CN116070471A - Wind driven generator simulation acceleration method and system based on reduced order decomposition processing - Google Patents
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Abstract
The invention discloses a wind driven generator simulation acceleration method and system based on reduced order decomposition processing, and belongs to the technical field of wind driven generator simulation data processing. The prior scheme does not disclose how to solve the problem of huge calculation consumption in the simulation process of the wind driven generator, and the wind driven generator simulation acceleration method based on the reduced order decomposition treatment carries out singular value decomposition on an initial data matrix, intercepts a plurality of singular values with larger energy occupation, and obtains a reduced order rank according to the quantity of the intercepted singular values; then according to the reduced rank, obtaining space-time characteristic quantity capable of decomposing the complex flow process into low rank; and then generating a reduced-order prediction matrix according to the space-time characteristic quantity, obtaining predicted flow field data of the time step to be calculated according to the reduced-order prediction matrix, realizing simulation acceleration of the wind driven generator, effectively reducing the calculation dimension and the calculation quantity, and effectively solving the problem of huge calculation consumption in the simulation process of the wind driven generator.
Description
Technical Field
The invention relates to a wind driven generator simulation acceleration method and system based on reduced order decomposition processing, and belongs to the technical field of wind driven generator simulation data processing.
Background
Chinese patent (publication No. CN 114997078A) discloses a wind driven generator flow field simulation test method and device, and the scheme comprises the following steps: based on a three-dimensional flow field model of the wind driven generator, performing transient fluid dynamics simulation on the wind driven generator at the current moment to acquire simulation data of the wind driven generator and the three-dimensional flow field model at the current moment; based on the simulation data and a control strategy of the wind driven generator, obtaining simulation control parameters of the wind driven generator at the next moment; based on the simulation control parameters, the rotation speed of the reference coordinate system of the revolution region in the three-dimensional flow field model and the rotation speed of the grid nodes of the autorotation region in the three-dimensional flow field model are updated. According to the wind driven generator flow field simulation test method and device, unsteady computational fluid dynamics analysis can be carried out on the wind wheel speed change and blade pitch process under the running state of the wind driven generator, the speed change and pitch action of the wind driven generator is considered in simulation, the obtained transient flow field calculation result is closer to the actual situation, and the more accurate simulation test result can be obtained.
However, the above solution does not disclose how to solve the problem of huge calculation consumption in the wind driven generator simulation process, and the data volume in the current three-dimensional numerical simulation process is generally large, which results in overlong simulation calculation time. Particularly, for the wind driven generator, the grid quantity and the flow field data characteristics are limited, the time consumed by numerical simulation is often far longer than the physical time in reality, and the conventional wind driven generator simulation technology cannot acquire the running states of part of key components of the wind driven generator in time, cannot acquire the running states of the key components in time, so that the running maintenance of the wind driven generator is affected, and the popularization and the utilization of the wind driven generator simulation technology are not facilitated.
Further, as the calculated amount and time consumption of the simulation technology of the wind driven generator are too large, the simulation technology of the wind driven generator is difficult to directly apply to digital twin, so that a benign interaction environment of a wind driven generator model and a digital model cannot be effectively constructed, a technical system of digital regeneration, data synchronization, accurate mapping and common progress of the wind driven generator model and the digital model cannot be formed, and full life cycle coverage of simulation modeling of the wind driven generator cannot be realized.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims at providing a method for carrying out singular value decomposition on an initial data matrix to obtain a plurality of singular values, deleting singular values with smaller energy, only intercepting a plurality of singular values with larger energy, and obtaining a reduced rank according to the quantity of intercepted singular values; then, according to the order reduction rank, carrying out dynamic mode order reduction decomposition on the initial data matrix to obtain space-time characteristic quantity capable of decomposing a complex flow process into a low rank; according to the space-time characteristic quantity, a reduced order prediction matrix is generated, and according to the reduced order prediction matrix, the predicted flow field data of the time step to be calculated is obtained, so that the simulation acceleration of the wind driven generator is realized, the scheme is scientific and reasonable, the calculation dimension can be effectively reduced, the calculation quantity is reduced, and the calculation time and the calculation load of a server are saved.
The second purpose of the invention is to provide a method for obtaining low-rank space-time characteristic quantity by carrying out reduced order decomposition treatment on initial flow field data, and predicting flow field data of future time steps according to the space-time characteristic quantity, thereby effectively reducing data processing quantity and simulation calculation time, and further effectively solving the problem of huge calculation consumption in the simulation process of the wind driven generator; the method is convenient for users to acquire the running states of part of key components of the wind driven generator in time, the running states of the key components are known in time, the running maintenance efficiency of the wind driven generator can be effectively improved, and the popularization and the utilization of wind driven generator simulation technology are facilitated.
The invention aims at providing a wind driven generator simulation acceleration method which realizes simulation acceleration of a wind driven generator by constructing a reduced-order calculation model and a data prediction reduced-order model, can effectively reduce calculation dimension, reduce calculation amount and save calculation time.
