Method and system for measuring overtopping sediment transport rate and scouring rate for physical water tank experiment

Abstract

The invention provides a method and system for measuring the overtopping sediment transport rate and the scouring rate for a physical water tank experiment, and the method comprises the steps: deploying a sensor array to collect wave parameters, water flow parameters and sediment characteristic parameters, and calculating the overtopping sediment transport rate and the scouring rate based on the collected data; measurement parameters are dynamically adjusted by analyzing water flow turbulence characteristics in real time, data correction is performed by combining inertia characteristics of sediment particles and response characteristics of a measurement system, and the method comprises the following specific steps: synchronously acquiring wave height, period, water flow speed, sediment content and particle size distribution; extracting the instantaneous turbulence intensity and the water velocity gradient based on the collected data; calculating the overtopping sediment transport rate according to the wave parameters, the turbulence characteristics and the sediment characteristics; calculating the scouring rate by combining the overtopping sediment transport rate and the bed surface characteristic parameters; calculating a dynamic correction factor according to the sediment transport rate, the scouring rate and the response time of the measuring device; according to the invention, the reliability of wave-overtopping sediment transport and scouring process measurement under complex water flow conditions can be improved.

Description

Technical Field

[0001] This invention relates to the field of physical experiment technology, and specifically to a method and system for measuring the overpass sediment transport rate and scour rate in physical water tank experiments. Background Technology

[0002] In physical flume experiments, the measurement results of overflight sediment transport rate and scour rate are crucial for analyzing coastal sediment movement and assessing the stability of engineering structures. Existing measurement techniques are mostly based on fixed-parameter models, using preset sampling frequencies and empirical formulas. Since these empirical formulas are generally based on average flow characteristics, they fail to effectively capture the impact of dynamic changes in turbulent flow characteristics on the measurement process. Specifically, when the flow is in a highly turbulent state, the random fluctuations in instantaneous turbulence intensity and the spatial abrupt changes in the flow velocity gradient significantly alter the suspension and transport patterns of sediment particles. However, existing techniques lack real-time perception and quantification of turbulent characteristics, preventing the measurement system from dynamically adjusting sampling strategies and calculation parameters. This results in calculations of sediment transport rate and scour rate often deviating from the actual physical process under complex flow field conditions, especially when the inertial characteristics of sediment particles differ significantly, such as in scenarios involving a mixture of coarse and fine particles. This measurement deviation, caused by neglecting the coupling effect of turbulent dynamics and particle inertia, directly limits the accuracy of physical model experiments in simulating real-world engineering scenarios.

[0003] There is an urgent need for a technical solution that can specifically address the above problems in order to improve the reliability of measurements of wave transport and scouring processes under complex water flow conditions. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the existing technology and propose a method for measuring the overtopping sediment transport rate and scour rate in physical flume experiments. The method is characterized by: deploying a sensor array to collect wave parameters, water flow parameters and sediment characteristic parameters, and calculating the overtopping sediment transport rate and scour rate based on the collected data. The measurement parameters are dynamically adjusted by analyzing the turbulence characteristics of the water flow in real time, and the data is corrected by combining the inertial characteristics of sediment particles with the response characteristics of the measurement system. The specific steps are: synchronously collecting wave height, period, water flow velocity, sediment content and particle size distribution. Instantaneous turbulence intensity and water flow velocity gradient are extracted based on the collected data; The over-wave sediment transport rate is calculated based on wave parameters, turbulence characteristics, and sediment properties. The scour rate was calculated by combining the over-wave sediment transport rate and bed surface characteristic parameters. The dynamic correction factor is calculated based on the sand transport rate, erosion rate, and the response time of the measuring device. The sensor sampling frequency and data weight are adjusted based on the dynamic correction factor; the corrected wave transport rate and scour rate are output.

[0005] Preferably, a distributed sensor array is used in the synchronous acquisition process. The distributed sensor array includes wave sensors, three-dimensional flow meters, optical sediment concentration meters and bed pressure sensors arranged along the cross-section of the water tank. All sensors achieve synchronous data acquisition with adjustable frequency through a synchronous controller.

[0006] Further preferably, a wavelet transform and spectrum analysis combined algorithm is used when extracting instantaneous turbulence intensity and water flow velocity gradient. The sampling frequency of the combined algorithm is twice the data acquisition frequency to avoid signal aliasing.

[0007] More preferably, when adjusting the sensor sampling frequency, a high-frequency sampling mode is activated when the dynamic correction factor exceeds a preset threshold. The sampling frequency of the high-frequency sampling mode is 3-5 times the base frequency, and the base sampling frequency is maintained when the dynamic correction factor does not exceed the preset threshold.

[0008] A further preferred formula for calculating the overtopping sediment transport rate is: ; Where Q is the overtopping sediment transport rate, in kg / (m·s); H is the wave height, in m; and T is the wave period, in s. d represents the instantaneous turbulence intensity, expressed in m / s. 50 The median particle size of sediment is expressed in mm. Reference particle size, in mm; u represents the water flow velocity gradient, measured in seconds (s). -1 g is the acceleration due to gravity, in m / s²; k1, a, b, c, e, and f are empirical coefficients.

[0009] A further preferred formula for calculating the scour rate is: ; Where E is the scour rate, in kg / (m²·s); S is the bed slope; This refers to the density of sediment, expressed in kg / m³. This is the density of water, expressed in kg / m³. The relaxation time of sediment particles is expressed in seconds. The reference flow velocity is expressed in m / s; k2, d, h, i, j, and k are empirical coefficients.

