of the pulsating wind is converted into the spatiotemporal information of the pulsating wind field.

[0040] In this embodiment, the pulsating wind cross-spectrum matrix is ​​transformed based on the harmonic superposition method. The method for obtaining spatiotemporal information of pulsating wind fields is the harmonic superposition method, which converts the signal spectrum into time history information. For pulsating wind fields, the signal is not a single time history signal, but rather a sum of time history information in a continuous space. To model the time history of pulsating wind fields, it is discretized into spatial... A point; the pulsating wind field can be approximated as a stationary random process with ergodicity.

[0041] Based on the principle of harmonic superposition, the pulsating wind field is discretized into spatial... Points to be simulated A zero-mean one-dimensional stationary random process with a cutoff frequency of . The number of discrete frequency points is ,but , Then the first The time history of pulsating wind at a given point is simulated using the following formula: Where: angular frequency , To distribute evenly in random phase, Let be the frequency step size, and ; Here are the component indices after Cholesky decomposition, and ; It is a frequency discrete index, and ; For time; for Point space pulsating wind cross spectrum matrix Cholesky decomposition matrix: in: for The Cholesky decomposition matrix; for The conjugate matrix; It is the angular frequency; for The phase, and: in: To extract the imaginary part; To take the real part.

[0042] Furthermore, the first Pulsating wind time history at each point In complex exponential form: make: in: and To simplify the time history of pulsating wind Use intermediate parameters in complex exponential form; Then the first Pulsating wind time history at each point The complex exponential form can be rewritten as: in: For time, and satisfy , For time step, Index for discrete time points; It is the imaginary unit.

[0043] Using the above formula to describe space Simulating the fluctuating wind time history at a specific point and adding the average wind speed yields the fluctuating wind field time history. The final generated fluctuating wind field time history file should contain... Each column represents the time history of pulsating wind at a single point in space.

[0044] Step 4: Import the spatiotemporal information of the pulsating wind field into the control program of the active control wind tunnel to generate the target wind field, and monitor the generated pulsating wind speed in real time. Adjust the model parameters according to the difference between the monitoring results and the expected target, and iterate until the generated pulsating wind speed spectrum reaches the expected value.

[0045] Specifically, the method for iteratively generating the pulsating wind speed spectrum is as follows: After importing the generated spatiotemporal information of the pulsating wind field into the active control wind tunnel control program, the wind speed is monitored and fed back in real time by placing a wind speed measuring instrument in the wind tunnel, the actual wind speed time history is obtained and spectral analysis is performed, the actual wind field characteristics are compared with the target wind field characteristics, and the model parameters of the generalized pulsating wind field spectrum model established in step one are fine-tuned according to the degree of difference. Then, steps one to three are re-executed to generate a new pulsating wind speed time history and import it into the active control wind tunnel control program again until the generated wind field reaches the expected target, thereby achieving accurate simulation of the target wind field.

[0046] This embodiment takes the import of generated spatiotemporal information of pulsating wind fields into the active control wind tunnel control program as an example, including spatial... The pulsed wind speed time history data file is imported into the control program of the active control wind tunnel. The active control wind tunnel has multiple independently controllable fans. The control program can independently adjust the fan speed according to the pulsed wind speed time history of each point to generate the target wind field. When generating the pulsed wind field, wind speed measuring instruments are placed in the wind tunnel to monitor and report the wind speed in real time. The actual wind speed time history is obtained through the computer control program and spectral analysis is performed on it. Generally, there will be some difference between the wind field generated by the active control wind tunnel and the target wind field. Therefore, it is necessary to fine-tune the model according to the degree of difference between the actual wind field characteristics and the target wind field characteristics, and regenerate the pulsed wind speed time history. This is then imported back into the active control wind tunnel control program to generate the pulsed wind field. Through iteration, accurate simulation of the target wind field is achieved. Specifically, in this embodiment, the method for fine-tuning the model and regenerating the pulsed wind speed time history is as follows: First, power spectrum analysis is performed on the measured wind speed time history to obtain the actual wind power spectrum. Then, compare the actual spectrum with the target spectrum. Compare the results within a preset frequency range and calculate the relative error on logarithmic coordinates. If the error is large in the low-frequency range, adjust the parameters. , This is done by changing the low-frequency slope and energy; if the error is large in the high-frequency band, the parameters are adjusted. , , This involves changing the high-frequency slope and inflection point; if the overall energy deviates across the entire frequency band, the parameters are adjusted. The adjustment direction is determined based on the sign of the error: if the measured spectrum is higher than the target spectrum, the energy-related parameters are reduced or the absolute value of the slope is increased accordingly, and vice versa. Through multiple iterations, after each adjustment, steps one to three are re-executed to generate a new fluctuating wind speed time history, which is then imported back into the wind tunnel control program until the relative error between the measured spectrum and the target spectrum at all frequencies of interest is less than a preset threshold. A schematic diagram of the above process is shown below. Figure 4 As shown.

