CN116629086A - Method and system for calculating sediment start of vegetation areas of compound river - Google Patents
Method and system for calculating sediment start of vegetation areas of compound river Download PDFInfo
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Abstract
The invention provides a method and a system for calculating sediment starting in a vegetation area of a compound river, wherein the method comprises the steps of obtaining physical characteristics of sediment particles, constructing an experimental water tank model and configuring a sensor; performing simulation experiments according to experimental parameters, and acquiring flow state parameters of water flow under each working condition from a constructed experiment water tank model based on a preconfigured sensor; constructing a sediment starting model, and simulating a sediment starting process by taking physical characteristics of sediment particles and flow state parameters of water flow as simulated input conditions to obtain sediment starting process and characteristic data; and comparing the simulation result of the sediment starting model with the actually measured data, evaluating the practicability and the accuracy of the sediment starting model, and outputting the sediment starting model if the conditions are met so as to calculate the sediment starting process of the vegetation area of the compound river. The invention improves the simulation precision and the data processing speed, and describes the interaction relation between the form of the aquatic plant and the river channel more accurately.
Description
Technical Field
The invention relates to a water conservancy simulation technology, in particular to a method and a system for calculating sediment start of a vegetation area of a compound river channel.
Background
Aquatic plants are important components of water ecosystems such as rivers, lakes, estuaries and coastal zones, which have a significant impact on the movement of water and sediment. On the one hand, the aquatic plants can increase the resistance of water flow, reduce the flow velocity of the water flow and weaken the impact of waves on the beach, thereby protecting the river bank and the coastline and maintaining the stability and diversity of the water area. On the other hand, aquatic plants can change the turbulence characteristics of the water body, and influence the sediment transportation and pollutant migration processes. The aquatic plants can promote sediment deposition and consolidation, and improve the organic matter content and nutrient salt concentration of the sediment, thereby improving water quality and the sediment. Meanwhile, the aquatic plants also influence the re-suspension and the transportation of sediment, and reduce the transparency and the illumination intensity of the water body, thereby influencing photosynthesis and primary productivity. The influence characteristics of aquatic plants on water flow and sediment movement are researched, and the method has important significance for understanding and protecting the water area ecological system. However, due to the variety of aquatic plants, their variety of species, morphology, density, rigidity, distribution, etc., and the complexity of water and sediment movement, there are still many problems and difficulties in this research field.
For example, the effect of aquatic vegetation geometry and stiffness on water flow structure and sediment transport is not clear, and most current studies use rigid cylinders to simulate aquatic vegetation, and cannot represent the complex features of natural true vegetation. The interaction mechanism of aquatic vegetation and river form evolution is not clear, and how to effectively couple and simulate river form adjustment and vegetation evolution growth on a space-time scale is still to be further studied. The distinguishing condition and the prediction model of the sediment starting are not perfect, and the existing sediment starting model is empirical or semi-empirical and is subject to the difficulty of simultaneously and accurately measuring the flow velocity and the sediment concentration in a vegetation area.
In a word, the sediment starting calculation simulation of the vegetation area of the compound river channel has a plurality of problems, and further research and innovation are needed.
Disclosure of Invention
The invention aims to: a method for calculating sediment start of vegetation areas of a duplex river is constructed to solve one of the problems in the prior art. According to another aspect of the present invention, there is provided an implementation system to materialize the above method.
The technical scheme is as follows: the utility model provides a method for calculating sediment start of vegetation areas of a duplex river, which comprises the following steps:
S1, acquiring physical characteristics of sediment particles, constructing an experimental water tank model, and configuring a sensor;
s2, performing a simulation experiment according to experimental parameters, and acquiring flow state parameters of water flow under each working condition from a constructed experiment water tank model based on a preconfigured sensor;
s3, constructing a sediment starting model, and simulating a sediment starting process by taking physical characteristics of sediment particles and flow state parameters of water flow as simulated input conditions to obtain sediment starting process and characteristic data;
and S4, comparing the simulation result of the sediment starting model with the actual measurement data, evaluating the practicability and the accuracy of the sediment starting model, and outputting the sediment starting model if the conditions are met, so as to calculate the sediment starting process of the vegetation area of the compound river.
According to one aspect of the present application, the step S1 is further:
step S11, selecting a preset type of sediment particles according to a research target, collecting the sediment particles, and measuring or calling the physical characteristics of the sediment particles, wherein the sediment particles at least comprise part of marked particles;
s12, constructing an experimental water tank model, wherein the experimental water tank model comprises at least one section of water tank body made of transparent glass; the bottom of the water tank body is provided with at least two rows of aquatic plant model fixing parts extending along the length direction of the water tank body; the aquatic plant model is detachably arranged on the aquatic plant model fixing part;
And S13, installing a water level measuring needle, a Doppler velocimeter and a camera device in the experimental water tank model, wherein the camera device is installed along the length and the height direction of the water tank, and the shooting range of the camera device at least covers the area where part of the aquatic plant model is located.
According to one aspect of the application, in the step S12, the aquatic plant model includes a hollow transparent rigid organic glass rod, a hollow transparent flexible organic polymer rod, an aquatic plant branch and leaf simulation model which can be communicated and fixed at the free ends of the rigid organic glass rod and the flexible organic polymer rod, and an indicator system which is communicated with the rigid organic glass rod, the flexible organic polymer rod and the aquatic plant branch and leaf simulation model.
