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CN114169605A - Physical field and performance collaborative prediction method suitable for pump and hydraulic turbine - Google Patents

Physical field and performance collaborative prediction method suitable for pump and hydraulic turbine Download PDF

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CN114169605A
CN114169605A CN202111479710.3A CN202111479710A CN114169605A CN 114169605 A CN114169605 A CN 114169605A CN 202111479710 A CN202111479710 A CN 202111479710A CN 114169605 A CN114169605 A CN 114169605A
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谢永慧
徐涛
李云珠
张荻
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Xian Jiaotong University
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Abstract

The invention discloses a physical field and performance collaborative prediction method suitable for a pump and a hydraulic turbine, which comprises the following steps: 1) acquiring geometric parameters of a pump model and a hydraulic turbine model and coding the geometric parameters; 2) respectively acquiring working condition parameters under two modes of a pump and a hydraulic turbine and acquiring a numerical simulation result; 3) normalizing input and output parameters of the pump and the hydraulic turbine; 4) dividing a training set and a verification set aiming at data of a pump model and a hydraulic turbine model; 5) respectively constructing two groups of physical fields and performance cooperative prediction point cloud convolution networks of a pump and a hydraulic turbine; 6) training two groups of point cloud convolution neural networks of a pump and a hydraulic turbine; 7) and (5) post-processing the performance prediction information. The method realizes the direct mapping from the related performance parameters under the pump mode to the related performance parameters under the hydraulic turbine mode, overcomes the defects of large calculation data amount and long calculation time of the traditional numerical simulation method, and has the advantages of easy data change and timely acquisition of the required performance parameters.

Description

Physical field and performance collaborative prediction method suitable for pump and hydraulic turbine
Technical Field
The invention belongs to the technical field of pumps and hydraulic turbines, and particularly relates to a physical field and performance collaborative prediction method suitable for the pumps and the hydraulic turbines.
Background
At present, a large amount of clean, green, pollution-free and surplus pressure energy is always generated in the industrial production and transmission process in the fields of petroleum, chemical engineering and the like, and the pressure energy can be fully utilized as a hydraulic turbine through pump reversal. Currently, a numerical simulation method is mostly adopted for research on hydraulic turbines for pump reversal, and the method is used for carrying out numerical simulation calculation on an internal flow field of a pump during forward and reverse rotation to obtain the flow field of the pump during forward and reverse rotation under different working conditions, so that the performance of the pump under different working conditions is obtained. The three-dimensional numerical simulation calculation consumes a large amount of resources and time, corresponding performance parameters cannot be obtained in time when relevant working condition parameters and geometric parameters change, and great resource waste and time lag exist.
In recent years, with the development of science and technology, the computing power of a computer is remarkably improved, the progress of artificial intelligence enables traditional problems to have a plurality of new solutions, and the deep learning method is popularized and propagated in various engineering fields.
Disclosure of Invention
The invention aims to provide a physical field and performance collaborative prediction method suitable for a pump and a hydraulic turbine. First P _ Net in first group1The network implements the prediction of the pump physical field from the pump's operating and geometric parameters, the second P _ Net2Network entityThe performance of the pump is now predicted by the pump physical field; first TNet of the second group1The network realizes the prediction of the physical field of the hydraulic turbine from the working condition parameters and the geometric parameters of the hydraulic turbine, and the second T _ Net2The network realizes the prediction of the performance of the hydraulic turbine by the physical field of the hydraulic turbine, and finally obtains a mapping curve from the performance of the pump to the performance of the hydraulic turbine under the variable working condition through fitting two groups of data, thereby completing the task of the multi-physical field and performance collaborative prediction of the hydraulic turbine under the variable working condition.
The invention is realized by adopting the following technical scheme:
a physical field and performance collaborative prediction method suitable for a pump and a hydraulic turbine comprises the following steps:
1) geometric parameters of pump and hydraulic turbine models are obtained and encoded
The obtained geometric parameters of the blades are the installation angle alpha of the control blade, the number a of the blades and the coordinates (x, y, z) of B points of a Bezier curve of the control blade, each pump corresponds to one blade, and because the same blade is adopted in the two modes of the pump and the hydraulic turbine, the initial geometric parameter sets in the two modes are obtained by the method
Figure BDA0003394498920000021
Modeling is carried out according to the obtained geometric parameters of the I pumps, and for each grid model, grid node information G of each grid model is obtainedi,p,qMesh node information Gi,p,qInitial set of geometric parameters associated with the blade
Figure BDA0003394498920000022
Integrating to obtain a geometric parameter set Ai,jAnd encoding the geometric parameters by using a coder-decoder combination, wherein the coder Encoder encodes the geometric parameter set A of the bladei,jMapping into a fixed-length vector form, decoding the vector obtained by encoding into a geometric parameter set A by a Decoder Decoderi,jCombining the vectors of I blades obtained by the encoder to form an indirect geometric parameter set A of the bladesi,f(ii) a Wherein, I1, 2,3,.. and I represent different pumps, I is the total number of pumps, and J1, 2, 3.. and J representDifferent geometric parameters, J is the total number of the geometric parameters, P is 1,2, 3., P represents different grid nodes, P is the total number of the grid nodes, q is the coordinate of each grid node, and comprises the coordinates in the x direction, the y direction and the z direction, if a two-dimensional model, the coordinates in the x direction and the y direction are comprised, F is 1,2, 3., F represents different indirect geometric parameter variables, and F is the total number of the indirect geometric parameter variables;
2) respectively obtaining working condition parameters of the pump and the hydraulic turbine under two modes and obtaining a numerical simulation result
