A Method and System for Optimizing Water Resistance of a Cleaning Robot Considering Boundary Layer Velocity Distribution

Abstract

This invention provides a method and system for optimizing water resistance in a cleaning robot, considering the boundary layer velocity distribution. It relates to the field of water resistance optimization technology, and includes: modeling the cleaning robot adsorbed onto the curved surface of a ship hull to obtain a robot model and a ship hull surface model; performing fluid dynamics simulation on the ship hull surface model while the ship is en route to obtain velocity distribution data of the ship hull boundary layer; performing fluid dynamics simulation on the robot model adsorbed onto the ship hull surface to obtain instantaneous resistance data of the robot; and optimizing the robot's structural and appearance design based on the instantaneous resistance data. This invention designs the non-uniform, spatially varying velocity inlet of the CFD simulation scheme based on the boundary layer velocity distribution of the ship hull wall, making the scheme more closely resemble actual engineering working conditions. It compensates for the deficiency of still water simulation in not considering the influence of high-speed water flow on the ship hull wall on robot resistance, providing strong technical support for the independent research and development and performance improvement of ships and underwater equipment.

Description

Technical Field

[0001] This invention relates to the field of water resistance optimization technology, and more specifically to a water resistance optimization method and system for cleaning robots that takes into account the boundary layer velocity distribution. Background Technology

[0002] With the rapid development of the shipbuilding industry, the demand for ship cleaning is constantly expanding. Currently, ship cleaning methods are divided into three main categories: dock cleaning, freshwater navigation, and underwater cleaning. All three methods are post-cleaning, i.e., static cleaning, which has many problems such as high cleaning costs, expensive equipment, limited cleaning effect, and environmental pollution. Therefore, there is an urgent need in this field for a safe, efficient, flexible, autonomous, and inexpensive ship cleaning robot that can be used during ship navigation.

[0003] Compared to static cleaning, cleaning during ship navigation presents the robot with continuous, high-speed water flow impacts. The high-speed dynamic water flow near the ship's hull will subject the cleaning robot to high resistance and overturning moment. To meet the requirements of being small, lightweight, having low water resistance, and being flexible so that the robot can work safely during ship operation and reliably adhere to the complex curved surface of the hull, it is necessary to perform hydrodynamic analysis and drag reduction optimization on the robot structure, and use specialized fluid dynamics design and verification to ensure its reliable adhesion to the ship's hull surface.

[0004] The computational fluid dynamics (CFD) resistance simulation scheme for the design of a ship-to-ship cleaning robot needs to consider the influence of water flow near the hull wall on the robot's resistance. This is because the flow field and boundary layer near the hull wall of a ship are very complex during navigation, and the water velocity distribution is uneven. Within the "boundary layer" close to the hull wall, the flow velocity gradually increases from zero (at the hull wall) to close to the free flow velocity, and along the hull from bow to stern, the boundary layer undergoes a series of significant and continuous changes. These non-uniformities and spatial variations in water velocity directly affect the robot's resistance performance.

[0005] Current fluid simulations for underwater cleaning robots are conducted in still water environments. The simulation schemes are designed under conditions where the water flow velocity is zero and the robot travels at a slow and constant speed. They do not consider the impact of high-speed dynamic turbulence conditions and the complex curvature of the ship's walls at different locations on the resistance of the cleaning robot, resulting in inaccurate simulation results and failing to solve the problem of simulating the resistance of cleaning robots on ships en route. Summary of the Invention

[0006] To address the aforementioned issues, this invention proposes a water resistance optimization method and system for cleaning robots that considers boundary layer velocity distribution. Based on the boundary layer velocity distribution on the hull wall, the non-uniform, spatially varying velocity inlet of this CFD simulation scheme is designed, making the scheme more closely resemble actual engineering working conditions. This overcomes the deficiency of still water simulation in not considering the impact of high-speed water flow on the hull wall on robot resistance, providing strong technical support for the independent research and development and performance improvement of ships and underwater equipment.

[0007] According to some embodiments, the present invention adopts the following technical solution:

[0008] Water resistance optimization methods for cleaning robots considering boundary layer velocity distribution include:

[0009] Modeling was performed on the curved surface of the ship's hull to obtain the robot model and the curved surface model of the ship's hull.

[0010] Fluid dynamics simulation was performed on the hull surface model of the ship while it was en route to simulate the actual fluid flow and obtain the velocity distribution data of the hull boundary layer.

[0011] Fluid dynamics simulation was performed on a robot model adsorbed on the curved surface of the ship to simulate the robot's motion while the ship was en route and obtain instantaneous resistance data of the robot. Before the simulation, the velocity inlet of the fluid computation domain was defined based on the velocity distribution data of the ship's boundary layer and the velocity distribution method that is non-uniform at the boundary and varies with space.

[0012] Based on instantaneous resistance data, the robot's structure and appearance design are optimized with the goal of reducing the resistance encountered by the robot when it travels with the ship.