[0010] A further preferred formula for calculating the dynamic correction factor is: ; Where C is the dynamic correction factor; Q0 and E0 are the reference sediment transport rate and scour rate; The measurement device response time is expressed in seconds (s). This is a reference length scale, with units in meters (m). u0 is the reference velocity gradient, in seconds. -1 k3, k4, m, n, and p are empirical coefficients.

[0011] A system for measuring the overtopping sediment transport rate and scour rate in physical flume experiments, applied to any of the above-described methods for measuring the overtopping sediment transport rate and scour rate in physical flume experiments, includes a multi-sensor data acquisition module, a data processing unit, and an output unit. The multi-sensor data acquisition module is electrically connected to the data processing unit, and the data processing unit is electrically connected to the output unit. The system also includes a turbulence feature extraction module, a particle characteristic analysis module, a multi-parameter collaborative calculation module, and a dynamic correction control module. The output of the multi-sensor data acquisition module is connected to the input of the turbulence feature extraction module and the particle characteristic analysis module, respectively. The outputs of the turbulence feature extraction module and the particle characteristic analysis module are both connected to the input of the multi-parameter collaborative calculation module. The output of the multi-parameter collaborative calculation module is connected to the input of the dynamic correction control module and the output unit, respectively. The output of the dynamic correction control module is connected to the input of the multi-sensor data acquisition module.

[0012] More preferably, the multi-sensor data acquisition module includes a wave sensor, a three-dimensional flow meter, an optical sediment concentration meter, a bed surface pressure sensor, and a synchronization controller. The synchronization controller is electrically connected to the wave sensor, the three-dimensional flow meter, the optical sediment concentration meter, and the bed surface pressure sensor, respectively, and is used to control all sensors to synchronously acquire data at an adjustable fundamental frequency.

[0013] Further preferably, the multi-parameter collaborative calculation module adopts a parallel computing architecture, which includes multiple processing cores for parallel execution of the calculation of the over-wave sediment transport rate, scour rate and dynamic correction factor. The calculation delay is controlled within a preset threshold. The dynamic correction control module outputs a control signal to the multi-sensor data acquisition module according to the dynamic correction factor to adjust its sampling frequency.

[0014] Technical effects: This invention solves the measurement deviation problem in complex flow fields caused by neglecting the coupling effect of turbulent dynamics and particle inertia in existing technologies by dynamically adjusting measurement parameters in real time through analysis of water flow turbulence characteristics and combining the inertial characteristics of sediment particles with the response characteristics of the measurement system for data correction. Its innovative technical point lies in linking dynamic turbulence perception with dynamic adjustment of measurement parameters, while incorporating particle inertia and system response correction mechanisms. This allows the measurement process to adapt to complex water flow changes, improving the reliability of overwater sediment transport rate and scour rate measurements, and is particularly suitable for scenarios with high turbulence and significant differences in sediment particle characteristics. Attached Figure Description

[0015] Figure 1This is a flowchart of the method for measuring the overpass sediment transport rate and scour rate used in the physical flume experiment of this application. Figure 2 This is a connection block diagram of the wave transport rate and scour rate measurement system used in the physical flume experiment of this application; Figure 3 This is a connection block diagram of the multi-sensor data acquisition module of this application. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0017] Traditional technical solutions have the following technical problems: In existing physical flume experiments, the methods for measuring the overflow sediment transport rate and scour rate fail to analyze the turbulence characteristics of the water flow in real time to dynamically adjust the measurement parameters, and do not integrate the inertial characteristics of sediment particles with the response characteristics of the measurement system for data correction. This results in insufficient measurement accuracy under complex water flow conditions and fails to accurately reflect the influence of turbulence, particle inertia and other factors on the sediment transport and scour process.

[0018] Based on this, please refer to Figure 1 This embodiment provides a method for measuring the overtopping sediment transport rate and scour rate in physical flume experiments, including: S1: Deploy a sensor array to collect wave parameters, water flow parameters, and sediment characteristic parameters, and calculate the over-wave sediment transport rate and scour rate based on the collected data; S2: By dynamically adjusting the measurement parameters through real-time analysis of water flow turbulence characteristics, and combining the inertial characteristics of sediment particles with the response characteristics of the measurement system, data correction is performed. The specific steps are as follows: S21: Simultaneously collect wave height, period, water flow velocity, sediment concentration, and particle size distribution; S22: Extract instantaneous turbulence intensity and water flow velocity gradient based on collected data; S23: Calculate the overtopping sediment transport rate based on wave parameters, turbulence characteristics, and sediment properties; S24: Calculate the scour rate by combining the over-wave sediment transport rate and bed surface characteristic parameters; S25: Calculate the dynamic correction factor based on the sand transport rate, scour rate and the response time of the measuring device; S26: Adjust the sensor sampling frequency and data weights based on dynamic correction factors; S27: Output corrected over-wave sediment transport rate and scour rate.

