A method for predicting the roughness of a zebra mussel attachment for water diversion projects

Abstract

The present application provides a kind of for water conservancy project's roughness prediction method of clam attachment, it is related to hydraulic design parameter determination technical field, the method is by collecting the distribution of clam attachment in water conservancy project, constructs clam attachment model;Through the analysis of clam attachment model, obtain the clam attachment data;Based on the clam attachment data, the size of section velocity and the hydraulic slope between section are analyzed, and the Manning roughness coefficient is obtained;Based on the Manning roughness coefficient and the density of clam attachment, the prediction result of clam attachment roughness is obtained by analysis, and the prediction of clam attachment roughness is completed.The present application solves the problem that the influence of clam attachment in water conservancy project on roughness is difficult to accurately predict.

Description

Technical Field

[0001] This manual relates to the field of hydraulic engineering design parameter measurement technology, and in particular to a method for predicting the roughness of clam attachment in water diversion projects. Background Technology

[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 causing species migration and invasion across biogeographical barriers. In these projects, the swarming clam (Stropharia spp.) readily attaches densely to the channel walls, with adhesion thicknesses reaching 3-5 cm. This increases surface roughness, erodes concrete surfaces, and causes water pollution, posing a serious challenge to the ecological and operational safety of these projects. Currently, biofouling caused by swarming clam attachment affects the water conveyance efficiency and quality of the main canals, while also placing immense pressure on the entire project in terms of water quality safety, pipeline safety, energy consumption, and operation and maintenance. Therefore, research into the hydraulic characteristics of swarming clam attachment is urgently needed.

[0003] The attachment of clam shells affects the roughness of hydraulic facilities, and the water conveyance capacity and hydraulic loss of water conveyance facilities are closely related to their surface roughness. When calculating the relationship between river level and flow rate, reservoir backwater curves, long-distance water transfer flow rates, and river flood control capacity, it is necessary to select an appropriate roughness. Traditional experiments on the roughness of clam-attached surfaces require measuring the frictional resistance after attachment in real or scaled-down models, which faces many challenges. For example, the large differences in clam density, distribution, and growth stage make it difficult to standardize attachment samples; controlling water flow conditions and repeatedly testing different density scenarios are necessary, resulting in high-precision flume experiments that are costly and time-consuming; and the dynamic changes in attachment (such as seasonal reproduction) in actual engineering projects are difficult to fully simulate experimentally. Summary of the Invention

[0004] To address the aforementioned shortcomings in the existing technology, this invention provides a method for predicting the roughness of clam attachment in water diversion projects, which solves the problem of accurately predicting the impact of clam attachment on roughness in water diversion projects.

[0005] To achieve the aforementioned objectives, the technical solution adopted by this invention is: a method for predicting the attachment roughness of clam shells in water diversion projects, comprising:

[0006] S1: Construct a clam attachment model by collecting data on the distribution of clam attachment in water diversion projects;

[0007] S2: By analyzing the clam attachment model, clam attachment data is obtained;

[0008] S3: Based on the clam attachment data, the cross-sectional flow velocity and hydraulic gradient between cross sections are analyzed to obtain the Manning roughness coefficient;

[0009] S4: Based on the Manning roughness coefficient and the clam attachment density, the clam attachment roughness prediction result is obtained through analysis, thus completing the prediction of the clam attachment roughness.

[0010] The beneficial effects of the present invention are as follows: A method for predicting the roughness of clam attachment in water diversion projects is used to predict the roughness of clam attachment by collecting the distribution of clam attachment in water diversion projects. (1) By using the clam attachment model, the roughness prediction results of clam attachment under different attachment densities and different water flow velocities 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, or 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 parameters usable in the project are directly output, avoiding secondary conversion errors. (3) By simulating the flow resistance distribution of actual water diversion projects, the problem that traditional experiments cannot reproduce the scale of the project is solved. At the same time, a global roughness distribution map is provided to guide the key areas of dredging or anti-attachment measures.

[0011] Further, S3 includes:

[0012] Based on the roughness coefficient relationship function and the frictional velocity function, a roughness coefficient function is constructed.

