A method for optimizing the drag of underwater robots based on Fluent and Lagrange interpolation.

Abstract

This invention relates to a method for optimizing the drag of underwater robots based on Fluent and Lagrange interpolation. The method includes: establishing and simplifying the geometric model of the underwater robot and importing it into ANSYS; using DesignModeler to create the flow region and name relevant components; using the meshing program Workbench Mesh to perform mesh generation and boundary layer settings; importing the file into the solver module; initializing the model and performing iterative calculations; performing post-processing and statistical analysis of drag coefficients; repeating the above steps with different Z-axis coordinate parameters of the underwater robot; statistically analyzing the obtained multiple drag coefficients and Z-axis coordinate parameters of the underwater robot; using the Lagrange interpolation polynomial in Matlab to generate drag coefficient curves; and determining the starting point where decreasing the Z-axis longitudinal height has little impact on the drag coefficient, thus selecting a motor with appropriate power. This invention can provide guidance for selecting the optimal motor for underwater robots and provide optimal height for subsequent streamlined structure improvements.

Description

Technical Field

[0001] This invention belongs to the field of underwater robot drag structural optimization, and specifically relates to a method for underwater robot drag optimization based on Fluent and Lagrange interpolation methods. Background Technology

[0002] As bridge piers are continuously constructed, underwater piers suffer varying degrees of damage due to age and collisions with ships. Many cross-river bridges have excessively fast and long currents, posing different safety risks to underwater professionals. However, the excessively fast currents lead to problems with the selection of water resistance and motor power, resulting in insufficient power or excessive water resistance for underwater robots operating in rapid currents.

[0003] Therefore, by combining Fluent with Matlab, and with sufficient power for the underwater robot, the lowest drag coefficient can be selected to provide guidance for subsequent streamline optimization, save energy, and improve hydrodynamics. Summary of the Invention

[0004] The purpose of this invention is to explain the above-mentioned technical problems and provide a convenient method for optimizing the resistance of underwater robots based on Fluent and Lagrange interpolation, which can improve hydrodynamics and reduce water resistance. The method uses Lagrange polynomials to generate the resistance coefficient distribution function of Fluent simulation results. Compared with the traditional method of manually adjusting parameters to modify resistance, which is complicated and has low accuracy, this method directly uses the function to find the optimal resistance coefficient in one go and improves the accuracy.

[0005] To achieve the above objectives, the technical solution of the present invention is: a method for optimizing the resistance of an underwater robot based on Fluent and Lagrange interpolation methods, comprising the following steps:

[0006] S1: Determine the structural parameters of the underwater robot;

[0007] S2: Use the 3D modeling software CREO to perform structural modeling of the underwater robot's geometry and simplify it;

[0008] S3: Import the simplified model into ANSYS;

[0009] S4: Use DesignModeler to create the flow field region and name the relevant regions;

[0010] S5: Set the boundary layer parameters and perform mesh generation in the meshing program WorkbenchMesh;

[0011] S6: Set the turbulence model to the SST model based on k-epsilon;

[0012] S7: Import the mesh into Fluent's solver module and set the boundary conditions, including the pressure and velocity at the inlet and outlet.

[0013] The continuity equation, momentum equation, and energy equation for the inlet and outlet flow fields are as follows:

[0014] The continuity equation is ρ1V1A1=ρ2V2A2

[0015] Momentum equation

[0016] Energy equation

[0017] To obtain an analytical solution, we also need to use the equation of state for a complete gas as follows:

[0018] P = ρRT

[0019] The expression for the enthalpy of a perfect gas is as follows:

[0020] h = c p T

[0021] S8: Initialize the model and perform iterative calculations;

[0022] S9: Determine whether the calculation has converged. If the calculation residual is lower than the set value or the index in the report definition tends to be stable, it means that the calculation has converged. Otherwise, it means that it has not converged. At this time, improve the grid quality and return to step S5.

[0023] S10: Observe the drag coefficient through Fluent post-processing and record the relevant data;

[0024] S11: Modify the Z-axis coordinate of the underwater robot. Repeat steps S1 to S10 to obtain and record multiple drag coefficients and Z-axis coordinate parameters.

[0025] S12: Statistically analyze the obtained resistance points using Excel and construct the interpolation function using Lagrange interpolation polynomial;

[0026] S13: Import the interpolation function into MATLAB to create the function graph;

[0027] S14: Statistically analyze the Z-axis height and power of motors that meet the power requirements, find the corresponding point on the resistance function, and compare them to obtain the optimal resistance model.

[0028] In one embodiment of the present invention, the simplification in step S2 includes the removal of rounded chamfers, the removal of sharp corners of the support block, and the simplification of other redundant modules that have little impact on resistance.

[0029] In one embodiment of the present invention, the underwater robot parameters in step S1 include the size of the floating top of the underwater robot, the height and overall size of the motor cover for mounting the motor, and the size parameters of the lower support.

[0030] In one embodiment of the present invention, step S5 involves dividing the boundary mesh and densifying the mesh of the internal underwater robot.