[0047] In this embodiment, regenerating the fluctuating wind speed time history means: based on the fine-tuned model parameters, re-execute step one to establish a new generalized fluctuating wind field spectrum model, step two to calculate a new cross-spectral matrix, and step three to generate a new fluctuating wind time sequence using the harmonic superposition method. This process is exactly the same as the initial generation, only the model parameter values ​​are different.

[0048] This embodiment also proposes an active control wind tunnel physical simulation system for arbitrary pulsating wind fields, used to implement the active control wind tunnel physical simulation method for arbitrary pulsating wind fields described above. Specifically, the active control wind tunnel physical simulation system for arbitrary pulsating wind fields includes a modeling module, a matrix calculation module, a time history generation module, and an active control wind tunnel execution module. The modeling module is used to establish a generalized pulsating wind field spectrum model; the matrix calculation module is used to calculate the cross spectrum of pulsating wind at multiple points in space based on the generalized pulsating wind field spectrum model using the Davenport coherence function model, forming a cross spectrum matrix of pulsating wind; the time history generation module is used to convert the cross spectrum matrix of pulsating wind into spatiotemporal information of pulsating wind field based on the harmonic superposition method; the active control wind tunnel execution module is used to import the spatiotemporal information of pulsating wind field into its control program to generate a target wind field, and includes a wind speed monitoring and feedback unit, used to adjust the model parameters in the modeling module according to the difference between the actual generated wind field and the expected target, forming an iterative control loop.

[0049] The following section provides a further explanation of the specific implementation methods and systems for the active control of arbitrary pulsating wind fields in this embodiment, using concrete examples.

[0050] like Figure 5 As shown, this embodiment is conducted in the TJ-5 active control wind tunnel, which has... Each fan is independently controlled, and its speed can be independently adjusted, providing the physical basis for generating wind fields in mountainous canyons. Therefore, the number of spatial points selected in this embodiment is [number missing]. This corresponds one-to-one with the number and spatial location of fans in the actively controlled wind tunnel. A cobra probe is used to measure the wind speed of the pulsating wind field and calculate the pulsating wind power spectrum.

[0051] The frequency domain model of the pulsating wind field and the method for generating wind tunnel tests in this embodiment include the following steps.

[0052] Step 1: The proposed generalized pulsating wind field spectrum model is shown below.

[0053] in: The power spectrum of pulsating wind; For frequency; , , , , and These are model parameters, and their adjustment controls the energy distribution of the fluctuating wind spectrum in the frequency domain. This example constructs two types of mountain canyon wind fields, with three wind fields for each condition to facilitate parameter analysis: Operating Condition 1: Different low-frequency slopes but the same high-frequency slope and energy (WF1~WF3). Operating Condition 2: The low-frequency slope and energy are the same, but the high-frequency slope is different (WF4~WF6).

[0054] Step 2: Determine the average wind speed for this example m / s. The Davenport coherence function model is expressed as follows: in: It is a coherence function; As the attenuation coefficient, in this embodiment, we take... ; The distance between two points in space; In this embodiment, the average wind speed is... m / s.

[0055] Using the Davenport coherence function model, according to The spatial distance between points generates the corresponding coherence function, and the cross spectrum of fluctuating wind is calculated according to the definition of the coherence function. And form a pulsed wind cross spectrum matrix. : in: The distance is spatial point and spatial points The pulsating wind spectrum; and For spatial points and spatial points The pulsating wind spectrum; For spatial points and spatial points The coherence function.

