CN106330197A - Building wind tunnel pressure measurement test data compression method - Google Patents

Abstract

The invention relates to a building wind tunnel pressure measurement test data compression method. The invention aims at solving the problem that the little attention is paid to frequency domain characteristics of wind load and correlation in the prior art, resulting in a data compression error and an actual application error. The statistical information of the data is reconstructed by the Hermite polynomial, and the spectrum information is reconstructed by the Beta function theory, and a compressed wind pressure filed can be reconstructed at last in combination with random simulation technology. By adoption of the building wind tunnel pressure measurement test data compression method provided by the invention, the building wind tunnel pressure measurement data of GB and TB levels can be compressed to the KB and MB levels, and a novel information extracting and modeling method is provided for the high-dimension time-course large data of the wind load changing with time and space so as to achieve the data compression purpose at last. The building wind tunnel pressure measurement test data compression method is applied to the field of building wind tunnel pressure measurement tests.

Description

A kind of building wind tunnel pressure measuring test data compression method

Technical field

The present invention relates to build wind tunnel pressure measuring test data compression method.

Background technology

Wind tunnel pressure measuring test is one of committed step of current large complicated engineering structure wind force proofing design.Wind tunnel pressure measuring is tested The wind loads time course data amount obtained is big, up to GB even TB rank, and contains abundant information.Big data quantity result in The difficulty that data store and analyze, therefore, it is necessary to carry out feature extraction and compression storage, in order to amassing of data to data Tire out, analyze and predict.The wind load time-history data characteristics that building wind tunnel test obtains can be attributed to the mark that non-gaussian part is relevant Amount field.Current wind tunnel test data compression, many employing eigenvector methods, pay close attention to the wind load field principal coordinate information in time domain, Or the Time-domain Statistics information of wind load time-history is modeled, wind load frequency domain characteristic and dependency are paid close attention to less, causes number Deviation and the error in actual application according to compression.

Summary of the invention

The present invention is to solve to pay close attention to less to wind load frequency domain characteristic and dependency in prior art, causing data pressure The problem of the error in the deviation of contracting and actual application, and a kind of building wind tunnel pressure measuring test data compression method proposed.

A kind of building wind tunnel pressure measuring test data compression method realizes according to the following steps:

Step one: blast time series data nondimensionalization building wind tunnel test pressure measurement obtained is coefficient of wind pres time-histories Data, and the unbiased esti-mator meansigma methods of rated wind pressure coefficient, root-mean-square value, degree of bias value and and kurtosis value；

Step 2: coefficient of wind pres time-histories carries out auto-power spectrum and estimates to obtain auto-power spectrum, calculates dimensionless power and sets a song to music The peak value of line and curve high band slope under log-log coordinate；

Step 3: use Welch method to estimate the coherent function of coefficient of wind pres field, with exponential function matching coherent function；

Step 4: solve the equation of band Bata function according to step 2 and obtain the expression formula of dimensionless auto-power spectrum, and root According to Beta function call to any ν rank dimensionless spectral moment；

Step 5: form the wind load data after compression according to step one, step 3 and step 4；

Step 6: according to Hermite multinomial transfer function, forms cross-spectrum matrix and is reconstructed wind-pressure field；

Step 7: estimate extreme value wind load；

Step 8: computation structure wind vibration response.

Invention effect:

The present invention is a kind of building wind tunnel pressure measuring test data compression side based on Hermite multinomial and Beta function Method, can by the building wind tunnel pressure measuring data compression of GB, TB rank to KB, MB rank, to wind load in time, spatial variations A kind of novel information of the big data of higher-dimension time-histories extracts and modeling method, is finally reached the purpose of data compression.The present invention's is excellent Gesture is, modeling process is the simplest, it is possible to obtain the data form of efficient storage and application, it is simple to the deep excavation of data. By Hermite multinomial, the statistical information of data can be reconstructed, and by Beta function theory, spectrum information be entered Line reconstruction, finally combines stochastic simulation technology and can rebuild the wind-pressure field of compression.From the application angle of structural wind resistance design, compress number According to the Wind resistant analysis of engine request can be carried out, simplify loaded down with trivial details time-histories and spectrum analysis process, more practical.