According to one aspect of the present application, the step S2 is further:
s21, experimental parameters are called and configured in an experimental water tank model and each sensor, tap water is injected into an aquatic plant model, simulation is carried out, a first flow rate and first image data in a preset time are obtained, and a first image data set is formed;
s22, injecting an indicator into the aquatic plant model, simulating again, and obtaining a second flow rate and second image data in a preset time to form a second image data set;
S23, calling a first image data set, and preprocessing to obtain a surface image of a sediment area; acquiring a motion track and a speed field of the sediment particles based on the surface image of the sediment region, deriving the speed of the sediment particles by adopting a difference method, acquiring the acceleration of the sediment particles, and forming acceleration field data;
s24, a second image data set is called, and preprocessing is carried out to obtain an aquatic plant branch and leaf simulation model and a bending angle of the flexible aquatic plant;
and S25, calculating the resistance coefficient of the aquatic plant model, the turbulence intensity and the Reynolds stress based on the measured data including the water body density and the water flow speed to form a flow state parameter set of the water flow under each working condition.
According to one aspect of the present application, the step S3 is further:
s31, constructing a sediment starting model describing the relation between the sediment starting critical shear force and the aquatic plant inundation degree, the average flow velocity and the turbulence intensity; the sediment starting model is a neural network model and comprises an input layer, a hidden layer and an output layer;
s32, inputting flow state parameters including the inundation degree, the average flow velocity and the turbulence intensity of the aquatic plant model and the physical characteristics of sediment particles into the layer, wherein the hidden layer adopts an S-shaped transfer function as an activation function, and the output layer comprises a node of a sediment starting critical shearing force; adopting a minimum mean square error as a performance function; using a Levenberg-Marquardt algorithm as a training algorithm; optimizing parameters of a silt starting model by adopting a genetic algorithm;
Step S33, simulating a sediment starting process after training is completed; and obtaining the sediment starting process and characteristic data.
According to one aspect of the present application, the step S4 further includes:
step S41, generating a sediment starting simulation image based on the sediment starting process and the characteristic data and comparing the sediment starting simulation image with the actual measurement image;
step S42, using the root mean square error, the average absolute percentage error and the determination coefficient as evaluation indexes; then evaluating the accuracy of the sediment starting model through an empirical formula of the sediment starting process;
and step S43, outputting a sediment starting model if the conditions are met.
According to an aspect of the present application, the step S32 further includes:
step S32a, simulating based on dynamic water flow, and acquiring a second image; acquiring water flow velocity field data based on the second image, and calculating velocity variation to form a velocity variation set;
step S32b, taking the speed variation as a node in the water flow speed network, calculating the similarity between the nodes based on the Euclidean distance, and establishing an edge of the water flow speed network according to the similarity;
step S32c, calculating statistics of the water flow speed network, wherein the statistics comprise degree distribution, clustering coefficients, average path length and betweenness;
And step S32d, analyzing the association relation between the statistic of the water flow speed network and the physical characteristics of the water flow speed field, and optimizing the silt starting process based on the association relation.
According to an aspect of the present application, the step S32a further includes:
acquiring water flow speed field data;
smoothing the horizontal velocity of the turbulent boundary layer to convert the water velocity data into at least two subsequences;
for each sub-sequence, the speed variation between two adjacent points in the horizontal direction is calculated and used as a node.
According to one aspect of the application, step S32a further comprises: judging whether turbulence exists according to the second image, if so, judging whether turbulence exists; radial and circumferential water flow velocity data of the jet flow in the turbulence process are acquired based on the second image, filtered and smoothed, and divided into at least two subsequences, and for each subsequence, velocity increment between two adjacent points in the radial and axial directions is calculated and used as a node.
According to another aspect of the present application, there is provided a multiple riverway vegetation area silt start-up calculation system, comprising:
at least one processor; and
a memory communicatively coupled to at least one of the processors; wherein,,
The memory stores instructions executable by the processor for execution by the processor to implement the multiple riverway vegetation area silt start-up calculation method of any one of the above technical schemes.
The beneficial effects are that: 1. by using the constructed simulation model, the simulation data can be acquired more accurately, and then the relation between the critical shear force for starting the sediment and the submergence degree, the average flow velocity and the turbulence intensity of the aquatic plants is analyzed and extracted through the neural network model, so that the limitations of the experience or semi-experience of the existing sediment starting model are overcome, and the accuracy and the applicability of the sediment starting model are improved. The parameters of the silt starting model are optimized by utilizing a genetic algorithm, so that subjectivity and randomness of parameter selection are overcome, and stability and reliability of the silt starting model are improved.
2. The physical characteristics of the water flow velocity field are analyzed through the water flow velocity network, so that the difficulty of simultaneous accurate measurement of the flow velocity and the sediment concentration in a vegetation area is overcome, and the effectiveness and the usability of the data of the water flow velocity field are improved. The water flow movement and sediment transport process in the experiment water tank is simulated, and compared and analyzed with experiment data, so that the cost and time limit of a physical model experiment are overcome, and the visualization and operability of the water flow movement and sediment transport process are improved;
3. Through designing a set of novel experiment basin model, solved the problem that current sensor influences the flow field, when having improved the precision, data acquisition is also more convenient.
Drawings
Fig. 1 is a flow chart of the present invention.
Fig. 2 is a flowchart of step S1 of the present invention.
Fig. 3 is a flow chart of step S2 of the present invention.
Fig. 4 is a flowchart of step S3 of the present invention.