[0019] This technical solution integrates real-time turbulence analysis, particle inertia considerations, and system response correction into the measurement process by constructing a complete workflow encompassing acquisition, extraction, calculation, correction, and final output. This enables comprehensive processing of multiple influencing factors under complex water flow conditions. Overwater sediment transport rate refers to the rate at which sediment on the top of a beach or sandbar is carried over by waves and transported to the landside under the influence of strong storms or extreme waves. Its core function is to describe the rate at which sediment migrates with the overwater current, commonly expressed in kilograms per meter per second. This rate is directly related to wave energy, water flow turbulence characteristics, and sediment particle characteristics; the greater the wave height and the longer the period, the greater the instantaneous intensity of turbulence, the higher the probability of fine-grained sediment initiation, and consequently, the greater the overwater sediment transport rate.

[0020] The scour rate refers to the mass of sediment carried away per unit area of ​​bed surface by water flow or waves per unit time. It essentially reflects the rate at which sediment is stripped from the bed surface, and is commonly measured in kilograms per square meter per second. This rate is influenced by factors such as the overcurrent transport rate, bed slope, density difference between sediment and water, sediment particle inertia, turbulence intensity, and wave backflow intensity. A steeper bed slope, higher sediment density, and stronger turbulence often result in a higher scour rate.

[0021] By synchronously collecting multi-dimensional parameters, the comprehensiveness of the data was ensured; turbulence characteristics were extracted to provide key dynamic parameters for subsequent calculations; the combination of multi-factor calculation of sediment transport rate and scour rate improved the fit of the formula to the actual physical process; and the dynamic correction mechanism further compensated for system errors, ultimately achieving dynamic adaptation of the measurement process and accurate optimization of the results.

[0022] Traditional technical solutions have the following technical problems: In existing measurement methods, sensor acquisition systems are mostly asynchronous, which cannot fully acquire wave, water flow and sediment parameters at different cross sections of the water tank, resulting in a lack of spatial correlation and temporal consistency in the data, affecting the accuracy of subsequent analysis.

[0023] Based on this, a distributed sensor array is used in the synchronous acquisition process. The distributed sensor array includes wave sensors, three-dimensional flow meters, optical sediment concentration meters and bed pressure sensors arranged along the cross-section of the water tank. All sensors achieve synchronous data acquisition with adjustable frequency through a synchronous controller.

[0024] This technical solution deploys a distributed sensor array covering different sections of the water tank, and combines multiple types of sensors to collect parameters such as wave, flow velocity, sediment content, and bed pressure, ensuring the spatial comprehensiveness of the measurement. The application of a synchronous controller enables all sensors to acquire data synchronously at an adjustable frequency, ensuring the temporal consistency of the data and avoiding parameter correlation deviations caused by asynchronous acquisition.

[0025] The water tank is a wave tank with a length of 30m, a width of 2.5m, and a height of 1.5m. A partition wall is set up along the center line of the water tank, with one side of the wall being the test section for arranging the model and the other side being the wave-damping section to reduce wave reflection. The sand used in the experiment is well-sorted fine sand with a median particle size of 0.18mm, a density of 2.6g / cm³, a porosity of 0.4, and a settling velocity of 2.0cm / s. The sensor array is evenly arranged along the effective test section of the water tank, which is 23m long and 115cm wide.

[0026] A vertical wall is installed on the bank of the flume, and a water and sediment collection device is installed behind the wall to collect the water and sediment from the waves, and to help verify the calculation results of the sediment transport rate from the waves. An optical sediment concentration meter is arranged above the vertical wall along the width of the flume to output the instantaneous sediment concentration of the water from the waves. Wave sensors, flow meters and bed pressure sensors are arranged in an array along the center line of the flume test section to output the dynamic changes of wave height, still water surface elevation, flow velocity and bed pressure along the bank of the flume cross-section test model.

[0027] The collaborative acquisition and synchronous control of multiple sensors provide rich and accurate basic data for subsequent turbulence feature extraction, sediment transport rate and scour rate calculation, overcoming the limitations of traditional single sensor or asynchronous acquisition.

[0028] Traditional technical solutions have the following technical problems: When extracting instantaneous turbulence intensity and water flow velocity gradient, existing methods often use a single algorithm or have unreasonable sampling frequency settings, resulting in signal aliasing or inaccurate feature extraction. This makes it impossible to accurately capture the dynamic turbulence characteristics of the water flow, affecting the accuracy of sediment transport rate and scour rate calculations based on these features.

[0029] Based on this, a combined algorithm of wavelet transform and spectrum analysis is used to extract instantaneous turbulence intensity and water flow velocity gradient. The sampling frequency of the combined algorithm is twice the data acquisition frequency to avoid signal aliasing.

[0030] This technical solution combines the advantages of wavelet transform and spectrum analysis in time-frequency analysis. Wavelet transform can effectively capture the instantaneous characteristics of the signal, while spectrum analysis can accurately extract frequency domain information. The two work together to improve the extraction accuracy of turbulence intensity and velocity gradient. By setting the sampling frequency of the combined algorithm to twice the data acquisition frequency, following the Nyquist sampling theorem, signal aliasing is effectively avoided, ensuring that the extracted turbulence features truly reflect the dynamic changes of the water flow.

[0031] Accurate turbulence feature extraction provides a reliable basis for the accurate calculation of subsequent sediment transport rate and scour rate, and solves the feature distortion problem caused by traditional single algorithm or unreasonable sampling frequency.

[0032] Traditional technical solutions have the following technical problems: existing measurement systems mostly use a fixed sampling frequency, which cannot adjust the sampling strategy according to the dynamic changes in water flow conditions. When the water flow changes drastically, insufficient sampling leads to data distortion, while oversampling when the water flow is stable causes waste of resources, affecting measurement efficiency and data validity.

