Simulation method of river jacking effect based on hydraulic experiment

Abstract

The invention discloses a river jacking effect simulation method based on a hydraulic experiment, and relates to the field of hydraulic research, and the method comprises the following steps: constructing a transparent organic glass water tank, arranging a slope-adjustable river channel at the upstream, connecting a rectangular water tank at the downstream, and independently regulating and controlling the river flow and the lake flow through an integrated flow control module; closing the lake flow, adjusting the river flow, collecting reference water level data after the water flow is stable, calculating the water surface gradient, and verifying the deviation between the water surface gradient and the theoretical value of the Manning formula; lake flow is started, working conditions are set according to the stable target water flow gradient, water level changes are calculated, and a reference temperature field is collected; according to the method, the river flow and the lake flow are closed, the super-hydrophobic coating is prepared in the water tank and connected with the micropore array, and the typical working condition is restarted, through the synergistic effect of the super-hydrophobic coating and the precise micropore array, active regulation and control of the Manning coefficient within the range of 0.015-0.06 are achieved, and the problem that the dynamic change of the roughness of a natural river cannot be simulated in a traditional experiment is solved.

Description

Technical Field

[0001] This invention relates to the field of hydraulics research, and in particular to a simulation method for the river backwater effect based on hydraulic experiments. Background Technology

[0002] In the field of hydraulics research, the backwater effect caused by the interaction between rivers and lakes is a key issue affecting flood control scheduling, waterway management, and ecological balance. Traditional research methods mainly rely on prototype observation and numerical simulation. By deploying water level gauges and current meters to obtain field data, and combining them with numerical models, the backwater range can be simulated. Although such techniques can reflect actual hydrological processes, they are limited by the high cost of prototype observation, the long data acquisition cycle, and the simplification error of numerical models for complex boundary conditions. It is difficult to achieve high-precision and repeatable mechanism research. In particular, when quantifying the nonlinear relationship between the backwater effect and the flow ratio and riverbed roughness, existing methods lack experimental verification means for the coupling effect of multiple physical fields, resulting in significant deviations between theoretical models and engineering practice.

[0003] The limitations of existing technologies are mainly reflected in the correlation analysis between dynamic roughness and the backwater effect. Through fixed roughness flume experiments, it was found that a small change in the Manning coefficient can cause the position of the backwater front to fluctuate by ±0.5m. However, this study did not solve the problem of dynamic roughness control. In natural rivers, roughness is affected by vegetation growth, sediment deposition, etc., and it varies seasonally. However, existing experimental devices can only simulate static roughness and cannot be actively intervened. Existing technologies do not pay enough attention to the coupling effect between temperature field and backwater effect, and ignore the potential impact of water temperature stratification on water flow density and energy loss, which further limits the prediction accuracy of the model. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a simulation method for the river backwater effect based on hydraulic experiments. Existing experimental methods cannot simulate the dynamic changes in riverbed roughness and quantify the key issues of temperature-backwater effect coupling.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a method for simulating the river backwater effect based on hydraulic experiments, which includes constructing a transparent plexiglass water tank, setting an adjustable slope river channel upstream, connecting a rectangular water tank downstream, and independently regulating the river flow and lake flow through an integrated flow control module.

[0008] The lake flow was closed, the river flow was regulated, and after the water flow stabilized, benchmark water level data was collected to calculate the water surface slope and verify the deviation from the theoretical value of the Manning formula.

[0009] Turn on the lake flow rate, set the operating conditions according to the stable target water flow gradient, calculate the water level change and collect the reference temperature field;

[0010] By shutting down river and lake flow, a superhydrophobic coating was prepared in a flume and connected to a micropore array. The typical working conditions were restarted, and the riverbed roughness was adjusted by microbubble units. Water level and temperature data were collected after the intervention to obtain a microbubble intervention dataset.

[0011] Real-time values ​​of lake and river flow, water level sensor sequences, and infrared thermal imager temperature fields are collected. Instantaneous flow ratio and water surface slope are calculated to form time series data, which are then processed to obtain a standard dataset.

[0012] Based on the standard dataset, the multi-field coupling support index is calculated, a support strength grading standard is established, the difference rate between microbubble working conditions and conventional working conditions is analyzed, and a support effect assessment report is obtained.

[0013] As a preferred embodiment of the simulation method for the river backwater effect based on hydraulic experiments described in this invention, the method includes: constructing a transparent plexiglass tank, setting up an adjustable-slope river channel upstream, connecting a rectangular water tank downstream, and independently regulating the river flow and lake flow through an integrated flow control module, comprising the following steps.

[0014] Transparent acrylic glass is processed into tank components and spliced ​​together. After curing for 24 hours, a full water test is conducted to obtain a properly sealed tank body.

[0015] An integrated stepper motor drive unit and a ball screw transmission unit are installed upstream of the water tank body. The slope is calibrated by an electronic inclinometer to obtain a river section with an adjustable slope.

[0016] The rectangular water tank is connected to the water tank body using a flange sealing unit to obtain an integrated rectangular water tank-water tank body;

[0017] The river flow is controlled by connecting the variable frequency pumping unit and the electromagnetic metering unit to the main body of the water tank through a DN50 pipeline unit.

[0018] The flow rate of the lake is controlled by using a screw pump unit and an ultrasonic metering unit, with a gate unit installed in the outlet pipeline.

[0019] As a preferred embodiment of the simulation method for the river backwater effect based on hydraulic experiments described in this invention, the method includes the following steps: closing the lake flow, adjusting the river flow, collecting benchmark water level data after the water flow stabilizes, calculating the water surface slope, and verifying the deviation from the theoretical value of the Manning formula.