Detailed Description
At present, scholars at home and abroad mainly adopt methods such as theoretical analysis, experimental observation, numerical simulation and the like to discuss the influence mechanism and rule of aquatic plants on water flow and sediment movement from different angles and levels. The calculation is mainly performed by adopting an empirical formula method, including a seneckaloff formula, a Dou Guoren formula, a Zhang Rui formula, a Tang Cunben formula, a Sha Yuqing formula and a Zhang Gongwu formula. However, these empirical formulas have some problems pointed out in the background art, and in order to improve the accuracy of the simulation calculation, new experimental apparatuses and simulation methods are required. For this, the applicant provides the following technical solution.
It should be noted that, since the solution is relatively complex, in order to highlight the innovation and improvement, the contents that are known in the prior art or those skilled in the art are omitted.
As shown in FIG. 1, a method for calculating the sediment start of a vegetation area of a compound river is provided, which comprises the following steps:
s1, acquiring physical characteristics of sediment particles, constructing an experimental water tank model, and configuring a sensor;
s2, performing a simulation experiment according to experimental parameters, and acquiring flow state parameters of water flow under each working condition from a constructed experiment water tank model based on a preconfigured sensor;
s3, constructing a sediment starting model, and simulating a sediment starting process by taking physical characteristics of sediment particles and flow state parameters of water flow as simulated input conditions to obtain sediment starting process and characteristic data;
and S4, comparing the simulation result of the sediment starting model with the actual measurement data, evaluating the practicability and the accuracy of the sediment starting model, and outputting the sediment starting model if the conditions are met, so as to calculate the sediment starting process of the vegetation area of the compound river.
According to one aspect of the present application, the step S1 is further:
step S11, selecting sediment particles of a preset type according to a research target, collecting the sediment particles, and measuring or retrieving physical characteristics of the sediment particles, wherein the sediment particles at least comprise part of marked particles.
In one embodiment, it may be: according to the research target, selecting proper parameters such as the particle size, density, adhesive force and the like of the sediment, and collecting the sediment particles from a natural river or artificial preparation; measuring or retrieving physical characteristics of the sediment particles, including particle size distribution, density, binding force and the like, by using instruments such as a sieving instrument, a gravimeter, a viscosimeter and the like, and recording the physical characteristics in a table; and marking part of the sediment particles by using a dye, a fluorescent agent or a magnetic material and the like so as to identify and track the movement track and the speed field of the sediment particles in subsequent image processing.
S12, constructing an experimental water tank model, wherein the experimental water tank model comprises at least one section of water tank body made of transparent glass; the bottom of the water tank body is provided with at least two rows of aquatic plant model fixing parts extending along the length direction of the water tank body; the aquatic plant model is detachably mounted on the aquatic plant model fixing part.
According to the research target, selecting proper water tank size, shape and material, and manufacturing or purchasing a water tank body made of transparent glass; the length, the width and the height of the water tank body can meet the experimental requirements and ensure the similarity of water flow and sediment movement; at least two rows of aquatic plant model fixing parts extending along the length direction of the water tank body are arranged at the bottom of the water tank body; the aquatic plant model fixing part can be in the forms of nails, screws, buckles and the like, and can firmly fix the aquatic plant model; according to the research target, selecting proper parameters such as type, form, density, rigidity and the like, and manufacturing or purchasing an aquatic plant model; the aquatic plant model can be in the forms of rigid cylinders, flexible cylinders, simulated branches and leaves and the like, and can simulate the complex characteristics of natural and real vegetation; detachably mounting the aquatic plant model on the aquatic plant model fixing part; according to different experimental parameters, the spacing and arrangement modes of the aquatic plant model in the water flow direction and the vertical direction are adjusted to form vegetation areas with different densities and distributions.
And S13, installing a water level measuring needle, a Doppler velocimeter and a camera device in the experimental water tank model, wherein the camera device is installed along the length and the height direction of the water tank, and the shooting range of the camera device at least covers the area where part of the aquatic plant model is located.
Installing water level measuring pins at the inlet and outlet of the experimental water tank model for measuring and controlling water level change in the water tank; doppler velocimeters are detachably arranged at different positions of the experimental water tank model and are used for measuring parameters such as flow speed, pressure, turbulence intensity and the like of water flow; the method comprises the steps that camera devices are arranged in two directions (namely, the water flow direction and the vertical direction) of an experimental water tank model and are used for shooting image data of water flow and sediment movement; the shooting range of the shooting device at least covers the area where part of the aquatic plant model is located so as to observe the starting process and characteristics of sediment; the shooting frequency of the shooting device can meet the experimental requirement, and the definition and the continuity of image data are ensured.
According to one aspect of the application, in the step S12, the aquatic plant model includes a hollow transparent rigid organic glass rod, a hollow transparent flexible organic polymer rod, an aquatic plant branch and leaf simulation model which can be communicated and fixed at the free ends of the rigid organic glass rod and the flexible organic polymer rod, and an indicator system which is communicated with the rigid organic glass rod, the flexible organic polymer rod and the aquatic plant branch and leaf simulation model. The indicator system is used for injecting indicators with different colors into the simulation model, so that the images can be better resolved when the images are acquired, and the image processing is facilitated. In a specific embodiment, the organic glass, the organic polymer rod and the like can be made of materials such as PMAA and PE, and it should be noted that the flexible aquatic plant simulation model is different in materials when simulating different plants, some plants have higher flexibility, can be made of plastics or rubber, and some plants have lower flexibility and can be made of flexible organic glass.