The working condition parameters of the pump and the hydraulic turbine comprise the mechanical rotating speed omega when the pump and the hydraulic turbine operate in two modesRInlet pressure P in two modes of pump and hydraulic turbineinAnd inlet temperature Tin(ii) a For each pump, under two modes, respectively adopting Latin hypercube sampling method to randomly sample D working conditions in respective parameter space, so that the working condition parameter set under the pump mode is Bi,dThe operating condition parameter in the hydraulic turbine mode is Ci,dAnd respectively carrying out CFD calculation on D working condition points of each pump model in two modes to obtain a three-dimensional physical field data set of each blade in the pump mode
Figure BDA0003394498920000023
Combining with three-dimensional physical field data in hydraulic turbine mode
Figure BDA0003394498920000031
Calculating to obtain a set of performance parameters of different pumps under the pump mode according to the physical field parameters
Figure BDA0003394498920000032
And performance parameter set in hydraulic turbine mode
Figure BDA0003394498920000033
D is 1,2,3, and D represents different operating parameters, D is the total number of operating points that need to be calculated in each mode, N is 1,2,3, and N represents different physical fields including speed, temperature, pressure, and vorticity, and N is the total recorded flow field parametersNumber, m ═ 1,2, representing efficiency and head, respectively;
3) pump and hydraulic turbine input and output parameter normalization
Normalizing the simplified set and other data to obtain normalized input data set { A }i,f,Bi,d,Ci,dThe physical field is collected as
Figure BDA0003394498920000034
The set of performance parameters is
Figure BDA0003394498920000035
The input data set after normalization is
Figure BDA0003394498920000036
Set of physical fields as
Figure BDA0003394498920000037
The set of performance parameters is
Figure BDA0003394498920000038
4) Partitioning training and validation sets for pump and hydraulic turbine model data
For neural networks such as point clouds, the input data set is
Figure BDA0003394498920000039
Collecting input data
Figure BDA00033944989200000310
Randomly scrambling, and setting the input set as 2: 2: 1 into training set
Figure BDA00033944989200000311
Verification set
Figure BDA00033944989200000312
And test set
Figure BDA00033944989200000313
Similarly, the set of physical fields is divided into equal parts
Figure BDA00033944989200000314
And
Figure BDA00033944989200000315
the performance parameter set is equally divided into
Figure BDA00033944989200000316
And
Figure BDA00033944989200000317
5) cooperative prediction point cloud convolution network for respectively constructing two groups of physical fields and performances of pump and hydraulic turbine
Constructing two groups of networks of pump physical field and performance prediction and hydraulic turbine physical field prediction and performance prediction, wherein each group comprises two point cloud convolution neural networks; for the first set of pump physical fields and performance prediction networks, the first P _ Net1Point cloud convolution neural network aggregation according to input in pump mode
Figure BDA00033944989200000318
Predicting a set of physical fields in a pump mode
Figure BDA00033944989200000319
Realizing the mapping of the design variables, i.e. the geometric parameters and the working condition parameters, to the physical field of the pump in the pump mode, the second P _ Net2The point cloud convolution neural network input is P _ Net1Network predicted set of pump physical fields
Figure BDA00033944989200000320
Predicting a set of performance parameters for a pump mode
Figure BDA00033944989200000321
Mapping from the pump physical field to the pump performance parameter is realized; for the second group of hydraulic turbine physical field and performance prediction network, the first T _ Net1Point cloud convolution neural network according to input set in hydraulic turbine mode
Figure BDA00033944989200000322
Predicting to obtain physical field in hydraulic turbine mode
Figure BDA0003394498920000041
Implementing a mapping of design variables to a physical field of the hydraulic turbine in the hydraulic turbine mode, a second T _ Net2The point cloud convolution neural network input is T _ Net1Physical field predicted by network
Figure BDA0003394498920000042
Predicting to obtain a set of performance parameters in a hydraulic turbine mode
Figure BDA0003394498920000043
Mapping from a hydraulic turbine physical field to hydraulic turbine performance parameters is realized;
6) convolutional neural network for training two groups of point clouds of pump and hydraulic turbine
7) Performance prediction information post-processing
Output aggregation from two parts
Figure BDA0003394498920000044
And
Figure BDA0003394498920000045
establishing direct relation between the performance parameters in the pump mode and the performance parameters in the hydraulic turbine mode, realizing direct mapping from the efficiency in the pump mode to the efficiency in the hydraulic turbine mode, and simultaneously obtaining the related physical field analysis of the pump and the hydraulic turbine for further analysis.
The invention is further improved in that the method also comprises the following steps:
8) algorithm maintenance
In the actual application process, if the information needing to be input is increased or reduced, an encoder and a decoder are adopted, the original input information is mapped into a vector with a fixed size to serve as a new input, four groups of point cloud convolution networks including a trained pump, a trained hydraulic turbine physical field and performance prediction are adopted to serve as pre-training models, and training is carried out again on the basis.
The further improvement of the invention is that in the step 1), the Encoder Encoder and the Decoder Decoder both adopt full connection layer to construct network, and the Encoder Encoder constructs encoding mapping function to realize the geometric parameter set Ai,jEncoding mapping to a fixed length vector, Decoder constructing decoding mapping function to realize the mapping from the fixed length vector to the geometric parameter set Ai,jDecoding the map of (1); the loss function of the process is a set of geometric parameters Ai,jThe result of the predicted geometric parameters after encoding and decoding and the original real geometric parameter set Ai,jThe difference between:
Figure BDA0003394498920000046
the further improvement of the present invention is that, in step 3), the normalization operation is Sigmoid function conversion, and the specific operation is as follows:
Figure BDA0003394498920000047
wherein, the expression of the Sigmoid function is as follows:
Figure BDA0003394498920000051
the normalization operation for the remaining data is similar:
Figure BDA0003394498920000052
Figure BDA0003394498920000053
Figure BDA0003394498920000054
Figure BDA0003394498920000055
Figure BDA0003394498920000056
Figure BDA0003394498920000057
the invention further improves the point cloud convolution network used for the cooperative prediction of the physical fields and the performance of the pump and the hydraulic turbine in the step 4).