[0020] The central control module sends a shutdown command to the screw pump unit, ultrasonic metering unit, and gate unit to shut off the lake flow. It also inputs an adjustment command to the integrated flow control module and uses a PID control algorithm to obtain a stable target water flow rate.

[0021] Based on a stable target water flow rate, raw water level data is acquired through a capacitive water level sensor. The raw data is then filtered to calculate the average water level value at each measurement point, thus obtaining the benchmark water level data.

[0022] The actual measured water surface slope value is calculated based on the difference between the water level measurements at the inlet and outlet of the main body of the water tank.

[0023] The theoretical water surface slope value is calculated based on Manning's formula;

[0024] The relative deviation is obtained by comparing the measured slope value with the theoretical slope value.

[0025] As a preferred embodiment of the simulation method for the river backwater effect based on hydraulic experiments described in this invention, the method includes the following steps: activating lake flow, setting the operating conditions according to a stable target flow gradient, calculating water level changes, and collecting a reference temperature field.

[0026] The stable target water flow rate is transmitted to the lake flow control module, and the gate unit opening is dynamically adjusted based on the fuzzy control algorithm to obtain the stable water flow rate;

[0027] Kalman filtering is applied to the raw water level data to eliminate noise, and the difference is compared with the reference water level data to obtain the real-time water level data;

[0028] The difference between real-time water level data and reference water level is calculated to obtain the water level change;

[0029] Raw temperature data was collected under initial steady-state conditions using an infrared thermal imager. The temperature field data was then spatially averaged to obtain a reference temperature field.

[0030] As a preferred embodiment of the simulation method for the river backwater effect based on hydraulic experiments described in this invention, the method includes the following steps: shutting off river and lake flow, preparing a superhydrophobic coating and connecting it to a microporous array in a water tank, and restarting the typical operating conditions.

[0031] The central control module sends a shutdown command to shut off the river and lake flow, activates the electromagnetic drainage valve unit, and discharges the water in the main body of the tank to obtain a dry tank body.

[0032] The inner wall of the dry water tank body is plasma cleaned to obtain an activated hydrophilic surface, and then sprayed with nano-silica to obtain a superhydrophobic coating. The microporous plate unit is embedded into the superhydrophobic coating to obtain a microbubble unit.

[0033] A restart command is issued by the central control module to start the variable frequency pumping unit, screw pumping unit, and microbubble unit, thereby obtaining the operating parameters.

[0034] As a preferred embodiment of the simulation method for the river backwater effect based on hydraulic experiments described in this invention, the method includes the following steps: adjusting the riverbed roughness using microbubble units, collecting water level and temperature data after intervention, and obtaining a microbubble intervention dataset.

[0035] Calculate the target pressure based on the stable target water flow rate, start the microbubble unit and adjust the air compressor unit, filter the water level change data, and obtain the water level rise sequence.

[0036] After performing temperature compensation calculations on the reference temperature field, the temperature data after intervention is obtained, and the maximum water level rise is extracted from the water level rise sequence.

[0037] The baseline roughness is obtained by inverting the measured surface slope of the river flow.

[0038] The effective roughness is calculated based on the Manning formula, and the drag reduction efficiency is calculated by comparing the reference roughness and the effective roughness.

[0039] By integrating the maximum water level rise, post-intervention temperature data, effective roughness, and drag reduction efficiency, a microbubble intervention dataset is obtained.

[0040] As a preferred embodiment of the simulation method for the river backwater effect based on hydraulic experiments described in this invention, the method includes: collecting real-time values ​​of lake and river flow, water level sensor sequences, and infrared thermal imager temperature fields; calculating instantaneous flow ratio and water surface slope; forming time series data; and processing the data to obtain a standard dataset. This includes the following steps:

[0041] The central control module triggers electromagnetic and ultrasonic flow meters to collect real-time values ​​of lake flow sources and river flow.

[0042] Real-time water level data is obtained by collecting the difference between the water level sensor data upstream and the water level sensor data downstream of the main body of the water tank;

[0043] The real-time temperature field is obtained by acquiring two-dimensional temperature distribution image data of the main surface of the water tank in real time using an infrared thermal imager.

[0044] The instantaneous water surface slope is obtained by calculating the real-time water level data;

[0045] The instantaneous flow ratio is calculated based on the real-time values ​​of lake flow and river flow.

[0046] Instantaneous flow ratio, real-time water level data, and real-time temperature field are integrated to form time series data. The time series data is then standardized to obtain a standard dataset.

[0047] As a preferred embodiment of the simulation method for river backwater effect based on hydraulic experiments described in this invention, the method includes the following steps: calculating a multi-field coupled backwater index based on a standard dataset, establishing a backwater strength grading standard, analyzing the difference rate between microbubble conditions and conventional conditions, and obtaining a backwater effect assessment report.

[0048] Based on the standard dataset and the maximum water level rise, the multi-field coupled jacking index is calculated.

[0049] A grading standard table is obtained by classifying the support strength level based on the multi-field coupled support index.

[0050] Extract the microbubble working condition and the frontal distance from the dataset to obtain a set of comparison parameters;

[0051] By integrating the multi-field coupling support index, the grading standard table, and the drag reduction difference rate, a support effect assessment report is obtained.

[0052] In a second aspect, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the simulation method for the river backwater effect based on hydraulic experiments as described in the first aspect of the present invention.

[0053] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the simulation method for the river backwater effect based on hydraulic experiments as described in the first aspect of the present invention.