According to one aspect of the present application, the step S2 is further:
and S21, adjusting experimental parameters, configuring the experimental parameters in an experimental water tank model and each sensor, injecting tap water into the aquatic plant model, simulating, acquiring a first flow rate and first image data in a preset time, and forming a first image data set.
In one embodiment, according to the research target, proper experimental parameters including inlet flow, outlet water level, water depth, water flow direction, water flow turbulence, sediment particle size, sediment density and the like are selected and configured in an experimental water tank model and each sensor; injecting tap water into the aquatic plant model to fill the whole water tank, and keeping a certain flow rate and pressure; starting simulation, and simultaneously starting each sensor and each camera to acquire a first flow rate and first image data in a preset time; the first flow rate data comprises parameters such as flow rate, pressure, turbulence intensity and the like at each position; the first image data includes image data in two directions; the first streaming data and the first image data are stored in a computer and a first set of image data is formed.
And S22, injecting an indicator into the aquatic plant model, simulating again, and acquiring a second flow rate and second image data within a preset time to form a second image data set.
In one embodiment, the experimental parameters are kept unchanged, and the indicator is injected into the aquatic plant model and mixed with tap water; the indicator can be dye, fluorescent agent, colored suspended particles or suspended magnetic materials, and the like, and can clearly display the track and the characteristics of the water flow movement in the image. The indicator in the sediment can be sediment particles or indicator particles with different colors from the sediment background, such as red, black or reflective sediment particles; performing simulation again, and starting each sensor and the camera device at the same time to acquire a second flow rate and second image data in a preset time; the second flow rate data comprises parameters such as flow rate, pressure, turbulence intensity and the like at each position; the second image data comprises image data in two directions and is capable of displaying the distribution and variation of the indicator in the water flow; the second streaming data and the second image data are stored in a computer and a second set of image data is formed.
S23, calling a first image data set, and preprocessing to obtain a surface image of a sediment area; and based on the surface image of the sediment area, acquiring the motion track and the speed field of sediment particles, deriving the speed of the sediment particles by adopting a difference method, acquiring the acceleration of the sediment particles, and forming acceleration field data.
In one embodiment, the first image data set is called up and pre-processed, including denoising, enhancement, segmentation, etc., to improve image quality and analyzability; extracting surface images of the silt areas from the image data in two directions by utilizing an image processing technology, and converting the surface images into gray level or binarization images; the sediment area is the bottom area covered with sediment particles; acquiring a motion track and a velocity field of the sediment particles based on the surface image of the sediment region by using a marked particle method or an optical flow method; the motion track of the sediment particles refers to the position change of the sediment particles in the image, and the speed field of the sediment particles refers to the speed distribution of the sediment particles in the image; the speed of the sediment particles is derived by utilizing a difference method, and the acceleration of the sediment particles is obtained; acceleration of the sediment particles refers to acceleration distribution of the sediment particles in the image; and storing the motion track, the speed field and the acceleration data of the sediment particles in a computer, and forming acceleration field data.
And S24, a second image data set is called, and preprocessing is carried out to obtain the aquatic plant branch and leaf simulation model and the bending angle of the flexible aquatic plant.
In one embodiment, the second image data set is retrieved and pre-processed, including denoising, enhancement, segmentation, etc., to improve image quality and analyzability; extracting outlines of the rigid aquatic plants and the flexible aquatic plants from the image data in two directions by utilizing an image processing technology, and converting the outlines into gray level or binary images; rigid aquatic plants (branch and leaf simulation models at the upper end) and flexible aquatic plants refer to aquatic plant models with different degrees of bending deformation in water flow; obtaining bending angles of the rigid aquatic plant upper branch and leaf simulation model and the flexible aquatic plant based on the outline image of the rigid aquatic plant (the upper branch and leaf simulation model) and the flexible aquatic plant by utilizing methods such as edge detection or curve fitting and the like; the bending angle refers to an included angle between the aquatic plant model and the vertical direction in water flow; the rigid bend angle data is stored in a computer.
And S25, calculating the resistance coefficient of the aquatic plant model, the turbulence intensity and the Reynolds stress based on the measured data including the water body density and the water flow speed to form a flow state parameter set of the water flow under each working condition.
In this embodiment, various information of plants and water flows is obtained through photographed image data by simulating aquatic plants (such as flexible aquatic plants and aquatic plant branch and leaf simulation models which are more in line with actual conditions), so that after optimization is completed, relevant information can be directly obtained through image data without sensors such as a flow meter, the influence of the sensors on a flow field is reduced, and the efficiency and accuracy are higher. Meanwhile, according to the embodiment, the simulation degree is gradually increased through the rigid aquatic plant model, the flexible aquatic plant model and the rigid aquatic plant model and the flexible aquatic plant model added with the branch and leaf simulation model, so that the simulation parameters are subjected to step optimization, the actual physical condition is gradually approximated, and the design parameters of the model are continuously optimized. Meanwhile, the influence of the branches and leaves of the aquatic plants on the bottom water flow and the sediment can be analyzed, so that more accurate analysis data are provided for the analysis of the sediment starting process. After the indicator is added into the sediment, the shot sediment texture is more complex, so that the image analysis and the processing are convenient, the dynamic change process of the sediment is acquired, and the starting process of the sediment can be studied more finely. The specific image analysis method can be as follows:
Sequentially acquiring image data of a sediment area, and extracting a sediment starting edge;
searching and extracting a silt starting edge from the image data of each subsequent frame;
and establishing a sediment starting edge change chart based on the position of the sediment starting edge.
