Bed load particle starting test method and device

Abstract

The invention discloses a bed load particle starting test method and device, and relates to the technical field of hydromechanics, the method comprises the following steps: laying sediment particles in a wave water tank, and adjusting the water flow rate through flow control until the bed surface shear stress is less than the critical shear stress; tracer particles are excited to emit fluorescence through a laser beam, and a camera array is used for synchronously collecting instantaneous flow velocity of a flow field and displacement data of sediment particles; analyzing spatial and temporal distribution characteristics of turbulence energy dissipation and pressure energy dissipation based on a turbulence burst process, and establishing a sediment particle starting mathematical model; experimental data and computational fluid mechanics numerical simulation are combined, and the mathematical model is verified and optimized. According to the technical scheme, the analysis precision and the experimental convergence speed of the sediment starting criterion under the complex hydrodynamic force condition can be remarkably improved, and full-chain efficient cooperation from micromechanical response to macroscopic engineering application is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of fluid mechanics, and particularly relates to a test method and device for the incipient motion of bed load particles. Background Art

[0002] The incipient motion mechanism of bed load sediment is a core research topic in the fields of river dynamics, coastal engineering, and environmental fluid mechanics. Its accurate prediction has important practical significance for riverbed evolution, waterway maintenance, and flood control projects. Traditional theories are mostly based on the Shields curve framework, and describe the incipient motion threshold of sediment particles through a critical shear stress model. This model assumes that the incipient motion of sediment is dominated by the average shear stress on the bed surface, and simplifies the critical incipient motion condition into a functional relationship between the dimensionless shear force and the particle Reynolds number.

[0003] However, the Shields curve shows significant limitations in complex hydrodynamic environments, and its prediction error can reach more than 30%. Research shows that such errors stem from the simplified treatment of the turbulent dynamic characteristics and energy dissipation mechanism in traditional models, which are specifically reflected in the following aspects: the method does not fully consider the instantaneous force of turbulent bursts in the near-wall region on sediment particles. Turbulent bursts cause violent fluctuations in the pulsating flow velocity and pressure near the bed surface, generating instantaneous drag and lift forces far exceeding the time-averaged shear stress, while traditional models only rely on the time-averaged shear stress and cannot capture such dynamic effects; the Shields curve only considers the work done by the fluid kinetic energy on sediment particles, ignoring the synergistic effect of turbulent kinetic energy dissipation and pressure energy dissipation on particle incipient motion; existing research mostly focuses on single flow conditions, and does not systematically quantify the coupling effects of multiple factors such as wave period, bed surface roughness, and inter-particle collision friction. For example, in the strong tidal area of the estuary, the interaction between periodic flow reversal and dynamic adjustment of the bed surface morphology significantly changes the sediment incipient motion threshold, but existing models lack adaptability to such complex scenarios; although traditional experimental devices can control single variables, their data are difficult to directly embed in computational fluid dynamics models. In addition, the arrangement method of pressure sensors easily interferes with the flow field and the sampling frequency is insufficient, resulting in a bottleneck in the coupling verification between experimental data and numerical simulation. Therefore, based on the above problems, the present invention proposes a test method and device for the incipient motion of bed load particles. Summary of the Invention

[0004] Technical Objectives To solve the above problems, the object of the present invention is to provide a test method and device for the incipient motion of bed load particles, aiming to solve the theoretical defects of traditional bed load sediment incipient motion prediction models in complex hydrodynamic environments, including the neglect of the dynamic effects of turbulent bursts, the non-linear mechanism of energy dissipation, and the coupling effects of multiple factors. By integrating the spatio-temporal analysis of turbulent kinetic energy and pressure energy dissipation, and the multi-scale collaboration of experimental data and numerical simulation, a high-precision sediment incipient motion mathematical model is constructed, breaking through the single dependence of the Shields curve on the time-averaged shear stress, improving the prediction reliability in complex scenarios such as strong turbulence and wave-current coupling, and providing theoretical support for riverbed evolution analysis and water conservancy project optimization.

[0005] Technical solution To achieve the above object, the present invention provides a test method and device for the incipient motion of bed load particles. Through the sediment particles with pressure sensors embedded in a wave flume, a laser-PIV velocity measurement system, and an array of high-speed cameras, synchronous acquisition of the instantaneous flow velocity in the flow field and the particle displacement is realized; by combining quadrant analysis and statistical methods, the contribution of turbulent coherent structures to the drag force and lift force is quantified, and a non-linear correlation model between the pressure energy dissipation rate and the turbulent kinetic energy spectrum is established; through OpenFOAM fluid-structure interaction simulation extended to low Reynolds number conditions, and embedding a dynamic monitoring module to optimize parameter regulation, a general prediction framework applicable to multi-scale hydrodynamic conditions is finally formed.

