CN114896909A - Open channel flow calculation method based on water level height - Google Patents

Open channel flow calculation method based on water level height Download PDF

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CN114896909A
CN114896909A CN202210560775.9A CN202210560775A CN114896909A CN 114896909 A CN114896909 A CN 114896909A CN 202210560775 A CN202210560775 A CN 202210560775A CN 114896909 A CN114896909 A CN 114896909A
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邱丽如
王与同
刘建乐
陈奕锟
邓懿斌
金诚
周新志
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Abstract

The invention discloses an open channel flow calculation method based on water level height, which comprises the following steps: measuring channel parameter information by using a radar ultrasonic reflection principle; measuring a small amount of point flow velocity information under different water level heights by using a cableway measuring method; establishing a CFD numerical simulation model by using the measured channel parameters, and optimizing the CFD numerical simulation model based on the measured data until the error requirement is met; expanding a data set by using the obtained simulation data, training a radial basis function neural network, optimizing a network structure and parameters based on an error result, and minimizing a root mean square error of reconstructed flow velocity distribution and real flow velocity distribution; and high-precision calculation of channel flow is realized by using the obtained flow velocity field distribution through a flow velocity-area method. The method utilizes finite element simulation software and a neural network algorithm to realize the construction of the flow velocity field of the cross section of the open channel, thereby realizing the high-precision estimation of the flow, and having important practical significance for improving the water management of the irrigation area, realizing the high-efficiency utilization of water resources and the like.