The relevant image may also be processed by pixel tracking or other methods. Compared with the existing analysis method, the method can directly acquire details and detailed processes of silt starting under different simulation parameters. Because the image texture is clearer, the resolution is higher and more accurate when the speed field and the acceleration field of the sediment particle motion are extracted.
In one embodiment, parameters such as average flow rate, maximum flow rate, minimum flow rate, average shear force, maximum shear force, minimum shear force, etc. at each location are calculated based on the first flow rate data or the second flow rate data and recorded in a table; based on the first flow rate data or the second flow rate data, calculating parameters such as turbulence intensity, reynolds stress and the like at each position, and recording the parameters in a table; turbulence intensity refers to the ratio of the difference between the average flow rate and the instantaneous flow rate to the average flow rate, and reynolds stress refers to the statistical average of the product of the difference between the instantaneous flow rate and the average flow rate; calculating parameters such as the submergence degree of the aquatic plant model at each position based on the bending angle data of the rigid aquatic plant and the flexible aquatic plant obtained in the second image data set, and recording the parameters in a table; the submergence degree refers to the ratio of the water depth to the height of the aquatic plant model; based on measurement data including water density and water flow speed, calculating parameters such as resistance coefficients of aquatic plant models at all positions by using a mol-coulman formula or other suitable formulas, and recording the parameters in a table; the resistance coefficient refers to the ratio of the resistance of water flow to the aquatic plant model to the product of the water flow pressure and the projected area of the aquatic plant model; and (3) summarizing parameters such as average flow velocity, maximum flow velocity, minimum flow velocity, average shearing force, maximum shearing force, minimum shearing force, turbulence intensity, reynolds stress, aquatic plant model inundation degree, aquatic plant model resistance coefficient and the like at each position to form a flow state parameter set of water flow under each working condition.
In this embodiment, because the hollow transparent aquatic plant simulation model is adopted, when shooting the silt starting process, the relevant pixel of aquatic plant model can be got rid of fast, the image of silt that has the indicator is more clear, goes the overall process that silt started after more easily, and is simpler to image processing, and efficiency is higher. And after the indicator is poured therein, the plants, especially the flexible plants, can be more easily extracted from the curved inclination and the movement state of the branches and leaves with the water flow. For image processing, the efficiency is higher, the water flow change condition around the branches and leaves can be accurately captured, in other words, the influence of the branches and leaves on the water flow can be analyzed more clearly.
According to one aspect of the present application, the step S3 is further:
s31, constructing a sediment starting model describing the relation between the sediment starting critical shear force and the aquatic plant inundation degree, the average flow velocity and the turbulence intensity; the sediment starting model is a neural network model and comprises an input layer, a hidden layer and an output layer.
In one embodiment, a three-layer feedforward neural network model is constructed using a neural network toolbox or other suitable similar software, including an input layer, a hidden layer, and an output layer; setting fluid state parameters including the inundation degree, average flow velocity and turbulent flow intensity of the aquatic plant model and the physical characteristics of sediment particles, N nodes in total; setting an output layer to comprise a node of a silt starting critical shearing force; setting a hidden layer to comprise m nodes, wherein m is a self-defined or optimized parameter; setting the hidden layer to adopt an S-shaped transfer function as an activation function, namely f (x) =1/(1+exp (-x)); setting the output layer to adopt a linear transfer function as an activation function, namely f (x) =x; setting a weight matrix and a bias vector of the neural network model as W 1 、W 2 、b 1 And b 2 The method comprises the steps of carrying out a first treatment on the surface of the Setting the input vector of the neural network model as X and the output vector as Y; the neural network model can be expressed as y=w 2 f(W 1 X+b 1 )+b 2 。
S32, inputting flow state parameters including the inundation degree, the average flow velocity and the turbulence intensity of the aquatic plant model and the physical characteristics of sediment particles into the layer, wherein the hidden layer adopts an S-shaped transfer function as an activation function, and the output layer comprises a node of a sediment starting critical shearing force; adopting a minimum mean square error as a performance function; using a Levenberg-Marquardt algorithm as a training algorithm; and optimizing parameters of the silt starting model by adopting a genetic algorithm.
In one embodiment, a set of training data sets and a set of test data sets are prepared based on experimental or literature data; both the training data set and the test data set include an input vector X and an output vector Y; inputting the training data set into a neural network model and setting a minimum mean square error as a performance function, i.e., e=0.5 (Y-Y') 2 Wherein Y' is the predicted output of the neural network model; training the neural network model by using a Levenberg-Marquardt algorithm as a training algorithm, namely iteratively updating a weight matrix and a bias vector to enable a performance function to reach a minimum value; in the training process, verifying the neural network model by using a test data set, and setting certain stopping conditions such as maximum iteration times, minimum performance targets, maximum verification failure times and the like; after training, optimizing parameters of the neural network model by using a genetic algorithm, namely searching the optimal hidden layer node number m and weight matrix W through operations such as selection, crossing, variation and the like 1 、W 2 And offset vector b 1 、b 2 To make the performance function reachTo a minimum value.
Step S33, simulating a sediment starting process after training is completed; and obtaining the sediment starting process and characteristic data.
