CN110779680B - Method for detecting extreme value wind pressure of building envelope structure - Google Patents
Method for detecting extreme value wind pressure of building envelope structure Download PDFInfo
- Publication number
- CN110779680B CN110779680B CN201910992004.5A CN201910992004A CN110779680B CN 110779680 B CN110779680 B CN 110779680B CN 201910992004 A CN201910992004 A CN 201910992004A CN 110779680 B CN110779680 B CN 110779680B
- Authority
- CN
- China
- Prior art keywords
- wind pressure
- wind
- building
- coefficient
- sequence
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M9/00—Aerodynamic testing; Arrangements in or on wind tunnels
- G01M9/08—Aerodynamic models
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M9/00—Aerodynamic testing; Arrangements in or on wind tunnels
- G01M9/02—Wind tunnels
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M9/00—Aerodynamic testing; Arrangements in or on wind tunnels
- G01M9/06—Measuring arrangements specially adapted for aerodynamic testing
- G01M9/065—Measuring arrangements specially adapted for aerodynamic testing dealing with flow
Landscapes
- Physics & Mathematics (AREA)
- Fluid Mechanics (AREA)
- General Physics & Mathematics (AREA)
- Aerodynamic Tests, Hydrodynamic Tests, Wind Tunnels, And Water Tanks (AREA)
Abstract
The invention belongs to the technical field of building envelope detection, and relates to a method for detecting extreme value wind pressure of a building envelope. Compared with a measuring method directly adopting a prototype time interval of 10 minutes as a segment scale, the method can obtain enough independent wind pressure coefficient subsequences, ensure the stability of a statistical result, has more accurate and reasonable calculation result, is particularly suitable for extreme value measurement of a short-time range wind pressure coefficient sequence of a building wind tunnel test, and is convenient for engineering application. The invention introduces the relation between the coefficient of the gunn bell distribution parameter and the number of the independent wind pressure coefficient sub-sequences, and obtains the extreme value wind pressure value of the building pressure measuring prototype through conversion according to the dynamic time-distance ratio, thereby supplementing and perfecting the defects in the prior art to a certain extent.
Description
Technical Field
The invention belongs to the technical field of building envelope detection, and relates to a method for detecting extreme value wind pressure of a building envelope.
Background
Multiple wind disaster investigation results show that the whole building main structure is not usually damaged under the action of wind load, but accidents caused by damage of the enclosure structure (mainly comprising curtain walls, doors and windows, roof panels, wall panels, purlines and the like) occur frequently, and besides the problems of wind speed exceeding design standards, structural wind resistance design defects, lack of effective dynamic and static detection processes, improper construction and the like, the fact that the wind pressure extreme value of the building enclosure structure is not accurately estimated is another important reason for building structure disasters. At present, the main technical means for obtaining the wind pressure extreme values (including the positive extreme value pressure and the negative extreme value pressure, and the corresponding maximum value and the minimum value) of the building envelope structure is to perform a pressure test in an atmospheric boundary layer wind tunnel by a building pressure test model manufactured according to the principles of similarity criteria, a model blocking ratio, measuring point arrangement and the like. The test acquires a wind pressure signal of a surface measuring point of a building pressure measuring model through an electronic pressure scanning system, a wind pressure extreme value of the building enclosure structure is obtained through conversion of a correlation method, and for a wind pressure sequence obtained by the building pressure measuring model test, the following two main methods are provided in the roof structure wind load standard in the report:
method (1): when the sampling time of the wind pressure prototype on the surface of the building envelope structure is longer than 240 minutes, the maximum value and the minimum value of the local wind pressure extreme value can be determined according to a peak value piecewise average method;
method (2): when the sampling time of the wind pressure prototype on the surface of the building envelope is longer than 30 minutes but shorter than 100 minutes, the maximum value and the minimum value of the local wind pressure extreme value can be determined according to the modified Hermite moment model method.
The method (1) is a method directly based on the distribution of the wind pressure extreme value, can better reflect the tail characteristics of the wind pressure distribution on the surface of the building envelope structure, obtains the extreme value wind pressure directly from the wind pressure sequence, has stronger reference value and practical significance, and has the defect that a more accurate result can be obtained only when a long-time-range sequence is processed. In the wind tunnel test of the actual building engineering, in consideration of economy and applicability, the long-time sequence sampling condition required by the method (1) is often difficult to meet, and the sequence length acquired by the pressure scanning systems of most building wind tunnels can only meet the short-time sequence sampling condition of the method (2). How to obtain more accurate extreme value wind pressure by using the measured short-time sequence is an important prerequisite for the wind resistance design of the building envelope structure. At present, common methods (such as an out-of-range peak method, an r-LOS method, an independent storm method and the like) for processing a short-time range sequence of a pressure measuring system based on extreme value distribution select an 'independent' peak value according to autocorrelation analysis in extreme value information selection, but independence requires no relation among wind pressure sequences, autocorrelation analysis only considers linear correlation of the wind pressure sequences but not nonlinear correlation, independence among the wind pressure sub-sequences cannot be guaranteed, and therefore certain deviation exists between measured extreme value wind pressure and a true value.
The method (2) is one of common methods for obtaining extreme value wind pressure based on wind pressure parent, under certain conditions, the method (2) is transformed into a peak factor method based on Gaussian distribution, 3.5 is taken as a peak factor, but the direct taking of 3.5 as the peak factor actually neglects the influence of sequence time distance and actual cyclic variation characteristics of signals, and results show larger deviation when the method is applied to extreme value wind pressure measurement.
