A method for analyzing fluctuating wind pressure loads on wind tunnel structures based on linear regression

Abstract

The present invention discloses a method for analyzing the pulsating wind pressure load of a wind tunnel structure based on linear regression. The invention includes: obtaining pulsating wind pressure data, the pulsating wind pressure data includes wind speed, peak frequency and pulsating wind pressure, the peak frequency includes upstream peak frequency and downstream peak frequency, and the pulsating wind pressure includes upstream pulsation and downstream pulsation; eliminating the pulsating wind pressure data affected by errors according to the coefficient of variation and the F statistic to obtain the preferred pulsating wind pressure data; obtaining the pulsating wind pressure parameters according to the preferred pulsating wind pressure data; obtaining the load analysis formula according to the pulsating wind pressure parameters, the load analysis formula includes the pulsating frequency function and the pulsating wind pressure function; obtaining the vibration load according to the load analysis formula, the vibration load includes the frequency load and the wind pressure load, and solves the problem that the structural design of large-scale wind tunnel laboratory facilities in the related technology is prone to resonance phenomenon, resulting in safety risks of the structure.

Description

Technical Field

[0001] The invention relates to the technical field of engineering vibration, and in particular to a method for analyzing pulsating wind pressure loads of a wind tunnel structure based on linear regression. Background Art

[0002] Large wind tunnel test facilities are equipment used to simulate air flow and test the performance and behavior of objects such as aircraft, cars, and buildings in the air. These facilities usually consist of a huge enclosed space with a powerful fan system installed inside to generate high-speed airflow. By controlling the wind speed and airflow direction, researchers can simulate various wind field conditions to evaluate the aerodynamic performance, airflow resistance, lift and other parameters of the object. Large wind tunnel test facilities play an important role in aerospace, automotive industry, architectural design and other fields. By conducting tests in wind tunnels, engineers and designers can optimize product design, improve performance, reduce risks, and save costs.

[0003] As the size of wind tunnel anechoic chambers increases, it will bring some difficulties to the design of building structures. Vibration and resonance are one of the important challenges in the design of wind tunnel anechoic chambers. Due to the high-speed airflow, pressure difference, and wind pressure pulsation in the wind tunnel anechoic chamber, huge forces are exerted on the building structure, causing vibration and resonance of the structure.

[0004] Vibration and resonance may have an adverse effect on the strength and stability of building structures, and even cause structural damage or deformation. In addition, vibration may also reduce the fatigue life of the structure and increase the cost of maintenance and repair. Therefore, when designing a structure, it is necessary to understand the vibration characteristics and resonance frequency of the structure through structural dynamic analysis and simulation, so as to predict and avoid the occurrence of resonance in the design stage.

[0005] The key issues that need to be considered in the structural dynamics design of large wind tunnel anechoic chambers include: (1) wind tunnel airflow loading characteristics, such as pulsating wind pressure and peak frequency; (2) structural dynamic characteristics, such as natural frequency and vibration mode state; (3) vibration response analysis, such as vibration response parameters such as displacement, velocity, acceleration and stress, to evaluate the stability and safety of the structure; (4) fatigue life assessment, evaluating the fatigue life of the anechoic chamber structure to ensure the safety and reliability of the structure in long-term use.

[0006] In the past, the structural design of large wind tunnel laboratory facilities was static design or quasi-static design. Due to the significant dynamic effect of pulsating wind pressure, coupled with the large span and low structural stiffness of large wind tunnels, vibration matching effect is prone to occur, resulting in resonance. Therefore, static design cannot meet the structural safety issues.

[0007] In the prior art, the structural design of large wind tunnel laboratory facilities is prone to resonance, which leads to technical problems such as safety risks in the structure. No effective solution has been proposed so far. Summary of the invention

[0008] The main purpose of the present application is to provide a method for analyzing the pulsating wind pressure load of a wind tunnel structure based on linear regression, so as to at least solve the problem in the related art that the structural design of large wind tunnel laboratory facilities is prone to resonance, resulting in safety risks to the structure.

[0009] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a method for analyzing the pulsating wind pressure load of a wind tunnel structure based on linear regression is provided. The method comprises: obtaining pulsating wind pressure data, the pulsating wind pressure data comprises wind speed, peak frequency and pulsating wind pressure, the peak frequency comprises upstream peak frequency and downstream peak frequency, and the pulsating wind pressure comprises upstream pulsation and downstream pulsation; eliminating the pulsating wind pressure data affected by errors according to the coefficient of variation and the F statistic to obtain the preferred pulsating wind pressure data; obtaining pulsating wind pressure parameters according to the preferred pulsating wind pressure data, wherein the pulsating wind pressure parameters comprise a fitting function, a unilateral tolerance coefficient and a variance, the fitting function comprises a frequency fitting function and a wind pressure fitting function, the unilateral tolerance coefficient comprises a frequency unilateral tolerance coefficient and a wind pressure unilateral tolerance coefficient, and the variance comprises a frequency variance and a wind pressure variance; obtaining a load analysis formula according to the pulsating wind pressure parameters, the load analysis formula comprises a pulsating frequency function and a pulsating wind pressure function; obtaining a vibration load according to the load analysis formula, and the vibration load comprises a frequency load and a wind pressure load.

[0010] Optionally, the pulsating wind pressure data is preprocessed according to the mean formula and the variance formula to obtain the variance and the mean, wherein the variance includes the variance corresponding to the peak frequency and the variance corresponding to the pulsating wind pressure, and the mean includes the mean corresponding to the peak frequency and the mean corresponding to the pulsating wind pressure; the pulsating wind pressure data corresponding to the coefficient of variation greater than 15% is eliminated; when the F statistic is less than the critical value of the F distribution, the pulsating wind pressure data corresponding to the F statistic is eliminated, wherein the critical value of the F distribution is .

[0011] Optionally, substitute the mean and variance into the coefficient of variation formula to obtain the coefficient of variation, where the coefficient of variation formula is ,in, is the coefficient of variation, is the variance, is the mean.

[0012] Optionally, calculate the F statistic, which is calculated as ,in, is the number of data sets in the fluctuating wind pressure data, where a data set includes a wind speed, a peak frequency, and a fluctuating wind pressure. is the fitting function, is the function data of the fitting function, is the mean of the fitted function.

[0013] Optionally, a linear relationship is established based on the fluctuating wind pressure data to obtain a fitting function, wherein the linear relationship includes a linear relationship between wind speed and peak frequency and a linear relationship between wind speed and fluctuating wind pressure.

[0014] Optionally, when the coefficient of variation is known, the one-sided tolerance coefficient is calculated using the formula: ,in, is the one-sided tolerance factor, is the standard normal distribution function of the corresponding quantile The value of the independent variable, is the number of data sets in the pulsating wind pressure data, is the exceedance probability; when the coefficient of variation is unknown, calculate the one-sided tolerance coefficient, the calculation formula is ,in, is the one-sided tolerance factor, Degree of freedom of Distribution Function The independent variable value corresponding to the quantile value, To exceed probability.