[0054] The beneficial effects of this invention are as follows: By employing the synergistic effect of superhydrophobic coating and precision microporous array, active control of the Manning coefficient range of 0.015-0.06 is achieved, solving the problem that traditional experiments cannot simulate the dynamic changes in the roughness of natural rivers, thus improving the positioning accuracy of the top-support front. It is the first to create a multi-field coupled top-support index, which establishes a three-level intensity classification standard by integrating water level rise, corrected temperature difference and flow ratio, and quantifies the multi-field coupling effect of hydraulic-thermal-dynamic. The two technologies work together to achieve a full-chain innovation from mechanism research to engineering control. Compared with existing technologies, it has significant advantages in parameter control accuracy and mechanism revelation depth, and provides a standardized experimental and evaluation system for top-support effect research. Attached Figure Description

[0055] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 This is a flowchart of a simulation method for the river backwater effect based on hydraulic experiments.

[0057] Figure 2 A schematic diagram for constructing a transparent acrylic water tank.

[0058] Figure 3 This is a schematic diagram of the relative deviation.

[0059] Figure 4 This is a schematic diagram of a standard dataset. Detailed Implementation

[0060] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0061] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0062] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0063] Reference Figures 1-4 This is one embodiment of the present invention, which provides a simulation method for the river backwater effect based on hydraulic experiments, including the following steps:

[0064] S1. Construct a transparent plexiglass water tank, with an adjustable slope river channel upstream and a rectangular water tank downstream. The river flow and lake flow are controlled by independent water pumps, and flow meters are installed for calibration.

[0065] S1.1. Process transparent plexiglass into tank components and splice them together. After curing for 24 hours, conduct a full water test to obtain a properly sealed tank body.

[0066] Furthermore, transparent acrylic sheets are CNC cut into tank components that are 6 meters long, 0.5 meters wide, and 0.5 meters high. The joints are then bonded with silicone structural adhesive. After curing for 24 hours, a full water test is conducted. During the test, all joints are checked for leaks, and a sealed and qualified water tank body is obtained.

[0067] S1.2 An integrated stepper motor drive unit and ball screw transmission unit are installed upstream of the water tank body. The slope is calibrated by an electronic inclinometer to obtain a river section with an adjustable slope.

[0068] Furthermore, an STM32F407-controlled stepper motor drive unit and ball screw transmission unit are installed at the bottom upstream of the water tank. The ball screw lead is 5 mm. The slope value is measured in real time by an electronic inclinometer and compared with the target slope value. When the deviation between the measured slope value and the target slope value exceeds 0.005%, the stepper motor drive unit automatically adjusts the extension and retraction of the ball screw transmission unit until the slope value stabilizes within the target range. This results in an adjustable slope river section with a slope adjustment range of 0.05% to 0.15%. During the slope adjustment process, a laser displacement sensor is used to monitor the change in the height of the support column in real time to ensure that the slope adjustment accuracy reaches ±0.001%.

[0069] S1.3 Connect the rectangular water tank to the water tank body using a flange sealing unit to obtain the integrated rectangular water tank-water tank body.

[0070] Furthermore, a rectangular water tank measuring 1 meter × 0.5 meters × 0.5 meters is connected to the downstream end face of the water tank body via a DN150 flange. A rubber sealing ring with a Shore hardness of 60±5 is installed on the flange mating surface. All bolts are tightened evenly with a torque of 25 N·m. After the connection is completed, a sealing test is performed. Water is filled into the integrated rectangular water tank-water tank body to a working water depth of 0.4 meters and observed for 24 hours. If there is no leakage, the leakage rate is less than 0.1 liters / minute. After the sealing test is passed, the water in the integrated rectangular water tank-water tank body is drained. The flange connection is cleaned with anhydrous ethanol and dried with nitrogen for storage, thus completing the assembly of the integrated rectangular water tank-water tank body.

[0071] S1.4. The river flow is controlled by connecting the variable frequency pumping unit and the electromagnetic metering unit to the main body of the water tank through a DN50 pipeline unit.

[0072] Furthermore, a Grundfos NBG 40-125 variable frequency centrifugal pump unit and an E+H Promag 50W electromagnetic flow meter were connected to the inlet of the main water tank via a DN50 pipeline. A flow rectifier was installed at the end of the pipeline. The electromagnetic flow meter has a measurement range of 0.1-10 L / s and an accuracy of ±0.5%, outputting real-time flow values ​​via a 4-20mA signal. The variable frequency centrifugal pump unit adjusts its speed according to a PID control algorithm, with PID parameters set to a proportional coefficient of 0.8, an integral coefficient of 0.05, and a derivative coefficient of 0.12, stabilizing the river flow within the target value of ±0.05 L / s. A 20-liter standard metering container was used for volumetric-time calibration, verifying that the electromagnetic flow meter's measurement error at a flow rate of 5.0 L / s did not exceed ±0.5%. After calibration, the control voltage values ​​corresponding to different flow rates were recorded.

[0073] S1.5. The flow rate of the lake is controlled by a screw pumping unit and an ultrasonic metering unit, and a gate unit is installed in the outlet pipeline.

[0074] Furthermore, a NEMO SY screw pump unit and a Fuji FLR ultrasonic flow meter were connected to the bottom of a rectangular water tank via an outlet pipe. A multi-stage adjustable gate unit was installed at the end of the outlet pipe, with an opening adjustment accuracy of 0.1 mm. The ultrasonic flow meter had a measurement range of 0.1-5.0 L / s and an accuracy of ±1%, outputting real-time flow data via the Modbus-RTU protocol. The screw pump adjusted its speed according to a fuzzy control algorithm, with control parameters set to a proportional coefficient of 0.3 and an integral coefficient of 0.01, stabilizing the lake flow rate within the target range of ±0.05 L / s. A 20-liter standard measuring container was used for volumetric-time calibration, verifying that the ultrasonic flow meter's measurement error at a flow rate of 2.5 L / s did not exceed ±1%. After calibration, the flow values ​​corresponding to different gate openings were recorded.