Although the method (1) is almost perfect in theory, the method (1) actually implies an assumption that 25 wind pressure subsequences are obtained by segmenting the measuring point wind pressure sequence based on the basic prototype time interval (10 minutes) of the building structure load specification in China. In the concrete implementation, the wind speed value of the building pressure-measuring prototype under the specific working condition, namely the prototype wind speed, needs to be known, according to the wind tunnel test similarity criterion (the time scale ratio is the geometric scale ratio/the wind speed scale ratio), when the model wind speed changes, the wind pressure subsequence length corresponding to the 10-minute prototype time interval collected by the system correspondingly changes, so that the segment length and the segment number of the wind pressure sequence change, and further the result of the extreme value wind pressure value of the measuring point is influenced, under the conditions that the wind pressure sequence length is limited and the actual wind speed is low, the segment number is less than 25 and does not meet the basic conditions of the method (1), even if the segment number meets the requirements, the finally measured wind pressure is extremely large and the extremely small value also correspondingly change due to the change of the segment length and the segment number of the wind pressure sequence of the measuring point, so that the extreme value wind pressure coefficient determined by adopting the fixed wind speed prototype value can not be directly applied to the And calculating the value wind pressure.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a method for detecting the extreme value wind pressure of a building envelope structure. The detection method is stable and high in precision.
The invention is realized by adopting the following technical scheme:
a method for detecting extreme value wind pressure of a building envelope structure is characterized by comprising the following steps:
s1, determining the arrangement of wind pressure measuring points and manufacturing a building pressure measuring model according to relevant standards and guidelines of a building wind tunnel test;
s2, determining test parameters and test working conditions to perform wind tunnel test, collecting the surface wind pressure of the building pressure measurement model in S1, and obtaining a wind pressure coefficient sequence through non-dimensionalization;
s3, analyzing the wind pressure coefficient sequence in S2 by adopting a mutual information method to obtain the minimum time interval for dividing the independent wind pressure coefficient subsequence;
s4, segmenting the wind pressure coefficient sequence according to the minimum time interval in the S3 to obtain an independent wind pressure coefficient subsequence, and if the number of segments is more than or equal to 25, carrying out S5; otherwise, turning to S2, prolonging the test sampling time, and carrying out wind pressure collection on the surface of the building pressure measurement model again;
s5, counting the extreme value sequence of the independent wind pressure coefficient subsequence and calculating the standard deviation of the extreme value sequence; calculating the coefficient of the gunbell distribution parameter based on the number of the stages of dividing the wind pressure coefficient sequence at the minimum time interval;
s6, converting the prototype target time interval of the divided wind pressure coefficient sequence into a model target time interval according to a similarity criterion, and determining the time interval ratio of the model target time interval to the minimum time interval;
s7, calculating the coefficient of the Gunn Bell distribution parameter based on the number of the stages of dividing the wind pressure coefficient sequence by the model target time distance in S6;
s8, substituting the extreme value sequence, the standard deviation of the extreme value sequence, the coefficient of the Gunn-Beel distribution parameter and the time interval ratio value obtained in S5, S6 and S7 into an extreme value conversion formula of a modified peak value segment average method, so that the extreme value wind pressure coefficient result based on the wind pressure coefficient sequence under the minimum time interval is converted into the extreme value wind pressure coefficient result based on the wind pressure coefficient sequence under the model target time interval;
and S9, converting the extreme value wind pressure coefficient result based on the wind pressure coefficient sequence under the model target time interval in S8 into the extreme value wind pressure of the building pressure measuring prototype.
Preferably, the extreme value transformation formula of the modified peak value piecewise averaging method is as follows:
wherein:
respectively representing model-based target time intervals t2The maximum value and the minimum value of the lower wind pressure coefficient sequence,
respectively based on the minimum time interval t1The maximum value and the minimum value of the lower independent wind pressure coefficient subsequence j,
respectively based on the minimum time interval t1The variance of the lower maximum value wind pressure coefficient sequence and the variance of the minimum value wind pressure coefficient sequence,
representation based on minimum time distance t1The number of the sub-sequences of the lower independent wind pressure coefficients,
respectively based on the minimum time interval t1The coefficients of the gunn bell distribution parameters,
representing model-based objectsDistance t2And n represents the time-distance ratio for dividing the wind pressure coefficient sequence.
Preferably, in step S9, the extreme value wind pressure coefficient result based on the wind pressure coefficient sequence at the model target time distance is converted into the extreme value wind pressure of the building pressure measuring prototype, and the conversion formula is as follows:
wherein, wk、w0Respectively represents the standard value of the extreme value wind pressure and the basic wind pressure of 10m height, muzIs the change coefficient of the wind pressure height,
for model-based target time interval t2And the extreme value wind pressure coefficient of the lower sequence.
Preferably, the influence of the number of stages of the wind pressure coefficient sequence on the extreme wind pressure result of the pressure measuring model of the building is considered by calculating the coefficient of the gunbel distribution parameter.
Preferably, step S1 includes: determining proper geometric scaling ratio lambda according to building wind tunnel test standard and guideLAnd reasonably arranging the measuring points according to a wind pressure measuring point arrangement principle, encrypting the wind pressure measuring points at the positions with severe wind pressure change, and manufacturing a building pressure measuring model.
Preferably, step S2 includes: the required landform flow field is simulated through wind field debugging, and the wind speed scaling ratio lambda is determinedU(ii) a And setting the sampling duration, sampling frequency and test wind direction angle of the test according to the test working condition.