[0015] Optionally, the pulsation peak frequency is calculated according to the pulsation frequency function, ,in, is the peak pulsation frequency, is the fitting function corresponding to the peak frequency, is the wind speed, is the one-sided tolerance coefficient corresponding to the peak frequency, The variance corresponding to the peak frequency; the peak value of the pulsating wind pressure is calculated based on the pulsating wind pressure function, ,in, is the peak value of pulsating wind pressure, is the fitting function corresponding to the pulsating wind pressure, is the wind speed, is the one-side tolerance coefficient corresponding to the pulsating wind pressure, Variance corresponding to pulsating wind pressure.

[0016] According to another aspect of the present application, a device for analyzing the pulsating wind pressure load of a wind tunnel structure based on linear regression is provided. The device includes: a first acquisition unit, used to acquire pulsating wind pressure data, the pulsating wind pressure data includes wind speed, peak frequency and pulsating wind pressure, the peak frequency includes upstream peak frequency and downstream peak frequency, and the pulsating wind pressure includes upstream pulsation and downstream pulsation; a elimination unit, used to eliminate the pulsating wind pressure data affected by errors according to the coefficient of variation and the F statistic, so as to obtain preferred pulsating wind pressure data; a second acquisition unit, used to acquire pulsating wind pressure parameters according to the preferred pulsating wind pressure data, wherein the pulsating wind pressure parameters include a fitting function, a one-sided tolerance coefficient and a variance, the fitting function includes a frequency fitting function and a wind pressure fitting function, the one-sided tolerance coefficient includes a frequency one-sided tolerance coefficient and a wind pressure one-sided tolerance coefficient, and the variance includes a frequency variance and a wind pressure variance; a third acquisition unit, used to acquire a load analysis formula according to the pulsating wind pressure parameters, the load analysis formula includes a pulsating frequency function and a pulsating wind pressure function; a fourth acquisition unit, used to acquire a vibration load according to the load analysis formula, the vibration load includes a frequency load and a wind pressure load.

[0017] In order to achieve the above-mentioned purpose, according to another aspect of the present application, a computer-readable storage medium is provided, which includes a stored program, wherein the program executes any one of the above-mentioned methods for analyzing the pulsating wind pressure load of a wind tunnel structure based on linear regression.

[0018] According to another aspect of the present application, an electronic device is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a method for executing any one of the items of analyzing the pulsating wind pressure load of a wind tunnel structure based on linear regression.

[0019] Through the present application, the following steps are adopted: obtaining pulsating wind pressure data, the pulsating wind pressure data includes wind speed, peak frequency and pulsating wind pressure, the peak frequency includes upstream peak frequency and downstream peak frequency, and the pulsating wind pressure includes upstream pulsation and downstream pulsation; eliminating the pulsating wind pressure data affected by errors according to the coefficient of variation and the F statistic to obtain the preferred pulsating wind pressure data; obtaining the pulsating wind pressure parameters according to the preferred pulsating wind pressure data, wherein the pulsating wind pressure parameters include a fitting function, a unilateral tolerance coefficient and a variance, the fitting function includes a frequency fitting function and a wind pressure fitting function, and the unilateral tolerance coefficient includes a frequency fitting function and a wind pressure fitting function. The limit coefficients include the single-sided tolerance coefficient of frequency and the single-sided tolerance coefficient of wind pressure, and the variance includes the frequency variance and the wind pressure variance; the load analysis formula is obtained based on the pulsating wind pressure parameters, and the load analysis formula includes the pulsating frequency function and the pulsating wind pressure function; the vibration load is obtained based on the load analysis formula, and the vibration load includes the frequency load and the wind pressure load, which solves the problem that the structural design of large wind tunnel laboratory facilities in related technologies is prone to resonance, resulting in safety risks of the structure, and thus achieves the effect of analyzing the wind pressure load capacity of large wind tunnels and ensuring the safety of wind tunnel tests. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 It is a flow chart of a method for analyzing pulsating wind pressure load of a wind tunnel structure based on linear regression according to an embodiment of the present application;

[0022] Figure 2 It is a schematic diagram of pulsating wind pressure;

[0023] Figure 3 This is the upstream frequency regression analysis graph;

[0024] Figure 4 This is the downstream frequency regression analysis graph;

[0025] Figure 5 is the regression analysis diagram of upstream wind pressure load;

[0026] Figure 6 This is the regression analysis diagram of the downstream wind pressure load;

[0027] Figure 7 It is a structural block diagram of a device for analyzing the pulsating wind pressure load of a wind tunnel structure based on linear regression according to an embodiment of the present application. DETAILED DESCRIPTION

[0028] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0029] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0030] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0031] As introduced in the background technology, in the prior art, due to the significant dynamic effect of pulsating wind pressure, coupled with the large span and low structural stiffness of large wind tunnels, a vibration matching effect is prone to occur, resulting in resonance. In order to solve the problem that the structural design of large wind tunnel laboratory facilities is prone to resonance, which leads to safety risks of the structure, an embodiment of the present application provides a method for analyzing the pulsating wind pressure load of a wind tunnel structure based on linear regression.

[0032] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0033] In this embodiment, a method for analyzing the pulsating wind pressure load of a wind tunnel structure based on linear regression is provided, which runs on a mobile terminal, a computer terminal or a similar computing device. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0034] Figure 1 FIG. 1 is a flow chart of a method for analyzing the fluctuating wind pressure load of a wind tunnel structure based on linear regression according to an embodiment of the present application. Figure 1 As shown, the method comprises the following steps:

[0035] Step S101, obtaining pulsating wind pressure data, the pulsating wind pressure data includes wind speed, peak frequency and pulsating wind pressure, the peak frequency includes upstream peak frequency and downstream peak frequency, and the pulsating wind pressure includes upstream pulsation and downstream pulsation.

[0036] Specifically, when conducting a wind tunnel experiment, it is necessary to measure the wind speed, peak frequency and fluctuating wind pressure at the quantile value. The peak frequency includes the upstream peak frequency and the downstream peak frequency, and the fluctuating wind pressure includes the upstream pulsation and the downstream pulsation. In fact, in the subsequent analysis process, four sets of data can be analyzed, namely the pulsating frequency function corresponding to the upstream peak frequency, the pulsating frequency function corresponding to the downstream peak frequency, the fluctuating wind pressure function corresponding to the upstream pulsation, and the fluctuating wind pressure function corresponding to the downstream pulsation.

[0037] Step S102: Eliminate the pulsating wind pressure data affected by errors according to the coefficient of variation and the F statistic to obtain the optimal pulsating wind pressure data.

[0038] Specifically, the coefficient of variation method can be used to eliminate items with large errors in the data. In practical applications, the F test can help us confirm the overall applicability of the regression model, thereby evaluating the quality and effectiveness of the model.

[0039] Step S103, obtaining pulsating wind pressure parameters according to the preferred pulsating wind pressure data, wherein the pulsating wind pressure parameters include a fitting function, a one-sided tolerance coefficient and a variance, the fitting function includes a frequency fitting function and a wind pressure fitting function, the one-sided tolerance coefficient includes a frequency one-sided tolerance coefficient and a wind pressure one-sided tolerance coefficient, and the variance includes a frequency variance and a wind pressure variance.

