Fluid pipeline topology optimization design method based on parameter simulation model

Abstract

The invention relates to the technical field of fluid pipeline topological optimization, in particular to a fluid pipeline topological optimization design method based on a parameter simulation model. The method comprises the following steps of performing parametric modeling on a fluid pipeline to obtain a pipeline topology geometric parameter set; according to topological feature identification of the pipeline topology geometric parameter set, obtaining pipeline topology description data; constructing a multi-physics field equation set according to the pipeline topology description data to obtain a pipeline coupling field basic equation set; carrying out characteristic decomposition on the pipeline coupling field basic equation set to obtain a pipeline decoupling equation set; performing iterative solution on the pipeline decoupling equation set to obtain a pipeline multi-physics field coupling solution; performing sensitivity coefficient evaluation according to the pipeline multi-physics field coupling solution to obtain pipeline physics field sensitivity; and performing time domain integral response analysis on the pipeline physical field sensitivity to obtain the contribution degree of the pipeline physical field. According to the method, the deviation between the topological design and the practical application of the fluid pipeline can be obviously reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of fluid pipeline topology optimization, and particularly to a fluid pipeline topology optimization design method based on a parametric simulation model. Background Art

[0002] In traditional fluid pipeline design, iterative optimization is usually carried out relying on a simulation model based on a fixed parameter set. This method has a certain feasibility and efficiency in the theoretical and preliminary design stages. However, in actual operation, the pipeline system often faces complex dynamic working conditions, and the changes of these conditions are multi-dimensional and multi-factor coupled. For example, in the layout design of the fuel delivery pipeline of an aerospace engine, there is an extremely complex strong coupling relationship among fluid pressure pulsation, mechanical vibration load and thermal deformation effect. Fluid pressure pulsation will cause transient changes in the internal stress of the pipeline, while mechanical vibration load will further exacerbate the dynamic response of the pipeline. At the same time, the thermal deformation effect will affect the structural integrity and sealing performance of the pipeline. These three effects are intertwined to form a complex dynamic system.

[0003] However, when dealing with such complex problems, traditional optimization algorithms often adopt the method of static weight allocation to simplify the influence of multi-physical fields. Specifically, engineers will assign weights to different physical fields according to experience or preset rules to balance the influence of various factors on the system performance. Although this method can simplify the calculation process to a certain extent, it ignores the complexity of the dynamic coupling among physical fields. Therefore, there are often significant deviations between the simulation results and the actual working conditions. Such deviations are likely to lead to serious consequences in actual engineering applications. Summary of the Invention

[0004] Based on this, it is necessary for the present invention to provide a fluid pipeline topology optimization design method based on a parametric simulation model to solve at least one of the above technical problems.

[0005] To achieve the above object, a fluid pipeline topology optimization design method based on a parametric simulation model includes the following steps:

[0006] Step S1: Perform parametric modeling on the fluid pipeline to obtain a pipeline topology geometric parameter set; identify the topological features according to the pipeline topology geometric parameter set to obtain pipeline topology description data;

[0007] Step S2: Construct a multi-physical field equation set according to the pipeline topology description data to obtain a pipeline coupled field basic equation set; perform eigenvalue decomposition on the pipeline coupled field basic equation set to obtain a pipeline decoupled equation set; perform iterative solution on the pipeline decoupled equation set to obtain a pipeline multi-physical field coupling solution;

[0008] Step S3: Evaluate the sensitivity coefficient based on the multi - physical - field coupling solution of the pipeline to obtain the pipeline physical - field sensitivity; perform time - domain integral response analysis on the pipeline physical - field sensitivity to obtain the pipeline physical - field contribution degree; update the topological weight coefficient of the fluid pipeline according to the pipeline physical - field contribution degree to obtain the pipeline weight configuration data;

[0009] Step S4: Based on the pipeline weight configuration data, perform level - set modeling on the pipeline topological description data to obtain the basic pipeline topology optimization model; solve the improved MMA algorithm for the basic pipeline topology optimization model to obtain the optimized pipeline topology data;

[0010] Step S5: Conduct comparative analysis of experimental data on the optimized pipeline topology data to obtain the pipeline spatial error distribution data; perform spatial interpolation on the pipeline spatial error distribution data to obtain the pipeline error correction data; update the pipeline topology geometric parameter set according to the pipeline error correction data to obtain the optimized pipeline topology geometric parameter set.