[0013] According to some embodiments, the present invention adopts the following technical solution:

[0014] A water resistance optimization system for a cleaning robot considering boundary layer velocity distribution includes:

[0015] The 3D modeling module is configured to: model the cleaning robot adsorbed on the curved surface of the ship hull, and obtain the robot model and the curved surface model of the ship hull respectively;

[0016] The hull simulation module is configured to perform fluid dynamics simulation on the hull surface model of the ship while it is en route, simulate the actual fluid flow, and obtain the velocity distribution data of the hull boundary layer.

[0017] The robot simulation module is configured to perform fluid dynamics simulation on the robot model adsorbed on the curved surface of the ship hull, simulate the robot's motion when the ship is en route, and obtain the robot's instantaneous resistance data. Before the simulation, the velocity inlet of the fluid computation domain is defined based on the velocity distribution data of the ship hull boundary layer through a non-uniform boundary velocity distribution method that varies with space.

[0018] The structure, appearance design and optimization module is configured to optimize the robot's structure and appearance design based on instantaneous resistance data and with the goal of reducing the resistance encountered by the robot when it travels with the ship.

[0019] According to some embodiments, the present invention adopts the following technical solution:

[0020] A computer program product includes a computer program that, when executed by a processor, implements the aforementioned water resistance optimization method for a cleaning robot that considers boundary layer velocity distribution.

[0021] According to some embodiments, the present invention adopts the following technical solution:

[0022] A non-transitory computer-readable storage medium is provided for storing computer instructions, which, when executed by a processor, implement the aforementioned water resistance optimization method for a cleaning robot considering boundary layer velocity distribution.

[0023] According to some embodiments, the present invention adopts the following technical solution:

[0024] An electronic device includes a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to implement the water resistance optimization method for a cleaning robot that considers the boundary layer velocity distribution.

[0025] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0026] 1. This invention combines ship hull simulation and robot simulation. Based on the velocity distribution data of the ship boundary layer obtained from ship hull simulation, a velocity inlet, i.e. the incoming flow velocity, is set in the fluid computation domain of robot simulation, so as to better simulate the working conditions of the ship cleaning robot in transit.

[0027] 2. This invention proposes a method for defining a non-uniform velocity inlet and spatially varying velocity distribution based on the velocity distribution of the boundary layer of the hull wall. By exploring the velocity distribution and mechanism within the boundary layer of the hull wall, it provides velocity inlet data for the in-transit cleaning simulation scheme of underwater cleaning robots, making the robot's water resistance analysis more accurate.

[0028] 3. This invention employs a high-precision prism-layer mesh strategy, which has the advantage of better capturing the flow details on the hull wall. Near-wall processing is performed near the hull wall, and the height of the first mesh layer is calculated based on the corresponding theoretical formula. + The calculated values ​​are determined, and the total thickness of the prism layer mesh is controlled between 0.5% and 1% of the ship's length to ensure sufficient boundary layer resolution. A full y-axis mesh is used. + Wall treatment method, using semi-empirical formulas to calculate y + Value and total thickness of the boundary layer. Attached Figure Description

[0029] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0030] Figure 1 This is a schematic diagram of a ship-in-transit cleaning robot scenario in Example 1.

[0031] Figure 2 This is a method framework diagram for Example 1.

[0032] Figure 3 This is a flow domain setting diagram for the CFD simulation scheme of the hull wall flow field in Example 1.

[0033] Figure 4 This is a schematic diagram of the grid division planar parting cut by the longitudinal section of the hull in Example 1.

[0034] Figure 5 This is a schematic diagram of the grid division plane cut by the waterline plane of the hull in Example 1.

[0035] Figure 6 This is a velocity distribution diagram after the wire-laying probe is inserted at the bow in Example 1.

[0036] Figure 7 This is a velocity distribution diagram after the probe is inserted mid-ship in Example 1.

[0037] Figure 8 This is a velocity distribution diagram after the wire-laying probe is inserted at the stern in Example 1.

[0038] Figure 9 Define a logic diagram for the field function expression in Example 1.

[0039] Figure 10 This is a simulation resistance analysis diagram of the ship-to-ship cleaning robot in Example 1.

[0040] Figure 11 The diagram shows the flow field distribution of the simulation scheme for the ship-in-transit cleaning robot in Example 1.

[0041] Figure 12 This is a schematic diagram of the shape optimization based on the pressure difference resistance distribution in Example 1. Detailed Implementation

[0042] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0043] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0044] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0045] Example 1

[0046] One embodiment of the present invention provides a water resistance optimization method for a cleaning robot that considers the boundary layer velocity distribution, comprising:

[0047] Step S1: Model the cleaning robot adsorbed on the curved surface of the ship hull to obtain the robot model and the curved surface model of the ship hull respectively;

[0048] Step S2: Perform fluid dynamics simulation on the hull surface model of the ship while it is en route to simulate the actual fluid flow and obtain the velocity distribution data of the hull boundary layer.