[0013] The attachment data of the swamp clam is input into the roughness coefficient function to analyze the cross-sectional flow velocity and the hydraulic gradient between cross sections, thereby obtaining the Manning roughness coefficient.

[0014] (1) By introducing clam attachment data, the impact of biological attachment on riverbed or pipeline roughness can be dynamically reflected, making the model applicable to complex flow environments affected by biological attachment and expanding the application scope of traditional hydraulic calculations. (2) Based on functional modeling, the limitations of traditional empirical values ​​are avoided, the workload of manually adjusting parameters is reduced, and the automation and computational efficiency of hydraulic gradient and velocity analysis are improved. (3) By quantifying the impact of clam and other biological attachments on roughness, a new method is provided for ecological-hydraulic coupling research, which helps to balance ecological protection and engineering needs.

[0015] Furthermore, the expression for the Manning roughness coefficient is as follows:

[0016] ;

[0017] ;

[0018] ;

[0019] ;

[0020] in, This represents the Manning roughness coefficient. Indicates flow rate, Indicates the hydraulic radius of the channel. Indicates the frictional flow velocity. Represents gravitational acceleration. Indicates water depth. Indicates hydraulic gradient, This indicates the density of water. This indicates the shear force at the bottom of the riverbed.

[0021] (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.

[0022] Further, S4 includes:

[0023] Based on the clam attachment density and inflow velocity, the wall shear stress was analyzed, and the predicted wall shear stress results were obtained.

[0024] Based on the predicted wall shear stress, the Manning roughness coefficient is used to calculate the predicted roughness of clam attachment, thus completing the prediction of clam attachment roughness.

[0025] (1) By analyzing the effect of clam attachment density and inflow velocity on wall shear force, the influence of biological attachment on wall resistance can be quantified more accurately, thereby improving the accuracy of roughness prediction. (2) Combining the wall shear force prediction results with the Manning roughness coefficient calculation, the model can reflect the roughness changes under different flow velocity conditions in real time, which is suitable for dynamic hydrological environments (such as tidal rivers, seasonal flow fluctuations, etc.). Attached Figure Description

[0026] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein:

[0027] Figure 1 This is a method for predicting the attachment roughness of clam shells for water diversion projects, as shown in some embodiments of this specification.

[0028] Figure 2 This is an exemplary schematic diagram of a clam attachment model according to some embodiments of this specification. Detailed Implementation

[0029] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0030] Example

[0031] Figure 1 This is an exemplary flowchart illustrating a method for predicting the attachment roughness of clam shells 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 may be executed by a processor.

[0032] S1: By collecting data on the distribution of clam attachment in water diversion projects, a clam attachment model is constructed.

[0033] The clam attachment model is a physical model that reflects the clam attachment characteristics and body length distribution patterns of the main canal of the water diversion project.

[0034] In some embodiments, such as Figure 2 As shown, the processor can collect the distribution of clam attachment in the water diversion project through 3D modeling, and use the open-source numerical simulation computing platform (OpenFOAM) to model the clam attachment based on the single-layer stacking situation to obtain the clam attachment model.

[0035] S2: By analyzing the clam attachment model, clam attachment data is obtained.

[0036] Clam attachment data reflects data related to clam attachment in the clam attachment model. For example, clam attachment data may include data such as maximum height, average height, porosity, and surface roughness.

[0037] In some embodiments, the processor can extract parameters from the clam attachment model to obtain clam attachment data. For example, the processor can obtain the maximum height by calculating the vertical distance between the horizontal plane of the lowest point and the horizontal plane of the highest point of the clam attachment model; the average height of the clam attachment model can be obtained by calculating the average of the maximum heights of all individual clam models; the porosity can be obtained by calculating the ratio of the volume occupied by pores in the attachment model to the total volume; and the vertical roughness can be obtained by obtaining the standard deviation of the height of a single clam, thus obtaining clam attachment data.

[0038] S3: Based on the clam attachment data, the cross-sectional flow velocity and hydraulic gradient between cross sections are analyzed to obtain the Manning roughness coefficient.

