CN111046605A - River channel-flood area flood routing different-dimension fusion simulation principle and calculation method based on artificial neural network - Google Patents
River channel-flood area flood routing different-dimension fusion simulation principle and calculation method based on artificial neural network Download PDFInfo
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Abstract
The invention discloses a river channel-flood area flood routing different-dimension fusion simulation principle and a calculation method based on an artificial neural network, wherein river channel water power is calculated according to a two-dimensional water power model; then judging whether the river channel hydraulic power reaches an embankment breach condition or not according to the river channel hydraulic power, if so, calculating the water depth and the flow velocity of the burst position of the flood area at the burst position of the river channel at the T moment by using a trained burst neural network model, then calculating the flood port hydraulic power by using a two-dimensional hydrodynamic model by using the water depth and the flow velocity U of the burst position of the flood area as inflow boundaries, and otherwise, returning to the river channel hydraulic power calculation; and judging whether the maximum calculation time Tmax is reached, if so, outputting a result, otherwise, updating the calculation time to be T + DT, and returning to the river hydraulic calculation. The method realizes river channel-flood area flood routing different-dimensional fusion simulation by utilizing the powerful function approximation capability of the artificial neural network, and effectively improves the speed and the precision of the dam-break flood routing simulation through the calculation coupled with the hydrodynamic model.
Description
Technical Field
The invention relates to the technical field of river channel-flood area flood routing simulation, in particular to a river channel-flood area flood routing different-dimension fusion simulation principle and a calculation method based on an artificial neural network.
Background
The river channel-flood area flood routing simulation based on the hydrodynamic theory is one of important non-engineering measures for emergency disaster reduction, and can provide technical support for flood dynamic risk display and flood early warning and forecasting. The current numerical model of the breakwater can be roughly divided into a one-dimensional coupling model method, a two-dimensional coupling model method and a full two-dimensional model method.
In the first and second dimension coupling model methods, one dimension saint wien equation is adopted to describe the river water flow movement, the flood area water flow movement is described by plane two dimension shallow water equation, the river and the flood area are connected through the break mouth, the first and second dimension coupling model can highlight the description of the water flow connection and the water exchange between the river and the flood area, and the coupling method based on the water level flow relation and the water conservation method based on the weir flow formula are generally adopted. The method has the advantages that when the flow process line at the break port is solved, factors such as local collapse, gradual collapse, the influence of the break port position and the like are convenient to consider, the discontinuity problem of the break bank wave can not be considered during calculation, and therefore the requirement on the calculation format is low. The defect is that the calculated flow precision at the break dam position is lower.
The river channel and the general area are considered as an integral model by the aid of the full-two-dimensional model method, and the method has the advantages that calculation accuracy is high, but a calculation format has the capability of capturing shock waves, and numerical calculation needs more resources and consumes more time, so that emergency disaster relief decisions are not facilitated.
The artificial neural network simulates the neuron activity by a mathematical model, is an information processing system established based on the simulation of the brain neural network structure and function, and has the capabilities of self-learning, associative storage and high-speed searching of an optimal solution, so that the establishment of a rapid and accurate river channel-flood area flood evolution different-dimension fusion calculation model based on the artificial neural network technology has important significance.
Disclosure of Invention
The invention aims to provide a river channel-flood area flood evolution multidimensional fusion simulation principle and a calculation method based on an artificial neural network, aiming at the problems of high difficulty in capturing the burst wave shock wave, poor calculation stability under complex terrains, more resources needed by a numerical calculation method and the like, so as to improve the water flow simulation precision and reduce the calculation time.
