CN108090614A - A kind of space wind field prediction model method for building up based on related coefficient - Google Patents
A kind of space wind field prediction model method for building up based on related coefficient Download PDFInfo
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Abstract
The present invention proposes a kind of space wind field prediction model method for building up based on related coefficient, and entire space wind field information is predicted using a small amount of observation point data.According to CFD numerical simulations space wind field as a result, calculating the relative coefficient of each spatial point position and observation point position first；Then according to two adjacent observation point information solution room any point wind speed.For related coefficient, if two adjacent observation points are more than critical value with any Point correlation coefficient in space, two observation points are then thought in same vortex, and the two correlation is very big, chooses the foundation analyzed with the larger observation point of space any point relative coefficient as prediction model at this time；If observation point is less than critical value with any Point correlation coefficient in space, solution is overlapped to space any point wind speed using two observation point data simultaneously.The Wind Velocity History of space any point can be predicted as stated above, obtain the Wind Data of whole field.The present invention has forecasting efficiency height, as a result accurately, true and comprehensive advantage.
Description
Technical field
The present invention relates to a kind of space wind field prediction model method for building up based on related coefficient.
Background technology
As national basis facility, Loads of Long-span Bridges and wind power plant are all inevitable at project evaluation initial stage and operation later stage
Need consider wind influence.Accurate space wind field can be used for the addressing of wind power plant and Loads of Long-span Bridges, be allowed to be respectively at
Optimal wind resource and minimum windburn position.Field monitoring is to obtain local space wind field best practice, however, due to needing
A large amount of observation points are measured simultaneously, implement extremely difficult and costly.In view of the openness of space monitoring point and not
How uniformity is particularly important using a small amount of observation point information acquisition space Wind Data.Existing prediction model main sides
The prediction of hour or per day wind speed is overweighted, on the basis of time delay is considered, energy fundamental forecasting mean wind speed, and to whole
A space wind field prediction is unsatisfactory；And model to topographical elevation difference, wind direction, roughness etc. the considerations of it is not comprehensive.It needs exist for
It is noted that correlation reduces with the increase of distance and topographical elevation difference；When distance vector direction and wind direction difference, correlation
Also can reduce；And related coefficient is also related with terrain roughness, height etc..Existing space wind field prediction model is mainly for flat
Equal information, to landform, wretched insufficiency the considerations of roughness and wind direction.
The content of the invention
The present invention proposes a kind of space wind field prediction model method for building up based on related coefficient, by computational fluid dynamics
Efficient spatial wind field prediction model derived from numerical simulation result for the prediction of space wind field, utilizes a small amount of observation point data
It can just predict entire space wind field information.
The technology used in the present invention is as follows：A kind of space wind field prediction model method for building up based on related coefficient, such as
Under：According to computational fluid dynamics numerical simulation space wind field as a result, calculating each spatial point position and observation point position first
Relative coefficient；Then according to two adjacent observation point information solution room any point wind speed, since model considers landform, wind direction etc.
Influence, it is necessary to divide situation discussion：The parameter rules of selection are space any point and observation point mean wind direction angular difference value and phase
Both coefficient magnitude being closed, if mean wind direction angular difference value is less than critical value, it is believed that space any point wind direction is consistent with observation point, then
Mean wind speed correlation；On the contrary, if mean wind direction angle is more than critical value, the two mean wind speed is in inverse correlation：For
Related coefficient, if two adjacent observation points are more than critical value with any Point correlation coefficient in space, then it is assumed that two observation points are same
In one vortex, the two correlation is very big, chooses the observation point larger with space any point relative coefficient at this time as prediction
The foundation of model analysis；On the contrary, if observation point is less than critical value with any Point correlation coefficient in space, two observation points are utilized simultaneously
Data is overlapped solution to space any point wind speed, can predict the Wind Velocity History of space any point as stated above, so as to
Obtain the Wind Data of whole field.