The further improvement of the invention is that the point cloud convolution Network adopts PointNet, PointNet + + or Kd-Network.
The invention further improves the method, in the step 5), for two modes of the pump and the hydraulic turbine, two point cloud convolution neural networks in each mode comprise two parts of loss functions, wherein one part is the loss function F _ loss predicted by the physical field and is used for reducing the difference between the predicted physical field and the real physical field; the other part is a loss function psi _ loss for performance prediction, which is used for reducing the gap between the predicted performance and the real performance;
Figure BDA0003394498920000058
Figure BDA0003394498920000059
Figure BDA00033944989200000510
Figure BDA00033944989200000511
wherein, wqPredicting weights of the squared differences of the physical field data and the true physical field data for each network node,
Figure BDA0003394498920000061
are respectively P _ Net1、T_Net1Two physical field prediction point cloud convolutional neural networks generated predicted physical field data,
Figure BDA0003394498920000062
real physical field data for the pump and hydraulic turbine respectively,
Figure BDA0003394498920000063
are respectively P _ Net2、T_Net2Two performance prediction point cloud convolutional neural network generated predicted performance data,
Figure BDA0003394498920000064
actual performance data for the pump and hydraulic turbine, respectively.
The further improvement of the invention is that in the step 6), in the process of training the network, firstly, an optimizer is set as Adam, the initial learning rate is set to be 0.01, and the training is carried out for 50 steps; the optimizer is then set to SGD, after which the learning rate is reduced to 1/10 per 100 steps of training, with a minimum learning rate of 0.0001.
The invention has at least the following beneficial technical effects:
the invention provides a physical field and performance collaborative prediction method suitable for a pump and a hydraulic turbine. First P _ Net in first group1The network implements the prediction of the pump physical field from the pump's operating and geometric parameters, the second P _ Net2The network realizes that the performance of the pump is predicted by the physical field of the pump; first TNet of the second group1The network realizes the prediction of the physical field of the hydraulic turbine from the working condition parameters and the geometric parameters of the hydraulic turbine, and the second T _ Net2The network realizes the prediction of the hydraulic turbine performance by the hydraulic turbine physical field, and finally obtains a mapping curve from the pump performance to the hydraulic turbine performance under variable working conditions through fitting of two groups of data. The method can realize direct mapping from the related performance parameters under the pump mode to the related performance parameters under the hydraulic turbine mode, overcomes the defects of large calculation data amount and long calculation time of the traditional numerical simulation method, has the advantages of easy data change and timely acquisition of the required performance parameters, can replace the traditional numerical simulation calculation, and has the advantages of low cost, high accuracy and the like compared with an experimental method.
Drawings
FIG. 1 is a flow chart of a data-driven variable-condition hydraulic turbine based multi-physical field and performance collaborative prediction method of the present invention;
FIG. 2 is a diagram illustrating the data processing of the present invention;
FIG. 3 is a schematic diagram of a flow field prediction point cloud convolutional neural network constructed by the present invention;
FIG. 4 is a schematic diagram of a performance prediction point cloud convolutional neural network constructed by the present invention.
Detailed Description
The present invention will be described in further detail with reference to examples in accordance with the summary of the invention. The following is an application of the present invention, but is not limited thereto, and the implementer may modify the parameters according to specific situations, the specific implementation method is shown in fig. 1, and the data processing process is shown in fig. 2.
As shown in FIG. 1, the invention provides a physical field and performance cooperative prediction method suitable for a pump and a hydraulic turbine, which comprises the following steps:
firstly, acquiring geometric parameters of a pump model and a hydraulic turbine model and coding the geometric parameters;
obtaining lamps such as a mounting angle alpha of a control blade, the number a of the blades and coordinates (x, y, z) of B points of a Bezier curve of the control bladeThe geometric parameters of the sheet are that each pump corresponds to one blade, and because the same blade is adopted in the two modes of the pump and the hydraulic turbine, the initial geometric parameter sets in the two modes are obtained
Figure BDA0003394498920000071
Modeling is carried out according to the obtained geometric parameters of the I pumps, and for each grid model, grid node information G of each grid model is obtainedi,p,qMesh node information Gi,p,qInitial set of geometric parameters associated with the blade
Figure BDA0003394498920000072
Integrating to obtain a geometric parameter set Ai,j. In order to prevent dimension disaster caused by more geometric parameters of the blade, the geometric parameters are coded by adopting a coder-decoder combination, and the coder can be used for coding a geometric parameter set A of the bladei,jMapping into a fixed-length vector, and decoding the vector into geometric parameter set Ai,jThe loss function of the process is a set of geometric parameters Ai,jThe result of the predicted geometric parameters after encoding and decoding and the original real geometric parameter set Ai,jThe difference between:
Figure BDA0003394498920000073
combining vectors of I blades obtained by an encoder to form an indirect geometric parameter set A of the bladesi,f. Wherein, I is 1,2, 3., I represents different pumps, I is the total number of pumps, J is 1,2, 3., J represents different geometric parameters, J is the total number of geometric parameters, P is 1,2, 3., P represents different grid nodes, P is the total number of grid nodes, q is the coordinate of each grid node, including the coordinates in the x direction, the y direction and the z direction, if a two-dimensional model, only includes the coordinates in the x direction and the y direction, F is 1,2, 3., F represents different indirect geometric parameter variables,
secondly, respectively acquiring working condition parameters under two modes of a pump and a hydraulic turbine and acquiring a numerical simulation result;
obtaining the mechanical rotation speed omega when the pump and the hydraulic turbine operate in two modesRInlet pressure P in two modes of pump and hydraulic turbineinAnd inlet temperature TinAnd (5) waiting for working condition parameters. For each pump, under two modes, respectively adopting Latin hypercube sampling method to randomly sample D working conditions in respective parameter space, so that the working condition parameter set under the pump mode is Bi,dThe operating condition parameter in the hydraulic turbine mode is Ci,dAnd respectively carrying out CFD calculation on D working condition points of each pump model in two modes to obtain a physical field data set of each blade in the pump mode
Figure BDA0003394498920000081
Combining with physical field data in hydraulic turbine mode
Figure BDA0003394498920000082
According to the physical field parameters, the performance parameter set of different pumps under the pump mode can be calculated
Figure BDA0003394498920000083
And performance parameter set in hydraulic turbine mode
Figure BDA0003394498920000084
D is 1,2,3, the., D represents different operating parameters, D is the total number of operating points required to be calculated in each mode, N is 1,2,3, the., N represents different physical fields including speed, temperature, pressure, vorticity and the like, N is the total number of recorded flow field parameters, and m is 1 and 2 respectively represent efficiency and a water head.
