Method for predicting adhesion roughness of limnoperna fortunei in water transfer project

Abstract

The invention provides a marsh clam attachment roughness prediction method for a water transfer project, and relates to the technical field of hydraulic design parameter determination.The method comprises the steps that marsh clam attachment distribution conditions in the water transfer project are collected, and a marsh clam attachment model is constructed; analyzing the limnoperna fortunei attachment model to obtain limnoperna fortunei attachment data; analyzing the flow velocity of the sections and the hydraulic gradient between the sections based on the attachment data of the limnoperna fortunei to obtain a Manning roughness coefficient; and based on the Manning roughness coefficient and the limnoperna fortunei attachment density, obtaining a limnoperna fortunei attachment roughness prediction result through analysis, and completing the prediction of the limnoperna fortunei attachment roughness. According to the method, the problem that the influence of the marsh clam attachment condition on the roughness in the water transfer project is difficult to accurately predict is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of hydraulic engineering design parameter determination, and in particular to a method for predicting the roughness of limpet clam attachment used in water diversion projects. Background Art

[0002] To alleviate the uneven spatial and temporal distribution of water resources, optimize national water resource allocation, and ensure water supply security, my country has constructed numerous inter-basin water transfer projects. However, these projects are prone to species migration and invasion across biogeographic barriers. In these projects, marsh clams tend to attach densely to channel walls, with attachments reaching a thickness of 3–5 cm. This increases wall roughness, erodes concrete surfaces, and causes water pollution, posing a serious challenge to the ecological and operational safety of inter-basin water transfer projects. Currently, biofouling caused by marsh clam attachment is compromising the efficiency and quality of water conveyance in the main channels of water transfer projects, placing significant pressure on water quality, pipeline safety, energy consumption, and operation and maintenance. Therefore, research on the hydraulic characteristics of marsh clam attachment is urgent.

[0003] The attachment of marsh clams affects the roughness of water conservancy facilities. The water transfer capacity and hydraulic losses of water transfer facilities are closely related to the roughness of their wall surfaces. Selecting an appropriate roughness is essential for hydraulic calculations such as river level-flow relationships, reservoir backwater curves, long-distance water transfer flows, and the combined flood discharge capacity of rivers. Traditional experiments to determine the roughness of marsh clam attachment surfaces require measuring the frictional resistance after bioattachment in real or scaled models, which presents numerous challenges. For example, the large variations in density, distribution, and growth stages of marsh clams make standardization of bioattachment samples difficult. High-precision flume experiments require controlling water flow conditions and repeating tests at different density levels, making them costly and time-consuming. Furthermore, the dynamic changes in attachment in actual projects (such as seasonal reproduction) are difficult to fully simulate experimentally. Summary of the Invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method for predicting the roughness of marsh clam attachment for water diversion projects, which solves the problem that it is difficult to accurately predict the impact of marsh clam attachment on roughness in water diversion projects.

[0005] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is: a method for predicting the roughness of the attachment of limpet clams for water diversion projects, comprising: S1: Construct a clam attachment model by collecting the distribution of clam attachment in water diversion projects; S2: Analyzing the attachment model of the marsh clam to obtain the attachment data of the marsh clam; S3: Based on the clam attachment data, the cross-sectional flow velocity and the hydraulic gradient between cross-sections are analyzed to obtain the Manning roughness coefficient; S4: Based on the Manning roughness coefficient and the attachment density of the marsh clam, a prediction result of the attachment roughness of the marsh clam is obtained through analysis, thereby completing the prediction of the attachment roughness of the marsh clam.

[0006] The beneficial effects of the present invention are: a method for predicting the roughness of clam attachment in water diversion projects, which predicts the roughness of clam attachment by collecting the distribution of clam attachment in water diversion projects, and obtains the prediction results of clam attachment roughness. (1) By using the clam attachment model, the prediction results of clam attachment roughness under different attachment densities and different water flow rates can be obtained. Compared with traditional experiments, this method uses parameterized input, and a single simulation only takes a few hours to a few days. It does not require building a physical model, controlling environmental conditions, and repeatedly testing different density scenarios, which can not only reduce the time cycle but also reduce economic costs. (2) Through the built-in Manning coefficient calculation formula, the available parameters of the project are directly output to avoid secondary conversion errors. (3) By simulating the flow resistance distribution of the actual water diversion project, the problem that traditional experiments cannot reproduce the project scale is solved, and at the same time, a global roughness distribution map is provided to guide the key areas of dredging or anti-adhesion measures.