[0033] Based on this, when adjusting the sensor sampling frequency, a high-frequency sampling mode is activated when the dynamic correction factor exceeds a preset threshold. The sampling frequency of the high-frequency sampling mode is 3-5 times the base frequency. When the dynamic correction factor does not exceed the preset threshold, the base sampling frequency is maintained.

[0034] This technical solution introduces a dynamic correction factor as the basis for adjusting the sampling frequency, thus constructing an adaptive sampling mechanism: when the dynamic correction factor exceeds the preset threshold, it indicates that the water flow conditions are complex or the measurement deviation is large. At this time, a high-frequency sampling mode is activated, such as 3-5 times the base frequency, to ensure that enough detailed data is captured; when the dynamic correction factor does not exceed the threshold, it indicates that the water flow is stable or the measurement deviation is within an acceptable range, and the base sampling frequency is maintained to save resources.

[0035] This dynamic adjustment strategy based on actual measurement conditions achieves the matching of sampling frequency with water flow conditions, ensuring data integrity under complex water flow conditions and improving measurement efficiency under stable conditions, thus overcoming the limitations of fixed sampling frequency.

[0036] Traditional technical solutions have the following technical problems: existing formulas for calculating the overflow sediment transport rate mostly use time-averaged values, ignoring dynamic factors such as instantaneous turbulence intensity and water velocity gradient, as well as the synergistic effect of sediment particle size and wave parameters. This results in the formulas not being comprehensive enough in describing complex physical processes, and the calculation results deviate from the actual sediment transport situation, especially under highly turbulent or non-uniform sediment conditions, the error is relatively large.

[0037] Based on this, the formula for calculating the over-wave sediment transport rate is: ; Where Q is the overpass sediment transport rate, in kg / (m·s), representing the mass of sediment passing through a unit width per unit time; H is the wave height, in m, where the effective wave height is used; and T is the wave period, in s, where the spectral peak period is used. It represents the instantaneous turbulence intensity, measured in m / s, and reflects the severity of the velocity fluctuations in the water flow. The median particle size of sediment, in mm, refers to the particle size that accounts for 50% of the mass of sediment particles. The reference particle size, in mm, serves as a benchmark for comparing sediment particle sizes. u represents the water flow velocity gradient, measured in seconds (s). -1, which represents the rate of change of water flow velocity in space; g is the acceleration due to gravity, with units of m / s², and a value of approximately 9.8 m / s²; k1, a, b, c, e, and f are empirical coefficients, determined through experimental calibration.

[0038] This formula achieves accurate quantification of the over-wave sediment transport process through the coupling and correlation of multiple physical parameters. The design of each part addresses the shortcomings of existing technologies that ignore the synergistic influence of dynamic flow field and sediment characteristics.

[0039] In the formula, the wave height With wave cycle As fundamental parameters, they respectively reflect the wave's energy intensity and time scale, which are expressed as power functions. The nonlinear contribution to sediment transport rate is reflected in the wave height and period—the greater the wave height and the longer the period, the stronger the sediment-carrying capacity. This relationship is demonstrated by empirical coefficients. and Dynamic adjustments are made to adapt to different wave patterns, such as shallow water waves and breaking waves, where the value of 'a' ranges from 1.5 to 3.0, and the value of 'b' ranges from 0.5 to 1.5.

[0040] Instantaneous turbulence intensity pass This study introduces a method to directly quantify the ability of water flow pulsations to entrain sediment particles. In highly turbulent environments, such as wave-breaking zones, random fluctuations in water flow velocity significantly enhance sediment suspension and transport. The setting of the value enables the formula to sensitively capture this effect, solving the problem of simplifying the influence of turbulence in traditional formulas.

[0041] Sediment characteristics through The items are reflected, including The median particle size of the sediment is... Using the reference particle size, the ratio of the two reflects the differences in sediment gradation. For fine-grained sediments, a smaller ratio... This will lower the value, reflecting its characteristic of being more easily carried by water flow; while coarse-grained silt will have the opposite effect. The value was calibrated experimentally to ensure that the relationship was consistent with the actual sediment transport law, thus overcoming the shortcomings of existing formulas in adapting to non-uniform sediments. By quantifying the degree to which the particle size of the target sand sample deviates from the benchmark, if the ratio of the median particle size to the reference particle size is greater than 1, it indicates that the median particle size of the sand sample is larger than the benchmark level, and the overall particles are coarser; if the ratio is less than 1, the median particle size of the sand sample is smaller than the benchmark, and the overall particles are finer. This relative index indirectly reflects the influence of sediment gradation differences on its initiation, settling, and transport characteristics. The reference particle size refers to the benchmark value for comparing sediment particle sizes. It needs to be determined in conjunction with the experimental purpose, sediment type, and industry standards or experimental settings, and should be close to the order of magnitude of the median particle size of the sediment to ensure that the ratio can effectively reflect the sediment gradation differences.

[0042] The core innovation of the formula lies in the exponent term. ,in For the water flow velocity gradient, The characteristic velocity of the wave reflects the inertial force of wave propagation. This exponential term describes the inhibitory effect of the velocity gradient on sediment transport—when the water flow velocity changes drastically in space, such as a sudden drop in velocity near the bed surface, a shear force that hinders sediment movement is formed. At this time, the value of this term decreases, thus reducing the overall sediment transport rate. The value was determined by matching experimental data under different flow velocity gradient scenarios to ensure that the formula can accurately reflect the sand transport attenuation law in complex flow fields.