[0040] Specifically, the fitting function is obtained through linear regression analysis, and the one-sided tolerance coefficient is obtained by measuring the quantile points. After obtaining the data, the one-sided tolerance coefficient is obtained according to the probability that the independent variable is greater than the limit value, and the variance is obtained through the variance formula.

[0041] Step S104, obtaining a load analysis formula according to the pulsating wind pressure parameter, wherein the load analysis formula includes a pulsating frequency function and a pulsating wind pressure function.

[0042] Specifically, the vibration load is multiplied by the one-sided tolerance coefficient to obtain the parameter functions of the vibration load setting values ​​used in the structural design: ; ; ; The four functions correspond to the upstream peak frequency, downstream peak frequency, upstream pulsation and downstream pulsation respectively.

[0043] Step S105, obtaining vibration load according to the load analysis formula, where the vibration load includes frequency load and wind pressure load.

[0044] Specifically, through the above steps, the vibration load design value can be determined, thus providing a basis for the reliability design of the structure. Since each wind speed condition corresponds to a wind pressure value, and the vibration load is constrained by the median line, the degree of freedom of the T distribution function can be taken as 1. The one-sided tolerance coefficient at the significance level of 0.05 is: Based on the linear regression analysis, the fitting function is selected as the best function to represent the trend term of wind pressure load according to the correlation test. Taking the fitted linear function as the benchmark and considering the unilateral tolerance coefficient, the upper limit of the vibration load is obtained.

[0045] It is very important to consider the dynamic effect of fluctuating wind pressure in the design of wind tunnel laboratory structures, especially in large wind tunnels. The amplitude of fluctuating wind pressure load is related to wind speed and has a certain randomness, which brings challenges to the design.

[0046] In order to ensure the bearing capacity and fatigue strength of the wind tunnel structure, it is necessary to conduct statistical analysis of the fluctuating wind pressure load during the design process. This can be achieved by collecting wind tunnel test data and processing and analyzing these data using statistical methods. After statistical analysis, the fluctuating wind pressure load with a certain guaranteed probability can be obtained, thereby better evaluating the safety of the structure.

[0047] When considering fluctuating wind pressure loads, in addition to considering amplitude and randomness, it is also necessary to consider the effect of frequency on the structure. The relationship between the natural frequency of the structure and the frequency of fluctuating wind pressure is crucial to avoid resonance. Therefore, when designing wind tunnel structures, it is also necessary to match and analyze the natural frequency of the structure and the frequency of fluctuating wind pressure.

[0048] In general, the safety and stability of large wind tunnel laboratory structures can be improved by comprehensively considering the characteristics of pulsating wind pressure loads, conducting effective statistical analysis, and fully considering the dynamic response and frequency characteristics of the structure in the design.

[0049] In the reliability design of large wind tunnel test facility structures, it is crucial to perform a probabilistic analysis of fluctuating wind pressure loads. In order to determine the statistical characteristic parameters of fluctuating wind pressure loads, the following steps can be performed:

[0050] 1. Collect data: Obtain test data of fluctuating wind pressure loads. These data usually cover the changes in fluctuating wind pressure within a certain time range.

[0051] 2. Data preprocessing: Preprocess the collected pulsating wind pressure data, including data cleaning, filtering, correction and other operations to ensure the accuracy and reliability of the data.

[0052] 3. Statistical analysis: Analyze the pulsating wind pressure data using statistical methods to determine its statistical characteristic parameters. Common statistical characteristic parameters include mean, standard deviation, peak factor, probability density function, etc.

[0053] 4. Frequency domain analysis: By performing frequency domain analysis on the pulsating wind pressure data, the frequency component information can be obtained, including the main frequency, frequency distribution, etc., which is very important for the dynamic response analysis of the structure.

[0054] 5. Probability density function fitting: According to the probability distribution characteristics of the pulsating wind pressure data, an appropriate probability density function (such as normal distribution, Weibull distribution, etc.) can be selected to fit the data to better describe the probability characteristics of the pulsating wind pressure.

[0055] 6. Reliability analysis: Based on the statistical characteristic parameters of the pulsating wind pressure load, the reliability analysis of the structure can be carried out to evaluate the safety and reliability level of the structure under the action of pulsating wind pressure, providing a basis for structural design.

[0056] Through the above steps, a comprehensive statistical analysis of the pulsating wind pressure load data can be carried out to determine the characteristic parameters with statistical significance, providing basic conditions for structural reliability design.

[0057] In an optional embodiment, the pulsating wind pressure data is preprocessed according to the mean formula and the variance formula to obtain the variance and the mean, wherein the variance includes the variance corresponding to the peak frequency and the variance corresponding to the pulsating wind pressure, and the mean includes the mean corresponding to the peak frequency and the mean corresponding to the pulsating wind pressure; the pulsating wind pressure data corresponding to the coefficient of variation greater than 15% is eliminated; when the F statistic is less than the F distribution critical value, the pulsating wind pressure data corresponding to the F statistic is eliminated, wherein the F distribution critical value is .generally It is 0.05, which means the exceedance coefficient is 5%.

[0058] Specifically, the wind tunnel pulsating wind pressure data needs to be preprocessed first, mainly the analysis of the mean and variance. ; , here Corresponding test wind speed , Corresponding wind speed The mean of .

[0059] In regression analysis, the F statistic is used to test the significance of the regression model, that is, to determine whether the independent variable has a significant impact on the dependent variable. Represents a significant difference at the 0.05 level (usually ), the critical value of the F distribution with degrees of freedom of 1 and n-1. If the calculated F statistic is greater than this critical value, that is, the F statistic falls within the rejection region, then the null hypothesis can be rejected.

[0060] In an optional embodiment, the mean and variance are substituted into the coefficient of variation formula to obtain the coefficient of variation, where the coefficient of variation formula is: ,in, is the coefficient of variation, is the variance, When performing statistical analysis on data, if the coefficient of variation is greater than 15%, the data may be considered abnormal and should be eliminated.

[0061] In an optional embodiment, the F statistic is calculated using the formula: ,in, is the number of data sets in the fluctuating wind pressure data, where a data set includes a wind speed, a peak frequency, and a fluctuating wind pressure. is the fitting function, is the function data of the fitting function, is the mean of the fitted function.

[0062] Specifically, the F test is a statistical method that is often used to judge the applicability of a regression analysis model. In regression analysis, the F test can be used to evaluate the overall significance of the independent variables (explanatory variables) in the regression model, that is, to determine whether the established regression equation can effectively explain the variation of the dependent variable (target variable). When performing linear regression analysis, the null hypothesis of the F test is that the coefficients of all independent variables are zero, that is, the independent variables in the model have no significant effect on the dependent variable. If the p-value of the F test is less than the set significance level (usually 0.05), the null hypothesis can be rejected, and it is believed that the regression model is significant, that is, at least one independent variable has a significant effect on the dependent variable. In practical applications, the F test is often used in conjunction with the t test, which is used to determine whether the coefficients of each independent variable are significant. Through the comprehensive analysis of the F test and the t test, the applicability of the regression model and the degree of influence of the independent variables can be comprehensively evaluated. In general, the F test is one of the commonly used tools in regression analysis, which is used to judge the significance of the overall regression model and help us understand the model's degree of fit and predictive ability for the data. Here, represents the critical value of the F distribution with 1 and n-1 degrees of freedom at a significance level of 0.05. Specifically, The test is often used to determine whether the coefficient of the independent variable in a regression model is significantly different from zero. In this case, we calculate the F statistic and then compare it with If the calculated F statistic is greater than , then we can reject the null hypothesis that at least one independent variable has a significant effect on the dependent variable and the regression model is significant. On the contrary, if the calculated F statistic is less than , then we accept the null hypothesis, that is, the independent variables in the regression model have no significant effect on the dependent variable. In regression analysis, this F distribution is usually used to perform the F test to evaluate the overall significance of the regression model. Rejecting the null hypothesis means that at least one independent variable has a significant effect on the dependent variable, that is, the regression model is significant, and the effect of the independent variable on the dependent variable is not caused by random factors. Such a conclusion supports the statistical validity and reliability of the established regression model.

