A computer-aided design method for silt dam system with full-process step-by-step optimization

Abstract

The present invention relates to the technical field of computer-aided design, and discloses a computer-aided silt dam system design method with full-process step-by-step optimization. In the present invention, based on the idea of ​​full-process step-by-step evaluation, the entire design process is divided into multiple levels from large to small, and each level has clear and feasible design variables and optimization targets, so that computer-aided optimization can be performed step by step, and the optimization results are iterated into the next level as the basis for optimization. Specifically, it includes: dividing the target area into multiple sub-basins, screening out the sub-basins with the greatest positive impact on the construction of silt dams based on the entropy weight-TOPSIS method - reasonably allocating dam sites to avoid overlapping control ranges of various dams and causing waste - fitting each constraint condition and earthwork volume as a function of a certain key size, optimizing the optimal dam size that meets the constraint conditions and minimizes the earthwork volume - cost-effectiveness analysis, and finding the dam construction sequence with the greatest cost-effectiveness ratio - CFD simulation to optimize the microstructure of the silt dam.

Description

Technical Field

[0001] The present invention relates to the technical field of computer-aided design, and in particular to a computer-aided silt dam system design method with full-process step-by-step optimization. Background Art

[0002] A silt dam refers to a dam structure built in ditches of various levels in soil erosion areas for the purpose of blocking silt and forming land. The land formed by the blocked silt is called a dam area.

[0003] Generally speaking, a check dam is a dam system consisting of a few large backbone check dams and a number of smaller supporting check dams. The names of these dams are confusing, and some literature will describe these small check dams and large backbone check dams as two different dams. But in fact, they are the same type of dam.

[0004] The main structures of check dams are similar, mainly including a dam body made of local materials according to local conditions (the most common is a rammed earth dam), a spillway (structurally similar to a vertical shaft in front of the dam) for slowly discharging water from the dam reservoir and leaving mud, and a spillway for discharging excess water during heavy rainfall (the name is not uniform, some places call it an overflow channel or overflow trough, but it is usually a waterway extending from one side of the dam to the back of the dam, with an overflow weir at the entrance).

[0005] Unlike other water conservancy facilities, the silt dam is a small dam in the Loess Plateau that originated from the people and mainly relies on the people's own preparation and construction. The theoretical research on such small local projects has long been blank in academia. The design and construction of silt dams have long lacked technical guidance and mainly rely on previous experience accumulation. After summary, demonstration and promotion by the water conservancy and water conservation departments, the current corresponding technical specifications are only SD175-86 "Interim Technical Specifications for Water Conservation and Ditch Control Backbone Projects", which is used to guide the design and construction of backbone silt dams among silt dams. In addition, there are some documents, such as the "Silt Dam Construction Management Measures", which more or less provide certain guidance on the design and construction of silt dams. However, these technical specifications and documents still remain at the level of experience summary.

[0006] With the development of information technology, a series of civil engineering construction, including dams, can be designed with the help of computers to reduce costs and increase efficiency. However, the silt dam has its own unique characteristics. Unlike other buildings, it does not bear weight or great water pressure. The optimization demand for its structural stability itself is not high. The biggest demand is to reduce construction costs (for example, reduce earthwork volume) and improve the effect of silting land reclamation (for example, avoid excessive stones in the surface soil of the dam to affect crop growth). The main function of the existing BIM software is to ensure that the structural stability meets the standards, resulting in the existing BIM software basically not being able to help the design of the silt dam. The design and construction of the silt dam still remain at the stage of summarizing experience. Summary of the invention

[0007] The present invention provides a computer-aided silt dam system design method with full-process step-by-step optimization.

[0008] The technical problem to be solved is: the demand for optimizing the structural stability of the silt dam is not high, and the biggest demand is to reduce the construction cost and improve the effect of silting land for farmland reclamation, which leads to the fact that the existing BIM software is basically unhelpful in the design of the silt dam.

[0009] In order to solve the above technical problems, the present invention adopts the following technical solution: a computer-aided silt dam system design method with full-process step-by-step optimization, comprising the following steps:

[0010] Step 1: Divide the target area into multiple sub-basins, record the natural environment conditions, social environment conditions and current silt dam factors of each sub-basin, and form a database;

[0011] Step 2: Import the database of step 1 into the data analysis platform, conduct a weighted analysis of the impact of the natural environment conditions, social environment conditions and current silt dam factors of each sub-basin on the construction of silt dams, and find out the sub-basin with the greatest positive impact on the construction of silt dams, which will be recorded as the sub-basin suitable for construction;

[0012] Step 3: A backbone check dam and the secondary check dams in the catchment area it controls are recorded as a unit dam system. According to the basin area controlled by the unit dam system and the total area of ​​the sub-basin to be built, the number of unit dam systems is calculated, and then the number of secondary check dams is calculated;

[0013] Step 4: Determine the location and type of each silt dam, and then use operations optimization software to perform nonlinear programming on the size of each silt dam, so as to minimize the total dam volume of the proposed silt dam, provided that the silt area, sediment interception volume and flood retention volume meet the requirements;

[0014] Step 5: Number the planned silt dams and optimize the construction sequence of the silt dams to maximize the ratio of the present value of construction benefits to the present value of engineering costs;

[0015] Step 6: Count the types of check dams used in steps 2 to 5, select one of each type for CFD simulation, simulate the flow and settlement in the reservoir during water storage, and optimize the structure of the check dam based on the simulation results.