[0043] empirical coefficient As an overall scaling factor, it takes into account basic conditions such as the size of the water tank and the density of the sediment. It is calibrated through multiple sets of comparative experiments to ensure that the calculation results of the formula are consistent with the measured data of the physical model.

[0044] The overtopping sediment transport rate formula constructed by this technical solution comprehensively considers wave parameters H, T, and turbulence characteristics. , u, Sediment characteristics , The synergistic effect of gravity factor g: wave height and period reflect the energy input of waves, instantaneous turbulence intensity reflects the ability of water flow pulsation to carry sediment, water flow velocity gradient describes its inhibitory effect on sediment transport through exponential terms, and the ratio of median sediment particle size to reference particle size quantifies the influence of sediment gradation on sediment transport.

[0045] The empirical coefficients k1, a, b, c, e, and f were calibrated using data from beach equilibrium experiments. The experiment included three scenarios: net sediment transport at low initial water level, net sediment transport after replenishment at average water level, and sediment transport over waves at high water level. The waves used were irregular waves generated based on the Pierson–Moskowitz (PM) spectrum, with an effective wave height of 17 cm and a peak period of 2.6 s.

[0046] The parameters are linked through a combination of power and exponential functions, which realizes a multi-factor coupled description of complex sediment transport processes. Compared with traditional formulas, it is closer to the actual physical mechanism and improves the accuracy of wave transport rate calculation. It is especially suitable for complex scenarios such as high turbulence and non-uniform sediment.

[0047] Traditional technical solutions have the following technical problems: the existing scour rate calculation formula does not fully consider the synergistic effect of the overpass sediment transport rate, the inertial characteristics of sediment particles, turbulence intensity, and the dynamic response of the bed slope. This results in a large deviation between the calculation results and the actual scour situation under complex bed conditions or non-uniform flow fields, and cannot accurately reflect the scour mechanism under the coupling of multiple factors.

[0048] Based on this, the formula for calculating the scour rate is: ; Where E is the scour rate, in kg / (m²·s), representing the mass of sediment carried away by scour per unit area of ​​the bed surface per unit time; Q is the over-wave sediment transport rate, in kg / (m·s); and S is the bed slope, dimensionless, reflecting the degree of inclination of the bed surface. This refers to the density of sediment, expressed in kg / m³. This is the density of water, expressed in kg / m³. This represents the relaxation time of sediment particles, measured in seconds, reflecting the inertial response speed of sediment particles to changes in water flow. The instantaneous turbulence intensity is expressed in m / s. The reference flow velocity, in m / s, serves as the benchmark for flow velocity comparison. , d, h, i, j, and k are empirical coefficients, determined through experimental calibration. The scour rate formula constructed by this technical solution realizes the coupled correlation of multiple physical quantities: the overwater sediment transport rate Q directly reflects the sediment supply intensity; the bed slope S quantifies the influence of topography on scour; and the density ratio of sediment to water... This method reflects the gravitational effect of sediment in water, and incorporates the inertial characteristics of particle relaxation time τ to regulate scouring. Specifically, it integrates the inertial characteristics of sediment particles with the response characteristics of the measurement system for data correction, improving measurement accuracy under complex flow conditions and accurately reflecting the influence of turbulence, particle inertia, and other factors on sediment transport and scouring processes. The ratio of turbulence intensity to reference velocity is also considered. This describes the ability of turbulent fluctuations to strip sediment from the bed surface.

[0049] This formula applies to the over-wave sediment transport rate. Based on this, the bed surface characteristics, sediment physical properties and turbulence effects are further coupled to quantify the rate at which sediment is eroded and stripped from the bed surface, solving the problems of the erosion rate being disconnected from the sediment transport process and the neglect of the influence of particle inertia in the existing technology.

[0050] Over-wave sediment transport rate pass This term, as a core input, directly reflects the positive correlation between sediment supply intensity and scour rate—the higher the sediment transport rate, the more abundant the source of sediment that can be scour the bed surface. The value was determined experimentally to adapt this relationship to different bed surface morphologies, such as gentle beaches and steep shorelines. Bed slope pass The term is introduced to describe the modulating effect of topography on erosion.

[0051] On steep slope bed surface A larger gravity value along the slope will enhance the downward tendency of sediment, increasing the erosion rate; conversely, a gentler bed surface will have the opposite effect. The value setting captures this terrain effect, making up for the limitations of traditional formulas that assume the bed surface is level.

[0052] The density ratio of silt to water This reflects the gravitational effect of sediment in water. The greater the density difference, such as gravel relative to water, the stronger the gravitational component of the force on the sediment, and the easier it is for it to detach from the bed surface and be washed away by gravity. The values ​​were determined by matching scour test data of sediments with different densities to ensure the applicability of the formula to various types of sediments such as sand and gravel.

[0053] The key innovation of the formula lies in introducing the relaxation time of sediment particles. ,pass This parameter describes the inertial response speed of particles to changes in water flow—fine particles of sediment have short relaxation times, can quickly follow the water flow, and are easily washed away; coarse particles, due to their greater inertia, require a stronger water flow to initiate their movement. The setting of the value enables the formula to sensitively distinguish this difference, solving the problem of neglecting the particle inertial properties in existing technologies.