[0063] In an optional embodiment, a linear relationship is established based on the fluctuating wind pressure data to obtain a fitting function, wherein the linear relationship includes a linear relationship between wind speed and peak frequency and a linear relationship between wind speed and fluctuating wind pressure.

[0064] Specifically,

[0065]

[0066]

[0067]

[0068]

[0069]

[0070]

[0071]

[0072] So we get the linear regression equation:

[0073]

[0074] Among them, , or .

[0075] In an optional embodiment, when the coefficient of variation is known, the one-sided tolerance coefficient is calculated using the formula: ,in, is the one-sided tolerance factor, is the standard normal distribution function of the corresponding quantile The value of the independent variable, is the number of data sets in the pulsating wind pressure data, is the exceedance probability; when the coefficient of variation is unknown, calculate the one-sided tolerance coefficient, the calculation formula is ,in, is the one-sided tolerance factor, Degree of freedom of Distribution Function The independent variable value corresponding to the quantile value, To exceed probability.

[0076] Specifically, in the reliability load statistical analysis, the standard value one-sided tolerance factor ( ) is used to determine whether a structure or system meets reliability requirements under given loading conditions. This factor is usually related to the variability of the load data. ) is divided into coefficient of variation " Known" and " The two situations of "unknown" have the following meanings in reliability analysis:

[0077] “ Known” situation: In this case, the coefficient of variation ( ) is known. This means that for a given load data, its variability or fluctuation has been well defined. Knowing the coefficient of variation helps to more accurately assess the reliability of a structure or system under specific loading conditions and determine whether it meets the design requirements.

[0078] “ Unknown” case: In this case, the coefficient of variation ( ) is unknown. This may mean that the variability of the load data needs to be estimated based on actual data or other information. Dealing with unknown coefficients of variation requires careful consideration, as the uncertainty in the load data may affect the results and conclusions of the reliability analysis.

[0079] The standard value one-sided tolerance factor ( ) is divided into coefficient of variation " Known" and " The two situations of "unknown" and "unknown" are helpful to consider the variability of load data in the reliability load statistical analysis, so as to more effectively evaluate the reliability of the structure or system.

[0080] In an optional embodiment, the pulsation peak frequency is calculated according to the pulsation frequency function. ,in, is the peak pulsation frequency, is the fitting function corresponding to the peak frequency, is the wind speed, is the one-sided tolerance coefficient corresponding to the peak frequency, The variance corresponding to the peak frequency; the peak value of the pulsating wind pressure is calculated based on the pulsating wind pressure function, ,in, is the peak value of pulsating wind pressure, is the fitting function corresponding to the pulsating wind pressure, is the wind speed, is the one-side tolerance coefficient corresponding to the pulsating wind pressure, Variance corresponding to pulsating wind pressure.

[0081] In addition, for statistical analysis of wind tunnel wind pressure data, it is usually necessary to calculate the mean and variance. For static data with only amplitude and data, the mean and variance are easy to calculate. For vibration load data, there is not only amplitude data but also frequency data related to it. So far, there is no statistical analysis method for vibration loads. Here, the variance related to frequency change is defined as ( ) using the residuals from the least squares method ( ) term. The variance is defined as: , .

[0082] Specifically, in order to determine the vibration load design value, the following steps can be used:

[0083] (1) Determine the median line of vibration load: Based on the experimental wind pressure data, regression analysis is used to obtain the fitting function, including the pulsation frequency function of each area. , and fluctuating wind pressure function , . All based on test wind speed is the vibration load median function of the independent variable. Among them, the vibration load median fitting function is a straight line shown in the image.

[0084] (2) Analysis of vibration loads within the design period: Based on the characteristics of the structure and working conditions, calculate the statistical characteristic parameters of the vibration loads within the design period, including the median function of each area. , , , and variance , , , . is the fitting function corresponding to the upstream peak frequency, is the fitting function corresponding to the downstream peak frequency, is the fitting function corresponding to the upstream pulsation, is the fitting function corresponding to the downstream pulsation, is the variance corresponding to the upstream peak frequency, is the variance corresponding to the downstream peak frequency, is the variance corresponding to the upstream pulsation, is the variance corresponding to the downstream pulsation.

[0085] (3) Determine the one-sided tolerance factor: The one-sided tolerance factor is used to take into account the uncertainty factors in the design and is usually given according to the design standards or relevant specifications. This factor takes into account the adverse conditions that the structure may encounter during its design life. is the unilateral tolerance coefficient corresponding to the upstream peak frequency, is the one-sided tolerance coefficient corresponding to the downstream peak frequency, is the one-sided tolerance coefficient corresponding to the upstream pulsation, is the one-sided tolerance factor corresponding to the downstream pulsation.

[0086] (4) Wind pressure load with guaranteed probability: Multiply the vibration load by the one-sided tolerance coefficient to obtain the parameter functions of the vibration load setting value used in the structural design, which are the pulsation frequency function corresponding to the upstream peak frequency and the downstream peak frequency, and the pulsation wind pressure function corresponding to the upstream pulsation and the downstream pulsation:

[0087]

[0088]

[0089]

[0090]

[0091] (5) Structural dynamic reliability design: By substituting the pulsating wind pressure load data obtained from the above analysis into the structural analysis model, structural dynamic reliability design can be carried out to ensure that the structure has the required safety and stability.

[0092] In an optional embodiment, the pulsation frequency function and the pulsation wind pressure function are calculated:

[0093] like Figure 2 As shown, this is the distribution diagram of the pulsating wind pressure load with a guarantee probability of 95% (exceedance probability of 5%).

[0094] Wind tunnel wind pressure test data:

[0095]

[0096] The relationship between frequency and wind speed is obtained based on wind tunnel wind pressure test data:

[0097]

[0098] The relationship between frequency and wind speed obtained according to the average fitting value is: ;

[0099] like Figure 3 and Figure 4 , Figure 3 This is the upstream frequency regression analysis diagram. Figure 4 This is the downstream frequency regression analysis diagram.

[0100]

[0101] The standard value of the one-sided tolerance factor is When , the upstream and downstream pulsating wind pressure envelopes with a guaranteed probability of 95% are:

[0102]

[0103]

[0104] like Figure 5 , Figure 5 For the upstream wind pressure load regression analysis, Figure 6 , Figure 6 Regression analysis of downstream wind pressure load.