[0016] Furthermore, in step 1, the PyFlwDir library is used to divide the sub-basins.

[0017] Furthermore, in step 1, the natural environment conditions include the elevation, slope, river network density, soil type, rainfall erosivity and vegetation coverage of the sub-basin; the social environment conditions include the GDP, population density and road network density of the sub-basin; the existing silt dam factors include the total proportion of large dams, series connection rate and reservoir balance of the existing silt dams in the sub-basin;

[0018] In step 2, weighted analysis was performed based on the entropy weight-TOPSIS method, and SPSSAU was selected as the data analysis platform.

[0019] Furthermore, in the same type of silt dam, there is a dimension data, which makes other dimension data its function; this dimension data is recorded as the key dimension;

[0020] In step 4, four functions with key dimensions as independent variables and siltation area, sediment retention capacity, flood retention capacity and the total dam volume of the proposed siltation dam as dependent variables are obtained through regression. Then, with the siltation area, sediment retention capacity and flood retention capacity meeting the requirements as constraints, operations research optimization software is used for nonlinear programming to obtain the key dimensions that minimize the total dam volume of the proposed siltation dam under the constraints.

[0021] Furthermore, the key dimension is the dam height, and the total dam volume of the proposed silt dam is converted into earthwork volume for calculation;

[0022] In step 4, after determining the dam site location and dam type of each silt dam, ArcMap was used to measure the dam height, silt area and dam axis length at the dam site; then the reservoir capacity and earthwork volume were calculated, and the dam height-silt area curve, dam height-reservoir capacity curve and dam height-earthwork volume curve were obtained through regression analysis; finally, Lingo software was used for nonlinear programming.

[0023] Furthermore, in the simulation process of step six, FLUENT is used to simulate the flow and settlement in the reservoir during the water storage process of the silt dam. The height of the overflow weir at the spillway entrance is recorded as h. During the water storage process, the volume flow of the upstream water in the silt dam reservoir is recorded as q1, the volume flow of the water on both sides of the channel is recorded as q2, and the volume flow of the precipitation directly collected in the reservoir is recorded as q3. The total water inflow Q=q1+q2+q3;

[0024] The simulation process includes the following sub-steps:

[0025] Step 6.1: Collect data of Q under different precipitation levels, as well as water samples of upstream water, water on both sides of the channel, and precipitation. Screen stones from the water on both sides of the channel, count the stone particle size and density, and take the average value D and ρ; the flow rate of water on both sides of the channel is recorded as u;

[0026] Step 6.2: After screening out the stones, the three water samples are mixed in the proportion of q1, q2, and q3, and the physical properties of the mixed water samples are measured as the physical properties of the simulated fluid;

[0027] Step 6.3: Simplify the fluid flow in the reservoir into a flow in a trench with a length equal to the length of the silt dam reservoir, a width equal to the width of the silt dam reservoir, a depth of h, and a rectangular cross-section. A mixed water sample with a flow rate of Q flows into one end of the trench, and an overflow weir with a height of h is provided at the other end. Simulate and obtain the flow velocity distribution in the trench and the viscosity of water at different depths after reaching a steady state.

[0028] Step 6.4: Simulate the process of a ball with a velocity of u, a particle size of D, and a density of ρ falling vertically into the groove of step 6.3.

[0029] Furthermore, in step six, the structure of the silt dam is optimized as follows: a valve is set at the bottom of the overflow weir, and the valve is opened when the liquid level in the reservoir reaches the top of the overflow weir, thereby changing the flow velocity distribution in the reservoir during overflow and introducing disturbance to the position below the overflow weir in the reservoir;

[0030] The valve is blocked with a gate plate, which is fixedly connected to a floating ball located at the top of the overflow weir through a connecting rod; the connecting rod is vertically penetrated in a pre-embedded sleeve on the side surface of the overflow weir and is slidably connected to the pre-embedded sleeve.

[0031] Compared with the prior art, the computer-aided silt dam system design method with full-process step-by-step optimization of the present invention has the following beneficial effects:

[0032] In the present invention, based on the idea of ​​full-process step-by-step evaluation, the entire design process is divided into multiple levels from large to small, and each level is provided with clear and feasible design variables and optimization targets (without clear design variables and optimization targets, there is no optimization), so that computer-aided optimization can be carried out step by step, and the optimization results are iterated into the next level as the basis for optimization;

[0033] The target area is divided into multiple sub-basins, and the natural environment conditions, social environment conditions and current silt dam factors of each sub-basin are recorded. The sub-basins with the greatest positive impact on silt dam construction are screened out based on the entropy weight-TOPSIS method.