[0075] S2. Close the lake flow, regulate the river flow, and collect benchmark water level data after the water flow stabilizes. Calculate the water surface slope and verify the deviation from the theoretical value of the Manning formula.

[0076] S2.1. The central control module sends a shutdown command to the screw pump unit, ultrasonic metering unit, and gate unit to shut off the lake flow. It also inputs an adjustment command to the integrated flow control module and uses a PID control algorithm to obtain a stable target water flow rate.

[0077] Furthermore, a shutdown command was sent to the NETZSCH NEMO SY screw pump unit, Fuji FLR ultrasonic flow meter, and multi-stage adjustable gate unit via a Siemens S7-1200 programmable logic controller to close the lake flow control channel. Subsequently, a target flow rate of 5.0 L / s was input to the integrated flow control module. A PID control algorithm with a proportional gain of 0.8, integral gain of 0.05, and derivative gain of 0.12 was used to adjust the speed of the Grundfos NBG 40-125 variable frequency centrifugal pump unit via a 4-20mA signal. The river flow rate value fed back by the E+H Promag 50W electromagnetic flow meter was monitored in real time. When the flow rate fluctuation did not exceed ±0.05 L / s for 60 consecutive seconds, a stable target flow rate was determined to have been reached. During the stabilization process, the PID control output was updated every 200 milliseconds to ensure a flow rate adjustment response time of less than 0.8 seconds. Ultimately, a stable target flow rate of 5.0 ± 0.05 L / s, meeting the Froude number similarity requirement, was obtained, providing constant flow conditions for subsequent baseline water level data acquisition.

[0078] S2.2 Based on the stable target water flow rate, the raw water level data is obtained through a capacitive water level sensor. The raw data is then filtered to calculate the average water level value of each measurement point, thus obtaining the benchmark water level data.

[0079] Furthermore, under stable target water flow conditions, 12 Honeywell 24PC capacitive water level sensors were activated to continuously collect raw water level data for 300 seconds at a sampling frequency of 100Hz. Kalman filtering algorithm was applied to the raw data to eliminate sensor noise and wave interference. The Kalman filtering parameters were set to process noise covariance and observation noise covariance. After filtering, outliers were removed by the ±3σ criterion to obtain the benchmark water level data.

[0080] S2.3 Calculate the actual measured water surface slope value based on the difference between the water level measurements at the inlet and outlet of the main body of the water tank.

[0081] Specifically, the expression is,

[0082]

[0083] Among them, WSS meas Z represents the actual measured water surface slope value. 入口 Z represents the water level measurement at the upstream inlet of the main body of the water tank. 出口 This is the water level measurement at the downstream outlet of the water tank.

[0084] S2.4. Calculate the theoretical water surface slope value according to Manning's formula.

[0085] Specifically, the expression is,

[0086]

[0087] Among them, WSS theory Here, m is the theoretical water surface slope, and Q is the Manning roughness coefficient. 河 Let A be the river flow rate, A be the cross-sectional area of ​​the flow path, and R be the hydraulic radius.

[0088] S2.5. The relative deviation is obtained by comparing the measured slope value with the theoretical slope value.

[0089] Specifically, the expression is,

[0090]

[0091] Where δ represents the relative deviation.

[0092] S3. Turn on lake flow, set the operating conditions according to the stable target water flow gradient, calculate water level changes and collect the reference temperature field.

[0093] S3.1. The stable target water flow rate is transmitted to the lake flow control module, and the gate unit opening is dynamically adjusted based on the fuzzy control algorithm to obtain the stable water flow rate.

[0094] Furthermore, the stable target water flow rate is transmitted to the lake flow control channel composed of a NEMO SY screw pump unit and a Fuji FLR ultrasonic flow meter. Based on the fuzzy control algorithm, the opening degree of the multi-stage adjustable gate unit is dynamically adjusted. The input variable of the fuzzy control algorithm is the flow deviation, and the output variable is the gate opening increment. The control rule base contains 25 IF-THEN type rules. Mamdani type inference and centroid method are used for defuzzification. The control quantity is updated every 200 milliseconds to stabilize the lake flow rate within the target value of ±0.05 liters / second within 60 seconds. After stabilization, the correspondence between gate opening and flow rate is recorded. The entire process is monitored in real time through the Modbus-RTU protocol using ultrasonic flow meter data.

[0095] S3.2. Perform Kalman filtering on the raw water level data to eliminate noise, and compare the difference with the reference water level data to obtain the real-time water level data.

[0096] Specifically, the expression is,

[0097]

[0098] in, This represents the filtered water level estimate for the i-th measuring point at time t. Let i be the predicted water level at the i-th measuring point. K represents the raw water level data of the i-th measuring point at time t. t Let i be the Kalman gain at time t, where i is the measurement point index and t is time.

[0099] S3.3 Calculate the difference between the real-time water level data and the reference water level to obtain the water level change.

[0100] Specifically, the expression is,

[0101]

[0102] Where, ΔZ i (t) represents the water level change at the i-th measuring point at time t. Let be the reference water level value for the i-th measuring point.

[0103] S3.4. Collect raw temperature data under initial steady-state conditions using an infrared thermal imager, and perform spatial averaging on the temperature field data to obtain a reference temperature field.

[0104] Furthermore, under initial steady-state conditions, a FLIR A655sc infrared thermal imager was used to continuously acquire surface temperature field data of the water tank at a sampling frequency of 30Hz. The thermal imager had a resolution of 640×512 pixels, an emissivity of 0.96, and a thermal sensitivity of 0.03℃. Spatial averaging was performed on the raw temperature data. First, abnormal pixels caused by water surface fluctuations were removed to obtain the cross-sectional average temperature at each time point as the reference temperature field. The temperature fluctuation range was controlled within ±0.1℃. The reference temperature field data and water level data were stored synchronously with a time alignment error of no more than 1 millisecond. During data processing, the laboratory ambient temperature was maintained at 25±1℃ and the relative humidity at 50±5%.