[0054] The ratio of turbulence intensity to reference velocity Quantify the ability of turbulent fluctuations to peel off the bed surface. Using a reference flow velocity, such as the average flow velocity, this ratio reflects the relative intensity of turbulence. In high relative turbulence environments, the impact of pulsating water flow on the bed surface is enhanced, increasing the scouring rate. The values ​​were calibrated through scouring experiments under different turbulence intensities to ensure that the formula accurately reflects the scouring mechanism dominated by turbulence.

[0055] empirical coefficient Taking into account fundamental parameters such as bed surface roughness and sediment adhesion, multiple sets of comparative experiments were conducted to ensure that the calculated results of the formula are consistent with the measured data of bed surface erosion.

[0056] The bed slope S is calculated based on the three-dimensional topographic data collected by the underwater sonar scanner. The scanner uses multiple single-point sonar sensors arranged in an array along the width of the flue and installed on a slide rail to record two-dimensional cross-sectional data at 2cm intervals along the centerline of the flue. The vertical and horizontal errors are controlled within 1mm. The reference flow velocity Uref is the experimentally measured average flow velocity along the vertical bank, with a measurement range of 2.9cm / s to 8.5cm / s.

[0057] The parameters are combined through power functions to form a linkage mechanism, which comprehensively covers the influence of sediment characteristics, hydrodynamics and bed topography. Compared with traditional formulas, it is closer to the actual scour physical process and improves the accuracy of scour rate calculation under complex conditions.

[0058] Traditional technical solutions have the following technical problems: existing measurement systems lack a dynamic correction mechanism based on sediment transport rate, scour rate and device response characteristics. The correction process does not associate dynamic parameters such as turbulence intensity and water flow velocity gradient, which means that when the measurement device has a response delay or the flow field changes drastically, the system error cannot be effectively compensated, affecting the reliability of the final measurement results.

[0059] Based on this, the formula for calculating the dynamic correction factor is: ; Where C is the dynamic correction factor, which is dimensionless and used to quantify the error compensation coefficient of the measurement system; Q0 and E0 are the reference sediment transport rate and scour rate, respectively, in kg / (m·s) and kg / (m²·s), which serve as the benchmark values ​​for correction. The measurement device's response time, measured in seconds, reflects the device's speed of response to signals. The instantaneous turbulence intensity is expressed in m / s. This is a reference length scale, in meters, and is related to the dimensions of the water tank. u represents the water flow velocity gradient, measured in seconds (s). -1 ; u0 is the reference velocity gradient, in seconds. -1 k3, k4, m, n, and p are empirical coefficients, determined through experimental calibration.

[0060] This formula addresses the errors caused by response delay and dynamic changes in the flow field in the measurement system. It achieves adaptive correction through multi-dimensional parameter coupling, solving the problem that static correction in existing technologies cannot compensate for dynamic errors.

[0061] The first term of the formula Correction is performed based on the deviation between the measured value and the reference value, whereby... , This serves as a reference for sediment transport rate and scour rate under a stable flow field. When measured values... If the deviation from the reference value is large, such as during a sudden change in the flow field, this value will be adjusted accordingly. The correction range can be increased or decreased to ensure a sensitive response to significant deviations.

[0062] Index Term The effect of response delay of the measurement device is specifically compensated. The device response time, such as sensor sampling delay, For reference length, such as the characteristic dimensions of a water tank.

[0063] In high turbulence ( (Large) or slow device response ( In a large scene, Increasing the value decreases the exponent term, weakening the correction effect of the first term and preventing overcorrection due to delay; conversely, decreasing the value strengthens the correction. The value is calibrated through dynamic characteristic experiments of the device to ensure that the correction amplitude matches the delay error.

[0064] Second item To address measurement errors caused by drastic changes in flow velocity gradient, This serves as the reference velocity gradient. When the velocity gradient significantly exceeds the reference value, such as a sudden change in velocity near the bed surface, the measurement system is prone to errors due to insufficient spatial resolution. This item is addressed by... The correction intensity is increased to compensate for such errors; in a stable flow field, the correction intensity is reduced to ensure measurement stability.

[0065] empirical coefficient , The weights of the two corrections are balanced separately, and the overall correction factor is determined by comparing the measurement error data before and after correction. It can optimally compensate for system errors.

[0066] The dynamic correction factor formula designed in this technical solution achieves multi-dimensional error compensation: the first term, the ratio of the actual sediment transport rate to the scour rate and the reference value, reflects the deviation between the measured value and the benchmark value. Combined with the product of the device response time, turbulence intensity, and reference length, it quantifies the error caused by device delay in the dynamic flow field. The second term, the ratio of the actual velocity gradient to the reference value, compensates for measurement deviations caused by drastic changes in flow velocity. The combination of exponential and power functions enables the correction factor to adapt to the dynamic characteristics of the flow field and the device performance, achieving an upgrade from static correction to dynamic adaptive correction and effectively reducing system errors.

[0067] Traditional technical solutions have the following technical problems: existing measurement system modules are simple and lack dedicated modules for turbulence feature extraction and particle characteristic analysis. Data interaction between modules is not coherent, and it is impossible to achieve full-process collaborative processing from raw data acquisition to dynamic correction. As a result, the system has weak adaptability to complex flow fields, and measurement efficiency and accuracy are limited.