Thirdly, normalizing input and output parameters of the pump and the hydraulic turbine;
normalizing the simplified set and other data to obtain normalized input data set { A }i,f,Bi,d,Ci,dThe physical field is collected as
Figure BDA0003394498920000085
The set of performance parameters is
Figure BDA0003394498920000086
The input data set after normalization is
Figure BDA0003394498920000087
Set of physical fields as
Figure BDA0003394498920000088
The set of performance parameters is
Figure BDA0003394498920000089
The normalization operation is Sigmoid function conversion, and the specific operation is as follows:
Figure BDA00033944989200000810
wherein, the expression of the Sigmoid function is as follows:
Figure BDA00033944989200000811
the normalization operation for the remaining data is similar:
Figure BDA0003394498920000091
Figure BDA0003394498920000092
Figure BDA0003394498920000093
Figure BDA0003394498920000094
Figure BDA0003394498920000095
Figure BDA0003394498920000096
the input data set after normalization is
Figure BDA0003394498920000097
Set of physical fields as
Figure BDA0003394498920000098
The set of performance parameters is
Figure BDA0003394498920000099
Fourthly, dividing a training set and a verification set aiming at the data of the pump and the hydraulic turbine model;
for the PointNet point cloud convolution neural network, the input data set is
Figure BDA00033944989200000910
Collecting input data
Figure BDA00033944989200000911
Randomly scrambling, and setting the input set as 2: 2: 1 into training set
Figure BDA00033944989200000912
Verification set
Figure BDA00033944989200000913
And test set
Figure BDA00033944989200000914
Similarly, the set of physical fields is divided into equal parts
Figure BDA00033944989200000915
And
Figure BDA00033944989200000916
the performance parameter sets are also classified into
Figure BDA00033944989200000917
And
Figure BDA00033944989200000918
fifthly, constructing two groups of physical fields and performance cooperative prediction point cloud convolution networks of the pump and the hydraulic turbine respectively;
two sets of networks for pump physical field and performance prediction and hydraulic turbine physical field prediction and performance prediction are constructed, each set comprising a point cloud convolutional neural network as shown in fig. 3 and 4. For the first set of pump physical fields and performance prediction networks, the first P _ Net1Point cloud convolution neural network aggregation according to input in pump mode
Figure BDA00033944989200000919
Predicting a set of physical fields in a pump mode
Figure BDA00033944989200000920
The mapping from design variables (geometric parameters and working condition parameters) to the physical field of the pump in the pump mode is realized, and the loss function of the network is as follows:
Figure BDA00033944989200000921
second P _ Net2Point cloud convolution neural network composed of P _ Net1Network predicted set of pump physical fields
Figure BDA0003394498920000101
Predicting a set of performance parameters for a pump mode
Figure BDA0003394498920000102
Implementing a mapping from the physical field of the pump to the performance parameters of the pump, of the networkThe loss function is as follows:
Figure BDA0003394498920000103
for the second group of hydraulic turbine physical field and performance prediction network, the first T _ Net1Point cloud convolution neural network according to input set in hydraulic turbine mode
Figure BDA0003394498920000104
Predicting to obtain physical field in hydraulic turbine mode
Figure BDA0003394498920000105
The method realizes the mapping from design variables (geometric parameters and working condition parameters) to a physical field of the hydraulic turbine in a hydraulic turbine mode, and the loss function of the network is as follows:
Figure BDA0003394498920000106
second T _ Net2Point cloud convolution neural network composed of T _ Net1Physical field predicted by network
Figure BDA0003394498920000107
Predicting to obtain a set of performance parameters in a hydraulic turbine mode
Figure BDA0003394498920000108
Mapping from the physical field of the hydraulic turbine to the performance parameter of the hydraulic turbine is realized, and the loss function of the network is as follows:
Figure BDA0003394498920000109
sixthly, training four groups of point cloud convolution neural networks of two parts of a pump and a hydraulic turbine;
in the process of training the network, firstly setting an optimizer Adam, setting an initial learning rate to be 0.01, and training for 50 steps; the optimizer is then set to SGD, after which the learning rate is reduced to 1/10 per 100 steps of training, with a minimum learning rate of 0.0001.
Seventhly, post-processing the performance prediction information;
output aggregation from two parts
Figure BDA00033944989200001010
And
Figure BDA00033944989200001011
establishing direct relation between the performance parameters in the pump mode and the performance parameters in the hydraulic turbine mode, realizing direct mapping from the efficiency in the pump mode to the efficiency in the hydraulic turbine mode, and simultaneously obtaining the related physical field analysis of the pump and the hydraulic turbine for further analysis.