Accompanying drawing explanation

Fig. 1 is flow chart of the present invention；

Fig. 2 is that blast based on Bata function composes modeling result figure；

Fig. 3 (a) is normalized coefficient of wind pres time-histories figure；In figure, abscissa is time t_kS (), vertical coordinate is normalized Coefficient of wind pres time-histories

Fig. 3 (b) is coefficient of wind pres probability density function figure, and in figure, abscissa is normalized coefficient of wind presVertical coordinate is probability density function；

Fig. 3 (c) coefficient of wind pres dimensionless power spectral density function figure；In figure, abscissa is frequency f (Hz)；Vertical coordinate is nothing Dimension power spectral density function S；

Fig. 4 is the comparison diagram of extreme value wind load estimated result based on Hermite multinomial transfer function method and actual value；

Fig. 5 is the comparison diagram of flat plate framed structure wind vibration response and the initial data result of calculation carried out based on compression data.

Detailed description of the invention

Detailed description of the invention one: as it is shown in figure 1, a kind of building wind tunnel pressure measuring test data compression method includes following step Rapid:

Step one: blast time series data nondimensionalization building wind tunnel test pressure measurement obtained is coefficient of wind pres time-histories Data, and the unbiased esti-mator meansigma methods of rated wind pressure coefficient, root-mean-square value, degree of bias value and and kurtosis value；

Step 2: coefficient of wind pres time-histories carries out auto-power spectrum and estimates to obtain auto-power spectrum, calculates dimensionless power and sets a song to music The peak value of line and curve high band slope under log-log coordinate；

Step 3: use Welch method to estimate the coherent function of coefficient of wind pres field, with exponential function matching coherent function；

Step 4: solve the equation of band Bata function according to step 2 and obtain the expression formula of dimensionless auto-power spectrum, and root According to Beta function call to any ν rank dimensionless spectral moment；

Step 5: form the wind load data after compression according to step one, step 3 and step 4；

Step 6: according to Hermite multinomial transfer function, forms cross-spectrum matrix and is reconstructed wind-pressure field；

Step 7: estimate extreme value wind load；

Step 8: computation structure wind vibration response.

Detailed description of the invention two: present embodiment is unlike detailed description of the invention one: will building in described step one The blast time series data nondimensionalization that wind tunnel test pressure measurement obtains is coefficient of wind pres time course data, the nothing of rated wind pressure coefficient Partially estimated mean value, root-mean-square value, degree of bias value and and kurtosis value particularly as follows:

Blast time series data p that building wind tunnel test pressure measurement is obtained_i(t_k), when nondimensionalization is coefficient of wind pres Number of passes evidenceWherein said i represents measuring point number, and t is the time, k express time serial number, and value is 1,2 ..., N, N are sampling length, and ρ is atmospheric density, and U represents and flows wind speed at reference altitude；And rated wind pressure coefficient Unbiased esti-mator meansigma methodsRoot-mean-square valueDegree of bias valueAnd kurtosis value

$C_{p,ku}=\[\frac{N+1}{N}{\left(\frac{N-1}{N}\right)}^2\underset{k=1}{\overset{N}{\Σ}}{\[\frac{C_p\left(t_k\right)-{\stackrel{\‾}{C}}_p}{{\tilde{C}}_p}\]}^4-3(N-1)\]\frac{N-1}{(N-2)(N-3)}+3.$

Other step and parameter are identical with detailed description of the invention one.

Detailed description of the invention three: present embodiment is unlike detailed description of the invention one or two: right in described step 2 Coefficient of wind pres time-histories carries out auto-power spectrum and estimates to obtain auto-power spectrum, and the peak value and the curve that calculate dimensionless power spectrum curve are high Frequency range slope under log-log coordinate particularly as follows:

Use autoregression AR model to carry out auto-power spectrum estimation to carrying out coefficient of wind pres time-histories, obtain auto-power spectrum S_Cp(f), To its nondimensionalization, it is expressed asFrequency f nondimensionalization isWherein L represents with reference to yardstick；Calculate The peak value of dimensionless power spectrum curve S-F curve, i.e. S_m=max{S (F) }, F_m=argmax{S (F) }；And curve high band Slope under log-log coordinate

$k_2=\frac{(K-j+1)\·\underset{k=j}{\overset{K}{\Σ}}ln\[S\left(F_k\right)\]\·ln\left(F_k\right)-\underset{k=j}{\overset{K}{\Σ}}ln\[S\left(F_k\right)\]\·\underset{k=j}{\overset{K}{\Σ}}ln\left(F_k\right)}{(K-j+1)\·\underset{k=j}{\overset{K}{\Σ}}{\[ln\left(F_k\right)\]}^2-{\[\underset{k=j}{\overset{K}{\Σ}}\ln \left(F_k\right)\]}^2}$

In formula, K is half Fourier transformation length, F_k(k=1,2 ..., K) it is discrete dimensionless frequency, j is high band The index of frequency, takes F_j=1.5F_m。

Other step and parameter are identical with detailed description of the invention one or two.