[0105] S4. Shut down river and lake flow, prepare a superhydrophobic coating in the water tank and connect it to a micropore array, then restart the typical operating conditions.

[0106] S4.1. A shutdown command is sent through the central control module to shut off the river and lake flow, and to activate the electromagnetic drainage valve unit to discharge the water in the main body of the tank, resulting in a dry main body of the tank.

[0107] Furthermore, a shutdown command was sent to the Grundfos NBG 40-125 variable frequency centrifugal pump unit and the Netzsch NEMO SY screw pump unit via a Siemens S7-1200 programmable logic controller, simultaneously shutting down the monitoring functions of the E+H Promag50W electromagnetic flowmeter and the Fuji FLR ultrasonic flowmeter. Then, the DN50 electromagnetic drain valve unit was activated to drain the deionized water from the tank body at a rate of 10 liters per minute. The residual water depth was monitored in real time during the drainage process. When the water depth dropped below 5 mm, the electromagnetic drain valve unit was closed. Compressed air was used to blow away any residual water film on the inner wall of the tank body and the surface of the microporous array, ensuring no visible water stains. Finally, a dry tank body with a relative humidity below 15% was obtained. During the drying process, the ambient temperature was maintained at 25±1℃ to prevent deformation of the acrylic sheet due to temperature differences. After drainage, the sealing of all sensor interfaces was checked, and after confirming no leaks, the superhydrophobic coating preparation process began.

[0108] S4.2. The inner wall of the dry water tank body is plasma cleaned to obtain an activated hydrophilic surface, and nano-silica is sprayed to obtain a superhydrophobic coating. The microporous plate unit is embedded into the superhydrophobic coating to obtain a microbubble unit.

[0109] Furthermore, in the 2.0-4.0 meter section of the inner wall of the dry water tank, the surface was treated with a 40kHz plasma cleaner at a power setting of 300W for 5 minutes to obtain an activated hydrophilic surface with a contact angle of <10°. Nano-SiO2 (20nm particle size) and PDMS (Dow Corning 184) were mixed at a 1:9 mass ratio, diluted with 2% n-hexane, and then evenly sprayed three times with a spray gun (0.3MPa air pressure, 20cm distance from the surface), with a 30-minute interval between each coat. The mixture was then cured at 80℃ for 2 hours to form a superhydrophobic coating with a contact angle of 152°±2° and a roll-off angle of <5°. A laser-processed 316L stainless steel microporous plate (pore size 50±2μm, pore density 400 pores / cm²) was then applied. 2 The microbubble unit is embedded in the coating area and connected to the air compressor pipeline via a flange. After a sealing test shows a leakage rate of <0.1L / min, a complete microbubble unit is obtained. The coating quality is verified using a contact angle meter (Krüss DSA25), with a standard deviation of ≤1° for 5 randomly selected contact angles. After activation, the microbubble unit can form a stable air film layer with a thickness of 0.1-0.3mm and a coverage of ≥95% under a pressure of 5-30kPa.

[0110] S4.3. A restart command is sent through the central control module to start the variable frequency pumping unit, screw pumping unit, and microbubble unit, and obtain the operating parameters.

[0111] Furthermore, a restart command is sent via a Siemens S7-1200 programmable logic controller to sequentially start the Grundfos NBG 40-125 variable frequency centrifugal pump unit, the Netzsch NEMO SY screw pump unit, and the microbubble unit. The variable frequency centrifugal pump unit adjusts its speed according to preset PID parameters (proportional coefficient 0.8, integral coefficient 0.05, derivative coefficient 0.12) to stabilize the river flow monitored by the E+H Promag 50W electromagnetic flowmeter at 5.0±0.05 liters / second. The screw pump dynamically adjusts the opening of the multi-stage adjustable gate unit through a fuzzy control algorithm to achieve a lake flow monitored by the Fuji FLR ultrasonic flowmeter at 2.5±0.05 liters / second. The air compressor of the microbubble unit adjusts its output pressure (α = Q_lake / Q_river) according to the formula P = 0.268α^(-0.4) to maintain the air pressure within the range of 20±1 kPa. After all devices are started, data from the E+HPromag 50W electromagnetic flow meter, Fuji FLR ultrasonic flow meter, Honeywell 24PC capacitive water level sensor, and FLIR A655sc infrared thermal imager are synchronously collected via the Modbus-RTU protocol, with a sampling interval of 200 milliseconds and a duration of 60 seconds to verify parameter stability.

[0112] S5. Adjust the riverbed roughness using microbubble units, collect water level and temperature data after intervention, and obtain a microbubble intervention dataset.

[0113] S5.1 Calculate the target pressure based on the stable target water flow rate, start the microbubble unit and adjust the air compressor unit, filter the water level change data, and obtain the water level rise sequence.

[0114] Furthermore, based on a stable target water flow rate and flow ratio, the air compressor of the microbubble unit is started and the output pressure is adjusted to 20±1 kPa. Raw water level data is acquired at a frequency of 100 Hz using a Honeywell 24PC capacitive water level sensor, and noise is eliminated using a Kalman filter algorithm to obtain the water level rise sequence.

[0115] S5.2 After performing temperature compensation calculations on the reference temperature field, the temperature data after intervention is obtained, and the maximum water level rise is extracted from the water level rise sequence.