8) Algorithm maintenance
In the actual application process, if the information needing to be input is increased or reduced, an encoder and a decoder are adopted, the original input information is mapped into a vector with a fixed size to serve as a new input, four groups of point cloud convolution networks including a trained pump, a trained hydraulic turbine physical field and performance prediction are adopted to serve as pre-training models, and training is carried out again on the basis.
In the step 2), the working condition is divided into Latin hypercube sampling, the point taking of the method in the working condition parameter space is multidimensional layered point taking, the method ensures that the obtained working condition points can well cover the whole parameter space, and the method is closest to random sampling.
Although the invention has been described in detail hereinabove with respect to a general description and specific embodiments thereof, it will be apparent to those skilled in the art that modifications or improvements may be made thereto based on the invention. Accordingly, such modifications and improvements are intended to be within the scope of the invention as claimed.

Claims (8)

1.一种适用于泵和水力透平的物理场和性能协同预测方法，其特征在于，包括以下步骤：1. a physical field and performance synergistic prediction method applicable to pump and hydraulic turbine, is characterized in that, comprises the following steps: 1)获取泵和水力透平模型几何参数并将其编码1) Obtain and code the pump and hydraulic turbine model geometry 获取的叶片的几何参数为控制叶片的安装角α，叶片数量a以及控制叶片Bezier曲线的B个点的坐标(x，y，z)，每一个泵对应一个叶片，由于泵和水力透平两种模式下采用的是相同的叶片，由此得到两种模式下的初始几何参数集合都为
Figure FDA0003394498910000011
根据获得的I个泵的几何参数进行建模，对每一个网格模型，获取其网格节点信息Gi，p，q，将网格节点信息Gi，p，q与叶片的初始几何参数集合
Figure FDA0003394498910000012
整合得到几何参数集合Ai，j，并采用编码器-解码器组合对几何参数进行编码，编码器Encoder将叶片的几何参数集合Ai，j映射为一个固定长度的向量的形式，解码器Decoder将编码获得的向量解码为几何参数集合Ai，j，将编码器获得的I个叶片的向量组合构成了叶片的间接几何参数集合Ai，f；其中，i＝1，2，3，...，I，代表不同的泵，I为泵的总数，j＝1，2，3，...，J，代表不同的几何参数，J为几何参数的总数，p＝1，2，3，...，P，代表不同的网格节点，P为网格节点总数，q为每个网格节点的坐标，包含x方向、y方向和z方向的坐标，若为二维模型，则包含x方向和y方向的坐标，f＝1，2，3，...，F，代表不同的间接几何参数变量，F为间接几何参数变量的总数；The obtained geometric parameters of the blade are the installation angle α of the control blade, the number of blades a, and the coordinates (x, y, z) of the B points of the Bezier curve of the control blade. Each pump corresponds to a blade. The same blade is used in the two modes, so the initial geometric parameter set in the two modes is
Figure FDA0003394498910000011
Modeling is carried out according to the obtained geometric parameters of I pumps, and for each mesh model, its mesh node information G i, p, q is obtained, and the mesh node information G i, p, q is compared with the initial geometric parameters of the blade gather
Figure FDA0003394498910000012
The geometric parameter set A i, j is obtained by integration, and the encoder-decoder combination is used to encode the geometric parameters. The encoder Encoder maps the geometric parameter set A i, j of the blade into a fixed-length vector form, and the decoder Decoder The vector obtained by encoding is decoded into the geometric parameter set A i,j , and the vector combination of the I blades obtained by the encoder constitutes the indirect geometric parameter set A i,f of the blade; wherein, i=1, 2, 3, . .., I, represent different pumps, I is the total number of pumps, j=1, 2, 3, ..., J, represent different geometric parameters, J is the total number of geometric parameters, p=1, 2, 3 , ..., P, represent different grid nodes, P is the total number of grid nodes, q is the coordinates of each grid node, including the coordinates of the x, y and z directions, if it is a two-dimensional model, then Contains the coordinates of the x-direction and the y-direction, f=1, 2, 3, ..., F, representing different indirect geometric parameter variables, F is the total number of indirect geometric parameter variables; 2)分别获取泵和水力透平两种模式下的工况参数并获得数值模拟结果2) Obtain the working parameters of the pump and the hydraulic turbine respectively and obtain the numerical simulation results 泵和水力透平的工况参数包括泵和水力透平两种模式运行时的机械转速ωR、泵和水力透平两种模式下的进口压力Pin和进口温度Tin；对每一种泵，在两种模式下，分别采用拉丁超立方采样方法在各自参数空间随机采样D个工况，由此泵模式下的工况参数集合为Bi，d，水力透平模式下的工况参数集合为Ci，d，并将每一种泵模型在两种模式下分别进行D个工况点的CFD计算，获得每一个叶片在泵模式下的三维物理场数据集合
Figure FDA0003394498910000013
和水力透平模式下的三维物理场数据结合
Figure FDA0003394498910000014
根据物理场参数计算得到泵模式下不同泵的性能参数集合
Figure FDA0003394498910000015
和水力透平模式下的性能参数集合
Figure FDA0003394498910000016
其中，d＝1，2，3，...，D，代表不同的工况参数，D为每种模式下需要计算的工况点总数，n＝1，2，3，...，N，代表不同的物理场，包括速度、温度、压力和涡量，N为记录的流场参数总数，m＝1,2，分别代表效率和水头；The operating parameters of the pump and hydraulic turbine include the mechanical speed ω R when the pump and the hydraulic turbine operate in two modes, the inlet pressure P in and the inlet temperature T in under the two modes of the pump and the hydraulic turbine; Pump, in the two modes, the Latin hypercube sampling method is used to randomly sample D working conditions in their respective parameter spaces, so the working condition parameter set in the pump mode is B i, d , and the working conditions in the hydraulic turbine mode The parameter set is C i, d , and the CFD calculation of D operating points is performed for each pump model in the two modes respectively, and the 3D physical field data set of each blade in the pump mode is obtained.