In one embodiment, according to the research objective, a proper input vector X is selected, and the flow state parameters including the inundation degree, the average flow velocity and the turbulence intensity of the aquatic plant model and the physical characteristics of sediment particles are included; inputting the input vector X into a neural network model, and obtaining an output vector Y, namely, a silt starting critical shearing force; judging whether the silt particles are started according to the silt starting critical shear force, and recording characteristic data such as starting time, starting position, starting speed and the like; and storing the silt starting process and characteristic data in a computer, and analyzing and evaluating the silt starting process and characteristic data.
In one embodiment, the following procedure may also be employed:
reading experimental data, acquiring image data of two directions at each moment, extracting regional image edges of a rigid vegetation plaque area from the image data, and acquiring an evolution diagram of the regional image edges along with time; acquiring data collected by a re-suspension bed sand collecting device; constructing a change relation between the re-suspended bed sand data of each aquatic plant model and the edge of the regional image, forming an association relation between the re-suspended bed sand data of each part in the experimental water tank and the edge of the regional image, and obtaining sediment settlement and bottom sand re-suspension change trend of each plaque area based on the association relation; and comparing and analyzing the sediment settling and sediment re-suspension change trend under different water flow and sediment conditions, and discussing the influence of the different water flow and sediment conditions on the sediment starting characteristic.
According to parameters such as geometric dimensions, a rigid vegetation model, inlet flow, outlet water level and the like in an experimental water tank, establishing a numerical simulation grid, and setting appropriate boundary conditions, initial conditions, a turbulence model, a sediment transport model and the like; running numerical simulation software, simulating the water flow movement and sediment transport process in the experimental water tank, and outputting parameters such as flow rate, pressure, sediment concentration and the like at each position at each moment; converting the numerical simulation result into an area image edge evolution diagram and re-suspension bed sand data with the same format as in the literature by using an image processing technology; and comparing and analyzing the numerical simulation result with test results in the literature, and evaluating the accuracy and applicability of the numerical simulation method.
According to one aspect of the present application, the step S4 further includes:
and S41, generating a sediment starting simulation image based on the sediment starting process and the characteristic data and comparing the sediment starting simulation image with the actual measurement image.
In one embodiment, an image generation technique is utilized to generate a simulated image of the silt initiation based on the silt initiation process and the feature data; the sediment-started simulation image refers to image data simulating water flow and sediment movement in two directions; preprocessing the sediment-started analog image by using an image processing technology, including denoising, enhancing, segmentation and other operations, so as to improve the image quality and the analyzability; comparing the simulated image started by the sediment with the actually measured image by utilizing an image matching technology, and calculating the similarity or difference of the simulated image; the actual measurement image refers to image data of water flow and sediment movement shot in an experimental water tank model; and storing the simulated image and the actually measured image of the sediment start and the similarity or difference data thereof in a computer, and analyzing and evaluating the simulated image and the actually measured image.
Step S42, using the root mean square error, the average absolute percentage error and the determination coefficient as evaluation indexes; then evaluating the accuracy of the sediment starting model through an empirical formula of the sediment starting process;
The root mean square error is the square root of the sum of squares of the differences between the predicted and measured outputs, the average absolute percentage error is the average of the ratios of the absolute values of the differences between the predicted and measured outputs to the measured output, and the decision coefficient is the square of the correlation between the predicted and measured outputs; the empirical formula of the sediment starting process refers to a formula which is obtained according to a physical principle or statistical analysis and describes the relation between the critical shear force of sediment starting and the submergence, average flow velocity and turbulence intensity of aquatic plants.
And step S43, outputting a sediment starting model if the conditions are met. According to the research target, setting certain conditions such as minimum root mean square error, maximum decision coefficient, minimum average absolute percentage error and the like, and judging whether the neural network model accords with the conditions; if the conditions are met, outputting a neural network model, and applying the neural network model to water area ecological systems under other types or working conditions or to actual engineering projects; if the condition is not met, returning, and reconstructing or adjusting the neural network model until the condition is met.
According to an aspect of the present application, the step S32 further includes:
step S32a, simulating based on dynamic water flow, and acquiring a second image; and acquiring water flow velocity field data based on the second image, and calculating velocity variation to form a velocity variation set.
In one embodiment, different water flow conditions such as flow rate, water level, water depth, water flow direction, water flow turbulence and the like are set in the water tank, and water flow movement and sediment transport process are observed; acquiring a second image by using the image pickup device, namely, shooting image data of water flow and sediment movement in two directions, and storing the second image in a computer; preprocessing the second image by using an image processing technology, including denoising, enhancing, segmentation and other operations, so as to improve the image quality and the analyzability; acquiring water flow velocity field data based on the second image by using an optical flow method or other suitable methods, and calculating a velocity variation; the water flow velocity field data refers to the water flow velocity distribution at each location; the speed variation refers to the speed difference between two adjacent points; the water flow velocity field data and the velocity variation data are stored in a computer, and a velocity variation set is formed.
Step S32b, taking the speed variation as nodes in the water flow speed network, calculating the similarity between the nodes based on the Euclidean distance, and establishing the edges of the water flow speed network according to the similarity.
In one embodiment, an undirected and unauthorized water velocity network is constructed using a complex network toolbox or other similar software module; the water flow speed network refers to a complex network consisting of nodes and edges and is used for describing the structural characteristics of a water flow speed field; taking the speed variation as a node in the water flow speed network; i.e. the speed variation at each location is taken as a node and is given a unique identifier; calculating the similarity between nodes based on the Euclidean distance, and establishing the edge of the water flow speed network according to the similarity; namely, calculating the Euclidean distance between every two nodes, namely, the absolute value of the difference between the speed variation amounts corresponding to the two nodes; if the Euclidean distance is smaller than or equal to a given threshold value, an edge is established between two nodes; otherwise, no edges are established.