The technical scheme adopted for realizing the purpose of the invention is as follows:
a river channel-flood area flood routing different-dimension fusion simulation principle and a calculation method based on an artificial neural network comprise the following steps:
s1, calculating river channel water power according to a two-dimensional water power model;
s2, judging whether an embankment bursting condition is met or not according to the river water power, and if so, enabling the water depth of the upstream section at the river break at the T-DT moment to be H0,T-DTDepth of water at downstream cross-section H1,T-DTDepth of water at breach position of flood area HT-DTSum flow rate UT-DTInputting a trained breach neural network model, and outputting and calculating the upstream section water depth H at the breach of the river channel at the time T0,TDepth of water at downstream cross-section H1,TDepth of water at breach position of flood area HTSum flow rate UTThen, the water depth H of the burst position of the general area output by the burst neural network modelTSum flow rate UTCalculating the flood water power by using a two-dimensional hydrodynamic model as an inflow boundary; if not, returning to the step S1;
s3, judging whether the maximum calculation time T is reachedmaxIf yes, outputting a result, otherwise, updating the calculation time to be T + DT, and returning to the step S1; DT is the calculated time step of the two-dimensional hydrodynamic model.
The method realizes the river channel-flood area flood routing different-dimensional fusion simulation by utilizing the powerful function approximation capability of the artificial neural network, effectively improves the speed and the precision of the dam break flood routing simulation through the calculation coupled with the hydrodynamic model, and provides a simple, convenient and reliable method for the river channel-flood area flood routing simulation.
Drawings
Fig. 1 is a schematic diagram of a river channel-flood area flood routing different-dimension fusion simulation principle and a calculation method based on an artificial neural network.
Detailed Description
The invention is described in further detail below with reference to the figures and specific examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in fig. 1, the river-flood routing multidimensional fusion simulation principle and the calculation method based on the artificial neural network provided by the invention are provided. The method comprises the following steps:
and S1, determining the calculated time step.
The calculation time step is uniformly taken as the calculation time step DT of the two-dimensional model.
S2, setting a break opening position, and constructing a high-resolution grid hydrodynamic model of the section interval between the upstream and downstream of the break opening of the river channel.
Through multiple simulations, a data set was created. Constructing a BP neural network model, and inputting an upstream section water depth (H) of a river course breach at the moment of T-DT as an input factor0,T-DT) Depth of water in downstream cross-section (H)1,T-DT) Depth of water at breach position of flood area (H)T-DT) And flow rate (U)T-DT) And the output factor is the water depth (H) of the upstream section at the breach of the river at the time T0,T) Depth of water in downstream cross-section (H)1,T) Depth of water at breach position of flood area (H)T) And flow rate (U)T). The data set was divided into two groups, one group (about 70%) for training the neural network model and about 30% for testing.
And S3, calculating river hydraulic power.
The river flow calculation is carried out by adopting a one-dimensional hydrodynamic model, the principle of the method is a Saint-Venn equation set, and the method is composed of a continuous equation and a motion equation:
in the formula: q is a section variable, m3S; a is the cross-sectional area of water passing, m2(ii) a q is the side inflow, m3X is distance along the way, m, t is time, s, α is power correction coefficient and is dimensionless quantity, g is gravity acceleration, m/s2(ii) a Z is the section water level, m; n is roughness and is dimensionless quantity; r is hydraulic radius, m.
And after the one-dimensional hydrodynamic model calculates a time step, updating the hydraulic element values of all the sections to the time T.
And 4S, judging whether the dike breaks down. And judging whether the burst condition is met, if so, skipping to the step 5, and if not, skipping to the step 7.
And S5, calculating a breach neural network model. The depth (H) of the upstream section of the breach of the river channel at the moment of T-DT0,T-DT) Depth of water in downstream cross-section (H)1,T-DT) Depth of water at breach position of flood area (H)T-DT) And flow rate (U)T-DT) Inputting a neural network model, and outputting the upstream section water depth (H) at the breach of the river at the time T0,T) Depth of water in downstream cross-section (H)1,T) Depth of water at breach position of flood area (H)T) And flow rate (U)T)。
And S6, calculating a two-dimensional hydrodynamic model.
Most rivers and lakes are shallow water flows, and the assumption is made that ① has a free surface, ② has a main driving force of gravity and a main dissipation force of frictional resistance between the water flow and a solid boundary and inside the water flow, ③ has a horizontal flow velocity approximately uniformly distributed along the water depth, and ④ has a negligible vertical flow velocity and vertical acceleration, and the water pressure is approximately distributed in a static pressure manner.