[0031] In one embodiment of the present invention, the flow field region in step S4 is a symmetrical rectangle, wherein X, Y, Z, -X, -Y, -Z are compensated to 0.5, 0.5, 2, 0.5, 0.5, 2 respectively, in meters, and the Body is an underwater robot.

[0032] In one embodiment of the present invention, the mesh division in step S5 is not individually localized, but the mesh is automatically refined when the bending angle is less than 5 degrees.

[0033] In one embodiment of the present invention, the flow field region in step S4 is set to have only fluid medium and no solids or gaps.

[0034] In one embodiment of the present invention, the direction of the water resistance in step S6 is the negative direction of the Z-axis, which is directly opposite to the underwater robot.

[0035] In one embodiment of the present invention, the data statistics in steps S11 and S12 are performed using Excel, and the function construction is performed using Matlab.

[0036] In one embodiment of the present invention, the outlet pressure condition in step S7 is standard atmospheric pressure, and the inlet velocity condition is water flow of 2 m / s.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] This invention optimizes water resistance and hydrodynamics by combining Fluent simulation and Matlab function construction, based on the structural water resistance of underwater robots in actual production. It utilizes a combination of Fluent and Lagrange interpolation methods to derive a drag coefficient curve, providing guidance for selecting the optimal motor for the underwater robot and offering an optimal height for subsequent streamlined structure improvements. This ensures the underwater robot's height is not too low, preventing insufficient power, while minimizing the drag coefficient, reducing energy consumption and drag, increasing power, and improving boundary economic efficiency. Attached Figure Description

[0039] Figure 1 This is a simulation flowchart of the present invention.

[0040] Figure 2 This is a flowchart of the data result processing.

[0041] Figure 3 This is a diagram of the overall structure of the underwater robot.

[0042] Figure 4 This is a simplified structural diagram of an underwater robot.

[0043] Figure 5 Convergence graph for a simplified model of an underwater robot.

[0044] Figure 6 Velocity cloud map for a simplified model of an underwater robot.

[0045] Figure 7 A streamline diagram of a simplified model of an underwater robot.

[0046] Figure 8 This is one of the drag coefficient diagrams for an underwater robot after its dimensions have been modified.

[0047] Figure 9 This is one of the drag coefficient diagrams for an underwater robot after its dimensions have been modified.

[0048] Figure 10 This is a graph showing the drag coefficient of an underwater robot coupled with the robot's height adjustment function, generated by Matlab. Detailed Implementation

[0049] The technical solution of the present invention will now be described in detail with reference to the accompanying drawings.

[0050] Figure 1 This is a simulation flowchart of the present invention. Figure 2 This is a flowchart of the data result processing.

[0051] For example Figure 3 As shown, the cylindrical object in the middle is the motor housing barrel whose height needs to be adjusted. Figure 4 This is a simplified structural diagram of an underwater robot. Figure 5 Convergence graph for a simplified model of an underwater robot. Figure 6 Velocity cloud map for a simplified model of an underwater robot. Figure 7 A streamline diagram of a simplified model of an underwater robot. Figure 8 This is one of the drag coefficient diagrams for an underwater robot after its dimensions have been modified. Figure 9 This is one of the drag coefficient diagrams for an underwater robot after its dimensions have been modified.

[0052] against Figure 10 As shown, the horizontal axis represents the reduction in height of the motor housing, and the vertical axis represents the drag coefficient. It was observed that the cd value decreased significantly after the height was reduced by 40%.

[0053] This invention discloses a method for optimizing the drag of an underwater robot based on Fluent and Lagrange interpolation, comprising the following steps:

[0054] S1: Determine the structural parameters of the underwater robot;

[0055] S2: Use the 3D modeling software CREO to perform structural modeling and simplification of the model;

[0056] S3: Import the simplified model into ANSYS;

[0057] S4: Use DesignModeler to create the flow field region and name the relevant regions;

[0058] S5: Set boundary layer parameters and perform mesh generation in Workbench Mesh;

[0059] S6: Set the turbulence model to the SST model based on k-epsilon;

[0060] S7: Import the mesh into Fluent's solver module and set boundary conditions, including pressure and velocity at the inlet and outlet.

[0061] The continuity equation, momentum equation, and energy equation for steady isentropic flow in the inlet and outlet flow fields are as follows:

[0062] The continuity equation is ρ1V1A1=ρ2V2A2

[0063] Momentum equation

[0064] Energy equation

[0065] To obtain an analytical solution, we also need to use the equation of state for a complete gas as follows:

[0066] P = ρRT

[0067] The expression for the enthalpy of a perfect gas is as follows:

[0068] h = c p T

[0069] S8: Initialize the model and perform iterative calculations;

[0070] S9: Determine whether the calculation has converged. If the calculation residual is lower than the set value or the index in the report definition tends to be stable, it means that the calculation has converged. Otherwise, it means that it has not converged. At this time, it is necessary to improve the grid quality and return to step S5.