[0049] Step S3: Perform fluid dynamics simulation on the robot model adsorbed on the curved surface of the ship to simulate the robot's motion while the ship is en route and obtain the robot's instantaneous resistance data. Before the simulation, the velocity inlet of the fluid computation domain is defined based on the velocity distribution data of the ship's boundary layer and the velocity distribution method that is non-uniform at the boundary and varies with space.

[0050] Step S4: Based on instantaneous resistance data, optimize the robot's structure and appearance design to reduce the resistance encountered by the robot as it travels with the ship.

[0051] As one embodiment, the water resistance optimization method for cleaning robots considering boundary layer velocity distribution of the present invention designs the non-uniform, space-varying velocity inlet of this CFD simulation scheme based on the boundary layer velocity distribution of the hull wall, making the scheme more in line with the actual working conditions in engineering, and making up for the deficiency of still water simulation in not considering the influence of high-speed water flow on the hull wall on the robot's resistance. It provides strong technical support for the independent research and development and performance improvement of ships and underwater equipment. The specific implementation process is described below.

[0052] For example Figure 1 The scenario depicting a ship cleaning robot en route illustrates a fluid simulation scheme designed for such robots under high-speed dynamic turbulence conditions and complex curvatures at different locations on the ship's walls. The technical problems this embodiment aims to address include:

[0053] (1) The velocity distribution of the flow field near the hull wall at different positions such as the bow, midship and stern of the ship is analyzed by computational fluid dynamics (CFD) numerical simulation.

[0054] (2) Formulate a simulation scheme for the resistance of the ship cleaning robot in transit. A method is adopted to define the non-uniform velocity inlet and the velocity distribution that varies with space based on the velocity distribution of the boundary layer of the hull wall. The resistance load, overturning moment and hydrodynamic characteristics of the robot under typical speed and different hull curvature positions are accurately quantified, the pressure distribution of the flow field around the robot is clarified, and the mechanism of resistance generation is explored in depth.

[0055] (3) Based on the simulation results, obtain accurate data on the pressure resistance and friction resistance of the robot, provide key input for the strength design of the adsorption system and the body structure, and perform multiple rounds of iterative optimization on the overall shape, corner transition and component layout of the robot to fundamentally reduce its resistance coefficient.

[0056] To address the aforementioned technical issues, one embodiment of the present invention provides a water resistance optimization method for a cleaning robot that considers boundary layer velocity distribution. Compared to static cleaning in a dock, the high-speed dynamic water flow near the hull surface during ship navigation causes the cleaning robot to experience higher resistance and overturning moment. This embodiment performs drag reduction analysis and optimization on the robot structure to meet the requirements of being compact, lightweight, having low water resistance, and being flexible, while also being able to perfectly conform to and reliably adhere to complex curved hull surfaces. The method framework is as follows: Figure 2 As shown, it includes:

[0057] Step 1: Hull Simulation: Flow Field Analysis of Hull Walls During Straight Sailing

[0058] A high-precision numerical calculation model was constructed using CFD technology. Overlapping mesh technology, the volume fraction method, and multiphase flow theory were then used to conduct a detailed analysis of the flow field during straight-line ship navigation. The specific steps are as follows:

[0059] 1. Input the ship hull surface model

[0060] A 3D model of the hull is created, and the hull surface model is imported into CFD software. The model quality is checked, and surface repair and feature patching are performed on puncture surfaces, free edges, etc., to ensure that the surfaces of key parts such as the bow, midships, and stern are intact.

[0061] 2. Develop a CFD simulation scheme for the ship's straight-line navigation condition based on the hull surface model.

[0062] ① Establish the computational domain and mesh it:

[0063] In CFD software, create a fluid computation domain that includes the ship's hull, such as... Figure 3 As shown, the computational domain is a cuboid flow domain, consisting of a velocity inlet, a pressure outlet, two symmetrical planes, a top surface, and a bottom surface, with corresponding dimensions set according to the ship's length. Furthermore, the ship's hull has a free surface (air-water contact surface) while sailing at sea. In order to capture the dynamic position of the free surface, the influence of the free surface waveform is considered, and the volume fraction (VOF) method is adopted, setting two VOF waveform domains and two free surface domains. The overlapping mesh method is used to process the ship's motion to simulate the ship's straight-line sailing condition.

[0064] Free surface treatment uses the volume fraction (VOF) method to simulate dynamic free surfaces. It determines the location of the free surface by analyzing the proportion of fluid volume occupied within the computational domain grid cells. Specifically:

[0065] First, a volume fraction function is defined to distinguish between the primary and secondary phase fluids in the computational domain. The volume fraction function is:

[0066]

[0067] Then, since the two fluids are immiscible and incompressible, let the velocity field of the fluid be... ,function The mass derivative is zero.