[0068] Based on this, please refer to Figure 2This embodiment provides a system for measuring the overtopping sediment transport rate and scour rate in a physical flume experiment, applicable to any of the above-described methods for measuring the overtopping sediment transport rate and scour rate in a physical flume experiment. The system includes a multi-sensor data acquisition module, a data processing unit, and an output unit. The multi-sensor data acquisition module is electrically connected to the data processing unit, and the data processing unit is electrically connected to the output unit. The system also includes a turbulence feature extraction module, a particle characteristic analysis module, a multi-parameter collaborative calculation module, and a dynamic correction control module. The output of the multi-sensor data acquisition module is connected to the input of the turbulence feature extraction module and the particle characteristic analysis module, respectively. The outputs of the turbulence feature extraction module and the particle characteristic analysis module are both connected to the input of the multi-parameter collaborative calculation module. The output of the multi-parameter collaborative calculation module is connected to the input of the dynamic correction control module and the output unit, respectively. The output of the dynamic correction control module is connected to the input of the multi-sensor data acquisition module.

[0069] This technical solution constructs a closed-loop system for acquisition, analysis, calculation, and correction by adding core units such as a turbulence feature extraction module and a particle characteristic analysis module. The multi-sensor data acquisition module provides the raw data foundation; the turbulence feature extraction module and the particle characteristic analysis module extract key features of water flow and sediment from the raw data, respectively; the multi-parameter collaborative calculation module integrates these features to calculate sediment transport rate, erosion rate, and correction factor; and the dynamic correction control module adjusts the parameters of the acquisition module in reverse based on the correction results. Each module achieves data interaction and functional collaboration through a clearly defined signal flow direction, solving the problems of isolated modules and fragmented data processing in traditional systems, and improving the system's adaptability to complex experimental conditions and overall measurement performance.

[0070] Traditional technical solutions have the following technical problems: the sensors used in existing data acquisition modules are mostly asynchronous, lacking a combination of dedicated sensors for waves, water flow, sediment and bed surface, and the synchronization control of sensors is insufficient, resulting in incomplete data dimensions and poor time consistency, which cannot provide comprehensive and accurate basic data for subsequent analysis.

[0071] Based on this, please refer to Figure 3 The multi-sensor data acquisition module includes a wave sensor, a three-dimensional flow meter, an optical sediment concentration meter, a bed surface pressure sensor, and a synchronization controller. The synchronization controller is electrically connected to the wave sensor, the three-dimensional flow meter, the optical sediment concentration meter, and the bed surface pressure sensor, respectively, and is used to control all sensors to synchronously acquire data at an adjustable fundamental frequency.

[0072] This technical solution achieves synchronous acquisition of multiple physical quantities by configuring a variety of dedicated sensors: wave sensors capture wave height and period, three-dimensional flow velocity meters acquire water flow velocity vectors, optical sediment concentration meters measure sediment concentration, and bed pressure sensors monitor the stress state of the bed surface; the synchronous controller uniformly regulates the acquisition frequency of all sensors through electrical connection to ensure that the data from different types of sensors remain consistent in the time dimension.

[0073] The combination of dedicated sensors covers the key parameters required for the experiment, while the synchronous control mechanism ensures the temporal correlation of the data, providing high-quality raw data support for subsequent turbulence feature extraction, sediment transport rate and scour rate calculation, and solving the problems of single parameters and asynchronous data in traditional acquisition modules.

[0074] Traditional technical solutions have the following technical problems: existing computing modules mostly adopt a single-core processing architecture, which cannot process the complex calculations of sand transport rate, scour rate and dynamic correction factor in parallel, resulting in excessive computing latency, which cannot meet the real-time requirements of dynamic correction and affects the system's ability to respond to rapidly changing flow fields.

[0075] Based on this, the multi-parameter collaborative calculation module adopts a parallel computing architecture, which includes multiple processing cores for parallel execution of the calculation of the over-wave sediment transport rate, scour rate and dynamic correction factor. The calculation delay is controlled within a preset threshold. The dynamic correction control module outputs control signals to the multi-sensor data acquisition module according to the dynamic correction factor to adjust its sampling frequency.

[0076] This technical solution employs a parallel computing architecture with multiple processing cores, distributing the calculation tasks of sand transport rate, scour rate, and dynamic correction factor to different cores for parallel processing. This significantly shortens the overall calculation time, ensures that the calculation delay is controlled within a preset threshold, and meets the real-time requirements of dynamic correction. Based on the correction factor obtained from parallel computing, the dynamic correction control module promptly outputs control signals to the multi-sensor data acquisition module to adjust the sampling frequency, achieving dynamic matching between computational efficiency and acquisition strategy.

[0077] The parallel computing architecture solves the efficiency bottleneck of traditional single-core processing. Combined with real-time interaction with the acquisition module, it improves the system's ability to respond quickly to complex flow fields and ensures the dynamic adaptability of the measurement process.