Preferably, step S2 includes: a pressure scanning system is used for collecting a wind pressure sequence p (t) of a wind pressure measuring point, and the wind pressure at the building pressure measuring model roof height is used as a dimensionless reference wind pressure to obtain a wind pressure coefficient sequence Cp(t)。
Preferably, step S3 includes: firstly, counting the maximum value of corresponding sampling time when the mutual information coefficient of all measuring points under each test wind direction angle is reduced from 1 to 0.05, and then comparing the system under the full wind direction angleCalculating the result and taking the envelope value as a division wind pressure coefficient sequence Cp(t) minimum time interval t1(ii) a Let random variable X represent wind pressure coefficient sequence Cp(t), Y represents a hysteresis wind pressure coefficient sequence Cp(t + τ), τ represents the lag time, and the mutual information method specifically comprises the following processes:
wherein: h (X), H (Y) are respectively X, Y edge entropy, H (X, Y) is X, Y joint entropy, MI (X, Y) and NMI (X, Y) are respectively X, Y mutual information and mutual information coefficient, and p (X, Y)b)、p(yd) X, Y, B ═ 1,2, …, B, D ═ 1,2, …, D, p (x, respectively)b,yd) For the joint distribution of X, Y, B, D is the data length of X, Y sequences, respectively.
Preferably, step S6 includes: according to the similarity criterion, the geometric scale ratio is lambdaLScaled ratio lambda of sum wind speedUCo-determining time scaling ratio lambdaTBy the time scale ratio lambdaTThe set prototype target time interval T2Conversion to model target time distance t2。
Preferably, by a time scaling ratio λTThe original purpose to be setTime scale distance T2Conversion to model target time distance t2The conversion relationship is as follows:
t2＝T2×λT
in the formula: lambda [ alpha ]T、λL、λURespectively representing the time scale ratio, the geometric scale ratio and the wind speed scale ratio, Lp、LmRespectively representing the dimension of a building pressure measurement prototype and the dimension of a building pressure measurement model, Wr,p、Wr,mAnd respectively representing the wind pressure at the reference height of the building pressure measuring prototype and the wind pressure at the reference height of the building pressure measuring model.
Compared with the prior art, the invention has the following beneficial effects:
(1) the method comprises the steps of carrying out independent segmentation according to mutual information characteristics of a wind pressure coefficient sequence, then calculating an extreme value wind pressure coefficient of the surface of a building pressure measurement model by applying a modified peak value segmentation average method, and obtaining a time-distance ratio according to specific wind speed in a prototype system to convert to obtain extreme value wind pressure of the surface of the building pressure measurement prototype. Compared with the measuring method which directly adopts the prototype time interval of 10 minutes as the segment scale, the method can obtain enough independent wind pressure coefficient subsequences, ensures the stability of the statistical result, has more accurate and reasonable calculation result, is particularly suitable for the extreme value measurement of the short-time wind pressure coefficient sequence of the building wind tunnel test, and is convenient for engineering application.
(2) According to the theoretical condition of independence requirement, the invention considers the linear correlation and the nonlinear correlation generated by uncertainty factors (such as noise interference, pressure measuring pipeline signal distortion and flow field turbulence) in a measuring system, and adopts the corresponding sampling time envelope value when the mutual information coefficient of the wind pressure coefficient sequence of each measuring point of the full wind direction angle model is attenuated from 1 to 0.05 as the time basis for dividing the short-time wind pressure coefficient sequence, thereby ensuring the independence among the wind pressure coefficient subsequences.
(3) The invention considers the influence of the comparison of the wind speed of the prototype and the model in the pressure measuring system on the number of segments and the extreme value of the wind pressure coefficient sequence, introduces the relation between the coefficient of the Gunn-Beel distribution parameter and the number of the independent wind pressure coefficient sub-sequences, converts according to the dynamic time-distance ratio to obtain the extreme value wind pressure value of the pressure measuring prototype of the building, and supplements and perfects the defects of the prior art to a certain extent.
Drawings
FIG. 1 is a flow chart of a method for detecting extreme wind pressure of a building envelope according to an embodiment of the present invention;
FIG. 2 is a schematic view of a plane roof construction pressure measurement model and 1/4 roof area measurement point arrangement in one embodiment of the present invention; wherein: the method comprises the following steps of (a) obtaining a pressure measurement model diagram of a plane roof building, (b) obtaining a measuring point arrangement diagram of 1/4 roof area (biaxial symmetry);
FIG. 3 is a schematic diagram showing coefficient fitting of the parameters of the Gunn Bell distribution in one embodiment of the present invention;
FIG. 4 is a diagram illustrating the variation of mutual information coefficients and autocorrelation coefficients with sampling time according to an embodiment of the present invention; wherein: (a) the schematic diagram is that the mutual information coefficient and the autocorrelation coefficient of a measuring point corresponding to the mutual information coefficient envelope value under the wind direction angle of 0 degree of the plane roof change along with the sampling time; (b) the schematic diagram is that the mutual information coefficient and the autocorrelation coefficient of a measuring point corresponding to the mutual information coefficient envelope value under the wind direction angle of 45 degrees of the plane roof change along with the sampling time;
fig. 5 is a schematic diagram of comparing the estimated extreme wind pressures of the 0 ° and 45 ° directional angles in an embodiment of the present invention, where: (a) a minimum value of 0 °; (b) a 45 ° minimum, (c) a 0 ° maximum, and (d) a 45 ° maximum.
Detailed Description
The present invention will be described in further detail below with reference to specific embodiments, but the embodiments of the present invention are not limited thereto.
A method for detecting extreme wind pressure of a building envelope, as shown in fig. 1, includes:
and S1, determining the arrangement of wind pressure measuring points and manufacturing a building pressure measuring model according to relevant standards and guidelines of the building wind tunnel test.