The present invention also has following technical characteristic：A kind of space wind field prediction model method for building up based on related coefficient,
It is as follows：
According to computational fluid dynamics (CFD, Computational Fluid Dynamics) numerical simulation space wind field
As a result, calculate the relative coefficient of each spatial point position and observation point position：
Corij=corrcoef (uxi,uxj) (1)
Ux in formulaiFor the fluctuating wind speed at observation point i, uxjFluctuating wind speed at any point j of space, CorijAppoint for space
Some relative coefficient with observation point；
According to two adjacent observation point information solution room any point wind speed.The parameter rules of selection are space any point with seeing
Measuring point mean wind direction angular difference value and related coefficient size.Taking the critical difference of average wind angle respectively, relative coefficient faces for 90 °
Dividing value is 0.7.
If mean wind direction angular difference value is less than 90 °, it is believed that space any point wind direction is consistent with observation point, then the two average wind
Fast correlation；
If abs(Degree(i)-Degree(j))≤90
uijmean=uimean*Corij (2-1)
Degree (i) represents observation point mean wind direction angle size in formula；Degree (j) is space any point mean wind direction angle
Size；uijmeanRepresent influence degree of the observation point to space any point mean wind speed；
On the contrary, if mean wind direction angle is more than 90 °, the two mean wind speed is in inverse correlation：
If abs (Degree (i)-Degree (j)) ＞ 90
uijmean=-uimean*Corij (2-2)
For related coefficient, if two adjacent observation points and any Point correlation coefficient in space are more than 0.7, then it is assumed that two see
For measuring point in same vortex, the two correlation is very big, chooses and the larger observation point of space any point relative coefficient at this time
Foundation as prediction model analysis；
If Corij≥0.7and Cori+1j≥0.7
Corij=max (Corij,Cori+1j)
uj=uijmean+uxi*Corij+uxi+1*Cori+1j (3-1)
Cor in formulai+1jFor the relative coefficient of space any point j and observation point i+1；uxi+1For the arteries and veins at observation point i+1
Dynamic wind speed；ujRepresentation space any point j wind speed values；
On the contrary, if observation point is less than 0.7 with any Point correlation coefficient in space, simultaneously using two observation point data to space
Any point wind speed is overlapped solution：
If Corij＜ 0.7or Cori+1j＜ 0.7
uj=uijmean+ui+1jmean+uxi*Corij+uxi+1*Cori+1j (3-2)
U in formulai+1jmeanIt is observation point i+1 to the influence degree of space any point j mean wind speeds；
The Wind Velocity History of space any point can be predicted by above-mentioned steps, so as to obtain the Wind Data of whole field, as wind
The basis of field characteristic analysis.
Beneficial effects of the present invention and advantage：
1. a small amount of spatial observation data predicts overall space wind field, more efficient；
2. considering the influence of landform, roughness, wind direction etc. simultaneously, prediction result is more accurate, really；
3. a pair Wind Velocity History is predicted, this use more more effective than simply prediction mean wind speed can be used for building knot simultaneously
Structure periphery is averaged wind field and fluctuating wind field specificity analysis.
Description of the drawings
Fig. 1 is spatial coherence figure between two adjacent observation points；
Fig. 2 is the height above sea level figure of each observation point；
Fig. 3 is space wind field prediction flow chart；
Fig. 4 is Wind Velocity History prediction result and measured result comparison diagram；
Fig. 5 is mean wind speed prediction result and measured result comparison diagram；
Fig. 6 is fluctuating wind speed prediction result and measured result comparison diagram；
Fig. 7 is turbulence integral scale prediction result and measured result comparison diagram；
Fig. 8 is fluctuating wind spectrum prediction result and measured result comparison diagram；
Fig. 9 is space any point Wind Velocity History prediction result and CFD analog result comparison diagrams.
Specific embodiment
The present invention is further explained below according to Figure of description citing：
Embodiment 1
A kind of space wind field prediction model method for building up based on related coefficient, step are specific as follows：
According to computational fluid dynamics (CFD Computational Fluid Dynamics) numerical simulation space wind field
As a result, calculate the relative coefficient of each spatial point position and observation point position：
Corij=corrcoef (uxi,uxj) (1)
Ux in formulaiFor the fluctuating wind speed at observation point i, uxjFluctuating wind speed at any point j of space, CorijAppoint for space
Some relative coefficient with observation point；
According to two adjacent observation point information solution room any point wind speed.Need a point situation discussion：The parameter rules of selection
For space any point and observation point mean wind direction angular difference value and related coefficient size.Here average wind angle critical difference is taken respectively
It is worth for 90 °, relative coefficient critical value is 0.7.