The invention aims at providing a wind driven generator simulation acceleration method and system which have the advantages of less data processing amount, short time consumption, capability of effectively accelerating calculation and high accuracy, and enable a numerical simulation technology to be more suitable for a digital twin model.
In order to achieve one of the above objects, a first technical solution of the present invention is:
a wind driven generator simulation acceleration method based on reduced order decomposition processing comprises the following steps:
firstly, performing full-order hydrodynamic simulation calculation on a wind driven generator to obtain full-order initial flow field data of a certain time step;
secondly, processing the initial flow field data to construct an initial data matrix;
thirdly, performing singular value decomposition on the initial data matrix to obtain a plurality of singular values, and determining a reduced rank according to the energy duty ratio of the singular values;
fourthly, performing dynamic modal reduced order decomposition on the initial data matrix according to the reduced order rank to obtain space-time characteristic quantity used for representing the flow process of the flow field;
fifthly, generating a reduced-order prediction matrix according to the space-time characteristic quantity, and obtaining predicted flow field data of the time step to be calculated according to the reduced-order prediction matrix;
and sixthly, carrying out simulation calculation on the wind driven generator according to the predicted flow field data, and realizing simulation acceleration of the wind driven generator.
According to the invention, through continuous exploration and experiment, singular value decomposition is carried out on an initial data matrix to obtain a plurality of singular values, and according to the principle that the larger the singular value is, the larger the contribution to the whole matrix is, some singular values with smaller energy are deleted, and only the singular values with larger energy are intercepted, so that the influence on simulation calculation accuracy can be reduced while the data processing capacity is reduced. Obtaining a reduced rank according to the number of intercepted singular values; then, according to the order reduction rank, carrying out dynamic mode order reduction decomposition on the initial data matrix to obtain space-time characteristic quantity capable of decomposing a complex flow process into a low rank; and generating a reduced-order prediction matrix according to the space-time characteristic quantity, and finally obtaining predicted flow field data of the time step to be calculated according to the reduced-order prediction matrix to realize simulation acceleration of the wind driven generator. The method has scientific and reasonable scheme, can effectively reduce the calculation dimension, reduce the calculation amount, save the calculation time and the calculation load of the server, and can effectively solve the problem of huge calculation consumption in the simulation process of the wind driven generator.
Furthermore, the invention obtains low-rank space-time characteristic quantity by carrying out reduced decomposition processing on the initial flow field data, predicts the flow field data of the future time step according to the space-time characteristic quantity, effectively reduces the data processing quantity and the simulation calculation time, and can effectively solve the problem of huge calculation consumption in the simulation process of the wind driven generator; and furthermore, a user can acquire the running states of part of key components of the wind driven generator in time, the running states of the key components can be known in time, the running maintenance efficiency of the wind driven generator can be effectively improved, and the popularization and the utilization of the simulation technology of the wind driven generator are facilitated.
Furthermore, compared with the full-order hydrodynamic simulation calculation in the prior art, the method for predicting the future flow field data has the advantages of being shorter in time consumption, capable of effectively accelerating calculation, high in accuracy, and capable of enabling a numerical simulation technology to be more suitable for a digital twin model, therefore, the simulation acceleration method can effectively construct a benign interaction environment of a wind driven generator model and a digital model, and a technical system of digital regeneration, data synchronization, accurate mapping and common progress of the wind driven generator model and the digital model is formed, and further full life cycle coverage of wind driven generator simulation modeling is achieved.
As a preferred technical measure:
in the first step, the method for performing full-order hydrodynamic simulation calculation on the wind driven generator comprises the following steps:
step 11, constructing a geometric model of the wind driven generator;
step 12, setting meteorological conditions and working conditions of a wind driven generator on the basis of a geometric model to obtain a calculation grid;
and 13, performing full-order flow field calculation on the calculation grid according to computational fluid dynamics to obtain initial flow field data comprising a plurality of time steps.
As a preferred technical measure:
meteorological conditions include wind speed and wind direction;
the working condition is the rotating speed of the wind driven generator;
the initial flow field data includes physical quantities on each computational grid;
the physical quantity comprises at least speed or/and pressure or/and temperature.
As a preferred technical measure:
in the second step, the method for constructing the initial data matrix is as follows:
step 21, obtaining physical quantity on each calculation grid;
step 22, the physical quantity which is taken out is stored into a digital matrix according to the requirement to form an initial data matrix;
the first column of the initial data matrix is the physical quantity of the 1 st time step, and the last column is the physical quantity of the N-1 st time step;
first of initial data matrixiThe row represents the time-dependent process information of the physical quantity on the ith computational grid, the first of whichjThe column represents the physical quantity of the jth time step.