Step S32c, calculating statistics of the water flow speed network, wherein the statistics comprise degree distribution, clustering coefficients, average path length and medium number.
In one embodiment, a degree distribution of the water velocity network is calculated; counting the number of neighbor nodes of each node, and classifying the number of neighbor nodes into different intervals to obtain the proportion of the number of nodes in each interval to the total number of nodes; calculating a clustering coefficient of a water flow speed network; counting the proportion of edges between each node and the neighboring nodes, and calculating the average value; calculating the average path length of the water flow speed network; counting the number of edges passing through the shortest path between every two nodes, and calculating the average value; calculating the medium number of the water flow speed network; counting the times of each node on all shortest paths, and calculating the average value; and storing statistic data of the water flow speed network in a computer, and analyzing and evaluating the statistic data.
And step S32d, analyzing the association relation between the statistic of the water flow speed network and the physical characteristics of the water flow speed field, and optimizing the silt starting process based on the association relation.
In one embodiment, the correlation between the statistics of the water flow velocity network and the physical characteristics of the water flow velocity field is analyzed using correlation analysis or other suitable methods; the physical characteristics of the water flow velocity field comprise parameters such as average flow velocity, maximum flow velocity, minimum flow velocity, average shearing force, maximum shearing force, minimum shearing force, turbulence intensity, reynolds stress and the like; based on the association relation, determining main factors influencing the silt starting process and optimizing the main factors; the optimization method can be to adjust the water flow condition, change the physical characteristics of silt particles, increase or decrease the aquatic plant model, etc.; repeating the steps S32a to S32c, simulating and analyzing the optimized silt starting process, comparing the results before and after optimization, and evaluating the optimization effect.
According to an aspect of the present application, the step S32a further includes:
1. acquiring water flow speed field data; acquiring water flow velocity field data through a second image or a velocimeter; the water flow velocity field data refers to the water flow velocity distribution at each location.
2. Smoothing the horizontal velocity of the turbulent boundary layer to convert the water velocity data into at least two subsequences; the turbulent boundary layer refers to a water flow layer near the bottom area, and the horizontal speed of the turbulent boundary layer is changed under the influence of sediment particles and an aquatic plant model; the smoothing method may be a moving average method, an exponential smoothing method, a wavelet transform method, or the like.
3. For each sub-sequence, the speed variation between two adjacent points in the horizontal direction is calculated and used as a node. The difference between the water velocities at each two adjacent locations is calculated and used as a node and is given a unique identifier.
According to one aspect of the application, step S32a further comprises:
1. judging whether turbulence exists according to the second image, if so, judging whether turbulence exists; turbulence refers to the phenomenon of irregular, unstable, highly mixed flow that occurs in water flow; the method of judgment may be to observe whether features such as vortex, jet, separation, etc. appear in the image.
2. Acquiring radial and circumferential water flow velocity data of jet flow in the turbulence process based on a second image, filtering and smoothing the data, and dividing the data into at least two subsequences, wherein the jet flow refers to a high-speed narrow-width water column formed by local pressure difference in water flow; the method of obtaining may be to calculate the water flow velocity distribution of the jet region in the image using an optical flow method or other suitable method; the filtering smoothing method may be a moving average method, an exponential smoothing method, a wavelet transform method, or the like.
3. For each sub-sequence, the velocity increment between two adjacent points in the radial and axial directions is calculated and used as a node. I.e. the difference between the water velocities at each two adjacent locations is calculated and taken as a node and given a unique identifier.
According to another aspect of the present application, there is provided a multiple riverway vegetation area silt start-up calculation system, comprising:
at least one processor; and
a memory communicatively coupled to at least one of the processors; wherein,,
the memory stores instructions executable by the processor for execution by the processor to implement the multiple riverway vegetation area silt start-up calculation method of any one of the above technical schemes.
The preferred embodiments of the present invention have been described in detail above, but the present invention is not limited to the specific details of the above embodiments, and various equivalent changes can be made to the technical solution of the present invention within the scope of the technical concept of the present invention, and all the equivalent changes belong to the protection scope of the present invention.
Claims (10)
1. The method for calculating the sediment start of the vegetation areas of the duplex river is characterized by comprising the following steps of:
s1, acquiring physical characteristics of sediment particles, constructing an experimental water tank model, and configuring a sensor;
s2, performing a simulation experiment according to experimental parameters, and acquiring flow state parameters of water flow under each working condition from a constructed experiment water tank model based on a preconfigured sensor;
s3, constructing a sediment starting model, and simulating a sediment starting process by taking physical characteristics of sediment particles and flow state parameters of water flow as simulated input conditions to obtain sediment starting process and characteristic data;
and S4, comparing the simulation result of the sediment starting model with the actual measurement data, evaluating the practicability and the accuracy of the sediment starting model, and outputting the sediment starting model if the conditions are met, so as to calculate the sediment starting process of the vegetation area of the compound river.