If mean wind direction angular difference value is less than 90 °, it is believed that space any point wind direction is consistent with observation point, then the two average wind
Fast correlation；
If abs(Degree(i)-Degree(j))≤90
uijmean=uimean*Corij (2-1)
Degree (i) represents observation point mean wind direction angle size in formula；Degree (j) is space any point mean wind direction angle
Size；uijmeanRepresent influence degree of the observation point to space any point mean wind speed；
On the contrary, if mean wind direction angle is more than 90 °, the two mean wind speed is in inverse correlation：
If abs (Degree (i)-Degree (j)) ＞ 90
uijmean=-uimean*Corij (2-2)
It is similar, for related coefficient, if two adjacent observation points with any Point correlation coefficient in space more than 0.7,
Two observation points are thought in same vortex, and the two correlation is very big, chooses at this time larger with space any point relative coefficient
The foundation analyzed as prediction model of observation point；
If Corij≥0.7and Cori+1j≥0.7
Corij=max (Corij,Cori+1j)
uj=uijmean+uxi*Corij+uxi+1*Cori+1j (3-1)
Cor in formulai+1jFor the relative coefficient of space any point j and observation point i+1；uxi+1For the arteries and veins at observation point i+1
Dynamic wind speed；ujRepresentation space any point j wind speed values；
On the contrary, if observation point is less than 0.7 with any Point correlation coefficient in space, simultaneously using two observation point data to space
Any point wind speed is overlapped solution：
If Corij＜ 0.7or Cori+1j＜ 0.7
uj=uijmean+ui+1jmean+uxi*Corij+uxi+1*Cori+1j (3-2)
U in formulai+1jmeanIt is observation point i+1 to the influence degree of space any point j mean wind speeds；
The Wind Velocity History of space any point can be predicted by above-mentioned steps, so as to obtain the Wind Data of whole field, as wind
The basis of field characteristic analysis.
The present embodiment uses formula as above, acquires the spatial coherence field between two adjacent observation points, as shown in Figure 1,
Landform and height above sea level at each observation point are as shown in Fig. 2, the pre- flow gauge of the Wind Velocity History of space any point is as shown in Figure 3.Specific space
Wind field Forecasting Methodology is as follows：
Spatial coherence field is obtained by formula (1) as shown in Figure 1, the specific height above sea level of each observation point is as shown in Figure 2；
According to formula (2-1), (2-2), (3-1), (3-2) is separated by among observation point actual measurement Wind Data prediction according to two and sees
Measuring point wind speed, and compared with intermediate sight point actual measurement wind speed, verification result is as Figure 4-8；
It according to formula (2-1), (2-2), (3-1), (3-2) prediction space any point wind speed, and is compared, ties with the analogue value
Fruit is as shown in Figure 9.
Claims (2)
1. a kind of space wind field prediction model method for building up based on related coefficient, which is characterized in that according to calculating fluid dynamic
Numerical simulation space wind field is learned as a result, calculating the relative coefficient of each spatial point position and observation point position first；Then foundation
Two adjacent observation point information solution room any point wind speed, since model considers the influence of landform, wind direction etc., it is necessary to which a point situation is begged for
By：The parameter rules of selection are space any point and observation point mean wind direction angular difference value and related coefficient size, if average
Wind angle difference is less than critical value, it is believed that space any point wind direction is consistent with observation point, then the two mean wind speed is proportionate pass
System；On the contrary, if mean wind direction angle is more than critical value, the two mean wind speed is in inverse correlation：For related coefficient, if two is adjacent
Observation point is more than critical value with any Point correlation coefficient in space, then it is assumed that for two observation points in same vortex, the two is related
Property it is very big, choose the foundation analyzed as prediction model with the larger observation point of space any point relative coefficient at this time；On the contrary,
If observation point is less than critical value with any Point correlation coefficient in space, simultaneously using two observation point data to space any point wind speed
Solution is overlapped, can according to said method predict the Wind Velocity History of space any point, so as to obtain the Wind Data of whole field.