Determining proper geometric scale ratio lambda according to relevant standards and guidelines of building wind tunnel testLThe test materials are ensured to have enough strength, the test points are reasonably arranged according to the wind pressure test point arrangement principle, the test points at the positions with severe wind pressure changes (such as wall corners, eave, roof corners and the like) are properly encrypted, and the rigid building pressure measurement model with reliable connection is manufactured.
And S2, determining wind tunnel test parameters and test working conditions to perform a wind tunnel test, collecting the surface wind pressure of the building pressure measurement model in S1, and obtaining a wind pressure coefficient sequence through non-dimensionalization.
Judging the type of the ground roughness according to the surrounding geomorphic environment of the building pressure measurement prototype proposed site, simulating a required geomorphic flow field through wind field debugging, and determining the wind speed scaling ratio lambdaU. And setting the sampling duration, sampling frequency, test wind direction angle and the like of the test according to the specific test working conditions. The wind tunnel test is carried out under the condition of ensuring various test parameters and the accuracy of the building pressure measurement model, a pressure scanning system is used for collecting a wind pressure sequence p (t) of a wind pressure measuring point, and the wind pressure at the height position of the building pressure measurement model roof is used as dimensionless reference wind pressure to obtain a wind pressure coefficient sequence Cp(t), the specific expression is as follows:
wherein the content of the first and second substances,
and pi(t) is a wind pressure coefficient sequence and a wind pressure sequence at measuring points i (i is 1,2, 3, … and K, K is the number of measuring points of the building pressure measurement model), and p is a wind pressure coefficient sequence and a wind pressure sequence0And paThe average static pressure and total pressure measured for the pitot tube at the reference height, respectively.
And S3, analyzing the wind pressure coefficient sequence in the S2 by adopting a mutual information method, and acquiring the minimum time interval for dividing the independent wind pressure coefficient subsequence.
The mutual information processing comprehensively analyzes the linear correlation and nonlinear correlation characteristics of the sequences, the value range of the mutual information coefficient is [0,1] and is an attenuation function of lag time, and when the value of the mutual information coefficient is 0, the linear and nonlinear correlation of the two sequences is represented, which means that the two sequences are statistically independent and can be used as the judgment standard of the independence of the two sequences. In consideration of uncertainty factors (such as noise interference, pressure measuring pipeline signal distortion and flow field turbulence) in a measuring system, in practical application, when the mutual information coefficient value is smaller than a certain set non-negative small quantity (the value of the invention is taken as 0.05), the approximate judgment basis of the independence of two sequences is taken.
To wind pressure coefficient sequence Cp(t) mutual information processing is performed. Firstly, counting the maximum value of corresponding sampling time when the mutual information coefficient of all measuring points under each test wind direction angle is reduced from 1 to 0.05, then comparing the statistical results under the full wind direction angle and taking an envelope value as a division Cp(t) minimum time interval t1And the independence among the wind pressure coefficient subsequences is ensured. Let random variable X represent wind pressure coefficient sequence Cp(t), Y represents a hysteresis wind pressure coefficient sequence Cp(t + τ), τ represents the lag time, and the mutual information method specifically includes the following steps:
wherein: h (X), H (Y) are respectively X, Y edge entropy, H (X, Y) is X, Y joint entropy, MI (X, Y) and NMI (X, Y) are respectively X, Y mutual information and mutual information coefficient, and p (X, Y)b)、p(yd) X, Y, B ═ 1,2, …, B, D ═ 1,2, …, D, p (x, respectively)b,yd) For the joint distribution of X, Y, B, D is the data length of X, Y sequences, respectively.
S4, segmenting the wind pressure coefficient sequence according to the minimum time interval in the S3 to obtain an independent wind pressure coefficient subsequence, and if the number of segments is more than or equal to 25, carrying out S5; otherwise, turning to S2, prolonging the test sampling time, and carrying out wind pressure collection on the surface of the building pressure measurement model again.
According to the minimum time interval t determined in the step S31To wind pressure coefficient sequence Cp(t) dividing to obtain the number of independent wind pressure coefficient subsequences
Judgment of
And 25. If it is
Conducting S5; if it is
And (4) properly prolonging the sampling time of the pressure measuring system, and carrying out pressure acquisition on the surface of the building pressure measuring model again.
S5, counting the extreme value sequence of the independent wind pressure coefficient subsequence and calculating the standard deviation of the extreme value sequence; and calculating the coefficient of the Gunn Bell distribution parameter based on the number of the stages of dividing the wind pressure coefficient sequence at the minimum time interval.
Statistic independent wind pressure coefficient subsequence
Maximum value of
And minimum value
Forming maximum and minimum sequences and counting the standard deviation of the two sequences
Calculating the number of sub-sequences of independent wind pressure coefficients according to the relationship between the number of sequences N (i.e., the number of stages in S4) and the coefficient of the Gunn distribution parameter
Corresponding coefficient
And
B1and B2The method is formed by adopting a Logistic model and fitting based on different numbers of sequences, a fitting schematic diagram is shown in FIG. 3, and a specific expression is as follows:
wherein N represents the number of sequences.
S6, converting the prototype target time interval of the divided wind pressure coefficient sequence into a model target time interval according to a similarity criterion, and determining the time interval ratio of the model target time interval to the minimum time interval;
according to the similarity criterion, the geometric scale ratio lambda is obtained in the steps S1 and S2LScaled ratio lambda of sum wind speedUCo-determining time scaling ratio lambdaTBy the time scale ratio lambdaTThe set prototype target time interval (namely the actual target time interval) T2Conversion to model target interval (i.e. trial target interval) t2. The specific conversion relationship is as follows:
t2＝T2×λT
in the formula: lambda [ alpha ]T、λL、λURespectively representing the time scale ratio, the geometric scale ratio and the wind speed scale ratio, Lp、LmRespectively representing the dimension of a building pressure measurement prototype and the dimension of a building pressure measurement model, Wr,p、Wr,mAnd respectively representing the wind pressure at the reference height of the building pressure measuring prototype and the wind pressure at the reference height of the building pressure measuring model. Divide wind pressureTime-distance ratio n ═ t of coefficient sequence2/t1。
For a particular building, since Wr,pCan change along with the change of the landform environment and the incoming flow wind direction of the building, thereby influencing the lambdaTThus for a certain prototype target time interval T2Model target time interval t obtained by conversion2And the number of sequence segments is not a fixed value.