[0068]

[0069] Next, expanding the matter derivative, we get:

[0070]

[0071] In two dimensions:

[0072]

[0073] Finally, the continuous volume fraction function Discretization for grid computing: in each grid Upper definition Integral on the grid The volume ratio of the main phase fluid in a grid cell can be represented by the VOF function, which is expressed as:

[0074]

[0075] in, For each grid The points on the top For each grid The volume fraction function is defined above, where Ω1 is the main phase fluid region and Ω2 is the secondary phase fluid region; For grid cells Volume; The value is between 0 and 1, when When = 0, the fluid in the mesh is a secondary phase; when When = 1, the main phase fluid is present in the mesh; when 0 < 1, the main phase fluid is present in the When the value is less than 1, it is defined as a grid containing free surfaces, thereby determining the location of the free surfaces.

[0076] The computational domain is meshed using an unstructured grid, such as... Figure 4 As shown, a cut volumetric mesh is used as the base mesh, and a volumetric mesh control strategy is adopted to implement a three-level volumetric mesh refinement. That is, the mesh density is different in the regions corresponding to the three levels, and the three levels are refined step by step to optimize the number and quality of meshes. The first level is the computational domain, the second level is the refinement domain, and the third level is the overlapping mesh domain.

[0077] Based on the three-level volumetric mesh refinement, to better capture the dynamics of the fluid within the boundary layer, near-wall processing is performed near the hull wall. This involves dividing the boundary layer near the hull wall into prismatic layers, typically numbered n (10-15 layers). The total thickness of the prismatic layers is calculated from the number of prismatic layers, the height of the first prismatic layer, and the elongation of the prismatic layers. Therefore, the height of the first prismatic layer is determined... This is especially important.

[0078] The height of the first layer of the prism mesh is based on y + The value is determined, y + This represents the dimensionless distance from the center of the first grid cell in the prism layer to the wall, using the full y-axis. + Wall treatment method, y + The value is usually between 30 and 300, and the total thickness of the prism layer mesh is controlled between 0.5% and 1% of the ship's length to ensure that the boundary layer is fully resolved.

[0079] First layer grid height The total boundary layer thickness δ is expressed by the formula:

[0080]

[0081]

[0082]

[0083] in, First grid height; y + Represents the dimensionless distance from the center of the first grid cell in the prism layer to the wall surface; υ is the wall shear stress; ρ is the kinematic viscosity of the fluid; and ρ is the density of the fluid. is the total thickness of the boundary layer; n is the number of prism layers; r is the elongation of the prism layer (the thickness ratio of two adjacent layers); Let be the thickness of the nth prism layer.

[0084] Local mesh refinement is applied in areas with significant curvature changes, such as the bow and stern; at the free surface (the interface between water and air) - see... Figure 4 VOF wave - see Figure 5 Two levels of mesh refinement are performed in the vicinity, and the anisotropic dimensions of the cut volume mesh generator are set to capture the flow field changes caused by waveform disturbances.

[0085] ② Set the computational domain boundaries and physical conditions:

[0086] The parameters of water flow velocity, pressure, and volume fraction are set at the velocity inlet, pressure outlet, and symmetry plane. The straight-line motion speed of the ship is set in the overlapping grid domain, and the hull surface is treated as a no-slip wall to simulate the ship during navigation.

[0087] Numerical wave suppression is performed at the boundary: a damping term perpendicular to the hull wall is introduced to reduce the reflection effect of ship waves at the boundary of the computational domain and prevent fluid backflow. The expression for vertical velocity damping is as follows:

[0088]

[0089]

[0090] in, For vertical velocity damping; x is the coordinate value of a point in the computational domain along the wave propagation direction. sd The starting point for wave suppression (x direction is the wave propagation direction); x ed This marks the endpoint or boundary of wave dissipation. f 1. f 2 and n d These are the wave suppression parameters; This represents the vertical velocity component.

[0091] ③ Define the solution method:

[0092] A three-dimensional, implicitly unsteady, multiphase flow, turbulent model was selected as the basic physical model. The SST k-ω turbulence model was used to simulate boundary layer flow and predict boundary layer separation under the opposite pressure gradient. A VOF multiphase flow model was enabled, and a still water VOF wave was set to capture the air-water free surface to simulate wave generation, along with gravity acceleration. An implicit unsteady solver was selected, employing either the PIMPLE or SIMPLE algorithm. The unsteady time step, maximum number of iterations per time step, and residual convergence criterion were set. A monitor was created to monitor hull resistance and surface pressure; the flow was considered fully developed once the time mean stabilized.

[0093] 3. Run CFD simulations to extract velocity distribution data of the boundary layer within the flow field at different locations on the hull wall.

[0094] After the CFD simulation stabilizes, cutting planes perpendicular to the wall are created at three monitoring points: bow, midship, and stern. Boundary layer thickness, displacement thickness, and momentum thickness are calculated. Gaussian point interpolation is used to map the hull surface velocity data to the boundary layer profile for quantitative analysis of the boundary layer development state, and to obtain velocity cloud maps, vector maps, and velocity profile curves of the flow field near the hull wall.