As a preferred technical measure:
in the third step, the method for performing singular value decomposition on the initial data matrix is as follows:
step 31, performing singular value decomposition operation on the initial data matrix to obtain a singular value matrix;
step 32, setting singular values arranged from large to small on diagonal lines of the singular value matrix;
step 33, obtaining the energy contribution degree of each singular value to the initial data matrix;
and step 32, accumulating the energy contribution degrees one by one from large to small to obtain energy contribution sums, and when the energy contribution sums are larger than an energy threshold value, counting the number of the energy contribution degrees participating in accumulation, and taking the number of the energy contribution degrees as a reduction rank.
As a preferred technical measure:
the fourth step, the method for carrying out dynamic mode reduced order decomposition on the initial data matrix is as follows:
step 41, constructing an initial prediction matrix differing by one time step according to the initial data matrix;
step 42, according to the reduced rank, carrying out truncated singular value decomposition on the initial flow field data to obtain a decomposition result;
step 43, constructing a decomposition similarity matrix between the initial flow field data and the initial prediction matrix according to the decomposition result and the initial prediction matrix;
step 44, performing eigenvalue decomposition on the decomposition similarity matrix to obtain eigenvalues and eigenvectors;
and 45, constructing space-time characteristic quantity for representing the flow process of the wind driven generator according to the decomposition result, the initial prediction matrix, the characteristic value and the characteristic vector.
As a preferred technical measure:
the method for constructing the initial prediction matrix is as follows:
the first column of the initial prediction matrix is the flow field data of the 2 nd time step, and the last column is the flow field data of the last time step;
first of initial prediction matrixiThe row represents the time-dependent process information of the physical quantity on the ith computational grid, the first of whichjThe column represents the physical quantity of the j+1th time step.
As a preferred technical measure:
in the fifth step, the method for obtaining the predicted flow field data of the time step to be calculated is as follows:
step 51, constructing a reduced-order prediction matrix according to the space-time characteristic quantity, the initial flow field data and the initial prediction matrix, wherein the reduced-order prediction matrix comprises flow field data of a time step to be calculated;
step 52, calculating the reduced order prediction matrix to obtain predicted flow field data of the time step to be calculated;
step 53, adding the predicted flow field data of the time step to be calculated to the initial prediction matrix and the initial flow field data, and constructing a new initial prediction matrix and a new initial data matrix;
and step 54, according to the new initial prediction matrix and the new initial data matrix, obtaining predicted flow field data which is different from the new initial prediction matrix by one time step, and circularly reciprocating to finish calculation of a plurality of time step flow field data to be calculated and realize future prediction of the flow field.
In order to achieve one of the above objects, a second technical solution of the present invention is:
a wind driven generator simulation acceleration method based on reduced order decomposition processing comprises the following steps:
carrying out full-order hydrodynamic simulation calculation on the wind driven generator by utilizing a pre-constructed hydrodynamic simulation model to obtain full-order initial flow field data of a certain time step;
processing initial flow field data through a pre-constructed reduced order calculation model, and constructing an initial data matrix; singular value decomposition is carried out on the initial data matrix to obtain a plurality of singular values, and a reduced rank is determined according to the energy duty ratio of the singular values;
predicting a reduced order model by utilizing pre-constructed data, and carrying out dynamic modal reduced order decomposition on an initial data matrix according to a reduced order rank to obtain space-time characteristic quantity for representing a flow process of a flow field; generating a reduced order prediction matrix according to the space-time characteristic quantity, and obtaining predicted flow field data of the time step to be calculated according to the reduced order prediction matrix;
and obtaining predicted flow field data through the reduced order calculation model and the data prediction reduced order model, and performing simulation calculation on the wind driven generator to realize simulation acceleration of the wind driven generator.
According to the invention, through continuous exploration and experiments, a reduced-order calculation model is constructed, singular value decomposition is carried out on an initial data matrix to obtain a plurality of singular values, and according to the principle that the larger the singular value is, the larger the contribution to the whole matrix is, some singular values with smaller energy are deleted, and only the singular values with larger energy are intercepted, so that the influence on simulation calculation accuracy is reduced while the data processing capacity is reduced. Obtaining a reduced rank according to the number of intercepted singular values; then, a data prediction order reduction model is utilized, dynamic modal order reduction decomposition is carried out on an initial data matrix according to order reduction rank, and space-time characteristic quantity capable of decomposing a complex flow process into low rank is obtained; and generating a reduced order prediction matrix according to the space-time characteristic quantity, and obtaining predicted flow field data of the time step to be calculated according to the reduced order prediction matrix to realize simulation acceleration of the wind driven generator. The method has scientific and reasonable scheme, can effectively reduce the calculation dimension, reduce the calculation amount and save the calculation time and the calculation load of the server.
Furthermore, the invention obtains low-rank space-time characteristic quantity by carrying out reduced decomposition processing on the initial flow field data, predicts the flow field data of the future time step according to the space-time characteristic quantity, effectively reduces the data processing quantity and the simulation calculation time, and can effectively solve the problem of huge calculation consumption in the simulation process of the wind driven generator; and furthermore, a user can acquire the running states of part of key components of the wind driven generator in time, the running states of the key components can be known in time, the running maintenance efficiency of the wind driven generator can be effectively improved, and the popularization and the utilization of the simulation technology of the wind driven generator are facilitated.