[0039] The Manning roughness coefficient is a comprehensive parameter reflecting the water-blocking effect of a rough channel. For example, the Manning roughness coefficient can reflect the combined effect of factors such as flow velocity, surface roughness of rough elements, rough element height, water depth, and hydraulic radius on the water-blocking effect of a rough channel.

[0040] In some embodiments, the processor may implement S3 based on the following steps: constructing a roughness coefficient function based on the roughness coefficient relationship function and the frictional velocity function; inputting the clam attachment data into the roughness coefficient function, analyzing the cross-sectional velocity and the hydraulic gradient between cross-sections, and obtaining the Manning roughness coefficient.

[0041] The roughness coefficient function is a function that reflects the relationship between the magnitude of the flow velocity at each cross section, the hydraulic gradient between cross sections, the average hydraulic radius between cross sections, and the Manning roughness coefficient.

[0042] In some embodiments, the processor can obtain the roughness coefficient function by analyzing the influencing factors of the Manning roughness coefficient based on the Manning formula and the Chezy formula.

[0043] In some embodiments, the expression for the Manning roughness coefficient can be:

[0044] ;

[0045] ;

[0046] ;

[0047] ;

[0048] in, This represents the Manning roughness coefficient. Indicates flow rate, Indicates the hydraulic radius of the channel. Indicates the frictional flow velocity. Represents gravitational acceleration. Indicates water depth. Indicates hydraulic gradient, This indicates the density of water. This indicates the shear force at the bottom of the riverbed.

[0049] S4: Based on the Manning roughness coefficient and the clam attachment density, the clam attachment roughness prediction result is obtained through analysis, thus completing the prediction of the clam attachment roughness.

[0050] The density of swamp clam attachment is a measure of the density of swamp clam attachments to the wall surface.

[0051] In some embodiments, the processor can calculate the clam attachment density based on the number and attachment area of ​​clam on the wall of the main canal in the water diversion project.

[0052] The prediction results of the roughness of clam attachment reflect the influence of clam attachment density on the roughness.

[0053] In some embodiments, the processor may implement S4 based on the following steps: analyzing the wall shear stress based on the clam attachment density and the incoming flow velocity to obtain the wall shear stress prediction result; and calculating the clam attachment roughness prediction result using the Manning roughness coefficient based on the wall shear stress prediction result to complete the prediction of the clam attachment roughness.

[0054] The wall shear stress prediction results are predictions of how the wall shear stress varies under the influence of clam attachment density and inflow velocity. For example, the wall shear stress prediction results can include predictions of the wall shear force on the channel floor and the shape drag of the clam.

[0055] In some embodiments, the processor can analyze the clam attachment density and the incoming flow velocity, calculate the effect of different clam densities on the wall shear stress at various incoming flow velocities, and obtain the predicted wall shear stress. For example, at an incoming flow velocity of 0.25 m / s, the wall shear stress is 11 Pa without clam attachment; for upstream clams, the shear stress is 11 Pa at a clam attachment density of 2500 clams / m³. 2 The maximum wall shear stress was 20.76 Pa, and the density was 5000 particles / m³. 2 Compared to the previous period, the change was not significant. As the adhesion density continued to increase, the wall shear stress began to decrease. When the density reached 10,000 particles / m², the stress decreased. 2 At the above levels, the wall shear stress stabilizes at around 12.8 Pa; for downstream clams, the maximum wall shear stress occurs at a density of 7500 clams / m³. 2 The maximum shear stress was 19.4 Pa. As the adhesion density increased, the wall shear stress gradually decreased, eventually stabilizing around 15.2 Pa. With increasing flow velocity, the wall shear stress increased accordingly. At a flow velocity of 0.20 m / s, the wall shear stress ranged from 0.007 to 0.01 Pa; at 0.25 m / s, it ranged from 10 to 25 Pa; and at 0.3 m / s, it ranged from 1 to 50 Pa. The variation trend of wall shear stress with clam adhesion density was roughly consistent across the three different flow velocities, i.e., for upstream clam populations, when the density was 2500-5000 clams / m³... 2 The maximum wall shear stress is obtained at this point, and the wall shear stress tends to stabilize under high-density adhesion; for downstream clams, the density is 5000-7500 clams / m³. 2The maximum wall shear stress is obtained at a certain velocity, and the wall shear stress tends to stabilize under high-density adhesion. However, when the incoming 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 turbulence state becomes more complex, and more vortex structures are generated on the wall of the swamp clam, which affects the wall shear stress.