[0116] Specifically, the expression is,

[0117] T corrected (x,y,t)=T(x,y,t)+k(T amb -T ref );

[0118] Among them, T corrected(x,y,t) represents the compensated temperature at coordinates (x,y) and time t, and T(x,y,t) represents the reference temperature field at coordinates (x,y) and time t. amb For ambient temperature, T ref This is the reference temperature.

[0119] Furthermore, based on the reference temperature field and the real-time ambient temperature, the temperature field after intervention acquired by the FLIR A655sc infrared thermal imager is compensated and calculated. The compensated temperature field is then spatially averaged to obtain the cross-sectional average temperature. At the same time, the maximum value is extracted from the water level rise sequence after Kalman filtering.

[0120] S5.3. The baseline roughness is obtained by inverting the measured surface slope of the river flow.

[0121] Furthermore, based on the measured water surface slope and stable river flow, the reference roughness is obtained by inverting the Manning formula, where the cross-sectional area of ​​the flow passage A = 0.25 square meters (flue width 0.5 meters × water depth 0.5 meters) and the hydraulic radius (wetted perimeter P = 2 × water depth + width = 1.5 meters) are consistent with the nominal roughness of PVC material, thus obtaining the reference roughness.

[0122] S5.4 Calculate the effective roughness based on Manning's formula, and calculate the drag reduction efficiency by comparing the reference roughness and the effective roughness.

[0123] Effective roughness, expressed as:

[0124]

[0125] Where, n eff For effective roughness, Q 河 This refers to the river's flow rate.

[0126] Drag reduction efficiency, expressed as:

[0127]

[0128] Where, η n For drag reduction efficiency, n0 is the reference roughness.

[0129] S5.5 Integrate the maximum water level rise, post-intervention temperature data, effective roughness, and drag reduction efficiency to obtain the microbubble intervention dataset.

[0130] Furthermore, the maximum water level rise, post-intervention temperature data, effective roughness, and drag reduction efficiency parameters are aligned and integrated by timestamp to construct a microbubble intervention dataset.

[0131] S6. Collect real-time values ​​of lake and river flow, water level sensor sequences, and infrared thermal imager temperature fields; calculate instantaneous flow ratio and water surface slope; form time series data and process it to obtain a standard dataset.

[0132] S6.1. The electromagnetic flowmeter and ultrasonic flowmeter are triggered by the central control module to collect real-time values ​​of lake flow and river flow.

[0133] Furthermore, the data acquisition functions of the electromagnetic flowmeter and ultrasonic flowmeter are synchronously triggered by the Siemens S7-1200 programmable logic controller. The E+H Promag 50W electromagnetic flowmeter acquires the real-time value of river flow Q_river(t) at a frequency of 50Hz, with a measurement range of 0.1-10 liters / second and an accuracy of ±0.5%. The Fuji FLR ultrasonic flowmeter acquires the real-time value of lake flow Q_lake(t) at a frequency of 50Hz, with a measurement range of 0.1-5 liters / second and an accuracy of ±1%. Both flowmeters transmit data to the central control module via the Modbus-RTU protocol (baud rate 19200), with the timestamp alignment error controlled within ±1 millisecond.

[0134] S6.2. Real-time water level data is obtained by collecting the difference between the upstream water level sensor data and the downstream water level sensor data of the main body of the water tank.

[0135] Furthermore, real-time water level data from the first upstream and twelfth downstream measuring points of the main body of the tank were synchronously acquired at a frequency of 100Hz using a Honeywell 24PC capacitive water level sensor array, with a measuring point spacing of 5.5 meters. A Kalman filter algorithm was used to reduce noise in the raw water level data. The filtered water level data was synchronized with the flow data acquired by the E+H Promag 50W electromagnetic flow meter and the Fuji FLR ultrasonic flow meter via the PTP protocol, with a time delay error of less than 1 millisecond. When a water level difference greater than 15 mm was detected, an abnormal alarm was triggered, eliminating invalid data caused by water turbulence or sensor malfunction, thus obtaining the real-time water level data.

[0136] S6.3. Real-time temperature field is obtained by acquiring two-dimensional temperature distribution image data of the main surface of the water tank through an infrared thermal imager.

[0137] Furthermore, a FLIR A655sc infrared thermal imager was used to acquire 640×512 pixel two-dimensional temperature distribution images of the main surface of the water tank at a sampling frequency of 30Hz. The compensated temperature field data was used to calculate the cross-sectional average temperature through spatial averaging. The temperature data was synchronized with the data from the Honeywell 24PC capacitive water level sensor via the PTP protocol, with a time alignment error of <1ms. This yielded the real-time temperature field.

[0138] S6.4 Calculate the instantaneous water surface slope by analyzing the real-time water level data.

[0139] Specifically, the expression is,

[0140]

[0141] Where WSS(t) is the instantaneous water surface slope at time t, Z1(t) is the real-time water level at the upstream inlet of the flume (the first measuring point) at time t, and Z 12 (t) represents the real-time water level at the downstream outlet of the water tank (the 12th measuring point) at time t.

[0142] S6.5. Based on the real-time values ​​of lake flow and river flow, the instantaneous flow ratio is calculated.

[0143] Specifically, the expression is,

[0144]

[0145] Where α(t) is the instantaneous flow ratio at time t, Q 湖 (t) represents the real-time value of the lake flow rate at time t, Q 河 (t) represents the real-time value of the river flow at time t.

[0146] S6.6 Integrate instantaneous flow ratio, real-time water level data, and real-time temperature field to form time series data. Standardize the time series data to obtain a standard dataset.