Based on the above assumptions, the motion equation of the three-dimensional water flow is integrated along the water depth, and a two-dimensional shallow water flow control equation can be obtained:
continuity equation:
equation of momentum along the X-axis:
momentum equation along the Y-axis:
wherein t is time, u and v are respectively the components of the flow velocity in the x and y directions, η is the river bed elevation, d is the still water depth, h is the water depth, h is d + η, g is the gravity acceleration, f is the Coriolis force, rho is the density of water, s is the flow velocity of waterxx,sxy,syx,syyIs a radiation stress component; p is a radical ofaIs atmospheric pressure; rho0Is the relative density of water; s is a source item; u. ofs,vsThe flow rate of the water flow is the source term;
the average value of the flow velocity along the water depth direction; t isiiFor the lateral stress, it was calculated from the Smagorinsky eddy coefficient equation.
The common numerical solving method of the two-dimensional shallow water equation set comprises three methods:
finite Difference Method (FDM), Finite Volume Method (FVM), and Finite Element Method (FEM). The finite difference method is to approximate the derivative of the shallow water equation in a difference mode, common finite difference formats of the shallow water equation comprise an alternate direction hidden format, a TVD format, a WENO format, a MADI format, a Godunov format and the like, and the finite difference method is generally suitable for rectangular grids and orthogonal curve grids. The finite element method is a volume approximation solution, and in order to achieve the goal of minimizing the space integral weighted residual of a differential equation, the element-by-element approximation is carried out. The finite element method is difficult to capture the discontinuous characteristic of water flow, so the method has certain limitation in application. The finite volume method is a local approximate solving method, discretization is carried out on an unstructured grid, and the finite volume method has no strict requirement on the structure of a control body, so that the finite volume method is more suitable for solving the problem of complex boundary. The discrete solving method used in the invention is a finite volume method with a central format, and the formula can be uniformly converted into the following form:
in the finite volume method discrete solving process, the calculation precision depends on the accuracy of the estimation of the interface numerical flux, common calculation formats of the interface flux comprise Godunov, Osher, Roe, HLL and the like, the Roe format based on the approximate Riemann solution is widely applied, and the solving format can better simulate the problem of large gradient flow in the flood propagation process. The method adopts the Roe format based on approximate Riemann solution to discretely solve the hydrodynamic flux. In order to enable the model to reach second-order precision, the flux on two sides of the interface is reconstructed by utilizing a linear gradient reconstruction technology. In order to prevent the oscillation problem in numerical solution, the stable TVD-MUSCL format is used for dispersing the flux.
Depth of water (H) at burst position of general area output by two-dimensional hydrodynamic model through neural network modelT) And flow rate (U)T) As inflow boundary. And after the two-dimensional hydrodynamic model calculates a time step, the hydraulic element values of all the grids are updated to the time T.
S7, judging whether the maximum calculation time T is reachedmaxIf yes, the result is output, and if not, the calculation time is updated to T + DT, and the process proceeds to step S3.
The method realizes the river channel-flood area flood routing different-dimensional fusion simulation by utilizing the powerful function approximation capability of the artificial neural network, effectively improves the speed and the precision of the dam break flood routing simulation through the calculation coupled with the hydrodynamic model, and provides a simple, convenient and reliable method for the river channel-flood area flood routing simulation.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (6)
1. A river channel-flood area flood routing different-dimension fusion simulation principle and a calculation method based on an artificial neural network are characterized by comprising the following steps;
s1, calculating river channel water power according to a two-dimensional water power model;
s2, judging whether an embankment bursting condition is met or not according to the river water power, and if so, enabling the water depth of the upstream section at the river break at the T-DT moment to be H0,T-DTDepth of water at downstream cross-section H1,T-DTDepth of water at breach position of flood area HT-DTSum flow rate UT-DTInputting a trained breach neural network model, and outputting and calculating the upstream section water depth H at the breach of the river channel at the time T0,TDepth of water at downstream cross-section H1,TDepth of water at breach position of flood area HTSum flow rate UTThen, the water depth H of the burst position of the general area output by the burst neural network modelTSum flow rate UTCalculating the flood water power by using a two-dimensional hydrodynamic model as an inflow boundary; if not, returning to the step S1;
s3, judging whether the maximum calculation time T is reachedmaxIf yes, outputting a result, otherwise, updating the calculation time to be T + DT, and returning to the step S1; DT is the calculated time step of the two-dimensional hydrodynamic model.