[0105] The embodiment of the present application also provides a device for analyzing the pulsating wind pressure load of a wind tunnel structure based on linear regression. It should be noted that the device for analyzing the pulsating wind pressure load of a wind tunnel structure based on linear regression in the embodiment of the present application can be used to execute the method for analyzing the pulsating wind pressure load of a wind tunnel structure based on linear regression provided in the embodiment of the present application. The device is used to implement the above-mentioned embodiments and preferred implementation modes, and those that have been explained will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0106] The following introduces a device for analyzing the pulsating wind pressure load of a wind tunnel structure based on linear regression provided in an embodiment of the present application.

[0107] Figure 7 1 is a structural block diagram of a device for analyzing the pulsating wind pressure load of a wind tunnel structure based on linear regression according to an embodiment of the present application. Figure 7As shown, the device includes: a first acquisition unit 701, which is used to acquire pulsating wind pressure data, the pulsating wind pressure data includes wind speed, peak frequency and pulsating wind pressure, the peak frequency includes upstream peak frequency and downstream peak frequency, and the pulsating wind pressure includes upstream pulsation and downstream pulsation; a removal unit 702, which is used to remove the pulsating wind pressure data affected by errors according to the coefficient of variation and the F statistic to obtain the preferred pulsating wind pressure data; a second acquisition unit 703, which is used to obtain the pulsating wind pressure parameters according to the preferred pulsating wind pressure data, wherein the pulsating wind pressure parameters include The first acquisition unit 704 is used to obtain a load analysis formula according to the pulsating wind pressure parameter, and the load analysis formula includes a pulsating frequency function and a pulsating wind pressure function; the fourth acquisition unit 705 is used to obtain a vibration load according to the load analysis formula, and the vibration load includes a frequency load and a wind pressure load.

[0108] In an optional embodiment, the elimination unit 702 includes: a preprocessing subunit, which is used to preprocess the pulsating wind pressure data according to the mean formula and the variance formula to obtain the variance and the mean, wherein the variance includes the variance corresponding to the peak frequency and the variance corresponding to the pulsating wind pressure, and the mean includes the mean corresponding to the peak frequency and the mean corresponding to the pulsating wind pressure; a first elimination subunit, which is used to eliminate the pulsating wind pressure data corresponding to the coefficient of variation greater than 15%; a second elimination subunit, which is used to eliminate the pulsating wind pressure data corresponding to the F statistic when the F statistic is less than the F distribution critical value, wherein the F distribution critical value is .

[0109] In an optional embodiment, the first elimination subunit includes: a substitution module, which is used to substitute the mean and variance into the coefficient of variation formula to obtain the coefficient of variation, wherein the coefficient of variation formula is: ,in, is the coefficient of variation, is the variance, is the mean.

[0110] In an optional embodiment, the first elimination subunit includes: a calculation module for calculating the F statistic, and the calculation formula is: ,in, is the number of data sets in the fluctuating wind pressure data, where a data set includes a wind speed, a peak frequency, and a fluctuating wind pressure. is the fitting function, is the function data of the fitting function, is the mean of the fitted function.

[0111] In an optional embodiment, the second acquisition unit 703 includes: a fitting subunit, which is used to establish a linear relationship based on the pulsating wind pressure data to obtain a fitting function, wherein the linear relationship includes a linear relationship between wind speed and peak frequency and a linear relationship between wind speed and pulsating wind pressure.

[0112] In an optional embodiment, the second acquisition unit 703 includes: a first calculation subunit, which is used to calculate the unilateral tolerance coefficient when the coefficient of variation is known, and the calculation formula is: ,in, is the one-sided tolerance factor, is the standard normal distribution function of the corresponding quantile The value of the independent variable, is the number of data sets in the pulsating wind pressure data, is the exceedance probability; the second calculation subunit is used to calculate the unilateral tolerance coefficient when the coefficient of variation is unknown. The calculation formula is: ,in, is the one-sided tolerance factor, Degree of freedom of Distribution Function The independent variable value corresponding to the quantile value, To exceed probability.

[0113] In an optional embodiment, the third acquisition unit 704 includes: a third calculation subunit, configured to calculate the pulsation peak frequency according to the pulsation frequency function, ,in, is the peak pulsation frequency, is the fitting function corresponding to the peak frequency, is the wind speed, is the one-sided tolerance coefficient corresponding to the peak frequency, The variance corresponding to the peak frequency; the fourth calculation subunit is used to calculate the peak value of the pulsating wind pressure according to the pulsating wind pressure function, ,in, is the peak value of pulsating wind pressure, is the fitting function corresponding to the pulsating wind pressure, is the wind speed, is the one-side tolerance coefficient corresponding to the pulsating wind pressure, Variance corresponding to pulsating wind pressure.

[0114] The device for analyzing the fluctuating wind pressure load of a wind tunnel structure based on linear regression includes a processor and a memory, wherein the first acquisition unit 703 and the like are stored in the memory as program units, and the processor executes the program units stored in the memory to implement corresponding functions. The modules are all located in the same processor; or, the modules are located in different processors in any combination.

[0115] The processor contains a kernel, which calls the corresponding program unit from the memory. One or more kernels can be set, and the technical problem of the structural design of large wind tunnel laboratory facilities being prone to resonance and causing safety risks to the structure can be solved by adjusting the kernel parameters.

[0116] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0117] An embodiment of the present invention provides a computer-readable storage medium, which includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the method of analyzing the pulsating wind pressure load of a wind tunnel structure based on linear regression.

[0118] Specifically, a method for analyzing the fluctuating wind pressure load of a wind tunnel structure based on linear regression includes:

[0119] Obtain pulsating wind pressure data, the pulsating wind pressure data includes wind speed, peak frequency and pulsating wind pressure, the peak frequency includes upstream peak frequency and downstream peak frequency, and the pulsating wind pressure includes upstream pulsation and downstream pulsation; eliminate the pulsating wind pressure data affected by errors according to the coefficient of variation and the F statistic to obtain the preferred pulsating wind pressure data; obtain pulsating wind pressure parameters according to the preferred pulsating wind pressure data, wherein the pulsating wind pressure parameters include a fitting function, a one-sided tolerance coefficient and a variance, the fitting function includes a frequency fitting function and a wind pressure fitting function, the one-sided tolerance coefficient includes a frequency one-sided tolerance coefficient and a wind pressure one-sided tolerance coefficient, and the variance includes a frequency variance and a wind pressure variance; obtain a load analysis formula according to the pulsating wind pressure parameters, the load analysis formula includes a pulsating frequency function and a pulsating wind pressure function; obtain a vibration load according to the load analysis formula, and the vibration load includes a frequency load and a wind pressure load.

[0120] Optionally, the pulsating wind pressure data is preprocessed according to the mean formula and the variance formula to obtain the variance and the mean, wherein the variance includes the variance corresponding to the peak frequency and the variance corresponding to the pulsating wind pressure, and the mean includes the mean corresponding to the peak frequency and the mean corresponding to the pulsating wind pressure; the pulsating wind pressure data corresponding to the coefficient of variation greater than 15% is eliminated; when the F statistic is less than the critical value of the F distribution, the pulsating wind pressure data corresponding to the F statistic is eliminated, wherein the critical value of the F distribution is .