Further, compared with full-order hydrodynamic simulation calculation in the prior art, the method for predicting the future flow field data is shorter in time consumption, can effectively accelerate calculation, has high accuracy, and enables the numerical simulation technology to be more suitable for a digital twin model.
In order to achieve one of the above objects, a third technical solution of the present invention is:
a wind driven generator simulation acceleration system based on reduced order decomposition processing comprises:
one or more processors;
a storage means for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement a wind turbine simulation acceleration method based on reduced order decomposition processing as described above.
Compared with the prior art, the invention has the following beneficial effects:
according to the invention, through continuous exploration and experiment, singular value decomposition is carried out on an initial data matrix to obtain a plurality of singular values, and according to the principle that the larger the singular value is, the larger the contribution to the whole matrix is, some singular values with smaller energy are deleted, and only the singular values with larger energy are intercepted, so that the influence on the simulation calculation accuracy is reduced while the data processing capacity is reduced. Obtaining a reduced rank according to the number of intercepted singular values; then, according to the order reduction rank, carrying out dynamic mode order reduction decomposition on the initial data matrix to obtain space-time characteristic quantity capable of decomposing a complex flow process into a low rank; and generating a reduced-order prediction matrix according to the space-time characteristic quantity, obtaining predicted flow field data of the time step to be calculated according to the reduced-order prediction matrix, realizing simulation acceleration of the wind driven generator, realizing scientific and reasonable scheme, effectively reducing the calculation dimension, reducing the calculation quantity, and saving the calculation time and the calculation load of a server.
Furthermore, the invention obtains low-rank space-time characteristic quantity by carrying out reduced decomposition processing on the initial flow field data, predicts the flow field data of the future time step according to the space-time characteristic quantity, effectively reduces the data processing quantity and the simulation calculation time, and can effectively solve the problem of huge calculation consumption in the simulation process of the wind driven generator; and furthermore, a user can acquire the running states of part of key components of the wind driven generator in time, the running states of the key components can be known in time, the running maintenance efficiency of the wind driven generator can be effectively improved, and the popularization and the utilization of the simulation technology of the wind driven generator are facilitated.
Further, compared with full-order hydrodynamic simulation calculation in the prior art, the method for predicting the future flow field data is shorter in time consumption, can effectively accelerate calculation, has high accuracy, and enables the numerical simulation technology to be more suitable for a digital twin model.
Drawings
FIG. 1 is a schematic flow chart of a wind turbine simulation acceleration method of the present invention;
FIG. 2 is a schematic flow chart of another method for simulating acceleration of a wind turbine according to the present invention;
FIG. 3 is a schematic diagram of a correspondence between singular values and energy contributions according to the present invention;
FIG. 4 is a schematic diagram of a variation of the sum of singular value order and energy contribution according to the present invention;
FIG. 5 is a graph showing the variation of maximum error and average error with predicted time steps according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
On the contrary, the invention is intended to cover any alternatives, modifications, equivalents, and variations as may be included within the spirit and scope of the invention as defined by the appended claims. Further, in the following detailed description of the present invention, certain specific details are set forth in order to provide a better understanding of the present invention. The present invention will be fully understood by those skilled in the art without the details described herein.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
As shown in fig. 1, a first specific embodiment of a wind driven generator simulation acceleration method based on reduced order decomposition processing of the present invention:
a wind driven generator simulation acceleration method based on reduced order decomposition processing comprises the following steps:
firstly, performing full-order hydrodynamic simulation calculation on a wind driven generator to obtain full-order initial flow field data of a certain time step;
secondly, processing the initial flow field data to construct an initial data matrix;
thirdly, performing singular value decomposition on the initial data matrix to obtain a plurality of singular values, and determining a reduced rank according to the energy duty ratio of the singular values;
fourthly, performing dynamic modal reduced order decomposition on the initial data matrix according to the reduced order rank to obtain space-time characteristic quantity used for representing the flow process of the flow field;
fifthly, generating a reduced-order prediction matrix according to the space-time characteristic quantity, and obtaining predicted flow field data of the time step to be calculated according to the reduced-order prediction matrix;
and sixthly, carrying out simulation calculation on the wind driven generator according to the predicted flow field data, and realizing simulation acceleration of the wind driven generator.