[0078] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for measuring the overtopping sediment transport rate and scour rate in physical flume experiments, characterized in that, include: Deploy a sensor array to collect wave parameters, flow parameters, and sediment characteristic parameters, and calculate the overtopping sediment transport rate and scour rate based on the collected data; The measurement parameters are dynamically adjusted by analyzing the turbulence characteristics of the water flow in real time, and the data is corrected by combining the inertial characteristics of sediment particles with the response characteristics of the measurement system. The specific steps are: synchronously collecting wave height, period, water flow velocity, sediment content and particle size distribution. Instantaneous turbulence intensity and water flow velocity gradient are extracted based on the collected data; The over-wave sediment transport rate is calculated based on wave parameters, turbulence characteristics, and sediment properties. The scour rate was calculated by combining the over-wave sediment transport rate and bed surface characteristic parameters. The dynamic correction factor is calculated based on the sand transport rate, erosion rate, and the response time of the measuring device. The sensor sampling frequency and data weight are adjusted based on the dynamic correction factor; the corrected wave transport rate and scour rate are output.

2. The method for measuring the overtopping sediment transport rate and scour rate in a physical flume experiment according to claim 1, characterized in that, A distributed sensor array is used during the synchronous acquisition process. The distributed sensor array includes wave sensors, three-dimensional flow meters, optical sediment concentration meters and bed pressure sensors arranged along the cross-section of the water tank. All sensors achieve synchronous data acquisition with adjustable frequency through a synchronous controller.

3. The method for measuring the overtopping sediment transport rate and scour rate in a physical flume experiment according to claim 1, characterized in that, When extracting instantaneous turbulence intensity and water flow velocity gradient, a combined algorithm of wavelet transform and spectrum analysis is used. The sampling frequency of the combined algorithm is twice the data acquisition frequency to avoid signal aliasing.

4. The method for measuring the overtopping sediment transport rate and scour rate in a physical flume experiment according to claim 1, characterized in that, When adjusting the sensor sampling frequency, a high-frequency sampling mode is activated when the dynamic correction factor exceeds a preset threshold. The sampling frequency of the high-frequency sampling mode is 3-5 times the base frequency. When the dynamic correction factor does not exceed the preset threshold, the base sampling frequency is maintained.

5. The method for measuring the overtopping sediment transport rate and scour rate in a physical flume experiment according to claim 1, characterized in that, The formula for calculating the over-wave sediment transport rate is: ； Where Q is the overtopping sediment transport rate, in kg / (m·s); H is the wave height, in m; and T is the wave period, in s. d represents the instantaneous turbulence intensity, expressed in m / s. 50 The median particle size of sediment is expressed in mm. Reference particle size, in mm; u represents the water flow velocity gradient, measured in seconds (s). -1 g is the acceleration due to gravity, in m / s²; k1, a, b, c, e, and f are empirical coefficients.

6. The method for measuring the overtopping sediment transport rate and scour rate in a physical flume experiment according to claim 5, characterized in that, The formula for calculating the scour rate is: ； Where E is the scour rate, in kg / (m²·s); S is the bed slope; This refers to the density of sediment, expressed in kg / m³. This is the density of water, expressed in kg / m³. The relaxation time of sediment particles is expressed in seconds. The reference flow velocity is expressed in m / s; k2, d, h, i, j, and k are empirical coefficients.

7. The method for measuring the overtopping sediment transport rate and scour rate in a physical flume experiment according to claim 6, characterized in that, The formula for calculating the dynamic correction factor is: ； Where C is the dynamic correction factor; Q0 and E0 are the reference sediment transport rate and scour rate; The measurement device response time is expressed in seconds (s). This is a reference length scale, with units in meters (m). u0 is the reference velocity gradient, in seconds. -1 k3, k4, m, n, and p are empirical coefficients.

8. A system for measuring the overtopping sediment transport rate and scour rate in a physical flume experiment, applied to the method for measuring the overtopping sediment transport rate and scour rate in a physical flume experiment as described in any one of claims 1-7, comprising a multi-sensor data acquisition module, a data processing unit, and an output unit, wherein the multi-sensor data acquisition module is electrically connected to the data processing unit, and the data processing unit is electrically connected to the output unit, characterized in that, It also includes a turbulence feature extraction module, a particle characteristic analysis module, a multi-parameter collaborative calculation module, and a dynamic correction control module. The output of the multi-sensor data acquisition module is connected to the input of the turbulence feature extraction module and the particle characteristic analysis module, respectively. The outputs of the turbulence feature extraction module and the particle characteristic analysis module are both connected to the input of the multi-parameter collaborative calculation module. The output of the multi-parameter collaborative calculation module is connected to the input of the dynamic correction control module and the output unit, respectively. The output of the dynamic correction control module is connected to the input of the multi-sensor data acquisition module.

9. The wave transport rate and scour rate measurement system for physical flume experiments according to claim 8, characterized in that, The multi-sensor data acquisition module includes a wave sensor, a three-dimensional flow meter, an optical sediment concentration meter, a bed surface pressure sensor, and a synchronization controller. The synchronization controller is electrically connected to the wave sensor, the three-dimensional flow meter, the optical sediment concentration meter, and the bed surface pressure sensor, and is used to control all sensors to synchronously acquire data at an adjustable fundamental frequency.

10. The wave transport and scour rate measurement system for physical flume experiments according to claim 8, characterized in that, The multi-parameter collaborative calculation module adopts a parallel computing architecture, which includes multiple processing cores for parallel calculation of the over-wave sediment transport rate, scour rate, and dynamic correction factor. The calculation delay is controlled within a preset threshold. The dynamic correction control module outputs control signals to the multi-sensor data acquisition module according to the dynamic correction factor to adjust its sampling frequency.

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