[0121] Optionally, substitute the mean and variance into the coefficient of variation formula to obtain the coefficient of variation, where the coefficient of variation formula is ,in, is the coefficient of variation, is the variance, is the mean.

[0122] Optionally, calculate the F statistic, which is calculated as ,in, is the number of data sets in the fluctuating wind pressure data, where a data set includes a wind speed, a peak frequency, and a fluctuating wind pressure. is the fitting function, is the function data of the fitting function, is the mean of the fitted function.

[0123] Optionally, a linear relationship is established based on the fluctuating wind pressure data to obtain a fitting function, wherein the linear relationship includes a linear relationship between wind speed and peak frequency and a linear relationship between wind speed and fluctuating wind pressure.

[0124] Optionally, when the coefficient of variation is known, the one-sided tolerance coefficient is calculated using the formula: ,in, is the one-sided tolerance factor, is the standard normal distribution function of the corresponding quantile The value of the independent variable, is the number of data sets in the pulsating wind pressure data, is the exceedance probability; when the coefficient of variation is unknown, calculate the one-sided tolerance coefficient, the calculation formula is ,in, is the one-sided tolerance factor, Degree of freedom of Distribution Function The independent variable value corresponding to the quantile value, To exceed probability.

[0125] Optionally, the pulsation peak frequency is calculated according to the pulsation frequency function, ,in, is the peak pulsation frequency, is the fitting function corresponding to the peak frequency, is the wind speed, is the one-sided tolerance coefficient corresponding to the peak frequency, The variance corresponding to the peak frequency; the peak value of the pulsating wind pressure is calculated based on the pulsating wind pressure function, ,in, is the peak value of pulsating wind pressure, is the fitting function corresponding to the pulsating wind pressure, is the wind speed, is the one-side tolerance coefficient corresponding to the pulsating wind pressure, Variance corresponding to pulsating wind pressure.

[0126] An embodiment of the present invention provides a processor, which is used to run a program, wherein the program executes the method of analyzing the pulsating wind pressure load of a wind tunnel structure based on linear regression when running.

[0127] Specifically, a method for analyzing the fluctuating wind pressure load of a wind tunnel structure based on linear regression includes:

[0128] Obtain pulsating wind pressure data, the pulsating wind pressure data includes wind speed, peak frequency and pulsating wind pressure, the peak frequency includes upstream peak frequency and downstream peak frequency, and the pulsating wind pressure includes upstream pulsation and downstream pulsation; eliminate the pulsating wind pressure data affected by errors according to the coefficient of variation and the F statistic to obtain the preferred pulsating wind pressure data; obtain pulsating wind pressure parameters according to the preferred pulsating wind pressure data, wherein the pulsating wind pressure parameters include a fitting function, a one-sided tolerance coefficient and a variance, the fitting function includes a frequency fitting function and a wind pressure fitting function, the one-sided tolerance coefficient includes a frequency one-sided tolerance coefficient and a wind pressure one-sided tolerance coefficient, and the variance includes a frequency variance and a wind pressure variance; obtain a load analysis formula according to the pulsating wind pressure parameters, the load analysis formula includes a pulsating frequency function and a pulsating wind pressure function; obtain a vibration load according to the load analysis formula, and the vibration load includes a frequency load and a wind pressure load.

[0129] Optionally, the pulsating wind pressure data is preprocessed according to the mean formula and the variance formula to obtain the variance and the mean, wherein the variance includes the variance corresponding to the peak frequency and the variance corresponding to the pulsating wind pressure, and the mean includes the mean corresponding to the peak frequency and the mean corresponding to the pulsating wind pressure; the pulsating wind pressure data corresponding to the coefficient of variation greater than 15% are eliminated; when the F statistic is less than When the distribution critical value is The pulsating wind pressure data corresponding to the statistic are eliminated, where the critical value of the F distribution is .

[0130] Optionally, substitute the mean and variance into the coefficient of variation formula to obtain the coefficient of variation, where the coefficient of variation formula is ,in, is the coefficient of variation, is the variance, is the mean.

[0131] Optionally, calculate the F statistic, which is calculated as ,in, is the number of data sets in the fluctuating wind pressure data, where a data set includes a wind speed, a peak frequency, and a fluctuating wind pressure. is the fitting function, is the function data of the fitting function, is the mean of the fitted function.

[0132] Optionally, a linear relationship is established based on the fluctuating wind pressure data to obtain a fitting function, wherein the linear relationship includes a linear relationship between wind speed and peak frequency and a linear relationship between wind speed and fluctuating wind pressure.

[0133] Optionally, when the coefficient of variation is known, the one-sided tolerance coefficient is calculated using the formula: ,in, is the one-sided tolerance factor, is the standard normal distribution function of the corresponding quantile The value of the independent variable, is the number of data sets in the pulsating wind pressure data, is the exceedance probability; when the coefficient of variation is unknown, calculate the one-sided tolerance coefficient, the calculation formula is ,in, is the one-sided tolerance factor, Degree of freedom of Distribution Function The independent variable value corresponding to the quantile value, To exceed probability.

[0134] Optionally, the pulsation peak frequency is calculated according to the pulsation frequency function, ,in, is the peak pulsation frequency, is the fitting function corresponding to the peak frequency, is the wind speed, is the one-sided tolerance coefficient corresponding to the peak frequency, The variance corresponding to the peak frequency; the peak value of the pulsating wind pressure is calculated based on the pulsating wind pressure function, ,in, is the peak value of pulsating wind pressure, is the fitting function corresponding to the pulsating wind pressure, is the wind speed, is the one-side tolerance coefficient corresponding to the pulsating wind pressure, Variance corresponding to pulsating wind pressure.

[0135] An embodiment of the present invention provides a device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, at least the following steps are implemented: obtaining pulsating wind pressure data, the pulsating wind pressure data including wind speed, peak frequency and pulsating wind pressure, the peak frequency including upstream peak frequency and downstream peak frequency, and the pulsating wind pressure including upstream pulsation and downstream pulsation; eliminating pulsating wind pressure data affected by errors based on the coefficient of variation and the F statistic to obtain preferred pulsating wind pressure data; obtaining pulsating wind pressure parameters based on the preferred pulsating wind pressure data, wherein the pulsating wind pressure parameters include a fitting function, a unilateral tolerance coefficient and a variance, the fitting function includes a frequency fitting function and a wind pressure fitting function, the unilateral tolerance coefficient includes a frequency unilateral tolerance coefficient and a wind pressure unilateral tolerance coefficient, and the variance includes a frequency variance and a wind pressure variance; obtaining a load analysis formula based on the pulsating wind pressure parameters, the load analysis formula includes a pulsating frequency function and a pulsating wind pressure function; obtaining a vibration load based on the load analysis formula, the vibration load includes a frequency load and a wind pressure load. The devices in this article can be servers, PCs, PADs, mobile phones, etc.