And S7, calculating the coefficient of the Gunn Bell distribution parameter based on the number of the stages of dividing the wind pressure coefficient sequence at the model target time distance in S6.
According to the model target time distance t determined in the step S62To wind pressure coefficient sequence Cp(t) dividing to obtain the number of independent wind pressure coefficient subsequences
Calculating coefficients according to equation (3)
S8, substituting the extreme value sequence, the standard deviation of the extreme value sequence, the coefficient of the Gunn-Beel distribution parameter and the time interval ratio value obtained in S5, S6 and S7 into an extreme value conversion formula of a modified peak value segment average method, so that the extreme value wind pressure coefficient result based on the wind pressure coefficient sequence under the minimum time interval is converted into the extreme value wind pressure coefficient result based on the wind pressure coefficient sequence under the model target time interval;
in this embodiment, a modified peak value piecewise averaging method is adopted based on the extreme value transformation relationship of the wind pressure coefficient sequences at different time intervals, so as to realize mutual transformation of extreme values based on the same wind pressure coefficient sequence at different time intervals.
The extreme value conversion formula of the modified peak value segment average method is as follows:
wherein:
respectively representing model-based target time intervals t2The maximum value and the minimum value of the lower wind pressure coefficient sequence,
respectively based on the minimum time interval t1The maximum value and the minimum value of the lower independent wind pressure coefficient subsequence j,
respectively based on the minimum time interval t1The variance of the lower maximum value wind pressure coefficient sequence and the variance of the minimum value wind pressure coefficient sequence,
representation based on minimum time distance t1The number of the sub-sequences of the lower independent wind pressure coefficients,
respectively based on the minimum time interval t1The coefficients of the gunn bell distribution parameters,
representing model-based target time distance t2And n represents the time-distance ratio for dividing the wind pressure coefficient sequence.
And S9, converting the extreme value wind pressure coefficient result based on the wind pressure coefficient sequence under the model target time interval in S8 into the extreme value wind pressure of the building pressure measuring prototype.
Specifically, the extreme value wind pressure coefficient result based on the wind pressure coefficient sequence at the model target time interval in step S8 is converted into the extreme value wind pressure of the building pressure measuring prototype, and the conversion formula is as follows:
wherein, wk、w0Respectively represents the standard value of the extreme value wind pressure and the basic wind pressure of 10m height, muzFor the wind pressure altitude change coefficient (including the terrain correction coefficient),
for model-based target time interval t2And (4) an extreme value wind pressure coefficient of the lower wind pressure coefficient sequence.
The invention will be further described with reference to the following embodiments and the accompanying drawings 2-5.
(1) Wind tunnel test
The test was carried out in an atmospheric boundary layer wind tunnel of 5m magnitude at southern China university of Engineers. The construction pressure measurement model is a large-span plane roof construction with the size of 200cm multiplied by 133.3cm multiplied by 26.7cm (corresponding to the construction pressure measurement prototype size of 300m multiplied by 200m multiplied by 40 m). The geometric, wind speed and time scale ratios of the test are 1/150, 1/5 and 1/30 respectively. Fig. 2 (a) and (b) show a pressure measurement model diagram of a planar roof building and an arrangement diagram of 1/4 roof area measurement points, respectively. In order to reflect the local characteristics of wind pressure, the wind pressure measuring points are arranged in the corner areas of the roof in an encrypted manner, and the middle area is arranged sparsely. The flow field landform is valued according to B-class landform specified in 'building structure load standard' GB5009-2012 in China, and the flow field is debugged by combining devices such as a pointed tower, a baffle plate, a rough element and the like. The pressure measuring device adopts an Initium pressure measuring system of PSI (American institute of Electrical and electronics Engineers), the test sampling frequency is set to be 300Hz, the single sampling time length is 68.3s (the corresponding prototype sampling time length is about 34min according to time scale ratio conversion), and the wind pressure coefficient sequence C is obtainedp(t) of (d). And repeating sampling for 10 times at wind direction angles of 0 degrees and 45 degrees respectively (corresponding to the sampling time length of the prototype is about 340min), and taking the sampling time length as long-range wind pressure coefficient sequence data.