[0034] Based on the optimal sub-basin, the dam sites are reasonably allocated to avoid overlapping control ranges of various dams and resulting in waste;

[0035] Based on the optimal dam site, each constraint condition and earthwork volume are fitted as a function of a certain key size, so that the optimal dam size that satisfies the constraint conditions and minimizes the earthwork volume can be optimized (the key to this step is to make all functions have a common independent variable);

[0036] Conduct cost-effectiveness analysis based on the optimal dam size to find the dam construction sequence that maximizes the cost-effectiveness ratio;

[0037] After all the above levels are completed, the parameters of the silt dam are determined. Based on this, CFD simulation can be carried out to understand the water and soil flow transfer process during the operation of the silt dam. The microstructure of the silt dam can be optimized based on the simulation results, thereby achieving process intensification and improving the effect of siltation and land reclamation.

[0038] By combining the above points, the design was completed and all aspects of the design of the entire silt dam system were optimized without blind spots. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A flow chart of a computer-aided silt dam system design method with full-process step-by-step optimization according to the present invention;

[0040] Figure 2 It is a schematic diagram of a structural optimization case of a silt dam;

[0041] In the figure, 1-overflow weir, 2-valve, 3-gate, 4-float, 5-connecting rod, 6-embedded sleeve. DETAILED DESCRIPTION

[0042] like Figure 1 As shown, a computer-aided silt dam system design method with full-process step-by-step optimization includes the following steps:

[0043] Step 1: Divide the target area into multiple sub-basins. In this embodiment, the PyFlwDir library is used to divide the sub-basins. This is a py toolkit that encapsulates various sub-basin division modules including graphics processing and DEM (digital elevation model) processing. After completing the sub-basin division using this tool, subsequent planning can be carried out more accurately. Because the silt dam system across sub-basins is weakly connected to each other and cannot promote each other, it is more difficult to measure the effectiveness of the silt dam system.

[0044] After the sub-basin division, the natural environment conditions, social environment conditions and current silt dam factors of each sub-basin are recorded and a database is formed;

[0045] Most of the data required for this step have existing databases that can be directly called. In this embodiment, the natural environmental conditions include the elevation, slope, river network density, soil type, rainfall erosion and vegetation coverage of the sub-basin; the social environmental conditions include the GDP, population density and road network density of the sub-basin; the existing silt dam factors include the total proportion of large dams, the series connection rate and the reservoir capacity balance of the existing silt dams in the sub-basin.

[0046] Step 2: Import the database of step 1 into the data analysis platform, conduct a weighted analysis of the impact of the natural environment conditions, social environment conditions and current silt dam factors of each sub-basin on the construction of silt dams, and find out the sub-basin with the greatest positive impact on the construction of silt dams, which will be recorded as the sub-basin suitable for construction;

[0047] If we put aside other things and only measure the impact of the silt dam itself, it is difficult to measure the effectiveness of the silt dam system. However, if it is included in the sub-basin where it is located to form a whole for evaluation, there will be a tool for evaluation, which is the so-called "entropy weight-TOPSIS method". The main principle of this method is to count all the factors that cause the impact in the entire sub-basin, judge whether each influencing factor is positive or negative, and then give an ideal model that maximizes all positive factors and minimizes all negative factors (positive processing will be carried out during the specific operation, so that all influencing factors are as large as possible), and then assign weights to each indicator, so that the weight of the indicator with a large impact is large. Finally, the deviation between all the factors of each tributary basin and the ideal model after integration is judged. The smaller the deviation, the more suitable the sub-basin is for the construction of a silt dam. In this embodiment, SPSSAU is selected as the data analysis platform, and the actual operation process is as follows:

[0048] Step 2.1 Construction of evaluation index system

[0049] Table 1 Evaluation index system for areas suitable for construction of silt dams

[0050]

[0051] Step 2.2 Data forward processing

[0052] According to the above description of the evaluation indicators, the type of each indicator is determined. In this study, elevation, surface slope, vegetation coverage and the total proportion of large dams are extremely small data, and other data are extremely large data. The TOPSIS model used in this study is generally calculated on the basis that all indicators are extremely large indicators (the larger the data, the better), so it is necessary to positively process the extremely small indicators. The specific method is as follows:

[0053]

[0054] In the formula, is the importance of the jth indicator of the i-th pixel x ij The positive results.

[0055] Step 2.2 Determine indicator weights using entropy weight method

[0056] In physics, the connection between information and entropy starts from the uncertainty of the system state. Entropy is a measure of the uncertainty of the system state and is the best tool and scale for measuring uncertainty. It quantitatively describes the complexity of the economic system, the uncertainty of the organizational state and the change of order. The increase of entropy value means the loss of information. The higher the order of a system, the smaller its entropy and the greater the amount of information it has. The larger the entropy value of a system, the smaller the degree of disorder of the system, the less information it contains, the smaller its role in the comprehensive evaluation, and the smaller its corresponding weight; conversely, the smaller the entropy value, the greater the degree of disorder of the system, the more information it contains, the greater its role in the comprehensive evaluation, and the greater its weight. Therefore, in the specific evaluation process, the weight determined by the entropy weight method is more objective and reliable.