[0056] Step 3: Determine the simulation cutoff frequency for this example using the harmonic superposition method as 1024Hz and the simulation time as 120s. Simulate the fluctuating wind time history using the harmonic superposition method; 120 points of fluctuating wind time history information can be obtained for each wind field. Note that the harmonic superposition method can only generate fluctuating wind time histories with a mean of 0; the average wind speed needs to be added to the generated fluctuating wind time histories to obtain the final fluctuating wind information. Two points are selected to demonstrate the fluctuating wind speed time histories generated by the harmonic superposition method, such as... Figure 6 As shown.

[0057] Step 4: Compile the time history data from 120 points in each wind field into a single file according to the requirements of the active control wind tunnel software. Import this file into the control software and start the wind tunnel. The wind tunnel will adjust the rotation speed according to the set wind speed of each turbine, thereby generating a pulsating wind field. Use a cobra probe in the wind tunnel to collect wind speed data and perform spectral analysis to check the simulation effect. If the error is large, adjust the parameters in the wind spectrum model and re-simulate until the collected pulsating wind spectrum reaches the expected target. Figure 4 As shown.

[0058] After repeated adjustments and iterations, this embodiment generated six turbulent fields in the target operating condition, and the measured power spectrum of the fluctuating wind is as follows. Figure 7 As shown, the coherence function of pulsating wind is as follows: Figure 8 As shown, the other parameters of the pulsating wind are shown in Table 1.

[0059] Table 1 Pulsating wind parameters Furthermore, wind tunnel tests were conducted using a rectangular model in six turbulent fields under the above two operating conditions to further verify the effectiveness of the method proposed in this embodiment. Figure 9 As shown, the buffeting force characteristics of a 4:1 rectangular model were tested in a wind tunnel. The model is 40cm wide, 10cm high, and 120cm long. Pressure gauges were placed on the model surface to collect surface pressure, and the buffeting force of the cross section was obtained by integrating the pressure gauge pressures. Spectral analysis of the cross section buffeting force under different wind fields was performed, and the results are shown below. Figure 10 As shown, in different wind fields, the cross-sectional buffeting force of the rectangular model also exhibits frequency domain characteristics consistent with pulsating wind, proving the effectiveness, accuracy, and applicability of the method proposed in this embodiment.

[0060] The above-described embodiments are merely preferred embodiments provided to fully illustrate the present invention, and the scope of protection of the present invention is not limited thereto. Equivalent substitutions or modifications made by those skilled in the art based on the present invention are all within the scope of protection of the present invention. The scope of protection of the present invention is defined by the claims.

Claims

1. A method for active control of wind tunnel physical simulation of arbitrary pulsating wind fields, characterized in that: Includes the following steps: Step 1: Establish a generalized pulsating wind field spectrum model, represented as: in: The power spectrum of pulsating wind; For frequency; , , , , and These are model parameters, and the energy distribution of the pulsating wind spectrum in the frequency domain is controlled by adjusting these model parameters; Step 2: Based on the generalized pulsating wind field spectrum model, calculate the cross spectrum of pulsating wind at multiple points in space using the Davenport coherence function model to form the cross spectrum matrix of pulsating wind; Step 3: Based on the harmonic superposition method, the cross-spectral matrix of the pulsating wind is converted into the spatiotemporal information of the pulsating wind field; Step 4: Import the spatiotemporal information of the pulsating wind field into the control program of the active control wind tunnel to generate the target wind field, and monitor the generated pulsating wind speed in real time. Adjust the model parameters according to the difference between the monitoring results and the expected target, and iterate until the generated pulsating wind speed spectrum reaches the expected value. In step one, the method for determining the pulsating wind spectrum by adjusting the model parameters is as follows: Adjust parameters Determine the slope of the pulsating wind spectrum at low frequencies; Adjust parameters and Determine the slope of the pulsating wind spectrum at high frequencies. And slope The value is less than 0; Adjust parameters and Determine the boundary between low and high frequencies. ; Adjust parameters Determine the overall energy of the pulsating wind spectrum .