The second concrete embodiment of the wind driven generator simulation acceleration method based on the reduced order decomposition treatment comprises the following steps:
a wind driven generator simulation acceleration method based on reduced order decomposition processing comprises the following steps:
firstly, performing full-order hydrodynamic simulation calculation on a wind driven generator to obtain full-order initial flow field data of a certain time step;
secondly, processing the initial flow field data to construct an initial data matrix and an initial prediction matrix which differ by one or more time steps;
thirdly, performing singular value decomposition on the initial data matrix to obtain a plurality of singular values, and determining a reduced rank according to the energy duty ratio of the singular values;
fourthly, according to the reduced rank, carrying out dynamic modal decomposition on the initial data matrix and the initial prediction matrix, and determining an approximate matrix between the initial data matrix and the initial prediction matrix;
fifthly, performing feature decomposition on the approximate matrix to obtain space-time feature quantity used for representing the flow process of the flow field of the wind driven generator;
sixth, generating a reduced-order prediction matrix according to the space-time characteristic quantity, and obtaining predicted flow field data of the next time step or a plurality of time steps according to the reduced-order prediction matrix;
and seventhly, carrying out simulation calculation on the wind driven generator according to the predicted flow field data, and realizing simulation acceleration of the wind driven generator.
The third concrete embodiment of the wind driven generator simulation acceleration method based on reduced order decomposition treatment comprises the following steps:
a wind driven generator simulation acceleration method based on reduced order decomposition processing comprises the following steps:
firstly, performing full-order hydrodynamic simulation calculation on a wind driven generator by utilizing a pre-constructed hydrodynamic simulation model to obtain full-order flow field data of a certain time step;
secondly, processing the data of the flow field through a pre-constructed reduced order calculation model to obtain an initial data matrix, performing singular value decomposition on the initial data matrix to obtain a plurality of singular values, and determining the reduced order rank of the initial data matrix according to the energy duty ratio of the singular values;
thirdly, processing the data of the flow field according to a pre-constructed data prediction order-reduction model to obtain a data prediction matrix, and then carrying out truncation decomposition on the data prediction matrix according to an order-reduction rank to obtain space-time characteristic quantity used for representing the flow process of the wind driven generator; reconstructing a data prediction matrix according to the space-time characteristic quantity to generate a reduced-order prediction matrix, wherein the reduced-order prediction matrix comprises flow field data of a time step to be calculated; according to the reduced order prediction matrix, obtaining predicted flow field data of the next time step or a plurality of time steps;
and fourthly, carrying out simulation acceleration on the wind driven generator according to the predicted flow field data.
The fourth concrete embodiment of the wind driven generator simulation acceleration method based on reduced order decomposition treatment comprises the following steps:
a wind driven generator simulation acceleration method based on reduced order decomposition processing comprises a computational fluid dynamics numerical solution model, a singular value decomposition model and a data dynamic modal decomposition model.
The computational fluid dynamics numerical solution model is used for calculating the wind driven generator flow field of the first N time steps, replaces an integral equation or a partial differential equation in a control equation through discrete algebraic forms, then solves a specific numerical solution for data comparison and explanation of experimental phenomena, and the control equation based on a back physical basis and different physical laws is listed below, wherein the control equation is derived from three physical laws, and is shown as follows:
conservation of materials:
conservation of momentum:
conservation of energy:
in the method, in the process of the invention,
for density (I)>
Is time, & lt>
In order to be able to achieve a speed,pfor pressure->
For shear force, & lt & gt>
For temperature, < >>
For heat conductivity, < >>
Is equal pressure specific heat capacity->
For heating/cooling the source item.
Singular value decompositionThe model is used for solving the flow mode of the flow field and the energy contained in each mode, and the interception rank of the whole flow field can be represented according to the interception of the energy of the modesr。
For the first N-step raw data matrix obtained from computational fluid dynamics simulation calculations
It can be decomposed into a positive definite matrix +.>
A diagonal matrix->
And the transpose of another positive definite matrix +.>
Is a product of (a) and (b).
First N steps of original data matrix
The expression of (2) is as follows:
the expression of the product is as follows:
the product expression represents a matrix
The represented linear transformation can be synthesized by a simpler rotation, stretching transformation.
Diagonal matrix in singular value decomposition model
The singular values are distributed on the diagonal of the singular value matrix, and are arranged from big to small. The larger the singular value, the greater the contribution to the overall matrix, according to this sourceIn addition, some small singular values can be deleted before interceptionrTerm singular values, retaining only the larger singular values, passing beforerTerm singular value calculation approximation matrix>
The original data matrix can be approximated, the expression of which is as follows:
wherein the matrix
，
，
Respectively +.>
、
、
Is included in the matrix of the block matrix.
The data dynamic modal decomposition model is a data driving method, can be used for analyzing the dynamic process of fluid (such as water flow), and can decompose the complex flow process into low-rank space-time characteristics. Dynamic modal decomposition has many advantages in describing some dynamic processes, including independence from any given dynamic system expression, and the possibility of short-term state prediction, the model itself being predictive.