[0007] Furthermore, the S3 includes: Based on the roughness coefficient relationship function and the friction velocity function, the roughness coefficient function is constructed; The attachment data of the limpet clam is input into the roughness coefficient function, and the cross-sectional flow velocity and the hydraulic gradient between cross sections are analyzed to obtain the Manning roughness coefficient.

[0008] (1) By introducing data on the attachment of limpet clams, the model can dynamically reflect the impact of bio-attachment on the roughness of riverbeds or pipes, making the model applicable to complex flow environments affected by bio-attachment and expanding the application scope of traditional hydraulic calculations. (2) Based on functional modeling, the model avoids the limitations of traditional empirical value selection, reduces the workload of manual parameter adjustment, and improves the automation and computational efficiency of hydraulic gradient and flow rate analysis. (3) By quantifying the impact of bio-attachment on roughness, the model provides a new method for ecological-hydraulic coupling research, which helps to balance ecological protection and engineering needs.

[0009] Furthermore, the expression of the Manning roughness coefficient is: ; ; ; ; in, represents the Manning roughness coefficient, Indicates flow rate, represents the hydraulic radius of the channel, represents the friction flow velocity, represents the acceleration due to gravity, Indicates water depth. represents the hydraulic gradient, represents the density of water, Represents the shear force at the bottom of the riverbed.

[0010] (1) By constructing a complete Manning roughness expression, a quantitative relationship is established between key hydraulic parameters, giving the calculation process a rigorous mathematical basis. (2) By adjusting the parameters in the expression, it can be applied to different flow conditions and different biological attachment situations.

[0011] Furthermore, the S4 includes: Based on the density of swamp clams and the incoming flow velocity, the wall shear stress was analyzed and the wall shear stress prediction results were obtained. Based on the wall shear force prediction result, the Manning roughness coefficient is used for calculation to obtain the prediction result of the attachment roughness of the marsh clam, thereby completing the prediction of the attachment roughness of the marsh clam.

[0012] (1) By analyzing the effects of clam attachment density and incoming flow velocity on wall shear force, we can more accurately quantify the impact of bioattachment on wall resistance, thereby improving the accuracy of roughness prediction. (2) By combining the wall shear force prediction results with the Manning roughness coefficient calculation, the model can reflect the changes in roughness under different flow conditions in real time, making it suitable for dynamic hydrological environments (such as tidal rivers and seasonal flow fluctuations). BRIEF DESCRIPTION OF THE DRAWINGS

[0013] This specification will be further described in the form of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting, and in these embodiments, like numbers represent like structures, wherein: Figure 1 A method for predicting the roughness of clam attachment used in water diversion projects according to some embodiments of this specification is provided; Figure 2 It is an exemplary schematic diagram of the clam attachment model shown in some embodiments of this specification. DETAILED DESCRIPTION

[0014] The specific embodiments of the present invention are described below to facilitate understanding of the present invention by those skilled in the art. However, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the appended claims, these changes are obvious, and all inventions and creations utilizing the concepts of the present invention are protected.

[0015] Example Figure 1This is an exemplary flow chart of a method for predicting the roughness of clam attachment for water diversion projects according to some embodiments of this specification. Figure 1 As shown, the process includes the following steps. In some embodiments, the process can be executed by a processor.

[0016] S1: Construct a clam attachment model by collecting the distribution of clam attachment in water diversion projects.

[0017] The marsh clam attachment model is a physical model that reflects the attachment characteristics of marsh clams and the distribution pattern of their body length in the main canal of the water diversion project.

[0018] In some embodiments, as Figure 2 As shown, the processor can collect the distribution of limpet clam attachment in the water diversion project through 3D modeling, and use the open source numerical simulation computing platform (OpenFOAM) to model based on the single-layer stacking situation to obtain the limpet clam attachment model.

[0019] S2: Obtaining the clam attachment data by analyzing the clam attachment model.

[0020] The clam attachment data is data related to clam attachment in the clam attachment model, for example, the clam attachment data may include data such as maximum height, average height, porosity, and surface roughness.

[0021] In some embodiments, the processor can extract parameters from the clam attachment model to obtain clam attachment data. For example, the processor can calculate the vertical distance between the horizontal plane at the lowest point of the clam attachment model and the horizontal plane at the highest point to obtain the maximum height; calculate the average height of the clam attachment model by averaging the maximum heights of all individual clam models; calculate the porosity by calculating the ratio of the volume occupied by pores in the attachment model to the total volume; and obtain the vertical roughness by obtaining the standard deviation of the height of individual clams, thereby obtaining clam attachment data.