[0095] Insert probes at three monitoring points—bow, midship, and stern—on the velocity cloud map to extract velocity distribution data at different heights above the hull wall within the flow field boundary layer. It means that, among them, For the first A collection of speeds at different heights from various monitoring points. It discretizes the boundary layer in terms of velocity. a height, For the first The first monitoring point The velocity value extracted by the line probe at each height. These correspond to the monitoring points at the bow, midships, and stern, respectively, and are used to define the velocity entry points for the resistance analysis scheme of the ship's in-transit cleaning robot. Figure 6 , Figure 7 , Figure 8 The velocity distribution after the wire-laying probes were inserted at the bow, midship, and stern of the ship is shown respectively.

[0096] Step 2: Robot Simulation: Transient Resistance Simulation of the In-Transit Cleaning Robot

[0097] Parametric modeling of the robot's shape is performed. Using two meshing strategies—overlapping mesh and sliding mesh—the robot's motion (straight-line, lateral, turning, etc.) at different monitoring points (bow, midship, stern, etc.) while the ship is en route is simulated. This predicts the robot's drag performance and provides a reliable basis for drag reduction optimization. Specifically:

[0098] 1. Input robot model

[0099] A 3D model of the ship-to-ship cleaning robot was created, and the robot model was imported into CFD software to check the model quality. Surface repair and feature patching were performed on puncture surfaces, free edges, etc., to ensure that the surface of key parts was intact.

[0100] 2. Develop a CFD simulation scheme based on the robot's motion conditions at different positions on the ship's hull.

[0101] ① Establish the computational domain and mesh it:

[0102] The CFD solver establishes a computational domain model including the robot and the external flow field. This computational domain model is then discretized into a flow domain mesh. Boundary conditions are set for different robot navigation conditions (including straight-line, lateral, and turning): for straight-line navigation, the direction of the incoming flow velocity and the robot's forward velocity are set; for lateral navigation, the direction of the incoming flow velocity and the robot's forward velocity are applied laterally; for turning, the incoming flow velocity and the rotational angular velocity around the vertical axis are set. A hybrid mesh strategy based on overlapping and sliding meshes is constructed to simulate the robot's complex motion in the flow field, and the boundary layer mesh is refined on the robot's walls. Since the water flow in areas with greater robot curvature is more complex, the mesh needs to be refined separately for these areas to better capture the flow field dynamics.

[0103] ② Define the boundary conditions of the computational domain:

[0104] The actual navigation conditions under which the robot operates while traveling with the ship need to consider the influence of the velocity distribution of the ship's wall boundary layer. This requires the velocity distribution of the ship's wall boundary layer obtained from the above CFD simulation of the ship's straight-line navigation conditions. The incoming flow velocity is set for the robot simulation; therefore, a velocity distribution based on the boundary layer velocity distribution of the ship's hull is designed. A method for defining a non-uniform, spatially varying velocity distribution at the velocity inlet is defined, wherein the non-uniformity and spatial variation are based on the velocity at different heights and different hull wall positions (i.e., bow, midships, and stern). Instead of using a uniform approach with the same speed at different heights, the velocity distribution at the robot's simulated velocity inlet is set. Taking the robot at the bow of a ship as an example, the velocity distribution data at the bow is used as a basis. The velocity distribution at the velocity inlet of the robot simulation is non-uniformly set, specifically as follows:

[0105] First, according to The height corresponding to each speed value is used to divide the speed entry area of ​​the robot simulation into regions;

[0106] Specifically, the velocity inlet is divided into Layer, in which, is the number of discrete heights of the boundary layer in terms of velocity. Based on the vertical distances of different velocity values of the extracted boundary layer from the hull wall (i.e., the heights corresponding to each velocity value in ), the Z coordinate is created. According to the mathematical relationship of the Z coordinate positions, the threshold for regional division is set, and a scalar field function is created to determine how to divide this

[0107] layer. For example, taking the division of the velocity inlet into three regions as an example, that is Figure 9 shows the field function divided into three regions. Using nested ternary operators, the heights corresponding to the first two velocity values in are respectively set as the thresholds for the three-region division. In this embodiment, they are 0.05 m and 0.2 m respectively. Then the conditions for regional division are: Region 1 is Z ≤ 0.05 m, Region 2 is 0.05 m < Z < 0.2 m, and Region 3 is Z ≥ 0.2 m. Thus, it is divided into three different regions.

[0108] Then, after dividing the regions, the velocities of different regions of the velocity inlet are set to the corresponding velocity values , that is, the velocity of the th region is set to , thereby completing the definition of the velocity distribution of the non-uniform and spatially varying velocity inlet.

[0109] Finally, the robot adopts the above method to define the velocity inlet at three typical positions of the bow, midship, and stern, realizing the setting of the boundary conditions of the computational domain.

[0110] ③ Set the solution method:

[0111] Select three-dimensional, implicit unsteady, turbulent physics, model, and use the SST k-ω turbulence model to simulate the flow separation and boundary layer effects around the robot; create cutting planes around the key parts of the robot as monitoring surfaces for extracting the local pressure and shear force distributions, calculate the normal pressure and tangential frictional resistance of each monitoring surface through the field function, and set the real-time output of the instantaneous data of the resultant force and moment.