The basic idea of dynamic modal decomposition is linear transformation. For a data matrix
，xFor each point value on one-dimensional data, the subscript indicates nullThe number of the sampling points is M;tfor the change of the whole data along with time, subscripts represent time sampling points, and the number of the subscripts is N; the physical quantity (speed, temperature, concentration, etc.) in the data matrix isuFor example +.>
Representing the physical quantity at the Mth grid point, data matrix +.>
The expression of (2) is as follows:
at the original momenttWhen=1, the data in the form of column vectors can be written as
The expression is as follows:
in the data dynamic modal decomposition model, if the system is a linear system, a matrix can be found
Such that:
so long as the original state is known
And the transformation matrix of the system->
It can be known at any time after the systemtStatus->
. For non-linear systems, an approximate matrix can also be found +.>
To perform the data dimension reduction processing, but generate a certain error +.>
The expression is as follows:
and constructing a data prediction matrix according to the read first N steps of dynamic modal decomposition.
Matrix array
The expression of the matrix consisting of the 1 st step to the N-1 st step is as follows:
matrix array
The expression of the matrix consisting of the steps 2 to N is as follows:
then
。
Wherein,,
for the cupmann matrix, approximation can be performed using a low rank structure. />
In a data dynamic modal decomposition model, solving a Coulomb man matrix
The method of (2) is as follows:
truncated rank according to singular value decompositionrCalculation of
The expression of the truncated singular value decomposition is as follows:
the following matrix was used:
for the Coulomb matrix
Performing approximate solution, wherein the matrix +.>
，
，
Respectively is
Is->
、
、
Truncated matrix, matrix->
。
For approximate Coulomb matrix
And (3) performing eigenvalue decomposition, wherein the calculation formula is as follows:
spatio-temporal feature analysis by means of dynamic modal decomposition, wherein
Diagonal matrix with diagonal elements as eigenvalues, matrix +.>
The space-time characteristics of the complex flow process can be analyzed by using the characteristic values and the characteristic vectors which are formed by the characteristic vectors. In dynamic modal decomposition, a decomposition matrix is constructed, the expression of which is as follows:
for short-term prediction matrix including next time step according to decomposition matrix
The calculation is performed with the following formula:
wherein,,
for matrix->
Is the inverse of the pseudo-inverse of the inverse matrix, which is the inverse of the inverse matrixIn sense form, due to singular matrices or matrices other than square matrices, e.g.>
There is no inverse matrix, so this problem is solved by a generalized inverse matrix, i.e. a pseudo-inverse matrix.
For any matrix
，
Pseudo inverse matrix->
Necessarily exist and->
The following conditions are satisfied:
thus matrix
Is an effective equivalent identity matrixIBut not identity matrixIIs a matrix of (a) in the matrix.
After predicting the n+1th step, adding the predicted physical field data to the prediction matrix
And->
Form a new prediction matrix->
And->
，
。
New prediction matrix
The expression of the matrix consisting of the 1 st to the N th steps is as follows:
new prediction matrix
The expression of the matrix consisting of the 2 nd step to the n+1 th step is as follows:
according to the same method
And->
And continuously predicting flow field data of the n+2 step, and iteratively circulating in this way to realize the prediction of a future flow field.
Compared with the original computational fluid dynamics simulation calculation, the method for carrying out future prediction through the reduced order model has the advantages of being short in time consumption, capable of accelerating calculation very fast, high in efficiency, high in accuracy, small in error and the like, and capable of solving the problems of large simulation calculation consumption, long calculation time and the like.
As shown in fig. 2, a fifth embodiment of the wind power generator simulation acceleration method based on the reduced order decomposition process of the present invention:
a wind driven generator simulation acceleration method based on reduced order decomposition processing comprises the following steps:
step 1: performing computational fluid dynamics simulation calculation to obtain flow field data of N time steps;
step 2: constructing an original data matrix through N-step flow field data
；
Step 3: for the original data matrix
Singular value decomposition is carried out, and a cut-off rank is selected according to the energy corresponding to each singular valuerThe model after the order reduction can cover most of information in a flow field;
step 4: constructing a prediction data matrix through N-step flow field data
、
The rows of the matrix of predicted data represent the amount of the grid, each column being data of one time step, wherein +.>
Including the flow field data for the first N-1 time steps, and (2)>
Flow field data comprising the 2 nd to nth time steps:
step 5: according to the truncated rank calculated in step 3rAnd the prediction data matrix in step 4
、
Performing dynamic modal decomposition to obtain a Coumann matrix +.>
And approximately solving the Coulomb matrix +.>
Kupman matrix->
The calculation formula of (2) is as follows:
wherein the matrix
、
、
For matrix->
Truncated singular value decomposition results of (2).
Then to the Coulomb matrix
And (3) performing eigenvalue decomposition:
Use matrix->
Is a feature value and feature vector of (a)To analyze the spatiotemporal characteristics of complex flow processes, the expression of which is as follows:
short-term prediction matrix
It can be calculated as:
Matrix->
The last column of (2) is the flow field data of the uncomputed n+1 time steps;
step 6: adding the predicted n+1th time step data to a matrix
And->
Constructing a new data prediction matrix
And the original data matrix->
At the same time the matrix->
Expanding to the N-th flow field to form a new matrix +.>
The expression is as follows:
and (3) circularly carrying out the steps 3-6 to realize the short-term prediction of the future flow field.