(2) Estimating extrema
By aligning the wind pressure coefficient sequence Cp(t) analyzing mutual information to obtain a divided wind pressure coefficient sequence Cp(t) minimum time interval. Fig. 4 shows a schematic diagram of mutual information coefficients and autocorrelation coefficients of measuring points corresponding to mutual information coefficient envelope values under wind angles of 0 ° and 45 ° of a plane roof varying with sampling time. As can be seen from FIG. 4, the 0 deg. azimuth angleThe mutual information coefficient and the autocorrelation coefficient of the measuring point 3 and the measuring point 13 at the 45-degree direction angle are rapidly reduced along with the increase of time, but the rate of the mutual information coefficient from 1 to 0.05 is smaller than the attenuation rate of the autocorrelation coefficient, the corresponding sampling time is respectively 2.01s and 1.71s when the mutual information coefficient of the two measuring points is reduced to 0.05, at the moment, the wind pressure coefficient subsequences are considered to be mutually independent, and the larger value of 2.01s is taken as a divided wind pressure coefficient sequence Cp(t) minimum time interval t1. Number of divided independent wind pressure coefficient subsequences
68.3/2.01 ≈ 34, and the fitting coefficient is calculated by the formula (3)
And
counting the maximum value in the independent wind pressure coefficient subsequence j
And minimum value
Composing a sequence of maxima
And sequence of minima
Calculating the standard deviation of the two-pole value sequence
Dividing the time scale ratio 1/30 of the system into wind pressure coefficient sequences Cp(t) substituting the prototype target time interval of 10min for the formula (4), and converting to obtain the model target time interval of t220s, based on the model target time interval t2Determining a wind pressure coefficient subsequence Nt2And pass through type (3)Calculating the fitting coefficient
And
the ratio of the two time distances is n is 20/2.01. The parameters are substituted into formula (5) to determine a model-based target time interval wind pressure coefficient sequence Cp(t) extreme value wind pressure coefficient.
(3) Standard extreme value
In this embodiment, the extreme value wind pressure coefficient is used as a standard value for measuring the measurement results of the present invention and other methods by using the long time sequence (10 times of repeated sampling of independent samples, corresponding to a prototype sampling time of about 340min) of the method (1) (peak value segment average method). The specific process of the peak segmentation average method is as follows:
when the wind pressure coefficient sequence Cp(t) when the time is more than 240 minutes, the wind pressure coefficient sequence C is processedp(t) equally divided into 10 minutes (corresponding to a model target interval of time t)220s) of
And (4) forming a maximum value sequence and a minimum value sequence by taking the maximum value and the minimum value of each sub-sequence. The maximum value and the minimum value of the local wind pressure coefficient extreme value are respectively determined according to the following formula:
in the formula
Represents a subsequence number;
respectively representing model-based target time intervals t2And the maximum value and the minimum value of the lower independent wind pressure coefficient subsequence j.
(4) Comparison of results
Fig. 5 shows a comparison of the minimum and maximum measurements of the model at 0 ° and 45 ° orientation angles. To better illustrate the effectiveness of the invention, the estimation result is analyzed by introducing a standard residual epsilon, and the expression is as follows:
in the formula
And respectively representing the estimated extreme value wind pressure coefficient and the standard extreme value wind pressure coefficient of the measuring point i, and K represents the number of measuring points of the building pressure measuring model.
As can be seen from (a) and (b) of FIG. 5, when the plane roof is at a 0-degree direction angle, the calculation result of the invention is closer to the standard value as a whole, has smaller discreteness and is uniformly distributed on both sides of the standard value compared with the result of the modified Hermite moment model change method, and the standard residual epsilon of the two method results is 0.094 and 0.140 respectively. Under a wind direction angle of 45 degrees, the minimum values of the two methods are uniformly distributed on two sides of the standard value on the whole, but the dispersion and the total residual error of the result of the method are smaller, and the epsilon of the two methods is 0.106 and 0.147 respectively. From fig. 5 (c) and (d), the maxima measured by the method of the present invention are distributed on both sides of the standard maxima at the 0 ° azimuth angle, and the overall deviation of the calculation result of the modified Hermite moment model method from the standard value is larger, and the positive pressure part is smaller than the standard value, which is unsafe, and the difference between the two is larger, which is 0.039 and 0.091, respectively. The calculation results of the method are better distributed on two sides of the standard maximum value under the wind direction angle of 45 degrees, the discreteness is small, epsilon is 0.069, the positive pressure deviation of partial measuring points of the corrected Hermite moment model method is larger than the standard value, the common point of the measuring points is that the positions of the measuring points are in the corner area of the roof, and the absolute value of the coefficient of variation is
The method is relatively large, the discreteness is large, and epsilon is 0.161, so that the method of the modified Hermite moment model has the defect.
In conclusion, the effectiveness of the method for detecting the short-time-range extreme value wind pressure is demonstrated, and compared with the prior art, the method is simpler in form, has stronger practical physical significance, is high in precision and stability, and is suitable for engineering application.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.
Claims (10)
1. A method for detecting extreme value wind pressure of a building envelope structure is characterized by comprising the following steps:
s1, determining the arrangement of wind pressure measuring points and manufacturing a building pressure measuring model according to relevant standards and guidelines of a building wind tunnel test;
s2, determining test parameters and test working conditions to perform wind tunnel test, collecting the surface wind pressure of the building pressure measurement model in S1, and obtaining a wind pressure coefficient sequence through non-dimensionalization;
s3, analyzing the wind pressure coefficient sequence in S2 by adopting a mutual information method to obtain the minimum time interval for dividing the independent wind pressure coefficient subsequence;
s4, segmenting the wind pressure coefficient sequence according to the minimum time interval in the S3 to obtain an independent wind pressure coefficient subsequence, and if the number of segments is more than or equal to 25, carrying out S5; otherwise, turning to S2, prolonging the test sampling time, and carrying out wind pressure collection on the surface of the building pressure measurement model again;
s5, counting the extreme value sequence of the independent wind pressure coefficient subsequence and calculating the standard deviation of the extreme value sequence; calculating the coefficient of the gunbell distribution parameter based on the number of the stages of dividing the wind pressure coefficient sequence at the minimum time interval;
s6, converting the prototype target time interval of the divided wind pressure coefficient sequence into a model target time interval according to a similarity criterion, and determining the time interval ratio of the model target time interval to the minimum time interval;
s7, calculating the coefficient of the Gunn Bell distribution parameter based on the number of the stages of dividing the wind pressure coefficient sequence by the model target time distance in S6;
s8, substituting the extreme value sequence, the standard deviation of the extreme value sequence, the coefficient of the Gunn-Beel distribution parameter and the time interval ratio value obtained in S5, S6 and S7 into an extreme value conversion formula of a modified peak value segment average method, so that the extreme value wind pressure coefficient result based on the wind pressure coefficient sequence under the minimum time interval is converted into the extreme value wind pressure coefficient result based on the wind pressure coefficient sequence under the model target time interval;
and S9, converting the extreme value wind pressure coefficient result based on the wind pressure coefficient sequence under the model target time interval in S8 into the extreme value wind pressure of the building pressure measuring prototype.