[0022] S3: Based on the clam attachment data, the cross-sectional flow velocity and the hydraulic gradient between cross-sections are analyzed to obtain the Manning roughness coefficient.

[0023] The Manning roughness coefficient is a comprehensive parameter that reflects the water resistance of a rough channel. For example, the Manning roughness coefficient can reflect the combined effects of factors such as flow velocity, surface roughness of the rough element, roughness element height, water depth, and hydraulic radius on the water resistance of a rough channel.

[0024] In some embodiments, the processor can implement S3 based on the following steps: constructing a roughness coefficient function based on the roughness coefficient relationship function and the friction flow velocity function; inputting the clam attachment data into the roughness coefficient function, analyzing the cross-sectional flow velocity and the hydraulic gradient between sections, and obtaining the Manning roughness coefficient.

[0025] The roughness coefficient function is a function that reflects the relationship between the flow velocity of each section, the hydraulic gradient between sections, the average hydraulic radius between sections and the Manning roughness coefficient.

[0026] In some embodiments, the processor may obtain the roughness coefficient function by analyzing factors affecting the Manning roughness coefficient based on the Manning formula and the Xie Cai formula.

[0027] In some embodiments, the expression for the Manning roughness coefficient may be: ; ; ; ; in, represents the Manning roughness coefficient, Indicates flow rate, represents the hydraulic radius of the channel, represents the friction flow velocity, represents the acceleration due to gravity, Indicates water depth. represents the hydraulic gradient, represents the density of water, Represents the shear force at the bottom of the riverbed.

[0028] S4: Based on the Manning roughness coefficient and the attachment density of the marsh clam, a prediction result of the attachment roughness of the marsh clam is obtained through analysis, thereby completing the prediction of the attachment roughness of the marsh clam.

[0029] The attachment density of marsh clams is data reflecting the density of marsh clams attached to the wall surface.

[0030] In some embodiments, the processor may calculate the attachment density of the clams based on the number and attachment area of ​​the clams on the wall of the main canal in the water diversion project.

[0031] The prediction results of the roughness of the attachment of the marsh clam reflect the influence of the density of the attachment of the marsh clam on the roughness.

[0032] In some embodiments, the processor can implement S4 based on the following steps: based on the attachment density of the swamp clam and the incoming flow velocity, the wall shear force is analyzed to obtain a wall shear force prediction result; based on the wall shear force prediction result, the Manning roughness coefficient is used for calculation to obtain a swamp clam attachment roughness prediction result, thereby completing the prediction of the swamp clam attachment roughness.

[0033] The wall shear force prediction result is a prediction result of the wall shear force affected by the density of the clams attached and the incoming flow velocity. For example, the wall shear force prediction result may include the wall shear force of the flow channel floor and the prediction result of the shape resistance of the clams.

[0034] In some embodiments, the processor can analyze the density of clam attachment and the incoming flow velocity, calculate the effect of different clam densities on the wall shear stress at each incoming flow velocity, and obtain the wall shear stress prediction result. For example, when the incoming flow velocity is 0.25 m / s, the wall shear stress value is 11 Pa when there are no clam attachments; for upstream clam attachments, when the density is 2500 / m 2 The maximum wall shear stress is 20.76 Pa and the density is 5000 / m 2 Compared with the above, the change is not significant. As the adhesion density continues to increase, the wall shear stress begins to decrease. When the density reaches 10,000 / m 2 When the wall shear stress is above 12.8 Pa, it is stable at about 12.8 Pa. For the downstream clams, the maximum wall shear stress is at a density of 7500 / m 2 The maximum shear stress was 19.4 Pa. As the attachment density increased, the wall shear stress gradually decreased and finally stabilized at around 15.2 Pa. As the incoming flow velocity increased, the wall shear stress increased accordingly. When the incoming flow velocity was 0.20 m / s, the wall shear stress was between 0.007 and 0.01 Pa; when the incoming flow velocity was 0.25 m / s, the wall shear stress was between 10 and 25 Pa; when the incoming flow velocity was 0.3 m / s, the wall shear stress was between 1 and 50 Pa. The changing trends of the wall shear stress with the attachment density of the swamp clams at three different incoming flow velocities were roughly the same, that is, for the upstream swamp clams, when the density was 2500-5000 / m 2 The maximum wall shear stress is obtained when the density of the wall is 5000-7500 / m 2 The maximum wall shear stress is obtained when the flow velocity is 0.3 m / s, and the wall shear stress tends to be stable under high density adhesion. However, when the flow velocity is 0.3 m / s, the stabilization trend of the wall shear stress is not obvious under high density adhesion. This is because the turbulence intensity increases, the turbulent flow state becomes more complex, and more vortex structures are generated on the wall of the clam, which affects the wall shear stress.