The wind driven generator simulation acceleration method based on reduced order decomposition processing provided by the embodiment of the invention can be used for carrying out short-term prediction of a flow field based on the existing computational fluid dynamics calculation data, so that the calculation time is saved.
The invention is applied to a specific embodiment for accelerating the calculation of the temperature field of the wind driven generator cabin:
in wind power operation and maintenance, heat dissipation is difficult due to long-time insolation and poor ventilation in high-temperature weather, local high temperature is easy to generate in a cabin, so that serious accidents such as fire disaster and the like are caused even if some electric elements are damaged, therefore, the development condition of a temperature field in the cabin for a period of time in the future can be timely predicted according to meteorological conditions in digital twinning, the operation and maintenance strategy of the wind driven generator can be timely adjusted, related adjustment is carried out, damage to electronic elements or serious accidents are avoided, and meanwhile, in order to reduce calculation amount and simulation time, the calculation of the temperature field of the cabin of the wind driven generator is required to be accelerated.
The concrete implementation flow for accelerating the calculation of the temperature field of the wind driven generator cabin by the invention is as follows:
according to the simplified geometric model, a computational fluid dynamics computational grid model of a certain model wind driven generator cabin is obtained, the grid quantity is 471184, and the grid type is a structured grid;
and 2, calculating the temperature rise condition of a certain model wind driven generator cabin under the meteorological conditions that the air temperature is 40 ℃ and the wind speed is 3m/s, calculating a transient flow field for 200 seconds, wherein the time step is 1 second, and selecting the first 100 seconds of temperature field as training data and the later 100 seconds of temperature field as verification data.
Constructing an original data matrix
Up to 471184 rows, 100 columns data, < ->
。
Through matrix
Calculating a final short-term prediction matrix for the 101 th time step temperature data>
The expression is as follows:
using matrices
'、
And->
Repeating the steps 3-5, and predicting the temperature of the 102 th stepAnd (3) repeating the process by the degree data, so as to realize the prediction of future flow field data and the calculation acceleration.
Through experiments, a CPU is used as an 8-core i7-9700 processor and a workstation with a memory of 8G is used for carrying out computational fluid mechanics simulation on the scene, and each step of computation requires 5 minutes; the wind driven generator simulation acceleration method based on the reduced order decomposition treatment can accelerate the calculation time of each time step to about 5 seconds.
As shown in fig. 5, although the maximum error K and the average error K of the present embodiment vary with the prediction time step, the error of the present embodiment may be controlled within 5%.
Therefore, the method and the device for accelerating the calculation of the temperature field of the wind driven generator cabin can accelerate the original calculation of computational fluid mechanics by 60 times, and still have good accuracy.
An embodiment of a device for applying the method of the invention:
a computer apparatus, comprising:
one or more processors;
a storage means for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement a wind turbine simulation acceleration method based on reduced order decomposition processing as described above.
A computer medium embodiment to which the method of the invention is applied:
a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a wind turbine simulation acceleration method based on a reduced order decomposition process as described above.
It will be apparent to those skilled in the art that embodiments of the present application may be provided as a method, system, computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (10)
1. A wind driven generator simulation acceleration method based on reduced order decomposition processing is characterized in that,
the method comprises the following steps:
firstly, performing full-order hydrodynamic simulation calculation on a wind driven generator to obtain full-order initial flow field data of a certain time step;
secondly, processing the initial flow field data to construct an initial data matrix;
thirdly, performing singular value decomposition on the initial data matrix to obtain a plurality of singular values, and determining a reduced rank according to the energy duty ratio of the singular values;
fourthly, performing dynamic modal reduced order decomposition on the initial data matrix according to the reduced order rank to obtain space-time characteristic quantity used for representing the flow process of the flow field;
fifthly, generating a reduced-order prediction matrix according to the space-time characteristic quantity, and obtaining predicted flow field data of the time step to be calculated according to the reduced-order prediction matrix;
and sixthly, carrying out simulation calculation on the wind driven generator according to the predicted flow field data, and realizing simulation acceleration of the wind driven generator.
2. The wind driven generator simulation acceleration method based on reduced order decomposition processing according to claim 1, wherein,
in the first step, the method for performing full-order hydrodynamic simulation calculation on the wind driven generator comprises the following steps:
step 11, constructing a geometric model of the wind driven generator;
step 12, setting meteorological conditions and working conditions of a wind driven generator on the basis of a geometric model to obtain a calculation grid;
and 13, performing full-order flow field calculation on the calculation grid according to computational fluid dynamics to obtain initial flow field data comprising a plurality of time steps.
3. A wind driven generator simulation acceleration method based on reduced order decomposition processing as set forth in claim 2, wherein,
meteorological conditions include wind speed and wind direction;
the working condition is the rotating speed of the wind driven generator;
the initial flow field data includes physical quantities on each computational grid;
the physical quantity comprises at least speed or/and pressure or/and temperature.