2. The riverway-flood-area-based heterogeneous fusion simulation principle and the calculation method according to claim 1, wherein the breach neural network model is a BP neural network model.
3. The artificial neural network-based river-flood routing different-dimension fusion simulation principle and calculation method according to claim 1, wherein the one-dimensional hydrodynamic model calculates that the hydraulic element values of all the sections are updated to the time T after a time step is finished.
4. The riverway-flood area flood routing different-dimension fusion simulation principle and calculation method based on the artificial neural network as claimed in claim 1, wherein the riverway hydraulic calculation is performed by adopting a one-dimensional hydrodynamic model based on the holy-south equation set:
the method is composed of a continuous equation and a motion equation:
wherein Q is a cross-sectional variable, m3S; a is the cross-sectional area of water passing, m2(ii) a q is the side inflow, m3X is distance along the way, m, t is time, s, α is power correction coefficient and is dimensionless quantity, g is gravity acceleration, m/s2(ii) a Z is the section water level, m; n is roughness and is dimensionless quantity; r is hydraulic radius, m.
5. The artificial neural network-based river-flood routing different-dimension fusion simulation principle and calculation method according to claim 1, wherein the two-dimensional hydrodynamic model calculates a time step later, and then the hydrodynamic element values of all the grids are updated to a time T.
6. The artificial neural network-based river-flood routing different-dimension fusion simulation principle and calculation method according to claim 1, wherein the two-dimensional hydrodynamic model calculation adopts a two-dimensional shallow water flow control equation, and comprises:
equation of continuity
Equation of momentum along the X axis
Equation of momentum along the Y-axis
Wherein t is time, u and v are respectively the components of the flow velocity in the x and y directions, η is the river bed elevation, d is the still water depth, h is the water depth, h is d + η, g is the gravity acceleration, f is the Coriolis force, rho is the density of water, s is the flow velocity of waterxx,sxy,syx,syyIs a radiation stress component; p is a radical ofaIs atmospheric pressure; rho0Is the relative density of water; s is a source item; u. ofs,vsThe flow rate of the water flow is the source term;
the average value of the flow velocity along the water depth direction; t isiiFor the lateral stress, it was calculated from the Smagorinsky eddy coefficient equation.
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| CN113792448A (en) * | 2021-08-03 | 2021-12-14 | 天津大学 | River course and flood area ice flood choking-levee breaking-submerging coupling simulation method |
| CN116151152A (en) * | 2023-03-01 | 2023-05-23 | 广西大学 | Hydrologic numerical simulation calculation method based on gridless calculation |
| CN116384266A (en) * | 2023-02-20 | 2023-07-04 | 北方工业大学 | Mud-rock flow evolution prediction method based on wave-breaking principle |
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111695304A (en) * | 2020-05-08 | 2020-09-22 | 长江水利委员会长江科学院 | Weighted average calculation method for water level gradient |
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| CN113792448A (en) * | 2021-08-03 | 2021-12-14 | 天津大学 | River course and flood area ice flood choking-levee breaking-submerging coupling simulation method |
| CN113792448B (en) * | 2021-08-03 | 2023-09-05 | 天津大学 | River channel and flood area water-break-inundation coupling simulation method |
| CN116384266A (en) * | 2023-02-20 | 2023-07-04 | 北方工业大学 | Mud-rock flow evolution prediction method based on wave-breaking principle |
| CN116384266B (en) * | 2023-02-20 | 2023-11-21 | 北方工业大学 | Mud-rock flow evolution prediction method based on wave-breaking principle |
| CN116151152A (en) * | 2023-03-01 | 2023-05-23 | 广西大学 | Hydrologic numerical simulation calculation method based on gridless calculation |
| CN116151152B (en) * | 2023-03-01 | 2023-08-08 | 广西大学 | Hydrodynamic force numerical simulation calculation method based on gridless calculation |
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