Figure FDA0003394498910000013
Combined with 3D physics data in hydraulic turbine mode
Figure FDA0003394498910000014
The set of performance parameters of different pumps in pump mode is calculated according to the physical field parameters
Figure FDA0003394498910000015
and a collection of performance parameters in hydraulic turbine mode
Figure FDA0003394498910000016
Among them, d=1, 2, 3, ..., D, representing different operating parameters, D is the total number of operating points to be calculated in each mode, n=1, 2, 3, ..., N , representing different physical fields, including velocity, temperature, pressure, and vorticity, N is the total number of recorded flow field parameters, m=1, 2, representing efficiency and head, respectively; 3)泵和水力透平输入输出参数归一化3) Normalization of input and output parameters of pumps and hydraulic turbines 将简化后的集合以及其他数据进行归一化操作，需要归一化的输入数据集合为{Ai,f,Bi,d,Ci,d}，物理场集合为
Figure FDA0003394498910000021
性能参数集合为
Figure FDA0003394498910000022
归一化之后的输入数据集合为
Figure FDA0003394498910000023
物理场集合为
Figure FDA0003394498910000024
性能参数集合为
Figure FDA0003394498910000025
Normalize the simplified set and other data. The input data set to be normalized is {A i,f ,B i,d ,C i,d }, and the physical field set is
Figure FDA0003394498910000021
The set of performance parameters is
Figure FDA0003394498910000022
The input data set after normalization is
Figure FDA0003394498910000023
The physics set is
Figure FDA0003394498910000024
The set of performance parameters is
Figure FDA0003394498910000025
 4)针对泵和水力透平模型数据划分训练集和验证集4) Divide training set and validation set for pump and hydraulic turbine model data 对于点云这一类的神经网络，输入数据集合为
Figure FDA0003394498910000026
将输入数据集合
Figure FDA0003394498910000027
随机打乱，并将输入集合按照2：2：1的比例分为训练集
Figure FDA0003394498910000028
验证集
Figure FDA0003394498910000029
和测试集
Figure FDA00033944989100000210
同样的，物理场集合按相同的比例分为
Figure FDA00033944989100000211
和
Figure FDA00033944989100000212
性能参数集合同样分为
Figure FDA00033944989100000213
和
Figure FDA00033944989100000214
For neural networks such as point clouds, the input data set is
Figure FDA0003394498910000026
set the input data
Figure FDA0003394498910000027
Randomly shuffle and divide the input set into the training set according to the ratio of 2:2:1
Figure FDA0003394498910000028
validation set
Figure FDA0003394498910000029
and test set
Figure FDA00033944989100000210
Likewise, the physics set is divided into equal proportions
Figure FDA00033944989100000211
and
Figure FDA00033944989100000212
The performance parameter set is also divided into
Figure FDA00033944989100000213
and
Figure FDA00033944989100000214
 5)分别构建泵和水力透平两组物理场和性能的协同预测点云卷积网络5) Construct a point cloud convolution network for cooperative prediction of the physical fields and performance of the pump and the hydraulic turbine respectively 构建泵物理场和性能预测与水力透平物理场预测和性能预测两组网络，每组包含两个点云卷积神经网络；对第一组泵物理场和性能预测网络，第一个P_Net1点云卷积神经网络根据泵模式下的输入集合
Figure FDA00033944989100000215
预测得到泵模式下的物理场集合
Figure FDA00033944989100000216
实现泵模式下由设计变量即几何参数和工况参数到泵物理场的映射，第二个P_Net2点云卷积神经网络输入为P_Net1网络预测得到的泵物理场集合
Figure FDA00033944989100000217
预测得到泵模式下的性能参数集合
Figure FDA00033944989100000218
实现由泵物理场到泵性能参数的映射；对第二组水力透平物理场和性能预测网络，第一个T_Net1点云卷积神经网络根据水力透平模式下的输入集合
Figure FDA00033944989100000219
预测得到水力透平模式下的物理场
Figure FDA00033944989100000220
实现水力透平模式下由设计变量到水力透平物理场的映射，第二个T_Net2点云卷积神经网络输入为T_Net1网络预测得到的物理场
Figure FDA00033944989100000221
预测得到水力透平模式下的性能参数集合
Figure FDA00033944989100000222
实现由水力透平物理场到水力透平性能参数的映射；Construct two sets of networks for pump physics and performance prediction and hydraulic turbine physics prediction and performance prediction, each containing two point cloud convolutional neural networks; for the first set of pump physics and performance prediction networks, the first P_Net 1 Point cloud convolutional neural network based on the set of inputs in pump mode
Figure FDA00033944989100000215
Predicted physics set in pump mode
Figure FDA00033944989100000216
Realize the mapping from design variables, namely geometric parameters and working condition parameters to pump physics in pump mode, and the second P_Net 2 point cloud convolutional neural network input is the pump physics set predicted by P_Net 1 network
Figure FDA00033944989100000217
Predicted set of performance parameters in pump mode
Figure FDA00033944989100000218
Realize the mapping from pump physics to pump performance parameters; for the second set of hydraulic turbine physics and performance prediction networks, the first T_Net 1 point cloud convolutional neural network is based on the input set in the hydraulic turbine mode
Figure FDA00033944989100000219
Predicted Physics in Hydraulic Turbine Mode
Figure FDA00033944989100000220
Realize the mapping from design variables to the physical field of the hydraulic turbine in the hydraulic turbine mode, the second T_Net 2 point cloud convolutional neural network input is the physical field predicted by the T_Net 1 network
Figure FDA00033944989100000221
Predict the set of performance parameters in the hydraulic turbine mode
Figure FDA00033944989100000222