[0057] The suitability evaluation indicators for the construction of silt dams include qualitative indicators (such as soil type) and quantitative indicators (such as elevation). Therefore, before the evaluation, the indicators must be preprocessed, that is, dimensionless. The processing method is as follows:

[0058]

[0059] In the formula, y ij is the decision matrix after dimensionless processing.

[0060] Calculate the proportion of the i-th pixel in the j-th indicator as follows:

[0061]

[0062] In the formula, p ij It is the proportion of the i-th pixel under the j-th indicator to the j-th indicator.

[0063] Calculate the information entropy and information utility value of each indicator, and finally obtain the weight of each evaluation indicator. The calculation method is as follows:

[0064]

[0065] In the formula, e j is the information entropy of the jth indicator among all n indicators; d j is its information utility value; Wj is the weight of the jth indicator.

[0066] Step 2.3 TOPSIS method to calculate comprehensive evaluation value

[0067] The basic principle of TOPSIS method is: on the basis of the normalized original data matrix of the indicators, find the ideal point in each candidate solution, that is, the best solution and the worst solution that do not necessarily exist in the set of alternative solutions. The indicator value of each attribute of the ideal point is the best or worst value of the attribute in the alternative solution, corresponding to the positive and negative ideal points respectively. The scheme to be evaluated is regarded as a point in space, and the schemes are ranked by detecting the distance between the point and the optimal solution and the worst solution (commonly used Euclidean distance), so as to obtain the relative proximity between each scheme and the optimal solution, and evaluate the pros and cons of the schemes. There are two concepts: ideal solution and negative ideal solution. The ideal solution refers to the optimal solution envisioned, and its various attribute values ​​reach the best value in each alternative solution; the negative ideal solution refers to the worst solution envisioned, and its various attribute values ​​are the worst values ​​of each alternative solution.

[0068] The calculation method is as follows:

[0069] First, define the positive and negative ideal solutions, and the calculation method is as follows:

[0070]

[0071] In the formula, Y + , Y - are the positive and negative ideal solutions respectively.

[0072] Secondly, define the distance between the evaluation object value and the maximum value. The calculation method is as follows:

[0073]

[0074] In the formula, d i + , d i - are the distances between the i-th pixel value and the maximum and minimum values ​​respectively.

[0075] Finally, the relative closeness between the index value of each evaluation object and the ideal solution is calculated, that is, the comprehensive evaluation score. The calculation method is:

[0076]

[0077] In the formula, S i is the comprehensive evaluation score of the i-th pixel.

[0078] Step 3: A backbone check dam and the secondary check dams in the catchment area it controls are recorded as a unit dam system. According to the basin area controlled by the unit dam system and the total area of ​​the sub-basin to be built, the number of unit dam systems is calculated, and then the number of secondary check dams is calculated;

[0079] It is very difficult to calculate the dam usage based on the controlled watershed area of ​​a single dam, because neither the terrain nor the size of the silt dam itself is standard, and the controlled area of ​​a single silt dam fluctuates greatly. However, if a group of silt dams are counted as a module, then the watershed area controlled by this group of silt dams is usually relatively consistent, and the fluctuations in the controlled area of ​​a single silt dam will offset each other. At this time, planning can be carried out. After planning, it is relatively easy to calculate the number of silt dams in each module. By planning in two levels, it can be ensured that the controlled areas of each silt dam do not overlap. The specific steps are as follows:

[0080] Step 3.1 According to the channel conditions in the watershed, the backbone check dam or the backbone check dam and several secondary check dams in the catchment area controlled by it are called unit dam systems. The number of backbone check dams can be basically determined by the number of unit dam systems, and the construction potential of check dams can be analyzed accordingly. The following formula is used when the number of unit dam systems in a small watershed is initially formulated:

[0081]

[0082] In the formula, N g is the number of unit dam systems, seats; S The planned controlled watershed area of ​​the dam system project, km 2 ; S 0 is the watershed area controlled by the dam system project, km 2 ; is the average drainage area controlled by a single dam of the backbone check dam, km 2 ; The value of is selected according to the different soil erosion intensity areas in the study basin.

[0083] Step 3.2 As a component of the sub-dam system, the area of ​​the unit dam system is not a simple factor of the sub-dam system. Therefore, in the process of allocating the unit dam system to the sub-dam system, the number of unit dam systems needs to be appropriately adjusted to ensure overall coordination and adaptation. The adjustment calculation method is as follows:

[0084]

[0085] In the formula, N gi is the number of unit dam systems in the ith sub-dam system, pieces; S i is the controlled area of ​​the ith sub-dam system, km2 ; N' gi is the number of unit dam systems in the i-th sub-dam system after adjustment, pieces.