Detailed description of the invention four: present embodiment is unlike one of detailed description of the invention one to three: described step 3 Middle employing Welch method estimates the coherent function of coefficient of wind pres field, by the detailed process of exponential function matching coherent function is:

Welch method is used to estimate the coherent function Coh of coefficient of wind pres field_ijF (), uses exponential function Coh_ij(f)=exp (-k_c ||f·D_ij/ U) matching coherent function, wherein D_ijRepresent the distance of point-to-point transmission, i.e.

$k_c=\{\underset{k=1}{\overset{K}{\Σ}}k\·\ln \[{\mathrm{Coh}}_{ij}(k\·\mathrm{\Δ}f)\]-\frac{K+1}{2}\·\underset{k=1}{\overset{K}{\Σ}}\ln \[{\mathrm{Coh}}_{ij}(k\·\mathrm{\Δ}f)\]\}\·\frac{6}{K(K+1)(K+2)}\·\frac{U}{D_{ij}\·\mathrm{\Δ}f}$

In formula, Δ f=f_s/ 2K is frequency interval, f_sFor sample frequency, k_cFor relevant index, U is for flowing with reference to wind speed.

Other step and parameter are identical with one of detailed description of the invention one to three.

Detailed description of the invention five: present embodiment is unlike one of detailed description of the invention one to four: described step 4 In solve the equation of band Bata function and obtain the expression formula of dimensionless auto-power spectrum, and according to Beta function call to any ν rank without The detailed process of dimension spectral moment is:

Solve the equation of band Bata function

$S_m\·\frac{1}{\mathrm{\α}}\·{\left(1-\frac{1}{k_2}\right)}^{\frac{1-k_2}{\mathrm{\α}}}\·{(-k_2)}^{\frac{1}{\mathrm{\α}}}\·B\left(\frac{1}{\mathrm{\α}},\frac{-k_2}{\mathrm{\α}}\right)=1$

Obtain frequency index α of blast spectrum, obtain the expression formula of dimensionless auto-power spectrum furtherWherein F '=F/F_m；According to Beta function, obtain any ν rank dimensionless spectral moment

$S_v={\∫}_0^{\mathrm{\∞}}{F}^{\′v-1}S{\mathrm{dF}}^{\′}={(-k_2)}^{v/\mathrm{\α}}\·B\left(\frac{1+v}{\mathrm{\α}},\frac{-k_2-v}{\mathrm{\α}}\right)/B\left(\frac{1}{\mathrm{\α}},\frac{-k_2}{\mathrm{\α}}\right)$

Obtain normalized second order spectral moment further:

${\mathrm{\λ}}_0=\sqrt{\frac{S_2}{S_0}}={(-k_2)}^{1/\mathrm{\α}}\·\sqrt{B\left(\frac{3}{\mathrm{\α}},\frac{-k_2-2}{\mathrm{\α}}\right)/B\left(\frac{1}{\mathrm{\α}},\frac{-k_2}{\mathrm{\α}}\right)}.$

Other step and parameter are identical with one of detailed description of the invention one to four.

Detailed description of the invention six: present embodiment is unlike one of detailed description of the invention one to five: described step 5 Middle formed compression after wind load data particularly as follows:

Form the wind load data after compression, be expressed as 13 column data:

$\left[\begin{array}{ccccccccccccc}x& y& z& {\stackrel{\‾}{C}}_p& {\tilde{C}}_p& C_{p,sk}& C_{p,ku}& F_m& S_m& k_2& k_c& \mathrm{\α}& {\mathrm{\λ}}_0\end{array}\right]$

Front 3 row are measuring point three-dimensional geometry coordinates；4～7 are classified as Fourth square before wind load, represent average blast system respectively Number, root-mean-square coefficient of wind pres, the coefficient of wind pres degree of bias, coefficient of wind pres kurtosis, 8～10 are classified as the auto-power spectrum model of wind load, point Not Biao Shi dimensionless spectrum peak frequency, dimensionless spectrum peak and blast spectrum attenuation slope, 11 are classified as wind load coherency function model, Representing relevant index, 12～13 are classified as derived parameter, represent the frequency index of blast spectrum, normalized second order spectral moment respectively.