Realize the mapping from hydraulic turbine physics to hydraulic turbine performance parameters; 6)训练泵和水力透平两组点云卷积神经网络6) Train two sets of point cloud convolutional neural networks for pumps and hydraulic turbines 7)性能预测信息后处理7) Post-processing of performance prediction information 根据两个部分的输出集合
Figure FDA0003394498910000031
和
Figure FDA0003394498910000032
建立泵模式下的性能参数与水力透平模式下性能参数之间的直接联系，实现泵模式下效率到水力透平模式下效率之间的直接映射，同时获得泵和水力透平的相关物理场分析，进行进一步的分析。output collection based on two parts
Figure FDA0003394498910000031
and
Figure FDA0003394498910000032
Establish a direct relationship between the performance parameters in the pump mode and the performance parameters in the hydraulic turbine mode, realize the direct mapping between the efficiency in the pump mode and the efficiency in the hydraulic turbine mode, and obtain the relevant physical fields of the pump and the hydraulic turbine at the same time analysis for further analysis. 2.根据权利要求1所述的一种适用于泵和水力透平的物理场和性能协同预测方法，其特征在于，还包括以下步骤：2. a kind of physical field and performance synergistic prediction method applicable to pump and hydraulic turbine according to claim 1, is characterized in that, also comprises the following steps: 8)算法维护8) Algorithm maintenance 在实际运用过程中，若需要输入的信息增加或减少，则采用编码器和解码器，将原输入信息映射为固定尺寸的向量作为新输入，采用已经训练完毕的泵和水力透平物理场和性能预测共四组点云卷积网络作为预训练模型，在此基础上重新进行训练。In the actual application process, if the input information needs to be increased or decreased, an encoder and a decoder are used to map the original input information to a fixed-size vector as a new input, and the trained pump and hydraulic turbine physical fields and Performance prediction A total of four groups of point cloud convolutional networks are used as pre-training models and retrained on this basis. 3.根据权利要求1所述的一种适用于泵和水力透平的物理场和性能协同预测方法，其特征在于，步骤1)中，编码器Encoder和解码器Decoder均采用全连接层构建网络，编码器Encoder构造编码映射函数实现从几何参数集合Ai,j到一个固定长度的向量的编码映射，解码器Decoder构造解码映射函数实现从固定长度向量到几何参数集合Ai,j的解码映射；该过程的损失函数为几何参数集合Ai,j经过编码解码后的预测几何参数结果与原始的真实几何参数集合Ai,j之间的差值：3. a kind of physics that is applicable to pump and hydraulic turbine according to claim 1 and performance collaborative prediction method, it is characterized in that, in step 1), encoder Encoder and decoder Decoder all adopt full connection layer to build network , the encoder Encoder constructs an encoding mapping function to implement the encoding mapping from the geometric parameter set A i,j to a fixed-length vector, and the decoder Decoder constructs a decoding mapping function to implement the fixed-length vector to the geometric parameter set A i,j decoding mapping ; The loss function of this process is the difference between the predicted geometric parameter result of the geometric parameter set A i,j after encoding and decoding and the original real geometric parameter set A i, j :
Figure FDA0003394498910000033
Figure FDA0003394498910000033
 4.根据权利要求1所述的一种适用于泵和水力透平的物理场和性能协同预测方法，其特征在于，步骤3)中，归一化的操作为Sigmoid函数转换，具体操作如下：4. a kind of physical field and performance synergistic prediction method that is applicable to pump and hydraulic turbine according to claim 1, is characterized in that, in step 3), the operation of normalization is Sigmoid function conversion, and concrete operation is as follows:
Figure FDA0003394498910000034
Figure FDA0003394498910000034
 其中，Sigmoid函数的表达式如下：Among them, the expression of the sigmoid function is as follows:
Figure FDA0003394498910000041
Figure FDA0003394498910000041
 其余数据的归一化操作类似：The normalization operation for the rest of the data is similar:
Figure FDA0003394498910000042
Figure FDA0003394498910000042
Figure FDA0003394498910000043
Figure FDA0003394498910000043
Figure FDA0003394498910000044
Figure FDA0003394498910000044
Figure FDA0003394498910000045
Figure FDA0003394498910000045
Figure FDA0003394498910000046
Figure FDA0003394498910000046
Figure FDA0003394498910000047
Figure FDA0003394498910000047
 5.根据权利要求1所述的一种适用于泵和水力透平的物理场和性能协同预测方法，其特征在于，步骤4)中，用于泵和水力透平物理场和性能协同预测的点云卷积网络。5. a kind of physical field and performance synergistic prediction method applicable to pump and hydraulic turbine according to claim 1, is characterized in that, in step 4), for pump and hydraulic turbine physics field and performance synergistic prediction method. Point cloud convolutional network. 6.根据权利要求5所述的一种适用于泵和水力透平的物理场和性能协同预测方法，其特征在于，点云卷积网络采用PointNet、PointNet++或Kd-Network。6 . The method for co-predicting the physical field and performance of a pump and a hydraulic turbine according to claim 5 , wherein the point cloud convolutional network adopts PointNet, PointNet++ or Kd-Network. 7 . 7.根据权利要求1所述的一种适用于泵和水力透平的物理场和性能协同预测方法，其特征在于，步骤5)中，对泵和水力透平两种模式，每一模式下的两个点云卷积神经网络包含两部分的损失函数，一部分为物理场预测的损失函数F_loss，用于减小预测物理场与真实物理场之间的差距；另一部分为性能预测的损失函数ψ_loss，用于减小预测性能与真实性能之间的差距；7. a kind of physical field and performance synergistic prediction method applicable to pump and hydraulic turbine according to claim 1, is characterized in that, in step 5), to two modes of pump and hydraulic turbine, under each mode The two point cloud convolutional neural networks contain two parts of the loss function, one part is the loss function F_loss for physics prediction, which is used to reduce the gap between the predicted physics field and the real physics field; the other part is the loss function for performance prediction ψ_loss, used to reduce the gap between predicted performance and real performance;