2. The wind tunnel physical simulation method for actively controlling arbitrary pulsating wind fields according to claim 1, characterized in that: The dividing point between low and high frequencies Represented as: Overall energy of the pulsating wind spectrum Represented as: 。 3. The wind tunnel physical simulation method for actively controlling arbitrary pulsating wind fields according to claim 1, characterized in that: In step one, when the model parameters take the following specific values, the generalized fluctuating wind field spectrum model degenerates into the traditional fluctuating wind spectrum model: when , , , , and At that time, the generalized pulsating wind field spectrum model is consistent with the von Kármán model; when , , , , and At that time, the generalized pulsating wind field spectrum model is consistent with the Simiu model; when , , , , and At that time, the generalized pulsating wind field spectrum model is consistent with the Panofsky model; in: The standard deviation of the downstream pulsating wind; The longitudinal integral scale; Average wind speed; For height; The frictional speed is denoted as .

4. The wind tunnel physical simulation method for actively controlling arbitrary pulsating wind fields according to claim 1, characterized in that: In step two, the Davenport coherence function model is expressed as: in: It is a coherence function; The attenuation coefficient; The distance between two points in space; Average wind speed; Based on the Davenport coherence function model and the generalized fluctuating wind field spectrum model, the cross spectrum of fluctuating wind is calculated: in: The distance is spatial point and spatial points The pulsating wind spectrum; and For spatial points and spatial points The pulsating wind spectrum; For spatial points and spatial points The coherence function; When space has When there are points, space is formed. Point-to-point pulsating wind cross-spectral matrix : in: For spatial points and spatial points The pulsating wind spectrum.

5. The wind tunnel physical simulation method for actively controlling arbitrary pulsating wind fields according to claim 1, characterized in that: In step three, the cross-spectral matrix of the pulsating wind is transformed based on the harmonic superposition method. The method for obtaining spatiotemporal information of pulsating wind fields is as follows: Discretize the pulsating wind field into space Point, cutoff frequency is The number of discrete frequency points is Then the first Pulsating wind time history at each point Simulate using the following formula: Where: angular frequency , To distribute evenly in random phase, Let be the frequency step size, and ; Here are the component indices after Cholesky decomposition, and ; It is a frequency discrete index, and ; For time; for Point space pulsating wind cross spectrum matrix Cholesky decomposition matrix: in: for The Cholesky decomposition matrix; for The conjugate matrix; It is the angular frequency; for The phase, and: in: To extract the imaginary part; To take the real part.

6. The wind tunnel physical simulation method for actively controlling arbitrary pulsating wind fields according to claim 5, characterized in that: The first Pulsating wind time history at each point In complex exponential form: make: in: and To simplify the time history of pulsating wind Use intermediate parameters in complex exponential form; Then the first Pulsating wind time history at each point The complex exponential form can be rewritten as: in: For time, and satisfy , For time step, Index for discrete time points; It is the imaginary unit.

7. The wind tunnel physical simulation method for actively controlling arbitrary pulsating wind fields according to claim 1, characterized in that: In step four, the method for iteratively generating the fluctuating wind speed spectrum is as follows: After the generated spatiotemporal information of the pulsating wind field is imported into the active control wind tunnel control program, the wind speed is monitored and fed back in real time by placing an anemometer in the wind tunnel. The actual wind speed time history is obtained and spectral analysis is performed. The actual wind field characteristics are compared with the target wind field characteristics. The model parameters of the generalized pulsating wind field spectrum model established in step one are fine-tuned according to the degree of difference. Steps one to three are then re-executed to generate a new pulsating wind speed time history and import it into the active control wind tunnel control program again until the generated wind field reaches the expected target, thus achieving accurate simulation of the target wind field.

8. A system for implementing the wind tunnel physical simulation method for active control of arbitrary pulsating wind fields as described in any one of claims 1-7, characterized in that: include: The modeling module is used to build a generalized pulsating wind field spectrum model; The matrix calculation module is used to calculate the cross spectrum of pulsating wind at multiple points in space based on the generalized pulsating wind field spectrum model and using the Davenport coherence function model to form the cross spectrum matrix of pulsating wind. The time history generation module is used to convert the cross-spectral matrix of the pulsating wind into the spatiotemporal information of the pulsating wind field based on the harmonic superposition method. The active control wind tunnel execution module is used to import the spatiotemporal information of the pulsating wind field into its control program to generate the target wind field, and includes a wind speed monitoring and feedback unit, which is used to adjust the model parameters in the modeling module according to the difference between the actual generated wind field and the expected target, forming an iterative control loop.

Images