[0086] Step 3.3 Calculate the number of secondary check dams based on the above-mentioned configuration ratio of secondary check dams to backbone check dams. The calculation method is as follows:

[0087]

[0088] In the formula, N mas To determine the number of secondary check dams, C For the configuration ratio of secondary check dams to backbone check dams, see above; N' g The number of backbone silt dams is .

[0089] Step 4: Determine the location and type of each silt dam, and then use operations optimization software to perform nonlinear programming on the size of each silt dam, so as to minimize the total dam volume of the proposed silt dam, provided that the silt area, sediment interception volume and flood retention volume meet the requirements;

[0090] This is a typical optimization problem under multiple constraints. For the design of the silt dam, there are multiple adjustable design variables. It is necessary to adjust a set of multiple coupled design variables and optimize under multiple constraints. This results in a high-dimensional solution space, making global optimization extremely difficult and generally no analytical solution can be obtained. It is usually necessary to use heuristic algorithms such as genetic algorithms to optimize. At the same time, due to the large dimension of the solution space, it is easy to fall into the local optimum, and the solution is likely to be invalid.

[0091] However, the silt dam itself also has some characteristics that are conducive to optimization. Since it does not bear weight and does not need to withstand a large amount of water pressure, there is a lot of room in the design. It is even possible to set each of the dimensional parameters as a function of a key dimensional parameter, that is, to specify a dimensional data and make the other dimensional data its function; this dimensional data is recorded as the key dimension; for example, the width of the dam is equal to the width of the channel, and the other dimensions are set to a certain multiple of the height of the dam. In this case, the dimension of the solution space is reduced to a level that is easy to optimize.

[0092] In this embodiment, the key dimension is the dam height, the proposed silt dam is a rammed earth dam, and the total dam volume of the silt dam is converted into earthwork volume for calculation, thereby further simplifying the optimization.

[0093] In step 4, four functions with key dimensions as independent variables and siltation area, sediment retention capacity, flood retention capacity and the total dam volume of the proposed siltation dam as dependent variables are obtained through regression. Then, with the siltation area, sediment retention capacity and flood retention capacity meeting the requirements as constraints, operations research optimization software is used for nonlinear programming to obtain the key dimensions that minimize the total dam volume of the proposed siltation dam under the constraints.

[0094] In step 4, after determining the location and type of each silt dam, ArcMap was used to measure the dam height, silt area and dam axis length at the dam site; then the reservoir capacity and earthwork volume were calculated, and the dam height-silt area curve, dam height-reservoir capacity curve and dam height-earthwork volume curve were obtained through regression analysis; finally, Lingo software was used for nonlinear programming.

[0095] Objective function: dam volume

[0096]

[0097] is the sum of the height of the mud retaining dam and the height of the flood detention dam at the ith dam site, m; a i , b i , c i is the coefficient of the dam height-earthwork volume regression equation at the i-th dam site.

[0098] Constraint 1: Siltation area constraint

[0099]

[0100] I c is a relatively stable critical value; S i is the area of ​​the i-th check dam, km 2 ; F is the drainage area controlled by the dam, km 2 .

[0101] Constraint 2: Mud blocking constraint

[0102]

[0103] In the formula, C i is the reservoir capacity at the dam site No. i, m 3 ; t is the sedimentation period, a; F is the dam system controlled area, km 2 ; M is the average erosion modulus of the basin, t / (km 2 a); γ is the dry density of sediment, t / m 3 , generally takes 1.3t / m 3 .

[0104] Constraint 3: Flood detention constraint

[0105]

[0106] C i L is the silt retention capacity at the dam site No. i, m 3 ; is the total amount of flood calculated based on a specific frequency at the dam site No. i, m 3 . is the total amount of flood at the dam site No. i calculated based on a certain flood frequency or a certain assumed condition (such as upstream dam breach, etc.), m 3 . In general, this constraint can be ignored.

[0107] In addition to the above three constraints, the value of the dam height cannot be negative, that is:

[0108] Constraint 4: Mud dam height constraint

[0109]

[0110] H L i It is the total dam height allowed by the terrain at the dam site No. i (excluding safety overheight).

[0111] Step 5: Number the planned silt dams and optimize the construction sequence of the silt dams to maximize the ratio of the present value of construction benefits to the present value of engineering costs;

[0112] This part of the prior art has been thoroughly studied, and there are many related planning schemes. The scheme adopted in this embodiment is as follows:

[0113] Benefit 1: Mud blocking benefit

[0114]

[0115] Where q is the cost of cleaning each cubic meter of sediment in the reservoir and river downstream of the river, RMB / m 3 ; K is the interception coefficient, i.e. the ratio of the amount of sediment intercepted by the silt dam to the total amount of sediment, dimensionless; M is the multi-year average erosion modulus within the basin area controlled by the silt dam, t / (km 2 a); A is the watershed area within the control range of the silt dam, km 2 ; v is the dry bulk density of sediment, t / m 3 .

[0116] Benefit 2: Production Benefit

[0117]

[0118] Where, d is the average output value per unit area of ​​dam land, yuan / mu; a and b are the corresponding coefficients and exponents of the regression equation; the rest are the same as above.