Figure FDA0003394498910000048
Figure FDA0003394498910000048
Figure FDA0003394498910000049
Figure FDA0003394498910000049
Figure FDA00033944989100000410
Figure FDA00033944989100000410
Figure FDA0003394498910000051
Figure FDA0003394498910000051
 其中，wq为每一个网络节点处预测物理场数据和真实物理场数据平方差的权值，
Figure FDA0003394498910000052
分别为P_Net1、T_Net1两个物理场预测点云卷积神经网络生成的预测物理场数据，
Figure FDA0003394498910000053
分别为泵和水力透平的真实物理场数据，
Figure FDA0003394498910000054
分别为P_Net2、T_Net2两个性能预测点云卷积神经网络生成的预测性能数据，
Figure FDA0003394498910000055
分别为泵和水力透平的真实性能数据。Among them, w q is the weight of the squared difference between the predicted physics data and the real physics data at each network node,
Figure FDA0003394498910000052
The predicted physical field data generated by the P_Net 1 and T_Net 1 physical field prediction point cloud convolutional neural networks, respectively,
Figure FDA0003394498910000053
are the real physics data of the pump and hydraulic turbine, respectively,
Figure FDA0003394498910000054
The prediction performance data generated by the two performance prediction point cloud convolutional neural networks of P_Net 2 and T_Net 2 , respectively,
Figure FDA0003394498910000055
Actual performance data for pumps and hydraulic turbines, respectively. 8.根据权利要求1所述的一种适用于泵和水力透平的物理场和性能协同预测方法，其特征在于，步骤6)中，在训练网络的过程中，首先设置优化器为Adam，初始学习率设置为0.01，训练50步；然后将优化器设置为SGD，之后在训练每100步时，将学习率降低为原来的1/10，最小的学习率为0.0001。8. a kind of physics field and performance synergistic prediction method applicable to pump and hydraulic turbine according to claim 1, is characterized in that, in step 6), in the process of training network, first set optimizer to be Adam, The initial learning rate is set to 0.01, and the training is performed for 50 steps; then the optimizer is set to SGD, and after every 100 steps of training, the learning rate is reduced to 1/10 of the original, and the minimum learning rate is 0.0001.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114547987A (en) * 2022-04-22 2022-05-27 中国计量大学 Centrifugal pump turbine performance prediction method based on improved artificial neural network

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106844904A (en) * 2017-01-04 2017-06-13 滨州东瑞机械有限公司 A kind of gas turbine vacuum pump impeller Optimization Design
CN111859746A (en) * 2020-07-10 2020-10-30 西安交通大学 A Performance Prediction Method for Turbomachinery under Variable Conditions Based on Flow Field Reconstruction
WO2021007812A1 (en) * 2019-07-17 2021-01-21 深圳大学 Deep neural network hyperparameter optimization method, electronic device and storage medium
CN112541572A (en) * 2020-11-25 2021-03-23 中国石油大学(华东) Residual oil distribution prediction method based on convolutional encoder-decoder network
CN112632728A (en) * 2020-12-30 2021-04-09 西安交通大学 Turbine mechanical blade profile design and performance prediction method based on deep learning
CN113256543A (en) * 2021-04-16 2021-08-13 南昌大学 Point cloud completion method based on graph convolution neural network model
CN113313830A (en) * 2021-05-24 2021-08-27 华南理工大学 Encoding point cloud feature extraction method based on multi-branch graph convolutional neural network

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106844904A (en) * 2017-01-04 2017-06-13 滨州东瑞机械有限公司 A kind of gas turbine vacuum pump impeller Optimization Design
WO2021007812A1 (en) * 2019-07-17 2021-01-21 深圳大学 Deep neural network hyperparameter optimization method, electronic device and storage medium
CN111859746A (en) * 2020-07-10 2020-10-30 西安交通大学 A Performance Prediction Method for Turbomachinery under Variable Conditions Based on Flow Field Reconstruction
CN112541572A (en) * 2020-11-25 2021-03-23 中国石油大学(华东) Residual oil distribution prediction method based on convolutional encoder-decoder network
CN112632728A (en) * 2020-12-30 2021-04-09 西安交通大学 Turbine mechanical blade profile design and performance prediction method based on deep learning
CN113256543A (en) * 2021-04-16 2021-08-13 南昌大学 Point cloud completion method based on graph convolution neural network model
CN113313830A (en) * 2021-05-24 2021-08-27 华南理工大学 Encoding point cloud feature extraction method based on multi-branch graph convolutional neural network

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
王春林 等: "双吸泵正反转内部流场数值模拟及性能预测", 排灌机械工程学报, vol. 33, no. 7, 31 July 2015 (2015-07-31), pages 577 - 582 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114547987A (en) * 2022-04-22 2022-05-27 中国计量大学 Centrifugal pump turbine performance prediction method based on improved artificial neural network
CN114547987B (en) * 2022-04-22 2022-07-26 中国计量大学 A Prediction Method for Turbine Performance of Centrifugal Pumps Based on Improved Artificial Neural Network

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