[0006] In the first aspect, the present invention provides a test method for the incipient motion of bed load particles, including: Laying sediment particles in a wave flume and adjusting the water flow velocity through flow control so that the bed shear stress is less than the critical shear stress; Exciting the fluorescence of tracer particles with a laser beam and synchronously collecting the instantaneous flow velocity in the flow field and the sediment particle displacement data using an array of high-speed cameras; Based on the analysis of the turbulent burst process, the spatio-temporal distribution characteristics of turbulent kinetic energy dissipation and pressure energy dissipation are analyzed, and a sediment particle incipient motion mathematical model is established; Combining experimental data with computational fluid dynamics numerical simulation to verify and optimize the mathematical model.

[0007] Further, according to the turbulent theory, the flow velocity and pressure at any point in the flow field show irregular pulsations over time. The formula for the instantaneous flow velocity of the fluid is:

[0008] In the formula, is the instantaneous flow velocity; is the average flow velocity; is the pulsating flow velocity.

[0009] Furthermore, by calculating the Reynolds stress and combining the quadrant analysis method to divide the contribution of the turbulent bursting process to the drag force and lift force of sediment particles, the statistical method is used to quantify the influence of each quadrant on the mean Reynolds stress.

[0010] Furthermore, the calculation formula of the Reynolds stress is as follows:

[0011] In the formula, is the Reynolds stress; is the fluid density; is the time-averaged product of the turbulent pulsating flow velocity, which characterizes the intensity of turbulent bursting.

[0012] Furthermore, the contribution of the Reynolds stress in the turbulent bursting process to the drag force and lift force of sediment particles is divided by the following formula:

[0013] In the formula, is the contribution amount of the turbulent bursting process to the mean Reynolds stress; is the time; is the pulsating flow velocity in the horizontal direction of the water flow at time t; is the pulsating flow velocity in the vertical direction of the water flow at time t; is the pulsating flow velocity in the horizontal direction of the water flow; is the pulsating flow velocity in the vertical direction of the water flow; is the behavior index of the turbulent bursting process at the i-th sampling point.

[0014] Furthermore, the mathematical model for sediment particle incipient motion is based on the correlation between the pressure energy dissipation rate and the turbulent kinetic energy spectrum, and by synchronously collecting the data of the pressure sensor and the particle displacement data, a non-linear relationship between the variance, skewness and kurtosis of the pressure energy fluctuation and the particle motion trajectory is established.

[0015] Furthermore, the fluctuation intensity of the turbulence is quantified by the root mean square of the velocity pulsation in each direction, providing basic data for analyzing the energy dissipation of the turbulent kinetic energy and the pressure fluctuation. The calculation formulas for the turbulence intensity in different directions are as follows:

[0016] In the formula, is the turbulence intensity in the water flow direction; is the turbulence intensity in the horizontal transverse direction; is the turbulence intensity in the vertical direction; is the total number of samples, that is, the number of samples in the time series; is the instantaneous water flow direction velocity component at the i-th sampling point; is the instantaneous horizontal transverse velocity component at the i-th sampling point; is the instantaneous vertical velocity component at the i-th sampling point; represents the average flow velocity in the water flow direction; represents the average flow velocity in the horizontal transverse direction; represents the average flow velocity in the vertical direction.

[0017] Furthermore, the characteristics of the drag force acting on the surface of the glass sphere over time are represented by the drag force spectrum, and the calculation formula is:

[0018] wherein, is the drag force spectrum output; is the drag force coefficient caused by the flow velocity; is the area where the glass sphere is exposed to the pulsating flow velocity region; is the sampling frequency; is the absolute value or the modulus of a complex number; is the dimensionless frequency function of the drag force caused by the flow velocity; is the flow velocity spectrum; is the pressure coefficient of the drag force; is the surface area of the pressure fluctuation region of the glass sphere; is the dimensionless frequency function of the drag force caused by the pressure; is the fluid pressure spectrum.

[0019] Furthermore, the instantaneous pressure at any point is decomposed into the sum of the time-averaged pressure and the fluctuating pressure through statistical analysis methods, and the calculation formula is:

[0020] wherein, is the sum of the time-averaged pressure and the fluctuating pressure; is the average pressure value at any point in time; is the fluctuating pressure at the i-th sampling point.

[0021] Even further, the calculation formula for the pressure fluctuation variance is:

[0022] wherein, is the probability density function of the pressure difference fluctuation; is the fluctuating pressure; is the total number of samples, that is, the number of samples in the time series.

[0023] Furthermore, the calculation formula for the skewness of the pressure is:

[0024] wherein, is the skewness of the pressure.

[0025] Furthermore, the formula for calculating the kurtosis of pressure is:

[0026] In the formula, is the kurtosis of pressure.

[0027] Furthermore, the open-source software OpenFOAM is used for fluid-structure interaction numerical simulation, and the bed surface roughness and particle shape parameters are adjusted. The model is extended to low Reynolds number conditions to generate a general prediction model applicable to complex hydrodynamic conditions.