2. The detection method according to claim 1, wherein the extreme value transformation formula of the modified peak value piecewise averaging method is as follows:
wherein:
respectively representing model-based target time intervals t2The maximum value and the minimum value of the lower wind pressure coefficient sequence,
respectively based on the minimum time interval t1The maximum value and the minimum value of the lower independent wind pressure coefficient subsequence j,
are respectively provided withRepresentation based on minimum time distance t1The variance of the lower maximum value wind pressure coefficient sequence and the variance of the minimum value wind pressure coefficient sequence,
representation based on minimum time distance t1The number of the sub-sequences of the lower independent wind pressure coefficients,
respectively based on the minimum time interval t1The coefficients of the gunn bell distribution parameters,
representing model-based target time distance t2And n represents the time-distance ratio for dividing the wind pressure coefficient sequence.
3. The detecting method according to claim 1, wherein in step S9, the extreme value wind pressure coefficient result based on the wind pressure coefficient sequence at the model target time distance is converted into the extreme value wind pressure of the building manometric prototype, and the conversion formula is as follows:
wherein, wk、w0Respectively represents the standard value of the extreme value wind pressure and the basic wind pressure of 10m height, muzIs the change coefficient of the wind pressure height,
for model-based target time interval t2And the extreme value wind pressure coefficient of the lower sequence.
4. The method according to claim 1, wherein the influence of the number of stages of the sequence of wind pressure coefficients on the extreme wind pressure result of the pressure measuring model of the building is taken into account by calculating the coefficients of the gunnel distribution parameters.
5. The detection method according to claim 1, wherein step S1 includes: determining proper geometric scaling ratio lambda according to building wind tunnel test standard and guideLAnd reasonably arranging the measuring points according to a wind pressure measuring point arrangement principle, encrypting the wind pressure measuring points at the positions with severe wind pressure change, and manufacturing a building pressure measuring model.
6. The detection method according to claim 5, wherein step S2 includes: the required landform flow field is simulated through wind field debugging, and the wind speed scaling ratio lambda is determinedU(ii) a And setting the sampling duration, sampling frequency and test wind direction angle of the test according to the test working condition.
7. The detection method according to claim 6, wherein step S2 includes: a pressure scanning system is used for collecting a wind pressure sequence p (t) of a wind pressure measuring point, and the wind pressure at the building pressure measuring model roof height is used as a dimensionless reference wind pressure to obtain a wind pressure coefficient sequence Cp(t)。
8. The detection method according to claim 7, wherein step S3 includes: firstly, counting the maximum value of corresponding sampling time when the mutual information coefficient of all measuring points under each test wind direction angle is reduced from 1 to 0.05, then comparing the statistical results under the full wind direction angle and taking an envelope value as a division wind pressure coefficient sequence Cp(t) minimum time interval t1(ii) a Let random variable X represent wind pressure coefficient sequence Cp(t), Y represents a hysteresis wind pressure coefficient sequence Cp(t + τ), τ represents the lag time, and the mutual information method specifically comprises the following processes:
wherein: h (X), H (Y) are respectively X, Y edge entropy, H (X, Y) is X, Y joint entropy, MI (X, Y) and NMI (X, Y) are respectively X, Y mutual information and mutual information coefficient, and p (X, Y)b)、p(yd) X, Y, B ═ 1,2, …, B, D ═ 1,2, …, D, p (x, respectively)b,yd) For the joint distribution of X, Y, B, D is the data length of X, Y sequences, respectively.