2. The method for calculating the silt start-up of vegetation areas of a duplex river according to claim 1, wherein the step S1 is further:
step S11, selecting a preset type of sediment particles according to a research target, collecting the sediment particles, and measuring or calling the physical characteristics of the sediment particles, wherein the sediment particles at least comprise part of marked particles;
s12, constructing an experimental water tank model, wherein the experimental water tank model comprises at least one section of water tank body made of transparent glass; the bottom of the water tank body is provided with at least two rows of aquatic plant model fixing parts extending along the length direction of the water tank body; the aquatic plant model is detachably arranged on the aquatic plant model fixing part;
and S13, installing a water level measuring needle, a Doppler velocimeter and a camera device in the experimental water tank model, wherein the camera device is installed along the length and the height direction of the water tank, and the shooting range of the camera device at least covers the area where part of the aquatic plant model is located.
3. The method for calculating silt start-up in vegetation areas of multiple river courses according to claim 2, wherein in the step S12, the aquatic plant model comprises a hollow transparent rigid organic glass rod, a hollow transparent flexible organic polymer rod, an aquatic plant branch and leaf simulation model which can be communicated and fixed at the free ends of the rigid organic glass rod and the flexible organic polymer rod, and an indicator system which is communicated with the rigid organic glass rod, the flexible organic polymer rod and the aquatic plant branch and leaf simulation model.
4. A method for calculating silt start-up in a vegetation area of a multiple river as claimed in claim 2 or 3 wherein said step S2 is further:
s21, experimental parameters are called and configured in an experimental water tank model and each sensor, tap water is injected into an aquatic plant model, simulation is carried out, a first flow rate and first image data in a preset time are obtained, and a first image data set is formed;
s22, injecting an indicator into the aquatic plant model, simulating again, and obtaining a second flow rate and second image data in a preset time to form a second image data set;
s23, calling a first image data set, and preprocessing to obtain a surface image of a sediment area; acquiring a motion track and a speed field of the sediment particles based on the surface image of the sediment region, deriving the speed of the sediment particles by adopting a difference method, acquiring the acceleration of the sediment particles, and forming acceleration field data;
s24, a second image data set is called, and preprocessing is carried out to obtain an aquatic plant branch and leaf simulation model and a bending angle of the flexible aquatic plant;
and S25, calculating the resistance coefficient of the aquatic plant model, the turbulence intensity and the Reynolds stress based on the measured data including the water body density and the water flow speed to form a flow state parameter set of the water flow under each working condition.
5. A method for calculating silt start-up in a vegetation area of a multiple river as claimed in claim 2 or 3 wherein said step S3 is further:
s31, constructing a sediment starting model describing the relation between the sediment starting critical shear force and the aquatic plant inundation degree, the average flow velocity and the turbulence intensity; the sediment starting model is a neural network model and comprises an input layer, a hidden layer and an output layer;
s32, inputting flow state parameters including the inundation degree, the average flow velocity and the turbulence intensity of the aquatic plant model and the physical characteristics of sediment particles into the layer, wherein the hidden layer adopts an S-shaped transfer function as an activation function, and the output layer comprises a node of a sediment starting critical shearing force; adopting a minimum mean square error as a performance function; using a Levenberg-Marquardt algorithm as a training algorithm; optimizing parameters of a silt starting model by adopting a genetic algorithm;
step S33, simulating a sediment starting process after training is completed; and obtaining the sediment starting process and characteristic data.
6. The method for calculating silt start-up in vegetation areas of multiple courses as defined in claim 5, wherein said step S4 further comprises:
step S41, generating a sediment starting simulation image based on the sediment starting process and the characteristic data and comparing the sediment starting simulation image with the actual measurement image;
Step S42, using the root mean square error, the average absolute percentage error and the determination coefficient as evaluation indexes; then evaluating the accuracy of the sediment starting model through an empirical formula of the sediment starting process;
and step S43, outputting a sediment starting model if the conditions are met.
7. The method for calculating silt start-up in vegetation areas of multiple courses as defined in claim 6, wherein said step S32 further comprises:
step S32a, simulating based on dynamic water flow, and acquiring a second image; acquiring water flow velocity field data based on the second image, and calculating velocity variation to form a velocity variation set;
step S32b, taking the speed variation as a node in the water flow speed network, calculating the similarity between the nodes based on the Euclidean distance, and establishing an edge of the water flow speed network according to the similarity;
step S32c, calculating statistics of the water flow speed network, wherein the statistics comprise degree distribution, clustering coefficients, average path length and betweenness;
and step S32d, analyzing the association relation between the statistic of the water flow speed network and the physical characteristics of the water flow speed field, and optimizing the silt starting process based on the association relation.
8. The method for calculating the sediment start of vegetation areas of a duplex river according to claim 7,
The step S32a further includes:
acquiring water flow speed field data;
smoothing the horizontal velocity of the turbulent boundary layer to convert the water velocity data into at least two subsequences;
for each sub-sequence, the speed variation between two adjacent points in the horizontal direction is calculated and used as a node.
9. The compound channel vegetation area silt start-up calculation method of claim 8, wherein step S32a further comprises: judging whether turbulence exists according to the second image, if so, judging whether turbulence exists; radial and circumferential water flow velocity data of the jet flow in the turbulence process are acquired based on the second image, filtered and smoothed, and divided into at least two subsequences, and for each subsequence, velocity increment between two adjacent points in the radial and axial directions is calculated and used as a node.
10. A compound riverway vegetation area silt start-up computing system, comprising:
at least one processor; and
a memory communicatively coupled to at least one of the processors; wherein,,
the memory stores instructions executable by the processor for execution by the processor to implement the multiple riverway vegetation area silt start-up calculation method of any one of claims 1 to 9.
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