[0112] 3. Perform transient numerical simulation in the CFD solver, output the instantaneous data of the water resistance, surface pressure distribution of the whole robot and key parts as shown in Figure 10 and the flow field information as shown in Figure 11 . Based on the output data, determine the magnitude of the resistance force on the robot, compare and analyze the resistance performance of the robot under different motion conditions, and provide a basis for the design of the robot adsorption mechanism and the optimization of the external shape for drag reduction.

[0113] Step Three: Structure, Appearance Design and Optimization

[0114] Based on the resistance simulation analysis results, a robot adsorption mechanism was designed, and the robot's external curvature, appendage shape, and streamlines were optimized to reduce the resistance encountered by the robot when it travels with the ship. This ensures reliable adsorption of the robot to the ship while achieving low water resistance, lightweight design, and perfect adaptation to complex curved surfaces. Specifically:

[0115] 1. By analyzing water resistance through simulation, the magnitude of the resistance is determined, and the force analysis of the robot is performed to infer the magnetic attraction force required by the adsorption system.

[0116] To ensure the robot reliably adheres to the ship's surface, the magnetic attraction force Fm provided by the adsorption system must be greater than or equal to the resultant force of the motor propulsion force F minus the resistance Fd, the net weight W, and other external forces f (such as friction). Since the instantaneous results of the water resistance simulation analysis have a certain fluctuation range, a quantitative analysis of the resistance must first be performed to determine its magnitude (Fd). By simulating the resistance at three typical positions—the bow, midship, and stern—of the robot, the maximum resistance Fd experienced by the robot is obtained. max Then, based on the maximum resistance value Fd max Given the robot's net weight W underwater (considering gravity and buoyancy), calculate the minimum magnetic attraction force required by the adsorption system: Fm ≥ F - (Fd) max Through iterative simulation, the rationality of Fm at different speeds is verified, so as to design a magnetic adsorption mechanism (selecting the magnetic force and material of the magnetic wheel) and optimize the magnet layout to distribute the attraction force evenly and avoid local stress concentration, thereby achieving lightweight and reliable adsorption, and ensuring the functionality and survivability of the robot.

[0117] 2. For example Figure 12 As shown, a surface pressure distribution cloud map of the robot is generated through CFD simulation, visually displaying the pressure gradient and high / low pressure regions. Areas with high resistance appear at the robot's leading edge, edges, and protrusions. Analysis of the cloud map data is performed, and shape optimization is carried out for areas with high resistance, including:

[0118] ① Optimize the curvature of the shape: Adopt a streamlined design, reduce the radius of curvature of the leading edge, and smooth the transition surface to reduce pressure drag. Change the front end of the robot to an ellipse or parabola to avoid abrupt right angle changes.

[0119] ②Optimize the shape of the attachment: reshape the protruding parts (camera) to make them fit the main body more linearly and reduce flow separation.

[0120] During the optimization process, the principle of lightweighting was combined with the use of topology optimization to reduce weight while ensuring structural strength. Finally, the optimization effect was verified through multiple CFD simulations to ensure that the drag was reduced and the fit between the robot and the complex hull surface was checked, achieving perfect compliance through flexible materials or adaptive mechanisms.

[0121] The above method has the advantages of "high precision, high efficiency, systematic approach, and strong engineering applicability." It not only solves the technical bottlenecks of traditional CFD simulation in specific scenarios (such as boundary layer analysis and complex motion simulation), but also directly links the simulation results with engineering optimization objectives (such as drag reduction and structural design). This provides strong technical support for the independent research and development and performance improvement of ships and underwater equipment. Its specific advantages are as follows:

[0122] 1. In-depth resistance analysis and optimization of the robot provides a reliable solution for resistance simulation analysis of underwater cleaning robots in transit, and provides key theoretical basis and technical support for a new mode of early preventive cleaning of ships in transit.

[0123] 2. The CFD simulation scheme achieves high boundary layer analytical accuracy and strong result reliability: by employing 10-15 prism-layer meshes and strictly controlling the height of the first mesh layer, the y... + With values ​​in the ideal range of 30-300, it can accurately capture the velocity gradient, shear stress, and boundary layer thickness near the hull wall, ensuring the physical authenticity of the flow field data within the boundary layer and overcoming the problem of insufficient accuracy caused by the rough mesh near the wall in traditional simulations.

[0124] 2. A systematic comparative analysis of the flow field at different locations on the ship's hull was achieved:

[0125] By setting up monitoring profiles at key locations such as the bow, midship, and stern in CFD simulations and quantitatively extracting parameters such as boundary layer thickness, displacement thickness, and momentum thickness, the differences and patterns of boundary layer development in different linear locations can be clearly revealed. This provides detailed data support for the entry velocity of robot drag analysis CFD simulations that cannot be provided by traditional methods.