Other step and parameter are identical with one of detailed description of the invention one to five.

Detailed description of the invention seven: present embodiment is unlike one of detailed description of the invention one to six: described step 6 Middle according to Hermite multinomial transfer function, form cross-spectrum matrix and wind-pressure field is reconstructed particularly as follows:

Wind-pressure field is rebuild based on Hermite multinomial transfer function method, according to Hermite multinomial transfer function, in conjunction with Characterize the statistical parameter γ of non-Gaussian feature₃=C_p,sk、γ₄=C_p,ku, set up nongausian process x (t) and Gaussian process u (t) Contact, it may be assumed that

Work as C_p,kuWhen >=3, x=h (u)=κ [u+h₃(u²-1)+h₄(u³-u)], Or be expressed asξ (x)=1.5b (a+x/ κ)-a³, a=h₃/ 3h₄, b=1/3h₄, c=(b-1-a²)³；

Work as C_p,ku< when 3, u=h^-1(x)=b₂x+b₃(x²-γ₃x-1)+b₄(x³-γ₄x-γ₃),

Expression formula in conjunction with crosspower spectrumCross-spectrum matrix [the S formed_Cp (ω)], press

$C_p\left(t_k\right)={\stackrel{\&OverBar;}{C}}_p+{\tilde{C}}_{p}2\sqrt{\mathrm{\&Delta;}\mathrm{\&omega;}}\underset{m=1}{\overset{k}{\&Sigma;}}\underset{l=1}{\overset{N}{\&Sigma;}}\left|H_{km}\left({\mathrm{\&omega;}}_{ml}\right)\right|cos\&lsqb;{\mathrm{\&omega;}}_{ml}t-{\mathrm{\&theta;}}_{km}\left({\mathrm{\&omega;}}_{ml}\right)+{\mathrm{\&phi;}}_{ml}\&rsqb;,k=1,2,...,n$

Wind-pressure field is reconstructed, wherein H_km(ω_ml) it is crosspower spectrum matrix [S_Cp(ω) Cholesky] decomposes, θ_km (ω_ml) it is H_km(ω_ml) explement,For discrete frequency, Δ ω is between circular frequency Every, φ_mlFor additive phase angle.

Other step and parameter are identical with one of detailed description of the invention one to six.

Detailed description of the invention eight: present embodiment is unlike one of detailed description of the invention one to seven: described step 7 Middle according to step 6 estimate extreme value wind load particularly as follows:

Hermite multinomial transfer function based on step 6 estimates extreme value wind load,g_NG=h (g),n₀=λ₀F_mU/L is average cross-over frequency, and T=600s is with reference to duration.H () is According to Hermite function x=h (u) determined by step 6=κ [u+h₃(u²-1)+h₄(u³-u)] or u=h^-1(x)=b₂x+ b₃(x²-γ₃x-1)+b₄(x³-γ₄x-γ₃)。

Other step and parameter are identical with one of detailed description of the invention one to seven.

Detailed description of the invention nine: present embodiment is unlike one of detailed description of the invention one to eight: described step 8 The middle detailed process according to step 6 computation structure wind vibration response is:

Press according to the wind load cross-spectrum matrix of reduction in step 6 Wherein said [H (ω)]={ ω²[M]+iω[C]+[K]}-¹For frequency response function matrix, [M] is mass matrix, and [C] is damping square Battle array, [K] is stiffness matrix,For imaginary unit, [S_Cp(ω) being] the cross-spectrum matrix calculated according to step 6, [R] is attached Belonging to area transition matrix, subscript * represents that conjugate transpose, T represent transposition, and-1 represents inverse matrix, computation structure wind vibration response association side Difference [∑_x]。

Other step and parameter are identical with one of detailed description of the invention one to eight.