[0028] Furthermore, it also includes a real-time monitoring method for the microscopic strain of sediment particles based on fiber Bragg gratings. The microscopic strain is measured by the reflection wavelength shift caused by the change of the grating period, and the dynamic stress distribution on the particle surface is deduced from the wavelength shift. Combining the spatio-temporal characteristics of the turbulent bursting process, the transient response mechanism of the particle force is quantified. The starting criterion formula is:

[0029] In the formula, is the effective shear stress; is the strain coupling coefficient; is the real-time strain value; is the critical strain threshold.

[0030] Furthermore, it also includes a multi-parameter adaptive optimization control method based on reinforcement learning. The interaction framework between the intelligent agent and the environment is constructed by using the deep reinforcement learning algorithm, and the parameters are dynamically optimized through the reward function. The agent explores the parameter space through the Q-learning strategy. The reward function is defined as:

[0031] In the formula, is the output of the reward function; || || is the Euclidean norm, which is used to quantify the absolute value of the deviation; is the particle displacement value; is the CFD prediction value of the particle displacement; is the weight coefficient; is the effective shear stress value; is the shear stress of the theoretical model.

[0032] This method drives the exploration of the parameter space through the reward function, realizes the efficient collaboration between experimental data and numerical models, significantly shortens the experimental convergence time and improves the prediction consistency, and deeply integrates data-driven adaptive control with physical models, providing a universal framework for parameter optimization under complex hydrodynamic conditions.

[0033] Second aspect, the present invention also provides a test device for the initiation of bed load particles. When the device operates, it executes the method described in the first aspect above, including: A wave flume (1), a laser beam (2), sediment particles (3), a high-speed camera array (4), a water-filled tetrahedron (5), a USB data cable (6), a computer (7), and a wave current (8); the sediment particles (3) are placed in the middle section of the wave flume (1), the laser beam (2) is vertically directed at the sediment particles (3) and perpendicular to the direction of the wave current (8), the high-speed camera array (4) is connected to the computer (7) through the USB data cable (6) for synchronously collecting the displacement and flow field data of the sediment particles (3); the water-filled tetrahedron (5) is arranged on the side wall of the wave flume (1) to reduce laser reflection interference.

[0034] Further, the sediment particles (3) are a mixed material of hollow glass balls and quartz sand. Pressure sensors are embedded on the surface of the hollow glass balls, the sensor wires are hidden inside the glass balls, and are led out through the side wall of the flume and connected to a data acquisition instrument.

[0035] Further, the high-speed camera array (4) includes three high-speed cameras, which are respectively arranged on the side and above the flume. Two of the cameras are at non-perpendicular angles to the flume for three-dimensional PIV velocity measurement, and the other camera is used to monitor the spatial displacement of the sediment particles (3) in real time.

[0036] By embedding the optimized sediment incipient motion mathematical model into the river dredging engineering control system, the cross-scale transformation from the theoretical model to engineering practice is realized. This technology breaks through the barriers between laboratory conditions and engineering scenarios, enables the sediment incipient motion prediction to drive the adaptive regulation of dredging equipment in real time, significantly improves the engineering efficiency and reduces the risk of environmental disturbance, reflecting the closed-loop innovation path from basic research to industrial implementation.

[0037] Third aspect, the present invention also provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a management platform, it realizes the test method for the initiation of bed load particles described above.

[0038] The present invention integrates a laser PIV velocimetry system, a high-speed camera array, and an embedded pressure sensor to achieve synchronous high-resolution acquisition of the instantaneous flow velocity and particle displacement in the flow field. By combining the fiber Bragg grating microscopic strain monitoring system with reinforcement learning multi-parameter adaptive optimization control, it breaks through the bottlenecks of the spatio-temporal resolution and parameter adjustment efficiency of traditional methods. It integrates multi-physical field dynamic analysis, data-driven intelligent decision-making, and cross-scale model coupling, significantly improving the analytical accuracy of the sediment incipient motion criterion and the experimental convergence speed under complex hydrodynamic conditions, realizing the full-chain efficient collaboration from microscopic mechanical response to macroscopic engineering application, and providing an innovative solution integrating theory and practice for sediment dynamics research and water conservancy project optimization.

[0039] Beneficial effects By implementing the test method and device for the incipient motion of bed load particles provided by the present invention, the following technical effects are achieved: (1) By embedding FBG sensors inside sediment particles, the present application realizes nanoscale resolution monitoring of the dynamic strain on the particle surface, breaking through the limitation of the interference of traditional external sensors on the flow field. By capturing the transient stress distribution induced by turbulent bursts in real time, the device can accurately identify the critical strain threshold for particle incipience, significantly improving the sensitivity and spatio-temporal resolution of the incipient motion criterion. Its technical contribution lies in coupling microscopic mechanical response with macroscopic flow field characteristics, providing a new experimental observation dimension for the nonlinear dynamics analysis of sediment incipient motion mechanism.

[0040] (2) The intelligent agent based on deep reinforcement learning dynamically optimizes parameters such as water flow velocity and wave frequency, solving the low-efficiency problem of traditional experiments relying on manual experience for parameter adjustment. By driving parameter space exploration through the reward function, it realizes the efficient collaboration between experimental data and numerical models, significantly shortening the experimental convergence time and improving the prediction consistency, deeply integrating data-driven adaptive control with physical models, and providing a general framework for parameter optimization under complex hydrodynamic conditions.

[0041] (3) Real-time automatic recognition of sediment particle motion patterns is achieved by integrating wireless pressure sensors and convolutional neural network algorithms. By combining multi-modal fusion analysis of high-frequency image data and mechanical signals, it enhances the correlation modeling ability between particle motion trajectories and turbulent energy dissipation, laying a data foundation for the dynamic prediction of refined incipient thresholds.

[0042] (4) By embedding the optimized sediment incipient motion mathematical model into the river dredging engineering control system, the cross-scale transformation from the theoretical model to engineering practice is realized. This technology breaks through the barriers between laboratory conditions and engineering scenarios, enabling sediment incipient motion prediction to drive the adaptive regulation of dredging equipment in real time, significantly improving engineering efficiency and reducing the risk of environmental disturbance, and reflecting the closed-loop innovation path from basic research to industrial implementation. Description of the drawings

[0043] To make the above test method and device for the incipient motion of bed load particles of the present invention more obvious and understandable, the drawings required for the specific implementation of the present invention will be briefly introduced below. Obviously, the drawings in the following description 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 efforts.

[0044] Figure 1 It shows the schematic diagram of the technical route of this application; Figure 2 It shows the composition diagram of the test device for the incipient motion of bed load particles (1 - wave flume; 2 - laser beam; 3 - sediment particles; 4 - high - speed camera array, 5 - water - filled tetrahedron, 6 - USB data cable, 7 - electronic computer; 8 - wave current); Figure 3 It shows the schematic diagram of the installation positions of glass balls and pressure sensors; Figure 4 It shows the front view of the flume layout; Figure 5 It shows the schematic diagram of the layout of the high - speed camera array; Figure 6 It shows the schematic diagram of different burial depths of particles; Figure 7 It shows the schematic diagram of the quadrant division in the X–Z plane. Specific embodiments

[0045] Example 1: A test method and device for the incipient motion of bed load particles are provided. The technical route of the method is as Figure 1 shown. The composition of the device is as Figure 2 shown, specifically including: a wave flume (1), a laser beam (2), sediment particles (3), a high - speed camera array (4), a water - filled tetrahedron (5), a USB data cable (6), an electronic computer (7) and a wave current (8); the sediment particles (3) are placed in the middle section of the wave flume (1), the laser beam (2) is vertically projected onto the sediment particles (3) and is perpendicular to the direction of the wave current (8), the high - speed camera array (4) is connected to the electronic computer (7) through the USB data cable (6) for synchronously collecting the displacement and flow field data of the sediment particles (3); the water - filled tetrahedron (5) is arranged on the side wall of the wave flume (1) to reduce the interference of laser reflection. Specifically, it is described as follows.

[0046] The installation positions of the glass balls and pressure sensors are as Figure 3 shown. The sediment particles (3) are a mixture of hollow glass balls and quartz sand. Pressure sensors are embedded on the surface of the hollow glass balls, and the sensor lines are hidden inside the glass balls and led out through the side wall of the flume to be connected to the data acquisition instrument.

[0047] Quartz sand with a particle size of 8-25 mm and hollow glass balls were selected as experimental materials. Quartz sand was laid in the front and back halves of the experimental area and at the same height as the glass balls. Glass balls were laid in 3 layers in the experimental area and used as model materials for the sediment initiation experiment. The glass balls in the experimental observation area were specially prepared with a density of about .like Figure 3 As shown, the surface of the glass ball at the pressure measurement positions P1, P2, P3 and P4 is marked, and the pressure sensor is installed after special production; there are three glass balls of the same size below the glass ball for starting experiment observation, according to Figure 3 Arrangement: The three glass balls where the pressure sensors are to be installed are specially treated so that the pressure sensors are completely hidden inside the glass balls; the four pressure sensors are as follows: Figure 3 As shown, the dotted line is the sensor line path.

[0048] The front view of the sink layout is as follows Figure 4 As shown, the average flow velocity and the distribution of flow velocity along the water depth are obtained through flow control and cross-sectional flow velocity measurement. The bed shear stress is calculated based on the logarithmic velocity curve of flow velocity along the water depth, and compared with the critical shear stress of sediment particles based on the Shields number curve; the water flow rate is repeatedly adjusted, and combined with experimental observations, the water flow rate with bed shear stress less than the critical shear stress but the glass ball partially starts is obtained; the water flow less than or equal to this flow velocity is the turbulence and bed condition before this experiment.

[0049] The high-speed camera array is arranged as follows Figure 5 As shown, the high-speed camera array (4) includes three high-speed cameras, which are arranged on the side and above the water tank, respectively. Two of the cameras are at a non-vertical angle to the water tank and are used for three-dimensional PIV flow velocity measurement, and the other camera is used for real-time monitoring of the spatial displacement of sediment particles (3).

[0050] Two high-speed cameras are placed on the side of the water tank to measure the instantaneous flow velocity in the x, y, and z directions in the plane. The x direction corresponds to the water flow direction, the y direction is in the same horizontal plane as the x direction and is perpendicular to the x direction, and the z direction is perpendicular to the gravity direction. A high-speed camera is placed on the side of the water tank to monitor the spatial position of the glass ball over time. The high-speed camera is controlled by an electronic computer to carry out synchronous high-frequency monitoring.

[0051] The tracer particles in the flow field emit fluorescence through the laser beam emitted from the top of the water tank, and the images are collected by two high-speed cameras on the side of the water tank, and then the instantaneous flow velocity near the sediment particles is calculated and displayed by the software of the PIV system. Among them, the two cameras are at a certain angle to the water tank, and a tetrahedral glass filled with water is installed on the side wall of the water tank to make the camera perpendicular to it, thereby reducing the influence of refraction.

[0052] The displacement image of the glass beads is collected by a high-speed camera at a perpendicular angle to the water tank. During the collection process, the start time and collection frequency are synchronously controlled by software. During the test, the three particle arrangement forms shown as Figure 6 are respectively experimented.

[0053] According to the turbulence theory, the flow velocity and pressure at any point in the flow field show irregular pulsations over time, that is, the sum of the time-averaged flow velocity and the pulsating flow velocity. The calculation formula is:

[0054] In the formula, is the instantaneous flow velocity; is the average flow velocity; is the pulsating flow velocity.

[0055] The characteristics of the turbulence bursting process are studied through the time series of the turbulence instantaneous flow velocity. Using the instantaneous flow velocity data at different depths of the boundary layer obtained by three-dimensional PIV velocimetry, it is plotted on a graph with the abscissa being time and the ordinate being the instantaneous velocity. According to the properties of the turbulent coherent structure in time and space, the intensity and time of the outward convection, ejection, inward convection, and sweeping actions, and the characteristics of the pulsating flow velocity and the instantaneous Reynolds stress are obtained. The velocity variance is expressed as the ratio of the time-average of the product of the pulsating flow velocities to the square of the friction velocity. The calculation formulas under each combination mode are respectively , , , , and , where represents the spatial average, and the abscissa is expressed as the ratio of the height above the bed surface to the sediment particle size.

[0056] By calculating the Reynolds stress and combining the quadrant analysis method to divide the contribution of the turbulence bursting process to the drag force and lift force of sediment particles, the influence of each quadrant on the average Reynolds stress is quantified by statistical methods.

[0057] According to the momentum theorem and Newton's second law, the time-averaged Reynolds stress is calculated through the pulsating flow velocity in the flow field. The calculation formula of the Reynolds stress is:

[0058] In the formula, is the Reynolds stress; is the fluid density; is the time-average product of the turbulent pulsating flow velocities, which characterizes the intensity of the turbulence bursting.

[0059] According to the quadrant analysis, the contribution of the Reynolds stress in the turbulence bursting process to the drag force and lift force of sediment particles is divided by the following formula:

[0060] In the formula, is the contribution of the turbulent bursting process to the mean Reynolds stress; is time; is the pulsating velocity of the water flow in the horizontal direction at time t; is the pulsating velocity of the water flow in the vertical direction at time t; is the pulsating velocity of the water flow in the horizontal direction; is the pulsating velocity of the water flow in the vertical direction; is the behavior index of the turbulent bursting process at the i-th sampling point.

[0061] Figure 7 In, quadrant I is outward convection, ; quadrant II is ejection, , quadrant III is inward convection, ; quadrant IV is sweeping, .

[0062] The mathematical model for sediment particle incipient motion is based on the correlation between pressure energy dissipation rate and turbulent kinetic energy spectrum, and by synchronously collecting pressure sensor data and particle displacement data, a non-linear relationship between the variance, skewness and kurtosis of pressure energy fluctuation and the particle motion trajectory is established.

[0063] The fluctuation intensity of turbulence is quantified by the root mean square of velocity pulsations in each direction, providing basic data for analyzing the energy dissipation of turbulent kinetic energy and pressure fluctuations. The calculation formulas for the turbulence intensity in different directions are:

[0064] In the formula, is the turbulence intensity in the water flow direction; is the turbulence intensity in the horizontal lateral direction; is the turbulence intensity in the vertical direction; is the total number of samples, that is, the number of samples in the time series; is the instantaneous water flow direction velocity component at the i-th sampling point; is the instantaneous horizontal lateral velocity component at the i-th sampling point; is the instantaneous vertical direction velocity component at the i-th sampling point; represents the mean flow velocity in the water flow direction; represents the mean flow velocity in the horizontal lateral direction; represents the mean flow velocity in the vertical direction.

[0065] During the experiment, the pressure measurement is obtained through the pressure sensor on the surface of the glass sphere, and the sampling frequency is consistent with the PIV velocity measurement sampling frequency. The characteristics of the drag force acting on the surface of the glass sphere over time are represented by the drag force spectrum, and the calculation formula is:

[0066] In the formula, is the output of the drag force spectrum; is the drag force coefficient caused by the flow velocity; is the area where the glass sphere is exposed to the pulsating flow velocity region; is the sampling frequency; is the absolute value or the modulus of a complex number; is the dimensionless frequency function of the drag force caused by the flow velocity; is the flow velocity spectrum; is the pressure coefficient of the drag force; is the surface area of the pressure fluctuation region of the glass sphere; is the dimensionless frequency function of the drag force caused by the pressure; is the fluid pressure spectrum.

[0067] Construct a mathematical model of pressure energy dissipation rate and sediment displacement by monitoring the pressure at four points on the surface of sediment particles.

[0068] Using the synchronously collected photos, obtain the spatial displacement data of the glass sphere changing with time, providing a basis for establishing a mathematical model related to the movement of the glass sphere.

[0069] Plot the pressure energy spectrum and the turbulent kinetic energy spectrum on the same graph with frequency as the abscissa. According to the changing trends of the shapes of the two spectra, analyze the response characteristics of the pressure energy to the energy dissipation of the turbulent kinetic energy. Then compare it with the time series of the spatial position of the glass sphere to obtain the regular characteristics of the relationship between the movement position of the glass sphere and the change of the pressure energy.

[0070] Decompose the instantaneous pressure at any point into the sum of the time-averaged pressure and the fluctuating pressure through statistical analysis methods. The calculation formula is:

[0071] In the formula, is the sum of the time-averaged pressure and the fluctuating pressure; is the average pressure value at any point in time; is the fluctuating pressure at the i-th sampling point.

[0072] The calculation formula for the variance of the pressure fluctuation is:

[0073] In the formula, is the probability density function of the pressure difference fluctuation; is the fluctuating pressure; is the total number of samples, that is, the number of samples in the time series.

[0074] The calculation formula for the skewness of the pressure is:

[0075] In the formula, is the skewness of the pressure.

[0076] The calculation formula for the kurtosis of the pressure is:

[0077] In the formula, is the kurtosis of the pressure.

[0078] The obtained pressure fluctuation parameters are respectively plotted on the x, y, and z coordinate systems, and the influences of the drag force and the lift force are divided according to the horizontal and vertical directions. At the same time, by comparing the time and space position distribution curves of the obtained instantaneous pressure, combined with the time and space displacement functions of the glass sphere, the characteristics of the pressure change with the movement distance and direction of the glass sphere are analyzed. Finally, the turbulent kinetic energy and the pressure energy dissipation are incorporated into the mathematical model of the glass sphere displacement.

[0079] Regarding the aforementioned turbulent bursting process and boundary layer destruction process, the characteristics of the outward convection, ejection, inward convection, and sweeping time series of the turbulent bursting are sorted out. Through the coherent structure scale and its movement, momentum transfer, turbulent kinetic energy, and pressure energy dissipation, statistical analysis is carried out on the turbulent kinetic energy based on the turbulent pulsating flow velocity and the pressure energy dissipation based on the pressure fluctuation. Connecting the time and space displacement of the glass sphere, using the established mathematical model, a correlation study is carried out.

[0080] Through the drag force and lift force models caused by the turbulent pulsating flow velocity and the drag force and lift force models caused by the pressure fluctuation, according to the energy required for the displacement change of the glass sphere, based on the turbulent kinetic energy dissipation and the pressure energy dissipation, starting mathematical models for the sliding, rolling, and saltating of the bed load sediment particles are respectively constructed. Based on the OpenFOAM open-source software, the obtained bed load sediment starting model from the experiment is added to the numerical model, the code of the fluid-structure interaction model is developed, and direct numerical simulation calculations are carried out. By comparing the calculation results of the numerical model with the experimental data, the accuracy of the numerical model is verified. Adjust the hydrodynamic parameters such as water depth and flow velocity and the parameters such as sediment particle size, particle shape, and bed surface roughness, and simulate a wider range of experimental conditions under low Reynolds number conditions to expand the experimental data and improve the mathematical model.

[0081] Example 2: On the basis of the aforementioned embodiment, a real-time monitoring method for the microscopic strain of sediment particles based on fiber Bragg gratings is added. The fiber Bragg grating sensor measures the microscopic strain with high precision through the reflection wavelength shift caused by the change of the grating period. When the sediment particles are displaced or deformed under the action of the fluid, the FBG sensor embedded inside the particles inversely calculates the dynamic stress distribution on the particle surface through the wavelength shift amount, and combines the spatio-temporal characteristics of the turbulent bursting process to quantify the transient response mechanism of the particle force.

[0082] An FBG sensor array is embedded inside the sediment particles, with a grating spacing of 1 mm and a wavelength resolution of 0.1 nm. The sensor lines are led out through microchannels inside the sediment particles to avoid interfering with the flow field.

[0083] The FBG demodulator is connected to an electronic computer to analyze the wavelength shift data in real time and convert it into particle surface strain values. The sampling frequency of the FBG demodulator is 10 kHz.

[0084] A strain-displacement correlation model is established by combining high-speed camera displacement data to define the critical strain threshold , when the local strain exceeds, it is judged that the particle starts, and the starting criterion formula is:

[0085] In the formula, For effective shear stress, the effects of turbulent pulsation, strain energy and turbulent energy dissipation are integrated; is the fluid density, reflecting the contribution of fluid inertia to shear stress; is the Reynolds stress, which is the time-averaged product of the turbulent pulsating flow velocity and represents the intensity of turbulent bursts; is the strain coupling coefficient, which is determined to be 0.3 through FBG calibration experiments; It is the real-time strain value, reflecting the local stress deformation; is the critical strain threshold, which is determined by the elastic modulus and particle size of the material.

[0086] Verification shows that the time resolution of particle start detection is improved to 10μs and the error rate is reduced to 2% while obtaining an average error similar to that of the above-mentioned embodiment. The results show that this method can realize non-invasive high-precision monitoring of the microscopic mechanical behavior of sediment particles and effectively capture the transient stress fluctuations on the particle surface during turbulent bursts. Compared with traditional external sensors, the experimental results show that the temporal and spatial resolution of particle force is significantly improved, and the flow field interference is greatly suppressed. Through the correlation analysis of dynamic strain and displacement, the device reveals the coupling mechanism of strain accumulation and energy dissipation during particle start, providing a more sensitive physical basis for the determination of critical start conditions. It further verifies the ability of this technology to analyze the nonlinear mechanical response of particles in complex turbulent environments, enabling the model to more accurately characterize the impact of actual hydrodynamic conditions on sediment movement.

[0087] Embodiment 3: On the basis of the foregoing embodiments, a multi-parameter adaptive optimization control method based on reinforcement learning is added. An interaction framework between the intelligent agent and the environment is constructed using the deep reinforcement learning algorithm. Parameters such as water flow velocity and wave frequency are dynamically optimized through the reward function, enabling the experiment to quickly converge to the target starting conditions. The agent explores the parameter space through the Q-learning strategy to maximize the coincidence degree between the experimental data and the CFD simulation.

[0088] Deploy the DRL module in the electronic computer. The input state space includes the real-time flow velocity , turbulence intensity , particle displacement . The action space is the opening degree of the flow valve and the frequency of the wave generator .

[0089] The reward function is defined as:

[0090] In the formula, is the output of the reward function; || || is the Euclidean norm, used to quantify the absolute value of the deviation; is the particle displacement value; is the CFD prediction value of the particle displacement; is the weight coefficient, a regulation factor used to balance the displacement error and the stress error; is the effective shear stress value; is the shear stress of the theoretical model.

[0091] Generate the optimal control strategy through offline training and adjust the experimental parameters online in real time.

[0092] Assume that the multi-parameter adaptive optimization control method based on reinforcement learning dynamically regulates the flow velocity and wave parameters for the bed load sediment incipient motion experiment. Under the strong disturbance condition of the turbulence intensity , optimize the water flow velocity and the wave frequency to minimize the mean square error between the sediment particle displacement and the CFD simulation value .

[0093] Let the initial flow velocity , the initial wave frequency , and the target critical flow velocity ; The DRL agent explores the parameter space through the Q-learning strategy, adjusts the opening degree of the flow valve and the wave frequency , and updates the strategy according to the reward function. After 72 minutes of online optimization, it converges to the optimal parameter combination.

[0094] The effects of the multi-parameter adaptive optimization control method based on reinforcement learning are shown in Table 1.

[0095] Table 1. Summary of the effects of the multi-parameter adaptive optimization control method based on reinforcement learning

[0096] According to the experimental table, the average optimization time of the DRL system is 71 minutes, which is 40.8% shorter than that of traditional manual adjustment. The control error of the flow valve opening is ±0.5%, and the adjustment error of the wave frequency is ±0.05 Hz. The critical flow velocity is calculated by the adaptive formula, and the mean prediction error is 2.5%. The particle displacement error is reduced from 0.45 mm² to 0.12 mm², a decrease of 73.3%. Under the conditions, the mean flow velocity after optimization is 1.06 m / s, and the deviation from the theoretical value is only 0.95%. The results show that this method realizes the efficient autonomous optimization of experimental parameters through the dynamic interaction between the intelligent agent and the flow field environment; it shows the ability to quickly converge to the target experimental conditions under strong turbulence and unsteady working conditions, significantly improving the robustness of parameter adjustment. During the experiment, the combination of the optimized flow velocity and wave frequency is highly consistent with the predicted value of the theoretical model, verifying the adaptability of the data-driven strategy to multi-physical field coupling problems. In addition, through the real-time feedback mechanism, the device effectively bridges the scale difference between experimental observations and numerical simulations, providing reliable technical support for model generalization in complex hydrodynamic scenarios.