4. A wind driven generator simulation acceleration method based on reduced order decomposition processing according to claim 3, wherein,
in the second step, the method for constructing the initial data matrix is as follows:
step 21, obtaining physical quantity on each calculation grid;
step 22, the physical quantity which is taken out is stored into a digital matrix according to the requirement to form an initial data matrix;
the first column of the initial data matrix is the physical quantity of the 1 st time step, and the last column is the physical quantity of the N-1 st time step;
first of initial data matrixiThe row represents the time-dependent process information of the physical quantity on the ith computational grid, the first of whichjThe column represents the physical quantity of the jth time step.
5. The wind driven generator simulation acceleration method based on reduced order decomposition processing according to claim 1, wherein,
in the third step, the method for performing singular value decomposition on the initial data matrix is as follows:
step 31, performing singular value decomposition operation on the initial data matrix to obtain a singular value matrix;
step 32, setting singular values arranged from large to small on diagonal lines of the singular value matrix;
step 33, obtaining the energy contribution degree of each singular value to the initial data matrix;
and step 32, accumulating the energy contribution degrees one by one from large to small to obtain energy contribution sums, and when the energy contribution sums are larger than an energy threshold value, counting the number of the energy contribution degrees participating in accumulation, and taking the number of the energy contribution degrees as a reduction rank.
6. The wind driven generator simulation acceleration method based on reduced order decomposition processing according to claim 1, wherein,
the fourth step, the method for carrying out dynamic mode reduced order decomposition on the initial data matrix is as follows:
step 41, constructing an initial prediction matrix differing by one time step according to the initial data matrix;
step 42, according to the reduced rank, carrying out truncated singular value decomposition on the initial flow field data to obtain a decomposition result;
step 43, constructing a decomposition similarity matrix between the initial flow field data and the initial prediction matrix according to the decomposition result and the initial prediction matrix;
step 44, performing eigenvalue decomposition on the decomposition similarity matrix to obtain eigenvalues and eigenvectors;
and 45, constructing space-time characteristic quantity for representing the flow process of the wind driven generator according to the decomposition result, the initial prediction matrix, the characteristic value and the characteristic vector.
7. The wind driven generator simulation acceleration method based on reduced order decomposition processing according to claim 6, wherein,
the method for constructing the initial prediction matrix is as follows:
the first column of the initial prediction matrix is the flow field data of the 2 nd time step, and the last column is the flow field data of the last time step;
first of initial prediction matrixiThe row represents the time-dependent process information of the physical quantity on the ith computational grid, the first of whichjThe column represents the physical quantity of the j+1th time step.
8. The wind driven generator simulation acceleration method based on reduced order decomposition processing according to claim 7, wherein,
in the fifth step, the method for obtaining the predicted flow field data of the time step to be calculated is as follows:
step 51, constructing a reduced-order prediction matrix according to the space-time characteristic quantity, the initial flow field data and the initial prediction matrix, wherein the reduced-order prediction matrix comprises flow field data of a time step to be calculated;
step 52, calculating the reduced order prediction matrix to obtain predicted flow field data of the time step to be calculated;
step 53, adding the predicted flow field data of the time step to be calculated to the initial prediction matrix and the initial flow field data, and constructing a new initial prediction matrix and a new initial data matrix;
and step 54, obtaining predicted flow field data which is different from the new initial prediction matrix by one time step according to the new initial prediction matrix and the new initial data matrix, and circularly reciprocating to finish the calculation of a plurality of time step flow field data to be calculated.
9. A wind driven generator simulation acceleration method based on reduced order decomposition processing is characterized in that,
the method comprises the following steps:
carrying out full-order hydrodynamic simulation calculation on the wind driven generator by utilizing a pre-constructed hydrodynamic simulation model to obtain full-order initial flow field data of a certain time step;
processing initial flow field data through a pre-constructed reduced order calculation model, and constructing an initial data matrix; singular value decomposition is carried out on the initial data matrix to obtain a plurality of singular values, and a reduced rank is determined according to the energy duty ratio of the singular values;
predicting a reduced order model by utilizing pre-constructed data, and carrying out dynamic modal reduced order decomposition on an initial data matrix according to a reduced order rank to obtain space-time characteristic quantity for representing a flow process of a flow field; generating a reduced order prediction matrix according to the space-time characteristic quantity, and obtaining predicted flow field data of the time step to be calculated according to the reduced order prediction matrix;
and obtaining predicted flow field data through the reduced order calculation model and the data prediction reduced order model, and performing simulation calculation on the wind driven generator to realize simulation acceleration of the wind driven generator.
10. A wind driven generator simulation acceleration system based on reduced order decomposition processing is characterized in that,
comprising the following steps:
one or more processors;
a storage means for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement a reduced order decomposition process based wind turbine simulation acceleration method as claimed in any one of claims 1-9.
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