[0071] S10: Observe the drag coefficient through Fluent post-processing and record the relevant data;

[0072] S11: Modify the Z-axis coordinate of the underwater robot. Repeat steps S1 to S10 to obtain and record multiple drag coefficients and Z-axis coordinate parameters.

[0073] S12: Statistically analyze the obtained resistance points using Excel and construct the interpolation function using Lagrange interpolation polynomial;

[0074] S13: Import the interpolation function into MATLAB to create the function graph;

[0075] S14: Statistically analyze the Z-axis height and power of motors that meet the power requirements on the market, find these points on the resistance function, and compare them to obtain the optimal resistance model;

[0076] Traditional methods for statistical analysis of Fluent data using Excel require analyzing large amounts of data and cannot accurately find the optimal point, relying instead on luck to find an approximate point. Furthermore, traditional experimental methods are time-consuming and labor-intensive. This invention patent application also incorporates high-precision Fluent simulation, saving experimental costs and time.

[0077] The above are preferred embodiments of the present invention. Any changes made to the technical solution of the present invention that do not exceed the scope of the technical solution of the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for optimizing the resistance of an underwater robot based on Fluent and Lagrange interpolation, characterized in that, Includes the following steps: S1: Determine the structural parameters of the underwater robot; S2: Use the 3D modeling software CREO to perform structural modeling of the underwater robot's geometry and simplify it; S3: Import the simplified model into ANSYS; S4: Use DesignModeler to create the flow field region and name the relevant regions; S5: Set the boundary layer parameters and perform mesh generation in the meshing program Workbench Mesh; S6: Set the turbulence model to the SST model based on k-epsilon; S7: Import the mesh into Fluent's solver module and set the boundary conditions, including the pressure and velocity at the inlet and outlet. The continuity equation, momentum equation, and energy equation for the inlet and outlet flow fields are as follows: Continuity equation = Momentum equation Energy equation = To obtain an analytical solution, we also need to use the equation of state for a complete gas as follows: P= The expression for the enthalpy of a perfect gas is as follows: h= S8: Initialize the model and perform iterative calculations; S9: Determine whether the calculation has converged. If the calculation residual is lower than the set value or the index in the report definition tends to be stable, it means that the calculation has converged. Otherwise, it means that it has not converged. At this time, improve the grid quality and return to step S5. S10: Observe the drag coefficient through Fluent post-processing and record the relevant data; S11: Modify the Z-axis coordinate of the underwater robot. Repeat steps S1 to S10 to obtain and record multiple drag coefficients and Z-axis coordinate parameters. S12: Statistically analyze the obtained resistance points using Excel and construct the interpolation function using Lagrange interpolation polynomial; S13: Import the interpolation function into MATLAB to create the function graph; S14: Statistically analyze the Z-axis height and power of motors that meet the power requirements, find the corresponding point on the resistance function, and compare them to obtain the optimal resistance model.

2. The method for optimizing the resistance of an underwater robot based on Fluent and Lagrange interpolation as described in claim 1, characterized in that, The simplification in step S2 includes the removal of rounded chamfers and the removal of sharp corners from the support blocks.

3. The method for optimizing the resistance of an underwater robot based on Fluent and Lagrange interpolation as described in claim 1, characterized in that, The underwater robot parameters in step S1 include the dimensions of the floating top of the underwater robot, the height and overall dimensions of the motor cover, and the dimensions of the lower support.

4. The method for optimizing the resistance of an underwater robot based on Fluent and Lagrange interpolation as described in claim 1, characterized in that, In step S5, boundary mesh is divided to refine the mesh of the internal underwater robot.

5. The method for optimizing the resistance of an underwater robot based on Fluent and Lagrange interpolation as described in claim 1, characterized in that, The flow field region in step S4 is a symmetrical rectangle, where X, Y, Z, -X, -Y, -Z are compensated to 0.5, 0.5, 2, 0.5, 0.5, 2 respectively, in meters. The Body is selected as an underwater robot.

6. The method for optimizing the resistance of an underwater robot based on Fluent and Lagrange interpolation as described in claim 1, characterized in that, In step S5, the mesh is not locally refined, but it is automatically refined when the bending angle is less than 5 degrees.

7. The method for optimizing the resistance of an underwater robot based on Fluent and Lagrange interpolation as described in claim 1, characterized in that, In step S4, the flow field region is set to contain only fluid medium, without solids or gaps.

8. The method for optimizing the resistance of an underwater robot based on Fluent and Lagrange interpolation as described in claim 1, characterized in that, In step S6, the direction of the water resistance is the negative direction of the Z-axis, which is directly opposite to the underwater robot.

9. The method for optimizing the resistance of an underwater robot based on Fluent and Lagrange interpolation as described in claim 1, characterized in that, The data statistics in steps S11 and S12 are performed using Excel, and the function construction is performed using Matlab.

10. The method for optimizing the resistance of an underwater robot based on Fluent and Lagrange interpolation as described in claim 1, characterized in that, In step S7, the outlet pressure condition is standard atmospheric pressure, and the inlet velocity condition is a water flow of 2 m / s.

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