Embodiment one:

Flat roof system series wind tunnel test, has investigated the length-width ratio of roof system, depth-width ratio, landforms, wind speed, scaling factor, wind direction Impact, has carried out the wind tunnel test of 286 operating modes altogether, and experimental data size is 84.9GB, after data compression of the present invention, and data Size is 12.9MB, is embodied as step as follows:

Step one: carry out statistical analysis after blast time-histories nondimensionalization wind tunnel test recorded, try to achieve coefficient of wind pres Front Fourth square.

Step 2: coefficient of wind pres time-histories is carried out auto-power spectrum analysis, has solved auto-power spectrum parameter S_m、F_m、k₂, Fig. 2 gives Go out modeling result.

Step 3: coefficient of wind pres time-histories has carried out analysis and the matching of coherent function, has solved being concerned with under each operating mode Index k_c。

Step 4: the auto-power spectrum parametric solution the obtaining step 3 equation containing Beta function, has obtained deriving ginseng Number α and λ₀。

Step 5: the result that step one～four obtain is integrally formed the output of the wind load data file after compression.

For verifying the effectiveness of this invention, the most also carry out reconstructing (step 6) by compression data, found based on this Bright method, compression data keep consistent with the statistics of initial data, frequency spectrum, correlation properties, illustrate the suitability of the method. Additionally, for ease of engineer applied, the most also use the compression data estimation extreme value wind load (step 7) of flat roof system, with various The exact method of this (1000 sample) is contrasted, and finds the actual value of analogue value energy envelope more than 95%, relative when underestimating Error, within 10%, may be used in engineering Design of Retaining Structure；For the wind-induced response of agent structure, according to step Eight result of calculations giving different flat plate framed structure, the error of discovery dynamic respond is within 5%, and result is more accurate, available In engineering structure wind force proofing design, experimental result is if Fig. 3 (a) is to shown in Fig. 5.

In sum, the present invention is in processing building wind tunnel pressure measuring test data, it is possible to the storage significantly reducing data is empty Between, the data result obtained has stronger reducibility, and relatively broad engineering use value.

Claims (9)

1. a building wind tunnel pressure measuring test data compression method, it is characterised in that described building wind tunnel pressure measuring test data pressure The detailed process of compression method is:

Step one: number of passes when blast time series data nondimensionalization building wind tunnel test pressure measurement obtained is coefficient of wind pres According to, and the unbiased esti-mator meansigma methods of rated wind pressure coefficient, root-mean-square value, degree of bias value and and kurtosis value；

Step 2: coefficient of wind pres time-histories is carried out auto-power spectrum and estimates to obtain auto-power spectrum, calculate dimensionless power spectrum curve Peak value and curve high band slope under log-log coordinate；

Step 3: use Welch method to estimate the coherent function of coefficient of wind pres field, with exponential function matching coherent function；

Step 4: solve the equation of band Bata function according to step 2 and obtain the expression formula of dimensionless auto-power spectrum, and according to Beta function call is to any ν rank dimensionless spectral moment；

Step 5: form the wind load data after compression according to step one, step 3 and step 4；

Step 6: according to Hermite multinomial transfer function, forms cross-spectrum matrix and is reconstructed wind-pressure field；

Step 7: estimate extreme value wind load；

Step 8: computation structure wind vibration response.

A kind of building wind tunnel pressure measuring test data compression method the most according to claim 1, it is characterised in that described step Blast time series data nondimensionalization building wind tunnel test pressure measurement obtained in one is coefficient of wind pres time course data, calculates wind Pressure the unbiased esti-mator meansigma methods of coefficient, root-mean-square value, degree of bias value and and kurtosis value particularly as follows:

Blast time series data p that building wind tunnel test pressure measurement is obtained_i(t_k), nondimensionalization is coefficient of wind pres time course dataWherein said i represents measuring point number, and t is the time, k express time serial number, and value is 1,2 ..., N, N are Sampling length, ρ is atmospheric density, and U represents and flows wind speed at reference altitude；And the unbiased esti-mator meansigma methods of rated wind pressure coefficientRoot-mean-square valueDegree of bias value And kurtosis value

$C_{p,ku}=\&lsqb;\frac{N+1}{N}{\left(\frac{N-1}{N}\right)}^2\underset{k=1}{\overset{N}{\&Sigma;}}{\&lsqb;\frac{C_p\left(t_k\right)-{\stackrel{\&OverBar;}{C}}_p}{{\tilde{C}}_p}\&rsqb;}^4-3(N-1)\&rsqb;\frac{N-1}{(N-2)(N-3)}+3.$