Description

Open channel flow calculation method based on water level height
Technical Field
The invention relates to the technical field of hydrological test application, in particular to an open channel flow calculation method based on water level height.
Background
An open channel is a channel with free surface (the points on the surface are subject to atmospheric pressure) water flow. The artificial open channel can be used for artificial water delivery channels, canals, pipelines which are not filled with water flow and the like. The manual open channel is more convenient to excavate, the overall arrangement is more free, materials are saved, and the geographic position does not need to be considered excessively. If the device is combined with certain scientific technology, the advantages of the open channel can be better exerted. The method has the advantages that the flow of the open channel is accurately measured, and the method has important significance for reasonably utilizing water resources, allocating the water quantity of the city and county drainage basins, knowing the trend of sewage and the like.
In hydrological analysis and measurement, the current open channel flow measurement method with a wide application range comprises the following steps: 1. a measuring weir and trough method. The Bernoulli equation is established between the front section and the contraction surface of the weir. But because the reason of building, lead to silt to pile up, influence water level flow relation, the precision reduces. Which requires regular maintenance and cleaning, adding to the cost. And the existing open channel needs to be modified, so that the investment is large. 2. Flow cross-section method. The method requires manual operation, and the requirement of automation cannot be met although the measurement precision is high. And for a narrow and deep open channel, the flow velocity distribution is not logarithmic and the precision is not high.
The CFD simulation method is mainly characterized in that numerical simulation of fluid motion is carried out under the control of physical equations (such as mass conservation equations, momentum conservation equations and the like), the numerical simulation can compare and extend experimental results, deep research on control equations is not needed, and only the physical essence and boundary conditions of problems and the explanation and analysis on calculation results are researched.
Hydrologic prediction models can be roughly divided into process-driven models and data-driven models. The process driving model realizes the forecasting of the flow process on the basis of hydrology; the data driving model basically does not consider the physical mechanism of the hydrological process, and realizes the flow forecasting by establishing an optimal mathematical relation. In the data driving model, the neural network depends on the complexity of the system, and the interconnection relationship among a large number of internal nodes is adjusted, so that the purpose of processing information is achieved, and the neural network is widely applied to the hydrology field.
Due to the imperfection of the current open channel flow velocity distribution research at home and abroad, the effective unification of high precision and low cost of flow measurement can not be realized. The invention realizes the construction of the flow velocity field by combining finite element simulation software and a neural network algorithm, thereby realizing the high-precision estimation of the flow. The open channel flow calculation method provided by the invention has important practical significance for improving water consumption management of an irrigation area, realizing efficient utilization of water resources and the like.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a method for calculating the flow of an open channel based on the height of a water level.
The technical scheme of the invention is realized as follows:
an open channel flow calculation method based on water level height comprises the following steps:
step 1: measuring channel parameter information by using a radar ultrasonic reflection principle;
step 2: measuring a small amount of point flow velocity information under different water level heights by using a cableway measuring method;
and step 3: establishing a CFD numerical simulation model by using the channel parameters measured in the step 1, and optimizing the CFD numerical simulation model based on the measured data in the step 2 until the error requirement is met;
and 4, step 4: expanding a data set by using the obtained simulation data, training a radial basis function neural network, optimizing a network structure and parameters based on an error result, and minimizing a root mean square error of reconstructed flow velocity distribution and real flow velocity distribution;
and 5: and high-precision calculation of channel flow is realized by utilizing the obtained flow velocity field distribution through a flow velocity-area method.
Further, the construction of the flow velocity distribution comprises the establishment of a CFD numerical simulation model and the establishment of a radial basis function neural network model.
Further, the training set of the neural network takes the height of the water level and horizontal and vertical coordinates of a certain point to be solved below the water level as input features, and takes a flow velocity value corresponding to the point to be solved as an output feature.
Further, the training set of the radial basis function neural network comprises measured data and simulation data generated by CFD.
Further, the channel parameter information comprises channel shape, channel bottom width, water level height, wall surface roughness and gradient.
The invention has the beneficial effects that: the invention solves the problem that the unification of high efficiency and low cost is difficult to realize in the water conservancy metering field, realizes the high-precision calculation of the flow of the cross section of the open channel by combining the CFD numerical simulation technology and the neural network model, and has important practical significance for improving the water consumption management of an irrigation area, realizing the high-efficiency utilization of water resources and the like.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of an open channel flow calculation method of the present invention;
FIG. 2 is a schematic diagram of a channel section real-time measuring point in the embodiment of the present invention;
FIG. 3 is a channel profile training set data diagram according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of an RBF neural network according to an embodiment of the present invention;
FIG. 5 is a schematic of a flow-area method according to an embodiment of the present invention.
Detailed Description
It should be understood that the embodiments described herein are only for the purpose of illustrating the present invention and are not to be construed as limiting the present invention.
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Embodiment 1, as shown in fig. 1, a method for calculating an open channel flow rate based on a water level height includes the following steps:
step 1: measuring channel parameter information by using a radar ultrasonic reflection principle;
step 2: measuring a small amount of point flow velocity information under different water level heights by using a cableway measuring method;
and step 3: establishing a CFD numerical simulation model by using the channel parameters measured in the step 1, and optimizing the CFD numerical simulation model based on the measured data in the step 2 until the error requirement is met;
and 4, step 4: expanding a data set by using the obtained simulation data, training a radial basis function neural network, optimizing a network structure and parameters based on an error result, and minimizing a root mean square error of reconstructed flow velocity distribution and real flow velocity distribution;
and 5: and high-precision calculation of channel flow is realized by utilizing the obtained flow velocity field distribution through a flow velocity-area method.
Further, the measured channel parameter information includes channel shape, channel bottom width, water level height, wall surface roughness and gradient.
In this embodiment, the cableway measurement method is used to measure the flow velocity of a single point at three different depths (0.2 h, 0.6h, 0.8 h) on 7 vertical lines uniformly distributed in a cross section of the channel shown in fig. 2 at 27 different water level heights, and the flow velocity values are 567 points as the measured data at the water level.
Further, the CFD numerical simulation model is established through channel parameters.
The CFD simulation process comprises the following steps:
in the implementation, as the channel model is simpler in geometry, a sweep mesh division method is adopted to remove unnecessary geometric features.
The computational solution in the CFD simulation process comprises the following steps:
based on the fact that the change of the water speed of the channel is related to time and the channel has a certain gradient, a transient model is selected, and the gravity and acceleration components in the z direction are added to simulate the gradient of the river channel.
The water is added based on the channel being affected by the flow of water in the canal and the flow of air at the surface.
Based on hydrodynamics, boundary condition setting is performed for parameters of a water inlet, a water outlet, a water surface and avoidance.
And based on the influence of the initial value on the convergence process, adopting a PISO algorithm as a solving algorithm.
Further, the CFD numerical simulation model is specifically optimized as follows: and continuously adjusting the model through the actually measured data until the error requirement that the root mean square error and the average relative error under all water levels are less than 5 percent is met. It is now shown that the CFD calculated flow velocity field is substantially the same as in reality.
Further, the root mean square error calculation formula:
Figure DEST_PATH_IMAGE001
(1)
average relative error calculation formula:
Figure DEST_PATH_IMAGE002
(2)
further, the training set of the radial basis function neural network comprises measured data and simulation data generated by CFD.
In this example, 27 sub-level CFD simulation results were used as a complement to the measured dataThe charging and replenishing method is shown in fig. 3. At the intersection of the two solid lines, the speed data is the measured value; at the intersection of the solid and dashed lines, the velocity data is calculated by CFD simulation experiments. Selecting a water levelh 0 And the abscissa of each intersection point at the water levelx i Ordinate and ordinate of they i As input, the coordinate point (c)x i , y i ) A training set is generated corresponding to the flow rate values as output.
Further, the training of the radial basis function neural network specifically includes:
by selection of radial basis functionsg m As follows.
Figure DEST_PATH_IMAGE003
(3)
In the formula:
Figure DEST_PATH_IMAGE004
is the input feature vector;
Figure DEST_PATH_IMAGE005
is as followsmThe center of each of the kernel functions is,
Figure DEST_PATH_IMAGE006
is as followsmThe kernel function expands the width parameter to a random value. Then the output vector of the hidden layer is known as
Figure DEST_PATH_IMAGE007
。
The network structure is shown in FIG. 4, the number of the hidden layer neurons ismOutput matrix
Figure DEST_PATH_IMAGE008
. Solving a connection weight matrix between a hidden layer and an output layer
Figure DEST_PATH_IMAGE009
. The target output of the known output layer is a matrix
Figure DEST_PATH_IMAGE010
There is the formula:
Figure DEST_PATH_IMAGE011
(4)
in this example, the number of training samples is much larger than the number of hidden layer neurons (i.e., the number of training samples is much larger than the number of hidden layer neuronsk≫m) Thus, therefore, it isHIs an irreversible matrix. At this time, least square method can be adopted to solve the matrixHMoore-Penrose amplification of (1) (ii)
Figure DEST_PATH_IMAGE012
). Usually in an orthogonal manner
Figure 539410DEST_PATH_IMAGE012
And (3) calculating:
Figure DEST_PATH_IMAGE013
(5)
then solve
Figure DEST_PATH_IMAGE014
Comprises the following steps:
Figure DEST_PATH_IMAGE015
(6)
further, the error analysis of the training set of the neural network specifically includes:
the structure (number of neurons) and the parameters (center and width of the radial basis function) of the neural network are continuously adjusted using the following formula. When the relative error of each point of flow velocity is within 5%, the model learns the flow velocity distribution rule of the channel, namely the construction of the open channel flow velocity field distribution model is completed.
Figure DEST_PATH_IMAGE016
(7)
Wherein,V rn for each measurement below the water levelThe measured speed of the speed point is measured,V in the velocities calculated for each point of the neural network below the water level,nis the first under the water levelnThe number of the measuring points is measured,Nthe total number of the measuring points under the water level.
Further, the high-precision calculation of the flow of the open channel is realized by a flow velocity-area method:
the fitted section is divided into sections by vertical and relative water depth, as shown in fig. 5. The flow rate of the portion can be obtained by multiplying the average flow velocity of the portion by the area of the portion, and the formula is as follows. The flow of each part is obtained as above, and finally the flow of each part is added to obtain the flow of the whole open channel section, namely the high-precision calculation of the flow of the open channel is realized.
Figure DEST_PATH_IMAGE017
(8)
The invention solves the problem that the unification of high efficiency and low cost is difficult to realize in the water conservancy metering field, realizes the high-precision calculation of the flow of the cross section of the open channel by combining the CFD numerical simulation technology and the neural network model, and has important practical significance for improving the water consumption management of an irrigation area, realizing the high-efficiency utilization of water resources and the like.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (5)