[0011] By constructing a multi - physical - field equation set and performing eigenvalue decomposition and iterative solution, the present invention can accurately capture the complex coupling relationship between fluid pressure pulsation, mechanical vibration load, and thermal deformation effect. This can dynamically reflect the interaction between physical fields, thereby significantly improving the accuracy and reliability of simulation results and making them closer to the actual working conditions. Through sensitivity coefficient evaluation and time - domain integral response analysis, the contribution degree of each physical field to the pipeline performance can be dynamically evaluated, and the topological weight coefficient can be updated accordingly. This enables the optimization design to adjust the optimization direction in real - time according to the changes in actual working conditions, thereby improving the adaptability and robustness of the design. Through level - set modeling and solving the improved MMA algorithm, complex topology optimization problems can be efficiently processed. The level - set method can flexibly handle the evolution of topological structures, while the improved MMA algorithm improves the convergence speed and accuracy of the optimization process by optimizing sub - problem construction and iterative solution. This can obtain a high - quality topology optimization scheme in a short time, significantly improving the design efficiency. Through comparative analysis of experimental data, the optimization results can be verified in practice, and the deviation between simulation and experiment can be found. Further, through spatial interpolation and error correction, the optimization results can be refined to ensure the reliability and accuracy of the design results. This effectively improves the credibility of the design and reduces the risks in practical applications. Through multi - physical - field coupling analysis and dynamic weight update, the comprehensive performance optimization of fluid pipelines in a multi - physical - field environment is achieved. It can not only effectively reduce the internal stress of the pipeline, reduce dynamic response, but also improve structural integrity and sealing performance. In summary, the present invention can significantly reduce the deviation between design and actual application. This effectively reduces the risks in engineering applications, improves the safety and reliability of the system, and is especially suitable for the aerospace and energy fields with extremely high safety requirements. Brief Description of the Drawings

[0012] Other features, objectives, and advantages of the present invention will become more apparent from the following detailed description with reference to the accompanying drawings:

[0013] Figure 1 The schematic diagram of the step flow of a fluid pipeline topology optimization design method based on a parametric simulation model in an embodiment is shown.

[0014] Figure 2 The detailed step flow schematic diagram of step S2 in an embodiment is shown.

[0015] Figure 3 The detailed step flow schematic diagram of step S26 in an embodiment is shown. Detailed implementation manners

[0016] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0017] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus their repeated description will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0018] It should be understood that although terms such as "first" and "second" may be used here to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit can be called the second unit, and similarly, the second unit can be called the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed related items.

[0019] To achieve the above object, please refer to Figures 1 to 3 , the present invention provides a fluid pipeline topology optimization design method based on a parametric simulation model, including the following steps:

[0020] Step S1: Perform parametric modeling on the fluid pipeline to obtain a pipeline topology geometric parameter set; identify the topology features according to the pipeline topology geometric parameter set to obtain pipeline topology description data;

[0021] Step S2: Construct a multi-physics field equation set based on the pipeline topology description data to obtain the basic pipeline coupling field equation set; perform eigenvalue decomposition on the basic pipeline coupling field equation set to obtain the pipeline decoupled equation set; perform iterative solution on the pipeline decoupled equation set to obtain the multi-physics field coupling solution of the pipeline.

[0022] Step S3: Evaluate the sensitivity coefficient according to the multi-physics field coupling solution of the pipeline to obtain the pipeline physical field sensitivity; perform time-domain integral response analysis on the pipeline physical field sensitivity to obtain the pipeline physical field contribution degree; update the topological weight coefficient of the fluid pipeline according to the pipeline physical field contribution degree to obtain the pipeline weight configuration data.