[0119] Benefit 3: Flood prevention benefits

[0120]

[0121] In the formula, f is the annual flood control benefit per unit flood detention reservoir capacity; V z is the storage capacity of the silt dam used for flood detention, m 3 ; Other details are the same as above.

[0122] Benefit 4: Irrigation Benefit

[0123]

[0124] In the formula, g is the irrigation benefit per unit flood detention reservoir capacity; others are the same as above.

[0125] Total Benefits:

[0126]

[0127] Dam system engineering cost:

[0128]

[0129] In the formula, V Li is the storage capacity of the i-th check dam used for mud retention, m 3 ; V zi is the storage capacity of the i-th check dam used for flood detention, m 3 ; S i is the other expenses of the i-th silt dam, such as survey and design, construction preparation, etc., RMB; the rest is the same as above. α, β —the corresponding coefficients and exponents of the regression equation; i s —Social discount rate, ; others are the same as above. x i The construction year of the i-th check dam in the small watershed dam system was started

[0130] Cost-effectiveness ratio:

[0131]

[0132] Substituting the construction example of a small watershed silt dam system into the above formula for analysis and calculation, when the ratio of the present value of the dam system construction benefit to the present value of the engineering cost reaches the maximum value, the obtained x i , which is the construction year of the i-th silt dam in the dam system. From this, we can get the construction sequence of silt dams in the small watershed dam system. Constructing the dam system according to this construction sequence can achieve the best benefit-cost ratio.

[0133] Step 6: Count the types of silt dams used in steps 2 to 5, select one of each dam type for CFD simulation, simulate the flow and settlement in the reservoir during the water storage process, and optimize the structure of the silt dam based on the simulation results. Of course, there is only one dam type in this embodiment, that is, the rammed earth dam.

[0134] The sedimentation here refers to the sedimentation of the stones washed down from both sides of the ditch in the water body in the reservoir. This process has a great impact on land reclamation, because if the stones washed down are difficult to settle to the deeper places in the reservoir but are concentrated in the topsoil, the formed land will cause stress to the growth of crop roots. This process has long been lacking research and corresponding control measures. However, with the development of technology, the problems in this process have been gradually solved. In this embodiment, the relevant data obtained in the previous steps are tried to perform simplified CFD simulation to find out the problem.

[0135] Considering that the flow in the reservoir of the silt dam is slow and there is a long steady flow section, and the slurry at the bottom is almost not flowing, the entire reservoir is simplified here, all the incoming flows are converted into one stream, and the fluid below the bottom of the overflow weir 1 is regarded as not flowing, so that the simulation of the silt dam, a complex large-scale structure that is usually difficult to simulate for CFD simulation, is feasible.

[0136] During the simulation of step six, FLUENT is used to simulate the flow and settlement in the reservoir during the water storage process of the silt dam. The height of the overflow weir 1 at the spillway entrance is recorded as h. During the water storage process, the volume flow of the upstream water in the silt dam reservoir is recorded as q1, the volume flow of the water on both sides of the channel is recorded as q2, and the volume flow of the precipitation directly collected in the reservoir (that is, the precipitation directly falling into the reservoir) is recorded as q3. The total water inflow Q=q1+q2+q3;

[0137] The simulation process includes the following sub-steps:

[0138] Step 6.1: Collect data of Q under different precipitation levels, as well as water samples of upstream water, water on both sides of the channel, and precipitation. Screen stones from the water on both sides of the channel, count the stone particle size and density, and take the average value D and ρ; the flow rate of water on both sides of the channel is recorded as u;

[0139] If the precipitation here is relatively stable, Q can also be measured directly from the cross-sectional area and flow velocity of the spillway.

[0140] Step 6.2: After screening out the stones, the three water samples are mixed in the ratio of q1, q2, and q3, and the physical properties of the mixed water samples are measured (mainly viscosity, density, and sediment content) as the physical properties of the simulated fluid;

[0141] Step 6.3: Simplify the fluid flow in the reservoir into a flow in a trench with a length equal to the length of the silt dam reservoir, a width equal to the width of the silt dam reservoir, a depth of h, and a rectangular cross-section. A mixed water sample with a flow rate of Q flows into one end of the trench, and an overflow weir 1 with a height of h is provided at the other end. Simulate and obtain the flow velocity distribution in the trench and the viscosity of water at different depths after reaching a steady state.

[0142] Step 6.4: simulate the process of a ball with a velocity of u, a particle size of D, and a density of ρ falling vertically into the groove of step 6.3. Here, steps 6.1 to 6.3 are actually one simulation step, and 6.4 is another simulation based on step 6.3. Steps 6.1 to 6.3 simulate the flow process, which serves as the basis for the sedimentation process simulation in step 6.3.

[0143] After simulation, it can be found that the muddy water that rushes down will form mud after a slight sedimentation. Mud is a typical non-Newtonian fluid. When it is static, its viscosity is extremely high and it is difficult for stones to sink. This will cause a large number of stones to be retained in the surface of the mud. After the water is drained, the mud becomes land, and the topsoil will contain a large number of stones.