[0136] Optionally, the pulsating wind pressure data is preprocessed according to the mean formula and the variance formula to obtain the variance and the mean, wherein the variance includes the variance corresponding to the peak frequency and the variance corresponding to the pulsating wind pressure, and the mean includes the mean corresponding to the peak frequency and the mean corresponding to the pulsating wind pressure; the pulsating wind pressure data corresponding to the coefficient of variation greater than 15% are eliminated; when the F statistic is less than When the distribution critical value is The pulsating wind pressure data corresponding to the statistic are eliminated, where the critical value of the F distribution is .

[0137] Optionally, substitute the mean and variance into the coefficient of variation formula to obtain the coefficient of variation, where the coefficient of variation formula is ,in, is the coefficient of variation, is the variance, is the mean.

[0138] Optionally, calculate the F statistic, which is calculated as ,in, is the number of data sets in the fluctuating wind pressure data, where a data set includes a wind speed, a peak frequency, and a fluctuating wind pressure. is the fitting function, is the function data of the fitting function, is the mean of the fitted function.

[0139] Optionally, a linear relationship is established based on the fluctuating wind pressure data to obtain a fitting function, wherein the linear relationship includes a linear relationship between wind speed and peak frequency and a linear relationship between wind speed and fluctuating wind pressure.

[0140] Optionally, when the coefficient of variation is known, the one-sided tolerance coefficient is calculated using the formula: ,in, is the one-sided tolerance factor, is the standard normal distribution function of the corresponding quantile The value of the independent variable, is the number of data sets in the pulsating wind pressure data, is the exceedance probability; when the coefficient of variation is unknown, calculate the one-sided tolerance coefficient, the calculation formula is ,in, is the one-sided tolerance factor, Degree of freedom of Distribution Function The independent variable value corresponding to the quantile value, To exceed probability.

[0141] Optionally, the pulsation peak frequency is calculated according to the pulsation frequency function, ,in, is the peak pulsation frequency, is the fitting function corresponding to the peak frequency, is the wind speed, is the one-sided tolerance coefficient corresponding to the peak frequency, The variance corresponding to the peak frequency; the peak value of the pulsating wind pressure is calculated based on the pulsating wind pressure function, ,in, is the peak value of pulsating wind pressure, is the fitting function corresponding to the pulsating wind pressure, is the wind speed, is the one-side tolerance coefficient corresponding to the pulsating wind pressure, Variance corresponding to pulsating wind pressure.

[0142] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program that is initialized with at least the following method steps: obtaining pulsating wind pressure data, the pulsating wind pressure data including wind speed, peak frequency and pulsating wind pressure, the peak frequency including upstream peak frequency and downstream peak frequency, and the pulsating wind pressure including upstream pulsation and downstream pulsation; eliminating pulsating wind pressure data affected by errors based on the coefficient of variation and the F statistic to obtain preferred pulsating wind pressure data; obtaining pulsating wind pressure parameters based on the preferred pulsating wind pressure data, wherein the pulsating wind pressure parameters include a fitting function, a one-sided tolerance coefficient and a variance, the fitting function includes a frequency fitting function and a wind pressure fitting function, the one-sided tolerance coefficient includes a frequency one-sided tolerance coefficient and a wind pressure one-sided tolerance coefficient, and the variance includes a frequency variance and a wind pressure variance; obtaining a load analysis formula based on the pulsating wind pressure parameters, the load analysis formula includes a pulsating frequency function and a pulsating wind pressure function; obtaining a vibration load based on the load analysis formula, the vibration load includes a frequency load and a wind pressure load.

[0143] Optionally, the pulsating wind pressure data is preprocessed according to the mean formula and the variance formula to obtain the variance and the mean, wherein the variance includes the variance corresponding to the peak frequency and the variance corresponding to the pulsating wind pressure, and the mean includes the mean corresponding to the peak frequency and the mean corresponding to the pulsating wind pressure; the pulsating wind pressure data corresponding to the coefficient of variation greater than 15% is eliminated; when the F statistic is less than the critical value of the F distribution, the pulsating wind pressure data corresponding to the F statistic is eliminated, wherein the critical value of the F distribution is .

[0144] Optionally, substitute the mean and variance into the coefficient of variation formula to obtain the coefficient of variation, where the coefficient of variation formula is ,in, is the coefficient of variation, is the variance, is the mean.

[0145] Optionally, calculate the F statistic, which is calculated as ,in, is the number of data sets in the fluctuating wind pressure data, where a data set includes a wind speed, a peak frequency, and a fluctuating wind pressure. is the fitting function, is the function data of the fitting function, is the mean of the fitted function.

[0146] Optionally, a linear relationship is established based on the fluctuating wind pressure data to obtain a fitting function, wherein the linear relationship includes a linear relationship between wind speed and peak frequency and a linear relationship between wind speed and fluctuating wind pressure.

[0147] Optionally, when the coefficient of variation is known, the one-sided tolerance coefficient is calculated using the formula: ,in, is the one-sided tolerance factor, is the standard normal distribution function of the corresponding quantile The value of the independent variable, is the number of data sets in the pulsating wind pressure data, is the exceedance probability; when the coefficient of variation is unknown, calculate the one-sided tolerance coefficient, the calculation formula is ,in, is the one-sided tolerance factor, Degree of freedom of Distribution Function The independent variable value corresponding to the quantile value, To exceed probability.

[0148] Optionally, the pulsation peak frequency is calculated according to the pulsation frequency function, ,in, is the peak pulsation frequency, is the fitting function corresponding to the peak frequency, is the wind speed, is the one-sided tolerance coefficient corresponding to the peak frequency, The variance corresponding to the peak frequency; the peak value of the pulsating wind pressure is calculated based on the pulsating wind pressure function, ,in, is the peak value of pulsating wind pressure, is the fitting function corresponding to the pulsating wind pressure, is the wind speed, is the one-side tolerance coefficient corresponding to the pulsating wind pressure, Variance corresponding to pulsating wind pressure.