[0023] Step S4: Perform level set modeling on the pipeline topology description data based on the pipeline weight configuration data to obtain the basic pipeline topology optimization model; solve the improved MMA algorithm for the basic pipeline topology optimization model to obtain the optimized pipeline topology data.

[0024] Step S5: Conduct comparative analysis of experimental data on the optimized pipeline topology data to obtain the pipeline spatial error distribution data; perform spatial interpolation on the pipeline spatial error distribution data to obtain the pipeline error correction data; update the pipeline topology geometric parameter set according to the pipeline error correction data to obtain the optimized pipeline topology geometric parameter set.

[0025] In this embodiment, first, the SolidWorks software is used to perform parametric modeling on the fluid pipeline. By defining the geometric parameters of the pipeline (such as diameter, length, wall thickness) and the topological structure (such as branches, connection points), a three-dimensional model of the pipeline is generated, and a set of pipeline topological geometric parameters is obtained. The MATLAB software is used to identify the topological features of these parameters. By analyzing the connection relationships and geometric features of the pipeline, the topological description data of the pipeline is extracted, such as node positions and the number of branches. Based on the pipeline topological description data, a multi-physics field equation set is constructed in the ANSYS Fluent software, including fluid dynamics equations, heat conduction equations, and structural mechanics equations, to obtain the basic equation set of the pipeline coupling field. The MATLAB software is used to perform eigenvalue decomposition on the basic equation set of the coupling field to separate the decoupled equation sets of different physical fields. The iterative solver of ANSYS Fluent is used to solve the decoupled equation sets to obtain the multi-physics field coupling solution of the pipeline, including the velocity field, pressure field, temperature field, and stress field. Based on the coupling solution, the sensitivity coefficient evaluation is performed in MATLAB, and the sensitivity of each physical field to the objective function (such as pressure loss, heat transfer efficiency, etc.) is calculated. The time-domain integral response analysis is performed on the sensitivity coefficients to evaluate the contribution degrees of each physical field at different time scales. According to the contribution degrees, the topological weight coefficients of the pipeline are updated to obtain the pipeline weight configuration data. The level set method in MATLAB is used to model the pipeline topological description data in combination with the weight configuration data to generate the basic model for pipeline topology optimization. The improved MMA (Method of Moving Asymptotes) algorithm is used to solve the basic model. The optimization algorithm code is written in MATLAB to adjust the topological structure of the pipeline to obtain the optimized pipeline topological data. The optimized topological data is used to manufacture the experimental model and is tested in the experimental environment. The laser Doppler vibrometer and pressure sensors are used to collect the experimental data to obtain the physical measurement characteristic data of the pipeline. The experimental data is compared with the numerical simulation results, and the difference between the two is calculated to obtain the pipeline spatial error distribution data. MATLAB is used to perform spatial interpolation on the error distribution data. The Kriging interpolation method is used to predict the error correction values at each point on the pipeline grid to obtain the pipeline error correction data. According to the error correction data, the pipeline topological geometric parameter set is updated, and the geometric shape and topological structure of the pipeline are adjusted. Finally, the optimized pipeline topological geometric parameter set is obtained, and the entire topological optimization design process is completed.

[0026] Preferably, step S1 includes the following steps:

[0027] Step S11: Collect the geometric profile surface features of the fluid pipeline to obtain the pipeline profile feature data;

[0028] Specifically, the fluid pipeline can be placed within the working area of the scanner. Then, start the 3D laser scanner and set the scanning resolution parameter to 0.1 mm. The scanner emits laser beams, scans the entire surface of the pipeline, and receives the reflected light through sensors to generate point cloud data of the pipeline surface. This point cloud data contains geometric contour information of the pipeline, including the diameter, bending radius, and surface shape of the pipeline, etc. After the scanning is completed, import the point cloud data into professional 3D modeling software (such as SolidWorks or AutoCAD), and through the point cloud processing function of the software, convert the point cloud data into a geometric contour surface model of the pipeline. Finally, extract the contour feature data of the pipeline from the software, including the center line, cross-sectional shape, and dimensions of the pipeline.