1. An open channel flow calculation method based on water level height is characterized by comprising the following steps:
step 1: measuring channel parameter information by using a radar ultrasonic reflection principle;
step 2: measuring a small amount of point flow velocity information under different water level heights by using a cableway measuring method;
and step 3: establishing a CFD numerical simulation model by using the channel parameters measured in the step 1, and optimizing the CFD numerical simulation model based on the measured data in the step 2 until the error requirement is met;
and 4, step 4: expanding a data set by using the obtained simulation data, training a radial basis function neural network, optimizing a network structure and parameters based on an error result, and minimizing a root mean square error of reconstructed flow velocity distribution and real flow velocity distribution;
and 5: and high-precision calculation of channel flow is realized by utilizing the obtained flow velocity field distribution through a flow velocity-area method.
2. The method for calculating the flow of the open channel based on the water level height according to claim 1, wherein: and the construction of the flow velocity distribution comprises the establishment of a CFD numerical simulation model and the establishment of a radial basis function neural network model.
3. The method for calculating the flow of the open channel based on the water level height according to claim 2, wherein: the training set of the radial basis function neural network comprises measured data and simulation data generated by CFD.
4. The method for calculating the flow rate of the open channel based on the water level height according to claim 3, wherein: the training set of the neural network takes the height of the water level and the horizontal and vertical coordinates of a certain point to be solved under the water level as input characteristics, and takes the corresponding flow velocity value to be solved as output characteristics.
5. The method for calculating the flow of the open channel based on the water level height according to claim 1, wherein: the channel parameter information comprises channel shape, channel bottom width, water level height, wall surface roughness and gradient.
CN202210560775.9A 2022-05-23 2022-05-23 Open channel flow calculation method based on water level height Pending CN114896909A (en)

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CN116384289A (en) * 2023-06-05 2023-07-04 江西省水利科学院(江西省大坝安全管理中心、江西省水资源管理中心) Method for predicting pier block type fishway flow through computational fluid dynamics
CN116384289B (en) * 2023-06-05 2023-08-08 江西省水利科学院(江西省大坝安全管理中心、江西省水资源管理中心) Method for predicting pier block type fishway flow through computational fluid dynamics

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