2. a kind of space wind field prediction model method for building up based on related coefficient according to claim 1, feature exist
In method is as follows：
According to computational fluid dynamics numerical simulation space wind field as a result, to calculate each spatial point position related to observation point position
Property coefficient：
Corij=corrcoef (uxi,uxj) (1)
Ux in formulaiFor the fluctuating wind speed at observation point i, uxjFluctuating wind speed at any point j of space, CorijFor space any point with
The relative coefficient of observation point；
According to two adjacent observation point information solution room any point wind speed, the parameter rules of selection are space any point and observation point
Mean wind direction angular difference value and related coefficient size, it is 90 ° to take the critical difference of average wind angle respectively, relative coefficient critical value
For 0.7；
If mean wind direction angular difference value is less than 90 °, it is believed that space any point wind direction is consistent with observation point, then the two mean wind speed is in
Positive correlation；
If abs(Degree(i)-Degree(j))≤90
uijmean=uimean*Corij (2-1)
Degree (i) represents observation point mean wind direction angle size in formula；Degree (j) is big for space any point mean wind direction angle
It is small；uijmeanRepresent influence degree of the observation point to space any point mean wind speed；
On the contrary, when mean wind direction angle is more than 90 °, then the two mean wind speed is in inverse correlation：
If abs (Degree (i)-Degree (j)) ＞ 90
uijmean=-uimean*Corij (2-2)
For related coefficient, if two adjacent observation points and any Point correlation coefficient in space are more than 0.7, then it is assumed that two observation points
In same vortex, the two correlation is very big, chooses and the larger observation point conduct of space any point relative coefficient at this time
The foundation of prediction model analysis；
If Corij≥0.7 and Cori+1j≥0.7
Corij=max (Corij,Cori+1j)
uj=uijmean+uxi*Corij+uxi+1*Cori+1j (3-1)
Cor in formulai+1jFor the relative coefficient of space any point j and observation point i+1；uxi+1For the fluctuating wind at observation point i+1
Speed；ujRepresentation space any point j wind speed values；
On the contrary, when observation point with any Point correlation coefficient in space less than 0.7, then it is any to space using two observation point data simultaneously
Point wind speed is overlapped solution：
If Corij0.7 or Cor of ＜i+1j＜ 0.7
uj=uijmean+ui+1jmean+uxi*Corij+uxi+1*Cori+1j (3-2)
U in formulai+1jmeanIt is observation point i+1 to the influence degree of space any point j mean wind speeds；
And then can predict the Wind Velocity History of space any point, so as to obtain the Wind Data of whole field, as wind field specificity analysis
Basis.
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| CN112395812A (en) * | 2020-11-26 | 2021-02-23 | 华北电力大学 | Method for evaluating wind speed time shifting performance |
| CN113468692A (en) * | 2021-07-19 | 2021-10-01 | 大连理工大学 | Three-dimensional wind field efficient simulation method based on delay effect |
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| CN112163259A (en) * | 2020-09-27 | 2021-01-01 | 西南交通大学 | Method for determining equivalent wind speed ratio of wind profile of typical railway infrastructure |
| CN112395812A (en) * | 2020-11-26 | 2021-02-23 | 华北电力大学 | Method for evaluating wind speed time shifting performance |
| CN112395812B (en) * | 2020-11-26 | 2024-03-26 | 华北电力大学 | Method for evaluating time shifting property of wind speed |
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| CN113468692A (en) * | 2021-07-19 | 2021-10-01 | 大连理工大学 | Three-dimensional wind field efficient simulation method based on delay effect |
| CN113468692B (en) * | 2021-07-19 | 2022-05-13 | 大连理工大学 | Three-dimensional wind field efficient simulation method based on delay effect |
| CN115577297A (en) * | 2022-10-21 | 2023-01-06 | 武汉理工大学 | Visual sea area wind field simulation and prediction system and method |
| CN115577297B (en) * | 2022-10-21 | 2023-05-30 | 武汉理工大学 | Visual sea area wind field simulation and prediction system and method thereof |
| CN117077558A (en) * | 2023-07-18 | 2023-11-17 | 西南林业大学 | Space-time refined wind speed field construction method |
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