[0149] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0150] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0151] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0152] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0153] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0154] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0155] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0156] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0157] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0158] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for analyzing the fluctuating wind pressure load of a wind tunnel structure based on linear regression, characterized in that: include: Acquire pulsating wind pressure data, the pulsating wind pressure data including wind speed, peak frequency and pulsating wind pressure, the peak frequency including upstream peak frequency and downstream peak frequency, the pulsating wind pressure including upstream pulsation and downstream pulsation; Eliminate the pulsating wind pressure data affected by errors according to the coefficient of variation and the F statistic to obtain the optimal pulsating wind pressure data; Obtaining pulsating wind pressure parameters according to the preferred pulsating wind pressure data, wherein the pulsating wind pressure parameters include a fitting function, a unilateral tolerance coefficient, and a variance, the fitting function includes a frequency fitting function and a wind pressure fitting function, the unilateral tolerance coefficient includes a frequency unilateral tolerance coefficient and a wind pressure unilateral tolerance coefficient, and the variance includes a frequency variance and a wind pressure variance; Acquiring the pulsating wind pressure parameter according to the preferred pulsating wind pressure data, comprising: establishing a linear relationship according to the pulsating wind pressure data to obtain the fitting function, wherein the linear relationship comprises a linear relationship between the wind speed and the peak frequency and a linear relationship between the wind speed and the pulsating wind pressure; Obtaining the pulsating wind pressure parameters according to the preferred pulsating wind pressure data includes: when the coefficient of variation is known, calculating the unilateral tolerance coefficient, the calculation formula is: Among them, k nk is the one-sided tolerance factor, u p is the standard normal distribution function P corresponding to the quantile Ф {x>u p }=p's independent variable value, n is the number of data groups in the pulsating wind pressure data, and p is the exceedance probability; when the coefficient of variation is unknown, the one-sided tolerance coefficient is calculated using the formula: Among them, k nk is the one-sided tolerance factor, t p,v is the t distribution function P with degrees of freedom v = n-1 t {x>t p,v }=p corresponds to the independent variable value of the quantile value, and p is the exceedance probability; according to the pulsating wind pressure parameter, a load analysis formula is obtained, and the load analysis formula includes a pulsating frequency function and a pulsating wind pressure function; According to the pulsating wind pressure parameter, a load analysis formula is obtained, and the load analysis formula includes a pulsating frequency function and a pulsating wind pressure function: according to the pulsating frequency function, the pulsating peak frequency is calculated, L f =μ f (v)+k f σ f , where L f is the pulsation peak frequency, μ f (v) is the fitting function corresponding to the peak frequency, v is the wind speed, k f is the one-sided tolerance coefficient corresponding to the peak frequency, σ f The variance corresponding to the peak frequency; Calculate the peak value of the pulsating wind pressure according to the pulsating wind pressure function, L p =μ p (v)+k p σ p , where L p is the peak value of the pulsating wind pressure, μ p (v) is the fitting function corresponding to the pulsating wind pressure, v is the wind speed, k p is the one-side tolerance coefficient corresponding to the pulsating wind pressure, σ p The variance corresponding to the pulsating wind pressure; The vibration load is obtained according to the load analysis formula, and the vibration load includes frequency load and wind pressure load.

2. The method according to claim 1, characterized in that According to the coefficient of variation and the F statistic, the pulsating wind pressure data affected by errors are eliminated to obtain the optimal pulsating wind pressure data, including: Preprocessing the pulsating wind pressure data according to the mean formula and the variance formula to obtain the variance and the mean, wherein the variance includes the variance corresponding to the peak frequency and the variance corresponding to the pulsating wind pressure, and the mean includes the mean corresponding to the peak frequency and the mean corresponding to the pulsating wind pressure; Eliminate the pulsating wind pressure data corresponding to the coefficient of variation greater than 15%; When the F statistic is less than the critical value of the F distribution, the pulsating wind pressure data corresponding to the F statistic is eliminated, wherein the critical value of the F distribution is F α (1,n-1).

3. The method according to claim 2, characterized in that Eliminating the pulsating wind pressure data corresponding to the coefficient of variation greater than 15%, including: Substitute the mean and the variance into the coefficient of variation formula to obtain the coefficient of variation, where the coefficient of variation formula is: Among them, δ x is the coefficient of variation, σ x is the variance, is the mean value.

4. The method according to claim 2, characterized in that: Eliminating the pulsating wind pressure data corresponding to the coefficient of variation greater than 15%, including: Calculate the F statistic, the calculation formula is Wherein, n is the number of data groups in the pulsating wind pressure data, and the data group includes one wind speed, one peak frequency and one pulsating wind pressure, is the fitting function, y i is the function data of the fitting function, is the mean of the fitting function.

5. A device for analyzing the pulsating wind pressure load of a wind tunnel structure based on linear regression, characterized in that: include: A first acquisition unit is used to acquire pulsating wind pressure data, wherein the pulsating wind pressure data includes wind speed, peak frequency and pulsating wind pressure, wherein the peak frequency includes upstream peak frequency and downstream peak frequency, and the pulsating wind pressure includes upstream pulsation and downstream pulsation; A elimination unit is used to eliminate the pulsating wind pressure data affected by errors according to the coefficient of variation and the F statistic to obtain the optimal pulsating wind pressure data; a second acquisition unit, configured to acquire a pulsating wind pressure parameter according to the preferred pulsating wind pressure data, wherein the pulsating wind pressure parameter includes a fitting function, a unilateral tolerance coefficient, and a variance, the fitting function includes a frequency fitting function and a wind pressure fitting function, the unilateral tolerance coefficient includes a frequency unilateral tolerance coefficient and a wind pressure unilateral tolerance coefficient, and the variance includes a frequency variance and a wind pressure variance; The second acquisition unit includes: a fitting subunit, which is used to acquire the fluctuating wind pressure parameters according to the preferred fluctuating wind pressure data, including: establishing a linear relationship according to the fluctuating wind pressure data to obtain the fitting function, wherein the linear relationship includes a linear relationship between the wind speed and the peak frequency and a linear relationship between the wind speed and the fluctuating wind pressure; The second acquisition unit includes: a first calculation subunit, which is used to obtain the pulsating wind pressure parameters according to the preferred pulsating wind pressure data, including: when the coefficient of variation is known, calculating the unilateral tolerance coefficient, the calculation formula is: Among them, k nk is the one-sided tolerance factor, u p is the standard normal distribution function P corresponding to the quantile Ф {x>u p }=p's independent variable value, n is the number of data groups in the pulsating wind pressure data, and p is the exceedance probability; the second calculation subunit is used to calculate the unilateral tolerance coefficient when the coefficient of variation is unknown, and the calculation formula is: Among them, k nk is the one-sided tolerance factor, t p,v is the t distribution function P with degrees of freedom ν = n-1 t {x>t p,v }=p is the value of the independent variable corresponding to the quantile value, and p is the probability of exceedance; A third acquisition unit, configured to acquire a load analysis formula according to the pulsating wind pressure parameter, wherein the load analysis formula includes a pulsating frequency function and a pulsating wind pressure function; The third acquisition unit includes: a third calculation subunit, which is used to obtain a load analysis formula according to the pulsating wind pressure parameter, wherein the load analysis formula includes a pulsating frequency function and a pulsating wind pressure function: the pulsating peak frequency is calculated according to the pulsating frequency function, L f =μ f (v)+k f σ f , where I f is the pulsation peak frequency, μ f (v) is the fitting function corresponding to the peak frequency, v is the wind speed, k f is the one-sided tolerance coefficient corresponding to the peak frequency, σ f The variance corresponding to the peak frequency; a fourth calculation subunit, used to calculate the peak value of the pulsating wind pressure according to the pulsating wind pressure function, L p =μ p (v)+k p ρ p , where L p is the peak value of the pulsating wind pressure, μ p (v) is the fitting function corresponding to the pulsating wind pressure, v is the wind speed, k p is the one-side tolerance coefficient corresponding to the pulsating wind pressure, σ p The variance corresponding to the pulsating wind pressure; The fourth acquisition unit is used to acquire the vibration load according to the load analysis formula, and the vibration load includes a frequency load and a wind pressure load.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute a method for analyzing the fluctuating wind pressure load of a wind tunnel structure based on linear regression as described in any one of claims 1 to 4.

7. An electronic device, characterized in that: include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a method for executing a method for analyzing pulsating wind pressure loads of a wind tunnel structure based on linear regression as described in any one of claims 1 to 4.

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