[0126] 3. The simulation scheme is comprehensive, taking into account both nonlinear waves and ship motion:

[0127] By combining the VOF multiphase flow model with overlapping grid technology, it is possible not only to accurately simulate the boundary layer of a ship in still water, but also to effectively account for the influence of unsteady wave-making generated by the ship's motion in waves on the boundary layer flow field. This makes the simulated environment closer to real navigation conditions and significantly improves the engineering practical value of the forecast.

[0128] 4. High-fidelity dynamic simulation of complex motions has been achieved:

[0129] By innovatively adopting a hybrid mesh strategy that combines overlapping meshes and sliding meshes, the technical challenge of mesh distortion and updating when the robot simultaneously performs complex multi-degree-of-freedom movements such as straight-line movement, lateral movement, and turning is effectively solved. This ensures the quality and stability of the computational mesh under large-amplitude movements and achieves high-precision dynamic simulation of the robot's real working conditions.

[0130] 5. Accurate prediction of hydrodynamic loads and clear local stress distribution:

[0131] By setting monitoring points and surfaces on key parts of the robot's surface, such as the bow, midship, and stern, this embodiment can not only obtain the robot's overall resistance, but also accurately decompose and quantify the resistance contribution of each local structure under different working conditions, providing an indispensable load input for the strength verification and lightweight design of the robot structure.

[0132] 6. High efficiency in shape optimization and fast design iteration:

[0133] By combining parametric geometric modeling with an automated multi-condition simulation process, this embodiment can quickly evaluate the differences in hydrodynamic performance of different shape designs under various motion modes. This changes the traditional lengthy design mode that relies on experience and experimentation, greatly shortening the design cycle and reducing R&D costs.

[0134] 7. Accurate turbulence simulation and effective prediction of flow separation:

[0135] By employing the SST k-ω turbulence model and performing fine boundary layer meshing on the robot's shape, this scheme can accurately simulate the flow separation phenomenon around the robot, especially in areas with drastic curvature changes. It reliably predicts the pressure drag caused by the separation vortex, providing key insights for suppressing flow separation and reducing drag through shape optimization.

[0136] 8. It has a strong systematic approach, establishing a standardized process for evaluating the performance of underwater cleaning robots en route:

[0137] This embodiment forms a complete, systematic, and repeatable high-precision prediction scheme for drag performance, from parametric modeling, hybrid mesh strategy, physical model selection to multi-condition analysis and local load monitoring. It provides reliable technical standards and tools for the research and development and performance evaluation of various underwater robots.

[0138] Example 2

[0139] One embodiment of the present invention provides a water resistance optimization system for a cleaning robot that considers the boundary layer velocity distribution, comprising:

[0140] The 3D modeling module is configured to: model the cleaning robot adsorbed on the curved surface of the ship hull, and obtain the robot model and the curved surface model of the ship hull respectively;

[0141] The hull simulation module is configured to perform fluid dynamics simulation on the hull surface model of the ship while it is en route, simulate the actual fluid flow, and obtain the velocity distribution data of the hull boundary layer.

[0142] The robot simulation module is configured to perform fluid dynamics simulation on the robot model adsorbed on the curved surface of the ship hull, simulate the robot's motion when the ship is en route, and obtain the robot's instantaneous resistance data. Before the simulation, the velocity inlet of the fluid computation domain is defined based on the velocity distribution data of the ship hull boundary layer through a non-uniform boundary velocity distribution method that varies with space.

[0143] The structure, appearance design and optimization module is configured to optimize the robot's structure and appearance design based on instantaneous resistance data and with the goal of reducing the resistance encountered by the robot when it travels with the ship.

[0144] Example 3

[0145] One embodiment of the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the aforementioned water resistance optimization method for a cleaning robot considering boundary layer velocity distribution.

[0146] Example 4

[0147] In one embodiment of the present invention, a non-transitory computer-readable storage medium is provided for storing computer instructions. When the computer instructions are executed by a processor, they implement the water resistance optimization method for a cleaning robot that considers the boundary layer velocity distribution.

[0148] Example 5

[0149] One embodiment of the present invention provides an electronic device, including: a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to implement the water resistance optimization method for cleaning robots that considers boundary layer velocity distribution.