[0144] Considering that the viscosity of mud will drop significantly after a slight disturbance, a microstructural improvement plan for the silt dam is proposed based on the above findings:

[0145] In step six, the structure of the silt dam is optimized as follows: a valve 2 is set at the bottom of the overflow weir 1, and the valve 2 is opened when the liquid level in the reservoir reaches the top of the overflow weir 1, thereby changing the flow velocity distribution in the reservoir during overflow, introducing disturbance to the position below the overflow weir 1 in the reservoir, thereby reducing the viscosity of the mud and allowing the stones to sink to a deeper position; note that the valve 2 here cannot be set at a position lower than the overflow weir 1, otherwise part of the mud will be discharged.

[0146] like Figure 2 As shown, the valve 2 is blocked with a gate plate 3, which is fixedly connected to a float 4 located at the top of the overflow weir 1 through a connecting rod 5; the connecting rod 5 is vertically penetrated into an embedded sleeve 6 on the side surface of the overflow weir 1 and is slidably connected to the embedded sleeve 6.

[0147] We tried this microstructure improvement in a silt dam on a tributary of the middle reaches of the Yellow River. The soil washed down from both sides of the silt dam contained a lot of stones, which led to poor growth of crops in the dam area. After the improvement, after the rainfall from July to October, a dam area was formed in the silt dam. We sampled and analyzed the surface soil of the dam area, and the results showed that there were no more stones in the average 28 cm thick topsoil. We will continue to conduct experiments in more different silt dams in the future.

[0148] This completes the entire design and optimization process. In the future, soil erosion can be predicted based on CMIP6 and CA-Markov models to provide a reference for subsequent maintenance:

[0149] Soil erosion forecast

[0150] Calculation based on soil erosion equation:

[0151]

[0152] Where A is the soil erosion modulus, t / km 2 a, predicted average annual soil loss per square kilometer; R is the rainfall erosivity factor MJ km -2 a -1 mm·h -1 , which indicates the potential ability of local precipitation conditions to cause soil erosion; K is the soil erodibility factor (t·h·MJ -1 mm -1 ), reflecting the difficulty of soil particle separation and transportation; L is the slope length factor; S is the slope factor, and the slope length and slope factor comprehensively reflect the undulating characteristics of the terrain where the fort site is located. Generally speaking, the shorter the slope length and the greater the slope, the more conducive it is to the transportation of soil particles. It is dimensionless; C is the vegetation coverage factor, reflecting the soil erosion under different vegetation attachment conditions. The closer the C value is to 1, the lower the vegetation coverage and the more serious the erosion. It is dimensionless; P is the management factor, which measures the artificial soil and water conservation measures at the site. If the location is undeveloped land, the P value is 1. If the site is located in an urban built-up area, it is considered that the soil and water conservation measures are good, and the P value is 0. It is dimensionless.

[0153] Calculation of future rainfall erosivity

[0154] Using the CMIP6 module in the ESGF official website, we extracted nc and converted it to tif through multi-model analysis to obtain future precipitation data in the study area for rainfall erosivity calculation.

[0155] Prediction of future vegetation cover

[0156] Based on the CA-Markov model, the vegetation coverage changes under natural scenarios were first predicted, and then other basins with similar conditions were selected. The research years were determined according to the construction time of the silt dam, and the vegetation coverage changes were analyzed. The conversion rate of vegetation coverage changes at different levels was obtained as the basis for the impact of silt dams on vegetation changes. Finally, the vegetation coverage of the target sub-basin was predicted based on the conversion rate.

[0157] Annual sedimentation forecast:

[0158] The amount of sediment intercepted in a certain year can be expressed as:

[0159]

[0160] In the formula, S i is the total sediment interception in year i, t; A i is the catchment area in year i, km 2 ; E i is the average soil erosion intensity of the controlled watershed surface in year i, t / (km 2 a); ξ i is the average sediment interception rate of the reservoir in the i-th year, %.

[0161] Prediction of landform after dam construction:

[0162] The calculated annual sediment interception volume is deposited in an ideal state, that is, the sediment is deposited layer by layer from the lowest point of the controlled basin. The flattening of sedimentation causes the lowest point of the controlled basin to rise, and the terrain change is visualized by editing the terrain contour lines. The contour lines are converted to dem, and the terrain changes are compared year by year.