9. The detection method according to claim 6, wherein step S6 includes: according to the similarity criterion, the geometric scale ratio is lambdaLScaled ratio lambda of sum wind speedUCo-determining time scaling ratio lambdaTBy the time scale ratio lambdaTThe set prototype target time interval T2Conversion to model target time distance t2。
10. Detection method according to claim 9, characterised in that the time scaling ratio λ is passedTThe set prototype target time interval T2Conversion to model target time distance t2The conversion relationship is as follows:
t2＝T2×λT
in the formula: lambda [ alpha ]T、λL、λURespectively representing the time scale ratio, the geometric scale ratio and the wind speed scale ratio, Lp、LmRespectively representing the dimension of a building pressure measurement prototype and the dimension of a building pressure measurement model, Wr,p、Wr,mAnd respectively representing the wind pressure at the reference height of the building pressure measuring prototype and the wind pressure at the reference height of the building pressure measuring model.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910992004.5A CN110779680B (en) | 2019-10-18 | 2019-10-18 | Method for detecting extreme value wind pressure of building envelope structure |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910992004.5A CN110779680B (en) | 2019-10-18 | 2019-10-18 | Method for detecting extreme value wind pressure of building envelope structure |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110779680A CN110779680A (en) | 2020-02-11 |
| CN110779680B true CN110779680B (en) | 2021-03-30 |
Family
ID=69385832
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910992004.5A Active CN110779680B (en) | 2019-10-18 | 2019-10-18 | Method for detecting extreme value wind pressure of building envelope structure |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110779680B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104535254A (en) * | 2014-12-23 | 2015-04-22 | 太原科技大学 | Building outer surface wind pressure measurement method |
| CN104715297A (en) * | 2015-04-10 | 2015-06-17 | 北京安澜尔雅科技有限公司 | Method for predicting wind-induced roof covering loss |
| KR20150106737A (en) * | 2014-03-12 | 2015-09-22 | 경북대학교 산학협력단 | Apparatus and method for calculating wind load considering topographic factor |
| CN106126787A (en) * | 2016-06-16 | 2016-11-16 | 西南交通大学 | A kind of roof boarding earthquake loss estimation methodology considering wind load dependency based on data |
| CN106202816A (en) * | 2016-07-26 | 2016-12-07 | 南京航空航天大学 | The determination method and device of straight tube cone segment type steel structure cooling tower blast extreme value |
| CN106250601A (en) * | 2016-07-26 | 2016-12-21 | 南京航空航天大学 | The determination method and device of hyperbolic steel construction cooling tower blast extreme value |
| CN106682283A (en) * | 2016-12-09 | 2017-05-17 | 西南交通大学 | Metal plate roof wind-induced loss estimating method |
| CN107229823A (en) * | 2017-05-18 | 2017-10-03 | 西南交通大学 | A kind of probabilistic analysis method of wind effect extreme value |
-
2019
- 2019-10-18 CN CN201910992004.5A patent/CN110779680B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20150106737A (en) * | 2014-03-12 | 2015-09-22 | 경북대학교 산학협력단 | Apparatus and method for calculating wind load considering topographic factor |
| CN104535254A (en) * | 2014-12-23 | 2015-04-22 | 太原科技大学 | Building outer surface wind pressure measurement method |
| CN104715297A (en) * | 2015-04-10 | 2015-06-17 | 北京安澜尔雅科技有限公司 | Method for predicting wind-induced roof covering loss |
| CN106126787A (en) * | 2016-06-16 | 2016-11-16 | 西南交通大学 | A kind of roof boarding earthquake loss estimation methodology considering wind load dependency based on data |
| CN106202816A (en) * | 2016-07-26 | 2016-12-07 | 南京航空航天大学 | The determination method and device of straight tube cone segment type steel structure cooling tower blast extreme value |
| CN106250601A (en) * | 2016-07-26 | 2016-12-21 | 南京航空航天大学 | The determination method and device of hyperbolic steel construction cooling tower blast extreme value |
| CN106682283A (en) * | 2016-12-09 | 2017-05-17 | 西南交通大学 | Metal plate roof wind-induced loss estimating method |
| CN107229823A (en) * | 2017-05-18 | 2017-10-03 | 西南交通大学 | A kind of probabilistic analysis method of wind effect extreme value |
Non-Patent Citations (3)
| Title |
|---|
| "The mutual information: Detecting and evaluating dependencies between variables";R.Steuer et al;《BIOINFORMATICS》;20021001;第18卷;S231-S240 * |
| "基于广义极值理论的非高斯风压极值计算方法";王飞 等;《工程力学》;20130228;第30卷(第2期);44-49 * |
| "超高层建筑围护结构风洞试验研究";孙秀丽;《铁道建筑技术》;20190831;138-141 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110779680A (en) | 2020-02-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2021218424A1 (en) | Rbf neural network-based method for sea surface wind speed inversion from marine radar image | |
| CN109143196B (en) | Three-point parameter estimation method based on K-distribution sea clutter amplitude model | |
| Greenway | An analytical approach to wind velocity gust factors | |
| CN111551927B (en) | Underground pipeline diameter measuring method based on three-dimensional ground penetrating radar | |
| KR20180091372A (en) | Method for tracking target position of radar | |
| CN111737912A (en) | MWHTS simulated bright temperature calculation method based on deep neural network | |
| CN111832176B (en) | Sea surface wind field inversion method and system of full-polarization microwave radiometer under rainfall condition | |
| CN110738275A (en) | UT-PHD-based multi-sensor sequential fusion tracking method | |
| CN113514833B (en) | Sea surface arbitrary point wave direction inversion method based on sea wave image | |
| CN110779680B (en) | Method for detecting extreme value wind pressure of building envelope structure | |
| CN103106332B (en) | A kind of analytical approach of uncertainty of measurement | |
| Sedlak et al. | Acoustic and electromagnetic emission as a tool for crack localization | |
| CN111308468B (en) | Method for automatically identifying deformation risk area based on InSAR technology | |
| CN115826004B (en) | Three-star cooperative direct positioning method based on two-dimensional angle and time difference combination | |
| CN109670143B (en) | Method for detecting statistical law of vibration frequency domain response signals of civil engineering structure under environmental excitation | |
| CN116522085A (en) | Full-automatic inhaul cable frequency extraction, fixed-order and cable force identification method and application | |
| CN114814779B (en) | Buoy surge wave height observation data error evaluation method, system, equipment and medium | |
| CN114691661B (en) | Assimilation-based cloud air guide and temperature and humidity profile pretreatment analysis method and system | |
| CN114577360B (en) | Automatic analysis calibration and inversion method for Raman temperature measurement radar signals | |
| Reinhold | Measurement of simultaneous fluctuating loads at multiple levels on a model of a tall building in a simulated urban boundary layer. | |
| CN112859002B (en) | Acoustic emission source positioning method and system | |
| CN111122813B (en) | Water quality category evaluation method based on regional groundwater flow field direction | |
| RU2714884C1 (en) | Method of determining the course of an object on a linear trajectory using measurements of its radial velocity | |
| CN107423545A (en) | A kind of simple and easy method based on Hermite polynomial rated wind pressure extreme value | |
| CN115618174B (en) | Soil humidity inversion method based on pixel scale surface roughness spectrum parameters |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |