Height optimization system for energy dissipation device mounted on trailing suction hopper dredger

Abstract

The present invention provides a height optimization system for an energy dissipation device mounted on a trailing suction hopper dredger. The optimization system comprises a core parameter input module, a hopper loading process simulation and prediction module, a hopper loading efficiency analysis module, a design optimization output module, and an automatic height adjustment module. During construction, the height of the energy dissipation device can be automatically and accurately adjusted in real time on the basis of different soil types and different slurry concentration requirements. The energy dissipation device is positioned neither too low, which would cause rapidly settling coarse-grained sediment to block the outlet of the energy dissipation device, nor too high, which would cause inflow into the hopper to disturb the sediment and increase overflow losses of suspended sediment. By accurately controlling the height of the energy dissipation device, the construction efficiency of the trailing suction hopper dredger is improved, while adverse impacts of hopper overflow on the construction water environment can be reduced, thereby providing engineering application value. The present invention enables automatic and flexible adjustment of the height of the energy dissipation device and, by reducing overflow losses, improves hopper loading efficiency and the uniformity of sediment distribution within the hopper.

Description

A highly optimized energy dissipation system for trailing suction hopper dredgers

[0001] This application is a divisional application of application number 202411411313.6, filed on October 10, 2024, entitled "An Optimization Method for Automatic Height Adjustment of Energy Dissipation Device of a Trailing Suction Hoist Dredger". Technical Field

[0002] This invention belongs to the field of dredging engineering technology, and specifically relates to a height optimization system for an energy dissipation device mounted on a trailing suction hopper dredger. Background Technology

[0003] Trailing suction hopper dredgers are the mainstay vessels in the dredging industry, playing a vital role in waterway dredging, land reclamation, and port maintenance. The mud hopper system is a crucial component of the trailing suction hopper dredger, responsible for loading dredged material into the hopper and flushing it to the shore. The loading process can be divided into two parts based on the characteristics of different stages: loading and overflow. Depending on the target soil properties, slurry (a mixture of water and sediment) continuously flows into the mud hopper through the inlet pipe. Some of the sediment settles at the bottom of the hopper under gravity, while the remaining sediment mixes with water and remains suspended. When the level of this water-sand mixture exceeds the height of the overflow pipe, varying degrees of overflow loss occur.

[0004] The efficiency of overflow loading for trailing suction hopper dredgers is directly related to the design and layout of their energy dissipation devices. Currently, most trailing suction hopper dredgers in China have fixed energy dissipation devices, which cannot be adjusted to reduce disturbance to the sediment inside the hopper caused by the incoming water flow under various complex working conditions such as different soil types and slurry concentrations. When the sediment particles are coarse, they settle quickly, and if the energy dissipation device is installed too low, it can easily clog the outlet. When the sediment particles are fine, if the energy dissipation device is installed too high, the fine sediment particles will be disturbed by the incoming water flow, causing most of them to remain suspended, increasing overflow losses, reducing loading efficiency, and adversely affecting the surrounding construction water environment. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides an adjustable-height energy dissipation device for trailing suction hopper dredgers, an automatic height adjustment optimization method, and an onboard system. This enables automatic and flexible adjustment of the energy dissipation device height during the dredging process. By minimizing disturbance of sediment within the hopper and reducing overflow losses, it improves the flatness of sediment loading and hopper loading efficiency. This meets the needs of various working conditions, including different soil types, hopper concentrations, hopper flow rates, and hopper structural dimensions of the trailing suction hopper.

[0006] One of the objectives of this invention is to provide an automatic height adjustment optimization method for the energy dissipation device of a trailing suction hopper dredger, comprising: Process 1: Constructing an adjustable height energy dissipation device inside the hopper of the trailing suction hopper dredger; Process 2: Determining the optimal energy dissipation height; Step 1: Determining core parameters through existing data related to trailing suction hopper dredger construction, geological survey reports of the area to be constructed, soil condition data, etc. Core parameters include: geometric model, boundary conditions, grid conditions, initial conditions, fluid motion, and sediment motion. Specifically: (1) The geometric model mainly includes: hopper structure dimensions, transverse and longitudinal triangular hopper dimensions, overflow device dimensions, energy dissipation device dimensions and height, and the relative positions of transverse and longitudinal triangular hoppers and energy dissipation overflow device to the hopper; (2) Boundary conditions include: the inlet boundary is the loading flow rate and loading mud concentration, the overflow boundary is the free outflow boundary, the upper boundary of the hopper is the atmospheric pressure boundary, and the hopper wall boundary is the wall boundary with a certain roughness material; (3) Grid conditions include: the grid range in the x, y, and z directions and the unit grid size.

[0007] (4) Initial conditions include: initial water level H0, and loading simulation duration T; (5) Fluid motion parameters include: fluid temperature T, fluid viscosity, and fluid motion equation.

[0008] (6) The fluid motion equation is selected from the RNG kε equation based on the characteristics of the loading compartment, as follows: Where: k—turbulent kinetic energy; u i — Velocity component; x i y i —Coordinate components; μ —Molecular viscosity coefficient; μ t — Turbulent viscosity coefficient; G k — Turbulent kinetic energy generation term; ε — Turbulent kinetic energy dissipation rate; α k α ε —The Prandtl numbers for turbulence at k and ε; Where C 1ε =1.42, η0=4.377, β=0.012; in C 2ε =1.68.

[0009] The main parameters of sediment movement include sediment particle size, sediment density, sediment porosity, critical Shields number, underwater angle of repose, bedload coefficient, and scour coefficient, involving four forms of sediment movement: suspension, deposition, scour, and bedload. The following empirical formulas for sediment are used to automatically calculate the movement of each sediment particle during the simulation process.

[0010] The empirical formulas of Mastbergen and Von den Berg were used to calculate the sediment initiation and scouring process.

[0011] In the formula, α refers to the entrainment coefficient, and n s The direction of the outward normal to the bed surface is θ, where θ refers to the Shield number of the sediment particles. cr The critical Shield number refers to the number of sediment particles, g is the acceleration due to gravity, μ is the dynamic viscosity of the fluid, and d is the velocity of the sediment. s Refers to the particle size of sediment, ρ s It is the density of sediment, ρ f It is the fluid density.

[0012] The Shield number θ of sediment particles is expressed as: In the formula, τ is the shear stress on the riverbed surface.

[0013] In the non-horizontal bed region, considering the influence of the sediment repose angle, the critical Shields number correction value θ is... cr,i 'for In the formula, κ is the angle between the vertical line of the sedimentary surface and the gravitational acceleration g; φ is the angle of repose of the sediment; and ψ is the angle between the upslope direction and the flow direction of the fluid.

[0014] The bedload transport process during the simulation was calculated using the empirical formula of Meyer-Peter & Müller equati.

[0015] In the formula, the thickness δ of the bedload sediment is calculated as follows: Therefore, the formula for calculating the bedload transport velocity is: Step 2: Conduct loading process simulation and prediction, which includes two steps: establishing a loading numerical model and loading simulation and prediction.

[0016] Establishing a loading numerical model: This refers to using CFD software (existing technology) to establish a numerical model that can simulate the loading process based on the input core parameters, so as to realize the loading simulation function.

[0017] Loading simulation and prediction: Using the above-mentioned loading numerical model, different energy dissipation device height positions are set for numerical simulation to simulate the entire loading process. This simulation predicts the changes in loading volume, earthwork volume, total overflow loss, and sediment height in the tank over loading time, yielding multiple sets of numerical simulation results corresponding to different energy dissipation device height positions. The energy dissipation device height position needs to be set manually.

[0018] The numerical simulation results also include: q t (3D mesh point cloud of flow rate at time t); ρ t (3D mesh point cloud of density at time t); v x (3D mesh point cloud of the x-direction velocity component at time t); v y(Three-dimensional grid point cloud of the y-direction component velocity at time t). The results of the numerical calculation are provided to Step 3.

[0019] Step 3: Based on the numerical simulation results, set the evaluation basis order according to four indicators including loading capacity, earthwork volume, overflow loss, and sediment flatness, and obtain the recommended optimized height of the energy dissipation device; among multiple groups of numerical simulation results, different heights of the energy dissipation device correspond to four indicators including loading capacity, earthwork volume, overflow loss, and sediment flatness. Set the evaluation basis order, and determine the recommended value of the optimized design of the energy dissipation device height according to the evaluation basis order (first gradient, second gradient). Usually, the loading efficiency represented by loading capacity, earthwork volume, and overflow loss is used as the first gradient discrimination criterion, and the sediment flatness is used as the second gradient discrimination criterion. Among them, the larger the loading capacity and earthwork volume, the smaller the overflow loss, and the higher the flatness of the sediment in the hold, the better the influence of the height of the energy dissipation device in this working condition on the hold loading effect.

[0020] Preferably, set the optimization goal as the optimal sediment flatness α and the maximum earthwork volume D, then the objective function of the optimization model is: max D=max∑ΔQ t ·Δt·ρ (16) Set the constraint conditions (x.t.): X={x1, x2, x3…, x i} (17) Where, Δt is the length of the calculation time period of the deposited earthwork volume; ΔQ i is the outflow flow rate within the time period Δt; ρ is the outflow concentration within the time period Δt; x i belongs to [0, H], H represents the height of the hold, and x (it) as the parameter for calculating flatness, represents the sediment height measured at the sampling position i at time t, represents the average sediment height of the longitudinal central axis section of the hold.

[0021] t < T (18) In the formula, T represents the initially set maximum time.

[0022] Set multiple groups of heights of the energy dissipation device, and respectively use the numerical calculation model in Step 2 to calculate q t 、ρ t 、v x and v y , and then calculate the sediment flatness and earthwork volume. Select the best group according to the above optimization goals (optimal flatness, maximum earthwork volume), and the corresponding height of the energy dissipation device is the optimal height of the energy dissipation device.

[0023] Considering time and computing power comprehensively, according to relevant numerical calculation and prediction experience, the number of groups of the energy dissipation device height is recommended to be 12 - 20 groups, with an interval of 0.25 - 0.5 m for each group, and the height change range is 5 - 6 m.

[0024] Step 4 of the process 3: Based on the optimal energy dissipation equipment height obtained in step 3, adjust the hydraulic struts of the adjustable height energy dissipation device to raise or lower the energy dissipation equipment height to the optimal height.

[0025] Furthermore, in step 3, to avoid complex numerical calculations, a neural network is introduced to predict sediment height and earthwork volume. The following is an example: A sediment height prediction model is established to predict sediment height, as follows: The calculation of X is achieved by establishing a prediction model of X(t) value and the initial and boundary conditions of the numerical simulation, as follows: Input value: q t ρ is the 3D grid point cloud of the flow rate at time t; t A 3D mesh point cloud with density at time t; v x A three-dimensional mesh point cloud of the velocity component in the x-direction at time t; v y The three-dimensional grid point cloud represents the y-direction velocity component at time t.

[0026] Output value: X(t+Δt) represents the sediment height matrix of the smoothness section calculated at the next time step Δt, serving as the label for the sediment height prediction model. The three-dimensional location of the energy dissipation device is set to 0; this setting characterizes the spatial features of the energy dissipation device.

[0027] Extract q from numerical simulation calculations at time intervals of Δt. t ρ t v x v y q for each time t ρ t v x v y Three-dimensional mesh data is subjected to convolutional kernel feature extraction to generate a feature matrix. The values ​​used to calculate the sediment height inside the chamber at time t+Δt are combined into a label matrix X, which is then used to train the network. Through the above operations, a sediment height prediction model for calculation points on the flatness calculation plane is obtained by training with numerical simulation data.

[0028] During the training phase, the input quantities and labels are calculated by the loading process simulation prediction model in step 2; after training is completed, the input quantities and labels are calculated based on the measured q. t ρ t v x v y Predicting the height of sediment at each point reflects the smoothness of the sediment.

[0029] An earthwork volume prediction model is established to predict the earthwork volume inside the chamber, as follows: As above, dt represents the 3D grid point cloud of the earthwork volume at time t, and d(t+Δt) represents the earthwork volume calculated at the next time Δt as a label item for the earthwork volume prediction model. Establish q at time t... t ρ t v x v y A network of data and three-dimensional data of the earthwork volume inside the mud chamber at time t+Δt is used to train an earthwork volume prediction model.

[0030] Based on the order of the two evaluation criteria, earthwork volume and flatness, set according to the prediction model, the recommended optimal height is obtained.

[0031] The second objective of this invention is to provide a height optimization system for an onboard energy dissipation device on a trailing suction hopper dredger, characterized by comprising a core parameter input module, a loading process simulation and prediction module, a design optimization output module, and an automatic height adjustment module. These software modules are embedded in and invoked by the onboard loading control system. The core parameter input module includes: The core parameters include geometric model, boundary conditions, mesh conditions, initial conditions, fluid motion, and sediment motion. The geometric model includes: the structural dimensions of the mud hopper, the dimensions of the transverse and longitudinal triangular hoppers, the dimensions of the overflow equipment, the dimensions and height of the energy dissipation equipment, and the relative positions of the transverse and longitudinal triangular hoppers and the energy dissipation overflow equipment to the mud hopper. The boundary conditions include the inlet boundary being the loading flow rate and mud concentration, the overflow boundary being the free outflow boundary, the upper boundary of the mud hopper being the atmospheric pressure boundary, and the hopper wall boundary being the wall boundary with a certain degree of roughness. The mesh conditions include: the mesh range in the x, y, and z directions and the unit mesh size. The initial conditions include the initial water level H0 and the simulation duration t. The fluid motion parameters include fluid temperature T, fluid viscosity μ, and the fluid motion equation. The sediment motion parameters mainly include sediment particle size, sediment density, sediment porosity, critical Shields number, underwater angle of repose, bedload coefficient, and scour coefficient, involving four motion forms: sediment suspension, sediment deposition, sediment scour, and sediment bedload. The loading process simulation and prediction module includes: Using the parameters input into the core parameter input module, numerical calculations are performed to simulate the loading process under different operating conditions. The loading process simulation and prediction module implements the simulation and prediction of the loading process, which includes two parts: establishing a loading numerical model and loading simulation and prediction.

[0032] Establish a numerical model for loading: Based on the input core parameters, use CFD software (existing technology) to establish a numerical model that can simulate the loading process, so as to realize the loading simulation function.

[0033] Loading simulation and prediction: Using the above-mentioned loading numerical model, different energy dissipation device height positions are set for numerical simulation to simulate the entire loading process. This simulation predicts the changes in loading volume, earthwork volume, total overflow loss, and sediment height in the tank over loading time, yielding multiple sets of numerical simulation results corresponding to different energy dissipation device height positions. The energy dissipation device height position needs to be set manually.

[0034] The numerical simulation results also include: q t (3D mesh point cloud of flow rate at time t); ρ t (3D mesh point cloud of density at time t); v x (3D mesh point cloud of the x-direction velocity component at time t); v y (3D mesh point cloud of the y-direction velocity component at time t). The numerical calculation results are provided to the design optimization output module.

[0035] This module can display various data during the calculation process in real time and present the simulation results in a visual manner within the 3D model. Users can gain a more intuitive and in-depth understanding of the loading process by viewing the specific flow rate, concentration, and earthwork volume outputs under various working conditions. This helps users analyze various phenomena during the loading process, such as flow rate distribution and sediment concentration changes, thereby optimizing the loading plan.

[0036] The design optimization output module, based on the numerical simulation results output by the loading process simulation and prediction module, sets the evaluation criteria order using four indicators: loading volume, earthwork volume, overflow loss, and sediment flatness, to obtain the optimized recommended height of the energy dissipation device. Users can click to select the order of evaluation criteria (loading efficiency, flatness) according to actual needs. The system will automatically output the optimized recommended value of the energy dissipation device height based on the evaluation criteria selected by the user.

[0037] Specifically, in multiple sets of numerical simulation results, different energy dissipation device heights correspond to different mud chamber loading capacity, earthwork volume, overflow loss, and sediment smoothness. An evaluation criterion order is set, and recommended optimal design values ​​for the energy dissipation device height are generated based on this order (first gradient, second gradient). Typically, loading efficiency, represented by loading capacity, earthwork volume, and overflow loss, is used as the first gradient criterion, and sediment smoothness as the second gradient criterion. Higher loading capacity and earthwork volume, lower overflow loss, and higher mud chamber smoothness indicate a better impact of the energy dissipation device height on the loading effect under that working condition.

[0038] The automatic height adjustment module, based on the optimized height recommendation of the energy dissipation device output by the design optimization output module, sends control signals via the shipboard loading control system to automatically control the hydraulic struts on the adjustable-height energy dissipation device. By precisely controlling the extension and retraction of the hydraulic struts, the height of the energy dissipation device is automatically and accurately adjusted. Alternatively, users can manually determine the soil conditions of the construction area based on existing geological survey data and, based on engineering experience, input the appropriate energy dissipation device height through the loading control system. Upon receiving the instruction, the system sends control signals to the hydraulic struts, enabling manual adjustment of the energy dissipation device's height. This adjustment method is more flexible and can adapt to different construction environments and needs.

[0039] The beneficial effect of this invention is that it provides an automatic height adjustment and optimization method and system for the energy dissipation device of a trailing suction hopper dredger. During construction, the height of the energy dissipation device can be automatically and precisely adjusted in real time according to different soil types and slurry concentration requirements. The energy dissipation device is neither too low, causing rapidly settling coarse particles of slurry to clog the outlet, nor too high, causing the incoming water flow to disturb the slurry and increase the overflow loss of suspended slurry. By precisely controlling the height of the energy dissipation device, the construction efficiency of the trailing suction hopper dredger is improved, while the adverse impact of overflow into the construction water environment is reduced, demonstrating its engineering application value. Attached Figure Description

[0040] Figure 1 is a front view of the adjustable height energy dissipation device of the trailing suction hopper dredger in the embodiment; Figure 2 is a side view of the adjustable height energy dissipation device of the trailing suction hopper dredger in the embodiment; Figure 3 is a schematic diagram of the telescopic pipe structure in the embodiment; Figure 4 is a schematic diagram of the hydraulic support rod structure for lifting in the embodiment; Figure 5 is a three-dimensional model of the mud chamber of the trailing suction hopper dredger in the embodiment; Figure 6 shows the mesh division of the three-dimensional model of the mud chamber of the trailing suction hopper dredger in the embodiment; Figure 7 is a three-dimensional numerical simulation of the mud chamber of the trailing suction hopper dredger in the embodiment; Figure 8 is a flowchart of the sediment height prediction model in the embodiment; Figure 9 is a flowchart of the earthwork volume prediction model in the embodiment; Figure 10 is a flowchart of the optimized height prediction of the energy dissipation equipment in the embodiment; Figure 11 is a simple artificial neural network prediction model for the optimized height of the energy dissipation equipment; Figure 12 is the software module of the height optimization system of the energy dissipation device on the trailing suction hopper dredger; Figure 13 shows the automated height adjustment process of the energy dissipation device of the trailing suction hopper dredger during construction; Reference numerals: 1-telescopic main pipe, 2-horizontal pipe, 3-connecting steel plate, 4-hydraulic support rod, 5-mud chamber inner beam, 6-mud chamber side wall; 11-Inner tube, 12-Outer tube, 13-Filling material, 14-Limiting block, 15-Main pipe connection flange; 21-Outlet, 22-Transverse pipe connection flange; 41-Rear cylinder head, 42-Cylinder barrel, 43-Piston, 44-Piston rod, 45-Guide sleeve, 46-Sealing ring, 47-Front cylinder head. Detailed Implementation

[0041] The adjustable height energy dissipation device, automatic height adjustment optimization method and system of the trailing suction hopper dredger of the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0042] The preparatory work involved structural modifications to the trailing suction hopper dredger, and the structural components of the adjustable height energy dissipation device are shown in Figures 1 to 4.

[0043] The height adjustment of the energy dissipation device is embedded in and controlled by the shipboard loading control system to adjust in a timely manner for optimal loading operations.

[0044] The adjustable height energy dissipation device of the trailing suction hopper dredger consists of a telescopic main pipe 1, a transverse pipe 2, a connecting steel plate 3, hydraulic struts 4, and a crossbeam 5 inside the mud hopper. The crossbeam 5 inside the mud hopper is fixed to the side wall 6 of the mud hopper by welding. Four lifting hydraulic struts 4 are fixed to the crossbeam 5 inside the mud hopper by bolts. The connecting steel plate 3 is placed above the hydraulic struts 4 and fixed to the upper end of the hydraulic struts 4 by bolts. The connecting steel plate 3 is connected to the outer pipe 12 of the telescopic main pipe 1 by welding.

[0045] The telescopic main pipe 1 is a sleeve-type structure. The inner pipe 11 and the outer pipe 12 are sealed by filler material 13 without affecting relative sliding. The limiting block 14 restricts the extension and retraction of the telescopic main pipe 1. Under the action of the hydraulic support rod 4, the telescopic main pipe 1 can freely extend and retract within a certain limit. The outer pipe 12 is connected to the transverse pipe 2 through the main pipe connecting flange 15.

[0046] The transverse pipe 2 is divided into three parts: left, middle and right, which are connected in sequence by flanges. The transverse pipe 2 has a total of 8 outlets 21, which are used for energy dissipation loading of mud.

[0047] The hydraulic strut 4 consists of a rear cylinder head 41, a cylinder 42, a piston 43, a piston rod 44, a guide sleeve 45, a sealing ring 46, and a front cylinder head 47. The rear cylinder head 41 is bolted to the crossbeam 5 inside the mud tank and simultaneously seals the rear of the cylinder 42. The piston 43 is subjected to force inside the cylinder, which is transmitted to the connecting steel plate 3 above through the piston rod 44. The guide sleeve 45 supports and ensures the coaxiality of the piston rod 44 and the cylinder 42. The sealing ring 46 seals the front cylinder head 46 and the piston rod 44 to prevent water, mud, and other contaminants from affecting the normal operation of the components inside the cylinder 42 during the loading process.

[0048] When adjusting the height of the energy dissipation device, the shipboard loading control system controls the lifting and lowering of the four hydraulic struts 4 in the embodiment, which in turn drives the outer pipe 12 and the transverse pipe 2 of the telescopic main pipe 1 to rise or fall through the connecting steel plate 3. The mud is then discharged into the mud tank along the inner pipe 11, the outer pipe 12, the transverse pipe 2, and the outlet 21 to carry out the loading operation.

[0049] Example 1: Optimization Method for Automatic Height Adjustment of Energy Dissipation Device in a Trailing Suction Hoist Dredger. Process 1: Following the preparatory work, the facilities of the trailing suction hopper dredger are modified. Details are omitted.

[0050] Process 2 determines the optimal adjustment height value, including the following steps: Step 1: Determine the core parameters, including key parameters of six parts: geometric model, boundary conditions, mesh conditions, initial conditions, fluid motion and sediment motion, which are used to automatically build a three-dimensional model of the mud tank system and set the scenario constraints for the loading simulation.

[0051] (1) Geometric model parameters, including setting the structural dimensions of the mud tank, the dimensions of the transverse and longitudinal triangular tanks, the dimensions of the overflow equipment, the dimensions of the energy dissipation equipment, and the relative positions of the transverse and longitudinal triangular tanks and the energy dissipation overflow equipment to the mud tank, are used to establish a three-dimensional structural model of the mud tank system. The established model is shown in Figure 5.

[0052] (2) Boundary conditions include setting the inlet boundary as the loading flow rate and loading mud concentration, selecting the overflow boundary as the free outflow boundary, the upper boundary of the mud tank as the atmospheric pressure boundary, and the tank wall boundary as the wall boundary with a certain roughness material; (3) As shown in Figure 6, the mesh is divided according to the three-dimensional model. The mesh conditions include setting the total mesh range in three directions that include the mud tank system structure model, and setting the unit mesh size to a suitable length, width and height dimension for identifying the mud tank structure in the simulation calculation; (4) Initial conditions include setting the initial water level and loading simulation duration; (5) Fluid motion parameters include setting the fluid temperature and fluid viscosity, and selecting the fluid motion equation as the RNG kε equation; (6) Sediment motion parameters include setting the sediment particle size, sediment density, sediment porosity, critical Shields number, underwater angle of repose, bedload motion coefficient and scour coefficient. The sediment initiation and scour process is calculated using the empirical formulas of Mastbergen and Von den Berg, and the bedload transport process in the simulation is calculated using the empirical formulas of Meyer-Peter & Mller equati.

[0053] Step 2: Establishing the Loading Numerical Model and Loading Simulation Prediction; Establishing the Loading Numerical Model: This refers to using CFD software to establish a numerical model capable of simulating the loading process based on the input core parameters, thereby realizing the loading simulation function; Loading Simulation Prediction: Using the above loading numerical model, different energy dissipation device height positions are set for numerical simulation to simulate the entire loading process, achieving simulation prediction of the changes in loading volume, earthwork volume, total overflow loss, and sediment height in the tank with loading time, obtaining multiple sets of numerical simulation calculation results corresponding to different energy dissipation device height positions; the energy dissipation device height positions need to be manually set; The numerical simulation calculation results also include: q t (3D mesh point cloud of flow rate at time t); ρ t(Three-dimensional grid point cloud of density at time t); v x (Three-dimensional grid point cloud of the x-directional velocity component at time t); v y (Three-dimensional grid point cloud of the y-directional velocity component at time t); The results of the numerical calculation are provided to step 3.

[0054] In the embodiment, using CFD software, according to the input core parameters, a numerical model of the loading construction process is automatically established by using the structured multi-block grid technology and the finite volume method, realizing the simulation of the loading construction changing with time. The simulation results are shown in Figure 7. Different heights of the energy dissipation device are set. The loading control system sets different sediment movement parameters according to the geological exploration report of the construction area and referring to the existing construction experience data for different soil types, and conducts the loading simulation calculation under multiple working conditions.

[0055] Step 3: Output of the optimized height of the energy dissipation device. Set the optimization goal as the optimal flatness α of the deposited sediment and the maximum earthwork volume D, then the objective function of the optimization model is: max D = max∑ΔQ t ·Δt·ρ (16) Set the constraint conditions (x.t.): X = {x1, x2, x3…, x i} (17) Where, Δt is the length of the time period for calculating the deposited earthwork volume; ΔQ i is the outflow discharge within the time period Δt; ρ is the outflow concentration within the time period Δt; x i belongs to [0, H], H represents the height of the mud tank, and x (it) is used as the parameter for calculating the flatness, representing the height of the deposited sediment measured at the sampling position i at time t, represents the average height of the deposited sediment in the cross-section of the longitudinal central axis of the mud tank.

[0056] t < T (18) In the formula, T represents the initially set maximum time.

[0057] Set multiple groups of heights of the energy dissipation device, and respectively use the numerical calculation model in step 2 to calculate q t , ρ t , v x and v y , and then calculate the flatness of the deposited sediment and the earthwork volume. Select the best group according to the above optimization goals (optimal flatness, maximum earthwork volume), and the corresponding height of the energy dissipation device is the optimal height of the energy dissipation device.

[0058] Optionally, the recommended design value for the energy dissipation device height can be determined based on the evaluation criteria (first-gradient, second-gradient) of four indicators: loading capacity, earthwork volume, overflow loss, and sediment flatness. Typically, loading efficiency, represented by loading capacity, earthwork volume, and overflow loss, is used as the first-gradient criterion, and sediment flatness as the second-gradient criterion. A higher loading capacity and earthwork volume, lower overflow loss, and higher sediment flatness indicate a better impact of the energy dissipation device height on the loading effect under that working condition.

[0059] Furthermore, in construction situations where the flatness of the loading compartment is highly critical, the order of evaluation criteria can be adjusted, with the flatness of the deposited sediment being used as the first priority.

[0060] In step 3, to avoid complex numerical calculations, a neural network is introduced to predict sediment height and earthwork volume. As shown in Figure 8, four variables are input into the input layer: qt (flow rate), ρt (density), vxt (x-direction velocity), and vyt (y-direction velocity). These input features, after feature selection and standardization preprocessing, are fed into the target Transformer neural network model to obtain the predicted sediment height.

[0061] The target Transformer neural network model includes an Embedding Layer, Multi-head Attention, and Softmax (probability distribution output). The Embedding Layer vectorizes the input features, converting them into a vector representation suitable for network processing. This process maps high-dimensional input features to a low-dimensional vector space for subsequent processing. Multi-head Attention captures different feature information through multiple attention heads and integrates this information to weight the input features. Softmax calculates the probability distribution of each output based on the result of the multi-head attention mechanism. The output values ​​are mapped to the range [0,1], and the sum of the probabilities of all outputs is 1, which helps the model learn the non-linear relationship between the input features and the output results. Finally, the predicted sediment deposition height sequence (x1,x2,...,x) is output through the output layer. n ).

[0062] The Transformer neural network model (sediment height prediction model) uses four input variables as training data and sediment height sequences as labels for training. After each iteration, the loss function is calculated using validation data and the labels, with mean squared error (MSE) as the loss function. In the formula, x i , These represent the sediment height obtained through iterative calculation and the actual sediment height used as a label value, respectively.

[0063] The model adjusts the network weights using the backpropagation algorithm to minimize the loss function. In each training iteration, the gradient of the loss function is calculated, and the network weights are updated until the model reaches the preset convergence condition, thus obtaining the target neural network model.

[0064] As shown in Figure 9, four variables are input to the input layer: qt (flow rate), ρt (density), vxt (x-direction velocity), and vyt (y-direction velocity). These input features are preprocessed (standardized) before being fed into the network. The BatchNorm layer calculates the mean and variance of all values ​​in the mini-batch, using these values ​​to normalize each batch of data. This ensures a relatively stable distribution of the input to each layer, accelerating training and improving model stability. The PointNet layer, a deep learning framework specifically designed for point cloud data, is used in this model to extract high-dimensional feature representations of the input features. The fully connected layer further processes the features extracted from the PointNet layer, mapping the high-dimensional features to the target output space, thereby enabling the prediction of earthwork volume.

[0065] Optionally, this invention also provides another simpler and faster method for recommending optimal parameters for energy dissipation equipment height, as shown in Figure 10, which illustrates the prediction process for the optimal energy dissipation equipment height. The prediction process includes 10 parts: data input, data preprocessing, partitioning of training and test sets, neural network model building, forward propagation, loss function calculation, back propagation, parameter update, model evaluation, and optimal value recommendation.

[0066] (1) Data input: As the starting point of the entire prediction model, the data such as qt (flow rate), ρt (density), vxt (x-direction velocity), vyt (y-direction velocity), and h (height of energy dissipation device) of the mud entering the mud chamber at time t are input in tabular form.

[0067] (2) Data Preprocessing: In this stage, the data described in (1) will be standardized. The earthwork volume D and the sediment smoothness α will be calculated. A new parameter λ (0 < λ < 1) will be set, and λD(1 λ)α will be calculated for each set of data. D-∑ΔQ t ·Δt·ρ (4) Where: Δt is the length of the time period for calculating the volume of deposited soil; ΔQ iρ is the outflow rate during the time interval Δt; ρ is the outflow concentration during the time interval Δt. (3) Training and test set division: Data such as qt (flow rate), ρt (density), vxt (x-direction velocity) and vyt (y-direction velocity), h (energy dissipation device height) will be divided into training and test sets. The training set is used to train the model, and the test set is used to evaluate the model's performance. The division ratio is set to 8:2 or 7:3.

[0068] (4) Neural Network Model Construction: A simple artificial neural network (MLP) model is constructed as shown in Figure 11. The neural network consists of multiple layers of neurons (nodes), with several neurons in each layer. The layer structure includes an input layer (receiving processed input data), a hidden layer (performing complex calculations and extracting high-order features of the input data), and an output layer (producing the final prediction result, i.e., the objective function λD(1 λ)α).

[0069] (5) Forward propagation: Data travels from the input layer through neurons in each layer, where activation functions are calculated and output, until the output layer. The calculation formula for each layer is as follows: a (l) =σ(z) (l) (6) Where: l is the number of activation layers; W (l) b is the weight matrix; (l) a is the bias vector; (l) With a (l-1) These are the activation values ​​for the current layer and the previous layer, respectively. In the input layer a, the corresponding input data such as qt (flow rate), ρt (density), vxt (x-direction velocity), vyt (y-direction velocity), and h (energy dissipation device height) can be used for calculation; σ is the activation function.

[0070] Furthermore, the activation function chosen is the ReLU function: σ(z) (l) )-ReLU(z (l) ) = max(0, z (l) (7) (6) Loss function calculation: The common mean squared error (MSE) is selected as the loss function: In the formula: y i For the true value, Let be the predicted value, where y = λD(1 λ)α, and n is the number of samples.

[0071] (7) Backpropagation: By calculating the gradient of the loss function with respect to each parameter, the parameters are updated to minimize the loss function. The gradient calculation formula is as follows: Where: δ (l) This is the error term, which is the difference between the network's predicted value and the actual label value.

[0072] (8) Parameter Update: Update the weight matrix and bias vector using the gradient descent optimization algorithm. The gradient descent update formula is as follows: In the formula: α is the learning rate.

[0073] (9) Model evaluation: The model performance is evaluated using the test set with metrics such as accuracy, precision, and recall.

[0074] (10) Optimal value recommendation: After the model training is completed, predictions can be made for the obtained input parameters qt (flow rate), ρt (density), vxt (x-direction velocity), vyt (y-direction velocity), and h (energy dissipation device height). In order to find the maximum value of the model, the model parameters are fixed, and for each parameter of each layer, the derivative is calculated using the chain rule to make them equal to zero, so as to obtain the extreme value candidates within a certain range, and the maximum value is selected. Backpropagation is then used to obtain the optimal energy dissipation device height h.

[0075] Step 4 of the process 3: Based on the optimal energy dissipation equipment height obtained in step 3, adjust the hydraulic struts of the adjustable height energy dissipation device to raise or lower the energy dissipation equipment height to the optimal height.

[0076] Example 2: A height optimization system for an onboard energy dissipation device on a trailing suction hopper dredger includes four developed software modules: a core parameter input module, a loading process simulation and prediction module, a design optimization output module, and an automatic height adjustment module. These software modules are embedded in and called by the onboard loading control system; as shown in Figure 12; Among them, the core parameter input module: The core parameters include geometric model, boundary conditions, mesh conditions, initial conditions, fluid motion, and sediment motion. The geometric model includes: the structural dimensions of the mud hopper, the dimensions of the transverse and longitudinal triangular hoppers, the dimensions of the overflow equipment, the dimensions and height of the energy dissipation equipment, and the relative positions of the transverse and longitudinal triangular hoppers and the energy dissipation overflow equipment to the mud hopper. The boundary conditions include: the inlet boundary being the loading flow rate and mud concentration; the overflow boundary being the free outflow boundary; the upper boundary of the mud hopper being the atmospheric pressure boundary; and the hopper wall boundary being a wall boundary with a certain degree of roughness. The mesh conditions include: the mesh range in the x, y, and z directions and the unit mesh size. The initial conditions include the initial water level H0 and the hopper loading simulation duration T. The fluid motion parameters include fluid temperature T, fluid viscosity μ, and the fluid motion equation. The sediment motion parameters mainly include sediment particle size, sediment density, sediment porosity, critical Shields number, underwater angle of repose, bedload coefficient, and scour coefficient, involving four motion forms: sediment suspension, sediment deposition, sediment scour, and sediment bedload. The hopper loading process simulation and prediction module includes: Using the parameters input into the core parameter input module, numerical calculations are performed to simulate the loading process under different operating conditions. The loading process simulation and prediction module implements the simulation and prediction of the loading process, which includes two parts: establishing a loading numerical model and loading simulation and prediction.

[0077] Establishing a loading numerical model: This refers to using CFD software (existing technology) to establish a numerical model that can simulate the loading process based on the input core parameters, so as to realize the loading simulation function.

[0078] Loading simulation and prediction: Using the above-mentioned loading numerical model, different energy dissipation device height positions are set for numerical simulation to simulate the entire loading process. This simulation predicts the changes in loading volume, earthwork volume, total overflow loss, and sediment height in the tank over loading time, yielding multiple sets of numerical simulation results corresponding to different energy dissipation device height positions. The energy dissipation device height position needs to be set manually.

[0079] The numerical simulation results also include: q t (3D mesh point cloud of flow rate at time t); ρ t (3D mesh point cloud of density at time t); v x (3D mesh point cloud of the x-direction velocity component at time t); v y (3D mesh point cloud of the y-direction velocity component at time t). The numerical calculation results are provided to the design optimization output module.

[0080] This module can display various data during the calculation process in real time and present the simulation results in a visual manner within the 3D model. Users can gain a more intuitive and in-depth understanding of the loading process by viewing the specific flow rate, concentration, and earthwork volume outputs under various working conditions. This helps users analyze various phenomena during the loading process, such as flow rate distribution and sediment concentration changes, thereby optimizing the loading plan.

[0081] The design optimization output module, based on the numerical simulation results output by the loading process simulation and prediction module, sets the evaluation criteria order using four indicators: loading volume, earthwork volume, overflow loss, and sediment flatness, to obtain the optimized recommended height of the energy dissipation device. Users can click to select the order of evaluation criteria (loading efficiency, flatness) according to actual needs. The system will automatically output the optimized recommended value of the energy dissipation device height based on the evaluation criteria selected by the user.

[0082] Specifically, in multiple sets of numerical simulation results, different energy dissipation device heights correspond to different loading capacity, earthwork volume, overflow loss, and sediment flatness. An evaluation criterion order was established, and the recommended optimal design value for the energy dissipation device height was determined based on this order (first gradient, second gradient). Typically, loading capacity, earthwork volume, and overflow loss, representing the tank loading efficiency, are used as the first gradient criterion, while sediment flatness is used as the second gradient criterion. Higher loading capacity and earthwork volume, lower overflow loss, and higher sediment flatness indicate a better impact of the energy dissipation device height on the loading effect under that working condition.

[0083] The automatic height adjustment module, based on the optimized height recommendation of the energy dissipation device output by the design optimization output module, sends control signals via the shipboard loading control system to automatically control the hydraulic struts on the adjustable-height energy dissipation device. By precisely controlling the extension and retraction of the hydraulic struts, the height of the energy dissipation device is automatically and accurately adjusted. Alternatively, users can manually determine the soil conditions of the construction area based on existing geological survey data and, based on engineering experience, input the appropriate energy dissipation device height through the loading control system. Upon receiving the instruction, the system sends control signals to the hydraulic struts, enabling manual adjustment of the energy dissipation device's height. This adjustment method is more flexible and can adapt to different construction environments and needs.

[0084] The specific modules are as follows: The loading process simulation and prediction module uses the parameters input in the core parameter input module to perform numerical simulation calculations of the loading process. It treats the sediment as solid particles, employing both fluid and sediment particle models to simulate four states: sediment suspension, deposition, resuspension, and shoveling. A partitioned structured mesh is used, with the energy dissipation device outlet area divided into separate zones and densified by a factor of 2. The inlet boundary is defined by the loading flow rate and slurry concentration; the overflow boundary is a free outflow boundary; the upper boundary of the slurry tank is an atmospheric pressure boundary; and the tank wall boundary is a wall boundary made of a material with a certain degree of roughness. The finite volume method is used to perform numerical simulation calculations of the loading process.

[0085] During the simulation, this module can display various data in real time, such as flow rate, concentration, and earthwork volume, and present the simulation results in a visual manner within the 3D model. Users can gain a deeper and more comprehensive understanding of the loading process through these intuitive data and images. Furthermore, users can analyze various phenomena during the loading process, such as flow rate distribution and sediment concentration changes, by viewing the output results under different operating conditions, thereby further optimizing the loading plan.

[0086] Furthermore, the loading volume, earthwork volume, overflow loss, and sediment flatness at different times during the loading process are calculated using the following formula: Loading volume: represents the total mass of mud mixture loaded into the mud tank that did not overflow.

[0087] M = M f +M ss +M sd In the formula: M represents the loading capacity, M f M represents the mass of the fluid inside the chamber. ss Indicates the mass of suspended sediment particles inside the chamber, M sd This indicates the mass of sediment particles deposited inside the chamber.

[0088] Earthwork volume: This refers to the volume of undisturbed soil that was loaded into the mud bin and did not overflow.

[0089] In the formula, ρ s ρ represents the density of sediment particles, e represents the porosity of the undisturbed soil, and ρ represents the density of sediment particles. f ρ represents fluid density. s1 This indicates the density of the original soil.

[0090] Overflow loss: refers to the total mass of mud mixture loaded into the mud tank but overflowing out.

[0091] M l =M o -M In the formula, M o The total mass of the mud mixture flowing out of the outlet of the energy dissipation device.

[0092] Flatness inside the mud chamber: expressed as the standard deviation of the height of sediment deposited in the longitudinal centerline section of the mud chamber.

[0093] In the formula, α represents the flatness of the cabin interior, and x i This indicates the height of the deposited sediment at position i on the longitudinal centerline section of the mud chamber. This represents the average height of sediment deposited along the longitudinal centerline of the mud chamber, where n represents the number of sediment deposit heights calculated along the longitudinal centerline of the mud chamber.

[0094] The automatic height adjustment module is a concrete manifestation of automation in the shipboard loading control system, eliminating the need for manual adjustment of the energy dissipation device height. This module automatically controls the hydraulic strut 4 installed on the adjustable-height energy dissipation device based on the optimized height recommendation value obtained from the design optimization output module. By precisely controlling the extension and retraction of the hydraulic strut 4, real-time, automated, and accurate adjustment of the energy dissipation device height is achieved. This automatic adjustment method not only improves the accuracy and efficiency of adjustment but also reduces the difficulty and error of manual operation.

[0095] Furthermore, users can also input the appropriate height of the energy dissipation device through the shipboard loading control system based on their own judgment and experience. Upon receiving the instruction, the system will send corresponding control signals to the hydraulic strut 4 to achieve manual adjustment of the energy dissipation device's height. This flexible adjustment method can adapt to different construction environments and needs, improving the system's adaptability and practicality.

[0096] Example 3 A novel trailing suction hopper dredger with automatically adjustable energy dissipation device height and its loading operation method: By modifying the original trailing suction hopper dredger or building a new one, the hardware is equipped with an adjustable-height energy dissipation device, and the preparatory work is completed. The previous description will not be repeated.

[0097] The height-adjustable energy dissipation device is managed by the shipboard loading control system, as described in Example 2.

[0098] The specific process of the loading operation method for the highly automated adjustment of the energy dissipation device of the trailing suction hopper dredger is shown in Figure 13, including the following steps: Step 1: The trailing suction hopper dredger enters the construction area; before the start of construction, the core parameters are manually input, mainly including six parts: geometric model, boundary conditions, mesh conditions, initial conditions, fluid motion and sediment motion. The specific content of the parameters is as described in Example 1.

[0099] Step 2: The shipboard loading control system automatically establishes a numerical model of the loading process based on the input core parameters, realizing the simulation function of loading changes over time.

[0100] Step 3: Set different energy dissipation equipment heights in the shipboard loading control system and perform loading simulation calculations under multiple working conditions.

[0101] Step 4: Recommend energy dissipation device height parameters through the design optimization parameter output module of the shipboard loading control system. Based on user requirements, select the evaluation criteria order (first gradient, second gradient) and the number of recommended values, and automatically generate recommended values ​​for the optimized design of the energy dissipation device height. The core parameter recommendation criteria for the energy dissipation device height location, under the default state, use loading efficiency (represented by loading capacity, earthwork volume, and overflow loss) as the first gradient criterion, and sediment flatness as the second gradient criterion.

[0102] Furthermore, in construction situations where the flatness of the loading compartment is highly critical, the order of evaluation criteria can be adjusted, with the flatness of the deposited sediment being used as the first priority.

[0103] Step 5: When the location and excavation depth of the ship construction area change significantly, refer to the geological survey report and the corresponding changes in the excavated soil. Based on the updated input values ​​such as qt, ρt, vxt, and vyt, repeat step 4 to recommend a new optimized value for the height of the energy dissipation equipment.

[0104] Specifically, as excavation progresses, the excavation location and depth change, and the soil conditions alter. To improve overall loading efficiency, the loading control system adjusts sediment transport parameters based on geological survey data, and re-performs numerical calculations and neural network training processes such as multi-condition loading simulation prediction, automatic optimization and recommendation of height parameters. The loading control system automatically re-controls the height of the hydraulic struts, thereby adjusting the height of the energy dissipation device to consistently ensure high loading efficiency. When the amount of fine particles in the excavated soil increases, the system automatically and precisely lowers the height of the energy dissipation device to reduce disturbance to fine sediment particles by the incoming water flow, allowing them to settle quickly and reducing overflow losses. When the amount of coarse particles in the loading slurry increases, the particles settle faster, and the system automatically and precisely increases the height of the energy dissipation device to prevent soil particles from clogging the outlet 21.

[0105] Step 6: When the trailing suction hopper dredger is operating at different construction locations and dredging depths, the corresponding recommended height of the energy dissipation equipment is called in combination with the dredger's position and the depth of the drag arm, and a command is sent to the hydraulic support rod 4 installed on the adjustable height energy dissipation device to realize the real-time height adjustment of the energy dissipation device.

Claims

1. A height optimization system for an energy dissipation device on a trailing suction hopper dredger, characterized in that: The height optimization system includes a core parameter input module, a loading process simulation and prediction module, a design optimization output module, and an automatic height adjustment module. These software modules are embedded in the shipboard loading control system and are called by it.

2. The height optimization system for the trailing suction hopper dredger's onboard energy dissipation device as described in claim 1, characterized in that: The core parameter input module includes a geometric model, boundary conditions, mesh conditions, initial conditions, fluid motion, and sediment motion. The geometric model includes: the structural dimensions of the mud hopper, the dimensions of the transverse and longitudinal triangular hoppers, the dimensions of the overflow equipment, the dimensions and height of the energy dissipation equipment, and the relative positions of the transverse and longitudinal triangular hoppers and the energy dissipation overflow equipment to the mud hopper. The boundary conditions include the inlet boundary being the loading flow rate and the loading mud concentration, the overflow boundary being the free outflow boundary, the upper boundary of the mud hopper being the atmospheric pressure boundary, and the hopper wall boundary being the wall boundary with a certain roughness material. The mesh conditions include: the mesh range in the x, y, and z directions and the unit mesh size. The initial conditions include the initial water level H0 and the loading simulation duration T. The fluid motion parameters include the fluid temperature T, the fluid viscosity, and the fluid motion equation. The sediment motion parameters mainly include sediment particle size, sediment density, sediment porosity, critical Shields number, underwater angle of repose, bedload coefficient, and scour coefficient, involving four motion forms: sediment suspension, sediment deposition, sediment scour, and sediment bedload.

3. The height optimization system for the trailing suction hopper dredger's onboard energy dissipation device as described in claim 1, characterized in that: The loading process simulation and prediction module uses the parameters input by the core parameter input module to perform numerical calculations and simulate the loading process under different working conditions. The loading process simulation and prediction module realizes the simulation and prediction of the loading process, including two parts: establishing a loading numerical model and loading simulation and prediction.

4. The height optimization system for the trailing suction hopper dredger's onboard energy dissipation device as described in claim 3, characterized in that: The establishment of the loading numerical model refers to the use of CFD software to establish a numerical model that can simulate the loading construction process based on the input core parameters, so as to realize the loading simulation function.

5. The height optimization system for the trailing suction hopper dredger's onboard energy dissipation device as described in claim 3, characterized in that: The loading simulation prediction: Using the loading numerical model, different working conditions with different height positions of the energy dissipation devices are set, and numerical simulation is performed to simulate the entire loading process. The simulation prediction of the changes in loading volume, earthwork volume, total overflow loss and sediment height in the tank with loading time is realized, and multiple sets of numerical simulation calculation results corresponding to different height positions of the energy dissipation devices are obtained. The height position of the energy dissipation devices needs to be set manually.

6. The height optimization system for the trailing suction hopper dredger's onboard energy dissipation device as described in claim 5, characterized in that: The numerical simulation results also include: q t (3D mesh point cloud of flow rate at time t); ρ t (3D mesh point cloud of density at time t); v x (3D mesh point cloud of the x-direction velocity component at time t); v y (3D mesh point cloud of the y-direction velocity component at time t), the numerical calculation results are provided to the design optimization output module; This module can display various data in the calculation process in real time and present the simulation results in a visual way in the 3D model. Users can view the specific flow rate, concentration and earthwork volume outputs during the loading process under various working conditions to gain a more intuitive and in-depth understanding of the loading process. This helps users analyze various phenomena during the loading process, such as flow rate distribution and sediment concentration changes, and thus optimize the loading plan.

7. The height optimization system for the trailing suction hopper dredger's onboard energy dissipation device as described in claim 1, characterized in that: The design optimization output module: Based on the numerical simulation results output by the loading process simulation and prediction module, it sets the evaluation criteria order based on four indicators, namely loading volume, earthwork volume, overflow loss, and sediment flatness, to obtain the recommended height for the energy dissipation device. Users can select the order of evaluation criteria (loading efficiency, flatness) according to their actual needs; the system will automatically output the optimized recommended value for the height of the energy dissipation equipment based on the evaluation criteria selected by the user.

8. The height optimization system for the trailing suction hopper dredger's onboard energy dissipation device as described in claim 1, characterized in that: The automatic height adjustment module, based on the optimized height recommendation value of the energy dissipation device output by the design optimization output module, sends a control signal via the shipboard loading control system to automatically control the hydraulic struts on the adjustable-height energy dissipation device. By precisely controlling the extension and retraction of the hydraulic struts, the height of the energy dissipation device is automatically and accurately adjusted. In addition, users can also manually determine the soil conditions of the construction area based on existing geological survey data and, based on engineering experience, input the appropriate height of the energy dissipation device through the loading control system. After receiving the instruction, the system will send a control signal to the hydraulic struts to realize manual assisted adjustment of the lifting and lowering of the energy dissipation device. This adjustment method is more flexible and can adapt to different construction environments and needs.

9. The height optimization system for the trailing suction hopper dredger-mounted energy dissipation device as described in claim 8, characterized in that: The shipboard loading control system manages the height-adjustable energy dissipation device, which is based on the original trailing suction hopper dredger or a new ship under construction. The adjustable height energy dissipation device is designed to include a telescopic main pipe (1), a transverse pipe (2), a connecting steel plate (3), a hydraulic support rod (4), and a crossbeam inside the mud chamber (5). The retractable main pipe (1) is a sleeve-type structure. The retractable main pipe (1) includes an inner pipe (11), an outer pipe (12), filler (13), a limiting block (14), and a main pipe connecting flange (15). The inner pipe (11) and the outer pipe (12) are sealed by the filler (13). The limiting block (14) limits the retraction range of the retractable main pipe (1). The inner pipe (11) is connected to the mud conveying pipeline of the trailing suction dredger. The outer pipe (12) is connected to the transverse pipe (2) through the main pipe connecting flange (15). The transverse pipe (2) is divided into three parts: left, middle, and right, which are connected in sequence through the transverse pipe connecting flange (22). It has a total of 8 outlets (21). The crossbeam (5) inside the mud chamber is fixed to the side wall (6) of the mud chamber by welding technology. Four hydraulic support rods (4) for lifting are fixed to the crossbeam (5) inside the mud chamber by bolts. A connecting steel plate (3) is placed above the hydraulic support rod (4) and fixed to the upper end of the hydraulic support rod (4) by bolts. The connecting steel plate (3) is connected to the outer tube (12) of the telescopic main tube (1) by welding technology. Four hydraulic struts (4) are evenly arranged on the crossbeam (5) inside the mud chamber to ensure that the crossbeam (5) inside the mud chamber and the connecting steel plate (3) are subjected to uniform force while providing sufficient force. The hydraulic strut (4) consists of a rear cylinder head (41), a cylinder (42), a piston (43), a piston rod (44), a guide sleeve (45), a sealing ring (46), and a front cylinder head (47). The rear cylinder head (41) is bolted to the crossbeam (5) inside the mud tank, and at the same time seals the rear of the cylinder (42). The piston (43) is subjected to force inside the cylinder, which is transmitted to the connecting steel plate (3) above through the piston rod (44). The guide sleeve (45) supports and ensures the coaxiality of the piston rod (44) and the cylinder (42). The sealing ring (46) seals the front cylinder head (46) and the piston rod (44) to prevent water, mud, sand, etc. from affecting the normal operation of the components inside the cylinder (42) during the loading process.

10. The height optimization system for the trailing suction hopper dredger's onboard energy dissipation device as described in claim 9, characterized in that: Obtain the optimized recommended height for the energy dissipation device, adjust the hydraulic struts of the adjustable-height energy dissipation device, raise or lower the height of the energy dissipation equipment to the optimal height, and carry out loading operations at the optimal energy dissipation height.

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