[0097] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable non-transitory storage media containing computer-usable program code.

[0098] The present invention can provide computer program instructions to the management platform of a general computer, a special computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed through the management platform of the computer or other programmable data processing devices generate a device for implementing the system.

[0099] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions of the system.

[0100] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are executed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing steps for implementing the functions of the system by the instructions executed on the computer or other programmable apparatus.

Claims

1. A test method for starting bed load particles, characterized in that: include: Sediment particles are laid in a wave flume, and the water flow rate is adjusted by flow control until the bed shear stress is less than the critical shear stress; The laser beam is used to excite the fluorescence of the tracer particles, and the camera array is used to synchronously collect the instantaneous flow velocity and sediment particle displacement data of the flow field; Based on the turbulent burst process, the temporal and spatial distribution characteristics of turbulent kinetic energy dissipation and pressure energy dissipation are analyzed, and a mathematical model of sediment particle initiation is established; Combine experimental data with computational fluid dynamics numerical simulations to verify and optimize mathematical models.

2. The method according to claim 1, characterized in that: The Reynolds stress is calculated and combined with the quadrant analysis method to divide the contribution of the turbulent burst process to the drag and lift of sediment particles, and the influence of each quadrant on the average Reynolds stress is quantified using statistical methods.

3. The method according to claim 1, characterized in that: The mathematical model of sediment particle initiation is based on the correlation between the pressure energy dissipation rate and the turbulent kinetic energy spectrum, and establishes a nonlinear relationship between the pressure energy fluctuation variance, skewness and kurtosis and the particle motion trajectory by synchronously collecting pressure sensor data and particle displacement data.

4. The method according to claim 1, characterized in that: Through fluid-solid coupling numerical simulation and adjustment of bed roughness and particle shape parameters, the model is extended to low Reynolds number conditions to generate a general prediction model suitable for complex hydrodynamic conditions.

5. The method according to any one of claims 1 to 4, characterized in that: It also includes a real-time monitoring method for microscopic strain of sediment particles based on fiber Bragg gratings, which measures microscopic strain by the reflection wavelength shift caused by the change of grating period, inverts the dynamic stress distribution on the particle surface by the wavelength shift, and quantifies the transient response mechanism of particle stress by combining the spatiotemporal characteristics of the turbulent burst process, wherein the starting criterion formula is: In the formula, is the effective shear stress; is the fluid density; is the time-averaged product of the turbulent pulsating flow velocity; is the strain coupling coefficient; is the real-time strain value; is the critical strain threshold.

6. The method according to any one of claims 1 to 4, characterized in that: It also includes a multi-parameter adaptive optimization control method based on reinforcement learning, which uses a deep reinforcement learning algorithm to build an interactive framework between the intelligent agent and the environment, dynamically optimizes parameters through a reward function, and the agent explores the parameter space through a Q-learning strategy. The reward function is defined as: In the formula, is the output of the reward function; || || is the Euclidean norm; is the particle displacement value; is the CFD predicted value of particle displacement; is the weight coefficient; is the effective shear stress value; is the shear stress of the theoretical model.

7. A test device for starting bed load particles, characterized in that: When the device is running, the method according to any one of claims 1 to 6 is executed: The device comprises: A wave flume (1), a laser beam (2), silt particles (3), a high-speed camera array (4), a water-filled tetrahedron (5), a USB data cable (6), an electronic computer (7), and a wave current (8); the silt particles (3) are placed in the middle section of the wave flume (1); the laser beam (2) is vertically directed toward the silt particles (3) and is perpendicular to the direction of the wave current (8); the high-speed camera array (4) is connected to the electronic computer (7) via the USB data cable (6); and the water-filled tetrahedron (5) is arranged on the side wall of the wave flume (1).

8. The device according to claim 7, characterized in that: The mud and sand particles (3) are a mixture of hollow glass balls and quartz sand, wherein a pressure sensor is embedded in the surface of the hollow glass ball, the sensor circuit is hidden inside the glass ball, and is led out through the side wall of the water tank to connect to the data acquisition instrument.

9. The device according to claim 7, characterized in that: The high-speed camera array (4) includes three high-speed cameras, which are arranged on the side and above the water tank, respectively. Two of the cameras are arranged at a non-perpendicular angle to the water tank and are used for three-dimensional PIV flow velocity measurement, and the other camera is used for real-time monitoring of the spatial displacement of sediment particles (3).

10. A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, characterized in that: When the computer program is executed, the method according to any one of claims 1 to 6 is implemented.

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