[0035] In some embodiments, the processor can combine the wall shear stress prediction result with the Manning's roughness coefficient, and through calculation, obtain the prediction result of the clam attachment roughness, thereby completing the prediction of the clam attachment roughness. For example, the processor can take the relative height as the hydraulic radius and use the wall shear stress calculation to obtain the friction velocity. The Manning's roughness coefficients under three flow rate conditions without clam attachment are all 0.012, while the Manning's coefficient for the concrete wall is 0.012-0.013, indicating that clam attachment causes the Manning's roughness coefficient to increase by between 0.001 and 0.01. When the flow state is slow flow, due to the attachment of clam, the increase in the Manning's roughness coefficient of the water pipeline can exceed 0.01. As the density of clam attachment increases, the roughness also increases, but the rate of increase slows and eventually decreases. The results showed that the Manning's roughness coefficient was higher when the upstream density of clams was around 5,000 per m²; and higher when the downstream density was between 5,000 and 7,500 per m². The Manning's roughness coefficient generally showed a trend of first slowly increasing, then decreasing, and finally stabilizing. Observation of the Manning's roughness coefficient at different flow rates revealed that the greater the flow rate, the greater the impact of clams on the Manning's roughness coefficient. At a flow rate of 0.3 m / s, the Manning's roughness coefficient reached 0.021, a 98% increase compared to a smooth surface.

[0036] In some embodiments of this specification, a method for predicting the roughness of clam attachment in water diversion projects is proposed. By collecting the distribution of clam attachment in water diversion projects, the roughness of clam attachment is predicted, and the prediction results of the roughness of clam attachment are obtained. (1) By using the clam attachment model, the prediction results of the roughness of clam attachment under different attachment densities and different water flow rates can be obtained. Compared with traditional experiments, this method uses parameterized input, and a single simulation only takes a few hours to a few days. It does not require building a physical model, controlling environmental conditions, and repeatedly testing different density scenarios. It can not only reduce the time period but also reduce economic costs. (2) Through the built-in Manning coefficient calculation formula, the available parameters of the project are directly output to avoid secondary conversion errors. (3) By simulating the flow resistance distribution of the actual water diversion project, the problem that traditional experiments cannot reproduce the project scale is solved, and a global roughness distribution map is provided to guide the key areas of dredging or anti-adhesion measures.

Claims

1. A method for predicting the roughness of clam attachment for water diversion projects, characterized in that: include: S1: Construct a clam attachment model by collecting the distribution of clam attachment in water diversion projects; S2: Analyzing the attachment model of the marsh clam to obtain the attachment data of the marsh clam; S3: Based on the clam attachment data, the cross-sectional flow velocity and the hydraulic gradient between cross-sections are analyzed to obtain the Manning roughness coefficient; S4: Based on the Manning roughness coefficient and the attachment density of the marsh clam, a prediction result of the attachment roughness of the marsh clam is obtained through analysis, thereby completing the prediction of the attachment roughness of the marsh clam.

2. The method for predicting the roughness of the clam adhesion used in water diversion projects according to claim 1, characterized in that: The S3 includes: Based on the roughness coefficient relationship function and the friction velocity function, the roughness coefficient function is constructed; The attachment data of the limpet clam is input into the roughness coefficient function, and the cross-sectional flow velocity and the hydraulic gradient between cross sections are analyzed to obtain the Manning roughness coefficient.

3. The method for predicting the roughness of the clam adhesion used in water diversion projects according to claim 2, characterized in that: The expression of the Manning roughness coefficient is: Where n is the Manning roughness coefficient, v is the flow velocity, R is the hydraulic radius of the channel, and u is * represents the friction velocity, g represents the acceleration due to gravity, H represents the water depth, J represents the hydraulic gradient, ρ represents the water density, τ w Represents the shear force at the bottom of the riverbed.

4. The method for predicting the roughness of the clam adhesion used in water diversion projects according to claim 1, characterized in that: The S4 includes: Based on the density of swamp clams and the incoming flow velocity, the wall shear stress was analyzed and the wall shear stress prediction results were obtained. Based on the wall shear force prediction result, the Manning roughness coefficient is used for calculation to obtain the prediction result of the attachment roughness of the marsh clam, thereby completing the prediction of the attachment roughness of the marsh clam.

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