[0163] The embodiments described above are merely descriptions of preferred implementation modes of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A computer-aided silt dam system design method with full-process step-by-step optimization, characterized by: The following steps are involved: Step 1: Divide the target area into multiple sub-basins, record the natural environment conditions, social environment conditions and current silt dam factors of each sub-basin, and form a database; Step 2: Import the database of step 1 into the data analysis platform, conduct a weighted analysis of the impact of the natural environment conditions, social environment conditions and current silt dam factors of each sub-basin on the construction of silt dams, and find out the sub-basin with the greatest positive impact on the construction of silt dams, which will be recorded as the sub-basin suitable for construction; Step 3: A backbone check dam and the secondary check dams in the catchment area it controls are recorded as a unit dam system. According to the basin area controlled by the unit dam system and the total area of ​​the sub-basin to be built, the number of unit dam systems is calculated, and then the number of secondary check dams is calculated; Step 4: Determine the location and type of each silt dam, and then use operations optimization software to perform nonlinear programming on the size of each silt dam, so as to minimize the total dam volume of the proposed silt dam, provided that the silt area, sediment interception volume and flood retention volume meet the requirements; Step 5: Number the planned silt dams and optimize the construction sequence of the silt dams to maximize the ratio of the present value of construction benefits to the present value of engineering costs; Step 6: Count the types of silt dams used in steps 2 to 5, select one of each dam type for CFD simulation, simulate the flow and settlement in the reservoir during the water storage process, and optimize the structure of the silt dam based on the simulation results; In the same type of silt dam, there is a dimension data that makes other dimension data its function; this dimension data is recorded as the key dimension; In step 4, regression is used to obtain four functions with key dimensions as independent variables and siltation area, silt retention, flood retention and the total dam volume of the proposed siltation dam as dependent variables; then, with the siltation area, silt retention and flood retention meeting the requirements as constraints, operations research optimization software is used for nonlinear programming to obtain the key dimensions that minimize the total dam volume of the proposed siltation dam under the constraints; The key dimension is the dam height, and the total dam volume of the proposed silt dam is converted into earthwork volume for calculation; In step 4, after determining the dam site location and dam type of each silt dam, ArcMap was used to measure the dam height, silt area and dam axis length at the dam site; then the reservoir capacity and earthwork volume were calculated, and the dam height-silt area curve, dam height-reservoir capacity curve and dam height-earthwork volume curve were obtained through regression analysis; finally, Lingo software was used for nonlinear programming.

2. According to claim 1, a computer-aided silt dam system design method with full-process step-by-step optimization is characterized in that: In step 1, the PyFlwDir library is used to divide the sub-basins.

3. The computer-aided design method for a silt dam system with full-process step-by-step optimization according to claim 1 is characterized in that: In step 1, natural environmental conditions include the sub-basin's elevation, slope, river network density, soil type, rainfall erosivity, and vegetation coverage; social environmental conditions include the sub-basin's GDP, population density, and road network density; existing silt dam factors include the total proportion of large dams, the series connection rate, and the reservoir capacity balance of the existing silt dams in the sub-basin; In step 2, weighted analysis was performed based on the entropy weight-TOPSIS method, and SPSSAU was selected as the data analysis platform.

4. The computer-aided silt dam system design method with full-process step-by-step optimization according to claim 1 is characterized in that: In the simulation process of step 6, FLUENT is used to simulate the flow and settlement in the reservoir during the impoundment process of the silt dam. The height of the overflow weir (1) at the spillway inlet is denoted as h. During the impoundment process, the volume flow of water from the upstream of the silt dam reservoir is denoted as q1, the volume flow of water from both sides of the channel is denoted as q2, and the volume flow of precipitation directly collected in the reservoir is denoted as q3. The total inflow is Q=q1+q2+q3. The simulation process includes the following sub-steps: Step 6.1: Collect data of Q under different precipitation levels, as well as water samples of upstream water, water on both sides of the channel, and precipitation. Screen stones from the water on both sides of the channel, count the stone particle size and density, and take the average value D and ρ; the flow rate of water on both sides of the channel is recorded as u; Step 6.2: After screening out the stones, the three water samples are mixed in the proportion of q1, q2, and q3, and the physical properties of the mixed water samples are measured as the physical properties of the simulated fluid; Step 6.3: Simplify the fluid flow in the reservoir into a flow in a trench with a length equal to the length of the dam reservoir, a width equal to the width of the dam reservoir, a depth of h, and a rectangular cross-section. A mixed water sample with a flow rate of Q flows into one end of the trench, and an overflow weir with a height of h is provided at the other end (1); simulate and obtain the flow velocity distribution in the trench and the viscosity of water at different depths after reaching a steady state; Step 6.4: Simulate the process of a ball with a velocity of u, a particle size of D, and a density of ρ falling vertically into the groove of step 6.

3.

5. The computer-aided silt dam system design method with full-process step-by-step optimization according to claim 4 is characterized in that: In step six, the structure of the silt dam is optimized as follows: a valve (2) is provided at the bottom of the overflow weir (1), and the valve (2) is opened when the liquid level in the reservoir reaches the top of the overflow weir (1), thereby changing the flow velocity distribution in the reservoir during overflow and introducing disturbance to a position in the reservoir below the overflow weir (1); The valve (2) is sealed with a gate plate (3), and the gate plate (3) is fixedly connected to a floating ball (4) located at the top of the overflow weir (1) through a connecting rod (5); the connecting rod (5) is vertically inserted into a pre-embedded sleeve (6) on the side surface of the overflow weir (1) and is slidably connected to the pre-embedded sleeve (6).

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