[0147] Furthermore, the real-time river flow values ​​collected by the E+H Promag 50W electromagnetic flowmeter, the real-time lake flow values ​​collected by the Fuji FLR ultrasonic flowmeter, the real-time water level data collected by the Honeywell 24PC capacitive water level sensor, and the real-time temperature field collected by the FLIR A655sc infrared thermal imager were integrated by timestamp alignment. The time series data were processed using the Z-score normalization method, instantaneous flow ratios were removed, wave interference was eliminated by applying a Butterworth low-pass filter (cutoff frequency 2Hz) to the real-time water level data, and environmental temperature compensation was applied to the real-time temperature field to obtain a standard dataset.

[0148] S7. Based on the standard dataset, calculate the multi-field coupling support index, establish a support strength grading standard, analyze the difference rate between microbubble working conditions and conventional working conditions, and obtain a support effect assessment report.

[0149] S7.1 Calculate the multi-field coupled backwater index based on the standard dataset and the maximum water level rise;

[0150]

[0151] Among them, I BS For the multi-field coupled top support index, ΔZ max The maximum water level rise is given by ΔT, the corrected temperature difference is given by ρ, the water density is given by α, and the flow rate ratio is given by α.

[0152] S7.2. Based on the multi-field coupling top support index, the top support strength level is divided into levels, and the grading standard table is obtained.

[0153] Furthermore, based on the calculation results of the multi-field coupled backing index, the backing intensity is divided into three levels: when I_BS<0.15, it is judged as a weak backing level, and the corresponding engineering measure is conventional monitoring; when 0.15≤I_BS≤0.4, it is judged as a medium backing level, and an early warning needs to be activated and the gate opening adjusted by 10%-30%; when I_BS>0.4, it is judged as a strong backing level, and emergency flood discharge measures must be taken.

[0154] S7.3 Extract the microbubble working condition and the frontal distance from the dataset to obtain the comparison parameter set and calculate the drag reduction difference rate.

[0155]

[0156] Where η is the drag reduction difference rate, This refers to the extension distance of the top support front under microbubble conditions. This refers to the extension distance of the top support front under normal operating conditions;

[0157] S7.4. Integrate the multi-field coupling support index, the grading standard table, and the drag reduction difference rate to obtain the support effect assessment report.

[0158] Furthermore, the three core data points—the multi-field coupling backwater index (I_BS) calculation results, the intensity level (weak / medium / strong) determined by the grading standard table, and the drag reduction difference rate—are integrated to generate a structured backwater effect assessment report. The report comprises four parts: a working condition parameter table listing the flow ratio, maximum water level rise, and corrected temperature difference; an analysis results table displaying the I_BS value, front retreat rate, and corresponding intensity level; a drag reduction effect assessment comparing the I_BS difference rate between microbubble working conditions and conventional working conditions; and engineering recommendations providing specific measures based on the level. The report is output in PDF format.

[0159] This embodiment also provides a computer device applicable to the simulation method of river backwater effect based on hydraulic experiments, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the simulation method of river backwater effect based on hydraulic experiments as proposed in the above embodiment.

[0160] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0161] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the simulation method for the river backwater effect based on hydraulic experiments as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0162] In summary, this invention achieves active control of the Manning coefficient within the range of 0.015-0.06 by employing a superhydrophobic coating and a precision microporous array, solving the problem that traditional experiments cannot simulate the dynamic changes in the roughness of natural rivers, thus improving the positioning accuracy of the top-support front. It also pioneers a multi-field coupled top-support index, establishing a three-level intensity grading standard by integrating water level rise, corrected temperature difference, and flow ratio, quantifying the multi-field coupling effect of hydraulics, thermodynamics, and dynamics. These two technologies work together to achieve a complete innovation chain from mechanism research to engineering control. Compared with existing technologies, it has significant advantages in parameter control accuracy and depth of mechanism revelation, providing a standardized experimental and evaluation system for top-support effect research.

[0163] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A simulation method for the river backwater effect based on hydraulic experiments, characterized in that: include, A transparent plexiglass water tank is constructed, with an adjustable-slope river channel upstream and a rectangular water tank downstream. The river flow and lake flow are independently regulated through an integrated flow control module. The lake flow was closed, the river flow was regulated, and after the water flow stabilized, benchmark water level data was collected to calculate the water surface slope and verify the deviation from the theoretical value of the Manning formula. Turn on the lake flow rate, set the operating conditions according to the stable target water flow gradient, calculate the water level change and collect the reference temperature field; By shutting down river and lake flow, a superhydrophobic coating was prepared in a flume and connected to a micropore array. The typical working conditions were restarted, and the riverbed roughness was adjusted by microbubble units. Water level and temperature data were collected after the intervention to obtain a microbubble intervention dataset. Real-time values ​​of lake and river flow, water level sensor sequences, and infrared thermal imager temperature fields are collected. Instantaneous flow ratio and water surface slope are calculated to form time series data, which are then processed to obtain a standard dataset. Based on the standard dataset, the multi-field coupling support index is calculated, a support strength grading standard is established, the difference rate between microbubble working conditions and conventional working conditions is analyzed, and a support effect assessment report is obtained.

2. The simulation method for river backwater effect based on hydraulic experiments as described in claim 1, characterized in that: A transparent acrylic glass water tank is constructed, with an adjustable-slope river channel upstream and a rectangular water tank downstream. The river and lake flow rates are independently regulated via an integrated flow control module. The process includes the following steps. Transparent acrylic glass is processed into tank components and spliced ​​together. After curing for 24 hours, a full water test is conducted to obtain a properly sealed tank body. An integrated stepper motor drive unit and a ball screw transmission unit are installed upstream of the water tank body. The slope is calibrated by an electronic inclinometer to obtain a river section with an adjustable slope. The rectangular water tank is connected to the water tank body using a flange sealing unit to obtain an integrated rectangular water tank-water tank body; The river flow is controlled by connecting the variable frequency pumping unit and the electromagnetic metering unit to the main body of the water tank through a DN50 pipeline unit. The flow rate of the lake is controlled by using a screw pump unit and an ultrasonic metering unit, with a gate unit installed in the outlet pipeline.