A kind of building wind tunnel pressure measuring test data compression method the most according to claim 2, it is characterised in that described step Coefficient of wind pres time-histories carries out in two auto-power spectrum estimate to obtain auto-power spectrum, calculate dimensionless power spectrum curve peak value and Curve high band slope under log-log coordinate particularly as follows:

Use autoregression AR model to carry out auto-power spectrum estimation to carrying out coefficient of wind pres time-histories, obtain auto-power spectrum S_CpF (), to it Nondimensionalization, is expressed asFrequency f nondimensionalization isWherein L represents with reference to yardstick；Calculate immeasurable The peak value of guiding principle power spectrum curve S-F curve, i.e. S_m=max{S (F) }, F_m=argmax{S (F) }；And curve high band is double Slope under logarithmic coordinates

$k_2=\frac{(K-j+1)\&CenterDot;\underset{k=j}{\overset{K}{\&Sigma;}}ln\&lsqb;S\left(F_k\right)\&rsqb;\&CenterDot;ln\left(F_k\right)-\underset{k=j}{\overset{K}{\&Sigma;}}ln\&lsqb;S\left(F_k\right)\&rsqb;\&CenterDot;\underset{k=j}{\overset{K}{\&Sigma;}}ln\left(F_k\right)}{(K-j+1)\&CenterDot;\underset{k=j}{\overset{K}{\&Sigma;}}{\&lsqb;ln\left(F_k\right)\&rsqb;}^2-{\&lsqb;\underset{k=j}{\overset{K}{\&Sigma;}}\ln \left(F_k\right)\&rsqb;}^2}$

In formula, K is half Fourier transformation length, F_kFor discrete dimensionless frequency, k value is 1,2 ..., K；J is high band frequency The index of rate, takes F_j=1.5F_m。

A kind of building wind tunnel pressure measuring test data compression method the most according to claim 3, it is characterised in that described step Use Welch method to estimate the coherent function of coefficient of wind pres field in three, by the detailed process of exponential function matching coherent function be:

Welch method is used to estimate the coherent function Coh of coefficient of wind pres field_ijF (), uses exponential function Coh_ij(f)=exp (-k_cf· D_ij/ U) matching coherent function, wherein D_ijRepresent the distance of point-to-point transmission, i.e.

$k_c=\left\{\underset{k=1}{\overset{K}{\mathrm{\&Sigma;}}}k\&CenterDot;\ln \&lsqb;{\mathrm{Coh}}_{ij}\left(k\&CenterDot;\mathrm{\&Delta;}f\right)\&rsqb;-\frac{K+1}{2}\&CenterDot;\underset{k=1}{\overset{K}{\mathrm{\&Sigma;}}}\ln \&lsqb;{\mathrm{Coh}}_{ij}\left(k\&CenterDot;\mathrm{\&Delta;}f\right)\&rsqb;\right\}\&CenterDot;\frac{6}{K\left(K+1\right)\left(K+2\right)}\&CenterDot;\frac{U}{D_{ij}\&CenterDot;\mathrm{\&Delta;}f}$

In formula, △ f=f_s/ 2K is frequency interval, f_sFor sample frequency.

A kind of building wind tunnel pressure measuring test data compression method the most according to claim 4, it is characterised in that described step The equation solving band Bata function in four obtains the expression formula of dimensionless auto-power spectrum, and according to Beta function call to any ν rank The detailed process of dimensionless spectral moment is:

Solve the equation of band Bata function

$S_m\&CenterDot;\frac{1}{\mathrm{\&alpha;}}\&CenterDot;{(1-\frac{1}{k_2})}^{\frac{1-k_2}{\mathrm{\&alpha;}}}\&CenterDot;{(-k_2)}^{\frac{1}{\mathrm{\&alpha;}}}\&CenterDot;B\left(\frac{1}{\mathrm{\&alpha;}},\frac{-k_2}{\mathrm{\&alpha;}}\right)=1$

Obtain frequency index α of blast spectrum, obtain the expression formula of dimensionless auto-power spectrum further Wherein F '=F/F_m；According to Beta function, obtain any ν rank dimensionless spectral moment