[0150] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0151] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0152] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A water resistance optimization method for a cleaning robot considering boundary layer velocity distribution, characterized in that, include: Modeling was performed on the curved surface of the ship's hull to obtain the robot model and the curved surface model of the ship's hull. Fluid dynamics simulation was performed on the hull surface model of the ship while it was en route to simulate the actual fluid flow and obtain the velocity distribution data of the hull boundary layer. A fluid dynamics simulation is performed on a robot model adsorbed onto the curved surface of a ship hull to simulate the robot's motion while the ship is en route, obtaining instantaneous resistance data for the robot. Before the simulation, based on the velocity distribution data of the ship's boundary layer, a velocity inlet for the fluid computational domain is defined using a non-uniform, spatially varying velocity distribution method. The fluid dynamics simulation of the robot model adsorbed onto the curved surface of the ship hull specifically involves: Import the robot model into the CFD software; A CFD simulation scheme was developed based on the robot's motion conditions at different positions on the ship's hull. Perform transient numerical simulation in the CFD solver to obtain instantaneous resistance data of the robot; The formulation of the CFD simulation scheme includes establishing a computational domain containing the robot and the external flow field and dividing it into meshes, setting the boundary conditions of the computational domain and setting the solution method; The setting of computational domain boundary conditions is based on the velocity distribution data of the ship's boundary layer, setting the incoming flow velocity, and defining the velocity inlet of the fluid computational domain, specifically: Based on the vertical distance between the hull boundary layer and the hull wall, a Z-coordinate is created. Based on the mathematical relationship of the Z-coordinate position, a scalar field function is created to define a complex velocity distribution. Based on the scalar field function, the velocity inlet is split, and finally the inlet boundary is divided into several independent regions with corresponding boundary layer velocity step distribution according to the Z coordinate, and boundary conditions are set for each region. Based on instantaneous resistance data, the robot's structure and appearance design are optimized with the goal of reducing the resistance encountered by the robot when it travels with the ship.

2. The water resistance optimization method for a cleaning robot considering boundary layer velocity distribution as described in claim 1, characterized in that, The specific steps for performing fluid dynamics simulation on the hull surface model of the ship while it is en route are as follows: Import the hull surface model into the CFD software; A CFD simulation scheme for the ship's straight-running operation was developed based on the ship's hull surface model. Run a CFD simulation to extract velocity distribution data of the boundary layer within the flow field at different locations on the hull wall; The formulation of the CFD simulation scheme for ship straight-running conditions includes establishing a fluid computational domain containing the ship hull and dividing it into meshes, setting the computational domain boundaries and physical conditions, and setting the solution method.

3. The water resistance optimization method for a cleaning robot considering boundary layer velocity distribution as described in claim 2, characterized in that, The aforementioned meshing involves using a cut volume mesh as the base mesh and employing a volume mesh control strategy to implement three-level volume mesh refinement in the fluid computation domain. Specifically, a prism layer mesh strategy is used on the hull surface to dynamically calculate the height and thickness of the mesh.

4. The water resistance optimization method for a cleaning robot considering boundary layer velocity distribution as described in claim 1, characterized in that, The optimization of the robot structure includes designing a magnetic adsorption mechanism and optimizing the robot's external curvature, appendage shape, and streamline.

5. A water resistance optimization system for a cleaning robot considering boundary layer velocity distribution, characterized in that, include: The 3D modeling module is configured to: model the cleaning robot adsorbed on the curved surface of the ship hull, and obtain the robot model and the curved surface model of the ship hull respectively; The hull simulation module is configured to perform fluid dynamics simulation on the hull surface model of the ship while it is en route, simulate the actual fluid flow, and obtain the velocity distribution data of the hull boundary layer. The robot simulation module is configured to: perform fluid dynamics simulation on a robot model adsorbed onto the curved surface of the ship hull, simulating the robot's motion while the ship is en route, and obtaining instantaneous resistance data of the robot. Specifically, before the simulation, based on the velocity distribution data of the ship's boundary layer, a velocity inlet for the fluid computation domain is defined using a non-uniform, spatially varying velocity distribution method. The fluid dynamics simulation of the robot model adsorbed onto the curved surface of the ship hull is as follows: Import the robot model into the CFD software; A CFD simulation scheme was developed based on the robot's motion conditions at different positions on the ship's hull. Perform transient numerical simulation in the CFD solver to obtain instantaneous resistance data of the robot; The formulation of the CFD simulation scheme includes establishing a computational domain containing the robot and the external flow field and dividing it into meshes, setting the boundary conditions of the computational domain and setting the solution method; The setting of computational domain boundary conditions is based on the velocity distribution data of the ship's boundary layer, setting the incoming flow velocity, and defining the velocity inlet of the fluid computational domain, specifically: Based on the vertical distance between the hull boundary layer and the hull wall, a Z-coordinate is created. Based on the mathematical relationship of the Z-coordinate position, a scalar field function is created to define a complex velocity distribution. Based on the scalar field function, the velocity inlet is split, and finally the inlet boundary is divided into several independent regions with corresponding boundary layer velocity step distribution according to the Z coordinate, and boundary conditions are set for each region. The structure, appearance design and optimization module is configured to optimize the robot's structure and appearance design based on instantaneous resistance data and with the goal of reducing the resistance encountered by the robot when it travels with the ship.

6. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the water resistance optimization method for cleaning robots that considers the boundary layer velocity distribution as described in any one of claims 1-4.

7. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium is used to store computer instructions, which, when executed by a processor, implement the water resistance optimization method for a cleaning robot considering the boundary layer velocity distribution as described in any one of claims 1-4.

8. An electronic device, characterized in that, include: The device includes a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to perform the water resistance optimization method for a cleaning robot considering the boundary layer velocity distribution as described in any one of claims 1-4.

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