3. The simulation method for river backwater effect based on hydraulic experiments as described in claim 2, characterized in that: The process involves closing the lake's flow, regulating the river's flow, collecting benchmark water level data after the water flow stabilizes, calculating the water surface slope, and verifying the deviation from the theoretical value of the Manning formula. This includes the following steps: The central control module sends a shutdown command to the screw pump unit, ultrasonic metering unit, and gate unit to shut off the lake flow. It also inputs an adjustment command to the integrated flow control module and uses a PID control algorithm to obtain a stable target water flow rate. Based on a stable target water flow rate, raw water level data is acquired through a capacitive water level sensor. The raw data is then filtered to calculate the average water level value at each measurement point, thus obtaining the benchmark water level data. The actual measured water surface slope value is calculated based on the difference between the water level measurements at the inlet and outlet of the main body of the water tank. The theoretical water surface slope value is calculated based on Manning's formula; The relative deviation is obtained by comparing the measured slope value with the theoretical slope value.

4. The simulation method for river backwater effect based on hydraulic experiments as described in claim 3, characterized in that: To enable lake flow, set the operating conditions according to the stable target flow gradient, calculate water level changes, and collect the reference temperature field, including the following steps. The stable target water flow rate is transmitted to the lake flow control module, and the gate unit opening is dynamically adjusted based on the fuzzy control algorithm to obtain the stable water flow rate. Kalman filtering is applied to the raw water level data to eliminate noise, and the difference is compared with the reference water level data to obtain the real-time water level data; The difference between real-time water level data and reference water level is calculated to obtain the water level change; Raw temperature data was collected under initial steady-state conditions using an infrared thermal imager. The temperature field data was then spatially averaged to obtain a reference temperature field.

5. The simulation method for river backwater effect based on hydraulic experiments as described in claim 4, characterized in that: By shutting down river and lake flow, preparing a superhydrophobic coating within a water tank and connecting it to a microporous array, and then restarting under typical operating conditions, the following steps are included. The central control module sends a shutdown command to shut off the river and lake flow, activates the electromagnetic drainage valve unit, and discharges the water in the main body of the tank to obtain a dry tank body. The inner wall of the dry water tank body is plasma cleaned to obtain an activated hydrophilic surface, and then sprayed with nano-silica to obtain a superhydrophobic coating. The microporous plate unit is embedded into the superhydrophobic coating to obtain a microbubble unit. A restart command is issued by the central control module to start the variable frequency pumping unit, screw pumping unit, and microbubble unit, thereby obtaining the operating parameters.

6. The simulation method for river backwater effect based on hydraulic experiments as described in claim 5, characterized in that: By adjusting the riverbed roughness using microbubble units, and collecting water level and temperature data after the intervention, a microbubble intervention dataset is obtained, including the following steps. Calculate the target pressure based on the stable target water flow rate, start the microbubble unit and adjust the air compressor unit, filter the water level change data, and obtain the water level rise sequence. After performing temperature compensation calculations on the reference temperature field, the temperature data after intervention is obtained, and the maximum water level rise is extracted from the water level rise sequence. The baseline roughness is obtained by inverting the measured surface slope of the river flow. The effective roughness is calculated based on the Manning formula, and the drag reduction efficiency is calculated by comparing the reference roughness and the effective roughness. By integrating the maximum water level rise, post-intervention temperature data, effective roughness, and drag reduction efficiency, a microbubble intervention dataset is obtained.

7. The simulation method for river backwater effect based on hydraulic experiments as described in claim 6, characterized in that: Real-time flow values ​​of lakes and rivers, water level sensor sequences, and infrared thermal imager temperature fields are collected. Instantaneous flow ratios and water surface slopes are calculated to form time-series data, which are then processed to obtain a standard dataset. This process includes the following steps: The central control module triggers electromagnetic and ultrasonic flow meters to collect real-time values ​​of lake flow sources and river flow. Real-time water level data is obtained by collecting the difference between the water level sensor data upstream and the water level sensor data downstream of the main body of the water tank; The real-time temperature field is obtained by acquiring two-dimensional temperature distribution image data of the main surface of the water tank in real time using an infrared thermal imager. The instantaneous water surface slope is obtained by calculating the real-time water level data; The instantaneous flow ratio is calculated based on the real-time values ​​of lake flow and river flow. Instantaneous flow ratio, real-time water level data, and real-time temperature field are integrated to form time series data. The time series data is then standardized to obtain a standard dataset.

8. The simulation method for river backwater effect based on hydraulic experiments as described in claim 7, characterized in that: Based on a standard dataset, a multi-field coupled support index is calculated, a support strength grading standard is established, the difference rate between microbubble working conditions and conventional working conditions is analyzed, and a support effect assessment report is obtained. Includes the following steps, Based on the standard dataset and the maximum water level rise, the multi-field coupled jacking index is calculated. A grading standard table is obtained by classifying the support strength level based on the multi-field coupled support index. Extract the frontal distance between the microbubble working condition and the conventional working condition from the dataset to obtain a set of comparison parameters and calculate the drag reduction difference rate. By integrating the multi-field coupling support index, the grading standard table, and the drag reduction difference rate, a support effect assessment report is obtained.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the simulation method for the river backwater effect based on hydraulic experiments as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the simulation method for the river backwater effect based on hydraulic experiments as described in any one of claims 1 to 8.

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