$S_{\mathrm{\&nu;}}={\&Integral;}_0^{\mathrm{\&infin;}}{F}^{\&prime;\mathrm{\&nu;}-1}S{\mathrm{dF}}^{\&prime;}={(-k_2)}^{\mathrm{\&nu;}/\mathrm{\&alpha;}}\&CenterDot;B\left(\frac{1+\mathrm{\&nu;}}{\mathrm{\&alpha;}},\frac{-k_2-\mathrm{\&nu;}}{\mathrm{\&alpha;}}\right)/B\left(\frac{1}{\mathrm{\&alpha;}},\frac{-k_2}{\mathrm{\&alpha;}}\right)$

Obtain normalized second order spectral moment further:

${\mathrm{\&lambda;}}_0=\sqrt{\frac{S_2}{S_0}}={(-k_2)}^{1/\mathrm{\&alpha;}}\&CenterDot;\sqrt{B\left(\frac{3}{\mathrm{\&alpha;}},\frac{-k_2-2}{\mathrm{\&alpha;}}\right)/B\left(\frac{1}{\mathrm{\&alpha;}},\frac{-k_2}{\mathrm{\&alpha;}}\right)}.$

A kind of building wind tunnel pressure measuring test data compression method the most according to claim 5, it is characterised in that described step In five formed compression after wind load data particularly as follows:

Form the wind load data after compression, be expressed as 13 column data:

$\left[\begin{array}{ccccccccccccc}x& y& z& {\stackrel{\&OverBar;}{C}}_p& {\tilde{C}}_p& C_{p,\mathrm{sk}}& C_{p,\mathrm{ku}}& F_m& S_m& k_2& k_c& \mathrm{\&alpha;}& {\mathrm{\&lambda;}}_0\end{array}\right]$

Front 3 row are measuring point three-dimensional geometry coordinates；4～7 are classified as Fourth square before wind load, 8～10 be classified as wind load from merit Rate spectrum model, 11 are classified as wind load coherency function model, and 12～13 are classified as derived parameter.

A kind of building wind tunnel pressure measuring test data compression method the most according to claim 6, it is characterised in that described step According to Hermite multinomial transfer function in six, formed cross-spectrum matrix and wind-pressure field is reconstructed particularly as follows:

Wind-pressure field is rebuild, according to Hermite multinomial transfer function, in conjunction with characterizing based on Hermite multinomial transfer function method The statistical parameter γ of non-Gaussian feature₃=C_p,sk、γ₄=C_p,ku, set up the connection of nongausian process x (t) and Gaussian process u (t) System, it may be assumed that

Work as C_p,kuWhen >=3, x=h (u)=κ [u+h₃(u²-1)+h₄(u³-u)], Or be expressed asξ (x)=1.5b (a+x/ κ)-a³, a=h₃/ 3h₄, b=1/3h₄, c=(b-1-a²)³；

Work as C_p,ku< when 3, u=h^-1(x)=b₂x+b₃(x²-γ₃x-1)+b₄(x³-γ₄x-γ₃),

Expression formula in conjunction with crosspower spectrumCross-spectrum matrix [the S formed_Cp (ω)], press

K=1,2 ..., n

Wind-pressure field is reconstructed, wherein H_km(ω_ml) it is crosspower spectrum matrix [S_Cp(ω) Cholesky] decomposes, θ_km(ω_ml) For H_km(ω_ml) explement,L=1,2 ..., N is discrete frequency, and Δ ω is circular frequency interval, φ_mlFor additive phase angle.

A kind of building wind tunnel pressure measuring test data compression method the most according to claim 7, it is characterised in that described step In seven according to step 6 estimate extreme value wind load particularly as follows:

Hermite multinomial transfer function based on step 6 estimates extreme value wind load,g_NG=h (g),n₀=λ₀F_mU/L is average cross-over frequency, and T=600s is with reference to duration.

A kind of building wind tunnel pressure measuring test data compression method the most according to claim 8, it is characterised in that described step In eight, the detailed process according to step 6 computation structure wind vibration response is:

Press according to the wind load cross-spectrum matrix of reduction in step 6Its Described in [H (ω)]={ ω²[M]+iω[C]+[K]}^-1For frequency response function matrix, [R] is attached area transition matrix, calculates Wind induced structural vibration response covariance [∑_x]。