[0056] In some embodiments, the processor can combine the predicted wall shear stress with the Manning roughness coefficient to calculate the predicted roughness of clam attachment, thus completing the prediction of clam attachment roughness. For example, the processor can take the relative height as the hydraulic radius and calculate the friction velocity using the wall shear stress to obtain the Manning roughness coefficient for the case without clam attachment under three flow velocity conditions, all of which are 0.012. The Manning coefficient for the concrete wall is 0.012-0.013, indicating that clam attachment increases the Manning roughness coefficient by 0.001 to 0.01. When the flow is slow, the increase in the Manning roughness coefficient of the water conveyance pipe due to clam attachment can exceed 0.01. As the clam attachment density increases, the roughness also increases, but the rate of increase slows down and eventually decreases. The results showed that the Manning roughness coefficient was relatively large when the upstream clam attachment density was around 5000 clams / m², and relatively large when the downstream clam attachment density was around 5000-7500 clams / m². The Manning roughness coefficient generally showed a trend of first increasing slowly, then decreasing, and finally stabilizing. Observation of the Manning roughness coefficient at different flow velocities revealed that the higher the flow velocity, the greater the impact of clam attachment on the Manning roughness coefficient. At a flow velocity of 0.3 m / s, the Manning roughness coefficient reached 0.021, which was 98% larger than that of a smooth wall surface.

[0057] 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 clam attachment roughness are obtained. (1) By using the clam attachment model, the prediction results of clam attachment roughness under different attachment densities and different water flow velocities 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, or 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 parameters usable in the project are directly output, avoiding secondary conversion errors. (3) By simulating the flow resistance distribution of actual water diversion projects, the problem that traditional experiments cannot reproduce the engineering scale is solved. At the same time, a global roughness distribution map is provided to guide the key areas of dredging or anti-attachment measures.

Claims

1. A method for predicting the attachment roughness of clam shells in water diversion projects, characterized in that, The method comprises the following steps: S1: constructing a zebra mussel attachment model by collecting the distribution of zebra mussel attachment in the water diversion project; the zebra mussel attachment model is a physical model reflecting the characteristics of zebra mussel attachment and the distribution law of zebra mussel length in the main canal of the water diversion project; S2: obtaining zebra mussel attachment data by analyzing the zebra mussel attachment model; the zebra mussel attachment data are data related to zebra mussel attachment in the zebra mussel attachment model, including maximum height, average height, porosity and surface roughness; S3: analyzing the size of the cross-section flow velocity and the hydraulic slope between the cross-sections based on the zebra mussel attachment data to obtain the Manning roughness coefficient; constructing a roughness coefficient function based on the relationship function of the roughness coefficient and the frictional flow velocity function; inputting the zebra mussel attachment data into the roughness coefficient function to analyze the size of the cross-section flow velocity and the hydraulic slope between the cross-sections, and obtaining the Manning roughness coefficient; the expression of the Manning roughness coefficient is: ； ； ； ； wherein, denotes the Manning roughness coefficient, denotes the flow velocity, denotes the hydraulic radius of the channel, denotes the friction velocity, denotes the gravitational acceleration, denotes the water depth, denotes the hydraulic slope, denotes the density of water, denotes the bed shear stress; S4: obtaining the prediction result of the zebra mussel attachment roughness based on the Manning roughness coefficient and the zebra mussel attachment density by analysis, and completing the prediction of the zebra mussel attachment roughness.

2. The method for predicting the attachment roughness of a zebra mussel for a water conveyance project of claim 1, wherein, The S4 comprises: analyzing the wall shear stress based on the zebra mussel attachment density and the incoming flow velocity to obtain the prediction result of the wall shear stress; calculating the Manning roughness coefficient based on the prediction result of the wall shear stress to obtain the prediction result of the zebra mussel attachment roughness, and completing the prediction of the zebra mussel attachment roughness.

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