[0029] Step S12: Arrange control points for the fluid pipeline according to the pipeline contour feature data to obtain initial control point distribution data;

[0030] Specifically, the pipeline contour feature data can be imported into CAD software to generate a 3D model of the pipeline. Then, select the "control point arrangement" function in the software, and according to the geometric shape and complexity of the pipeline, select an appropriate control point distribution strategy. For example, for a straight pipeline with a diameter of 100 mm and a length of 1 m, a control point can be arranged every 10 cm on the center line of the pipeline, and 8 control points can be evenly arranged on the cross-section of the pipeline. The initial positions of the control points can be adjusted according to the pipeline contour feature data to match the actual shape of the pipeline. Through the parametric modeling function of the software, parameterize the positions of the control points. Finally, generate initial control point distribution data, including the coordinate positions and weight information of each control point.

[0031] Step S13: Allocate node weights for the surface curvature of the fluid pipeline based on the initial control point distribution data to obtain the surface curvature weight coefficients of the pipeline;

[0032] Specifically, the initial control point distribution data can be imported into MATLAB, and this data includes the coordinate positions of the control points. Use the numerical analysis toolbox of MATLAB to calculate the surface curvature of the pipeline at each control point. For example, for a pipeline with a complex bending shape, the gradient function in MATLAB can be used to calculate the curvature gradient at the control points, and interpolation can be performed through the interp1 function. According to the magnitude of the curvature value, allocate weight coefficients for each control point. For example, for areas with a larger curvature (such as the bending part of the pipeline), a higher weight can be allocated (such as a weight coefficient of 0.8), while for areas with a smaller curvature (such as the straight part of the pipeline), a lower weight can be allocated (such as a weight coefficient of 0.2). The allocation of weight coefficients can be achieved through MATLAB scripts, and the weights are dynamically adjusted according to the curvature values. Finally, obtain the surface curvature weight coefficients of the pipeline.

[0033] Step S14: Perform spline curve fitting on the fluid pipeline according to the pipeline surface curvature weight coefficient to obtain pipeline cross-section profile data;

[0034] Specifically, the pipeline surface curvature weight coefficient and the initial control point distribution data can be imported into SolidWorks. In SolidWorks, select the "Spline Curve" tool and input the control points as the nodes of the spline curve. Adjust the shape of the spline curve according to the curvature weight coefficient. For example, for the control points with a higher weight coefficient, the shape of the spline curve can be made closer to the actual shape of the pipeline by increasing the weight of the nodes. The specific operation is to select the "Weight" option in the "Spline Curve" property bar of SolidWorks and input the corresponding weight coefficient value. By adjusting the weight, the spline curve can better reflect the bending degree and shape change of the pipeline. Finally, the cross-section profile data of the pipeline, including the shape, size, and curvature distribution information of the cross-section, is obtained through spline curve fitting.

[0035] Step S15: Perform axial scanning modeling on the fluid pipeline based on the pipeline cross-section profile data to obtain three-dimensional pipeline skeleton data;

[0036] Specifically, the pipeline cross-section profile data can be imported into SolidWorks. Select the "Sweep" tool in SolidWorks and set the sweep path as the central axis of the pipeline. Assuming the length of the pipeline is 2 meters and the central axis is along the Z-axis direction, a straight line can be drawn as the sweep path through sketching. Select the cross-section profile as the cross-section shape of the sweep and set the sweep direction as the positive direction along the Z-axis. In the sweep parameter settings, select the "Sweep Along Path" option and ensure that the alignment method between the sweep path and the cross-section profile is correct. After starting the sweep operation, SolidWorks will generate a three-dimensional pipeline skeleton model along the set path. Finally, the obtained three-dimensional pipeline skeleton data includes the center line of the pipeline, the cross-section shape, and the distribution along the axis.

[0037] Step S16: Parametrize the wall thickness of the three-dimensional pipeline skeleton data to obtain a pipeline solid model, and extract the characteristic dimensions of the pipeline solid model to obtain a pipeline topological geometric parameter set;

[0038] Specifically, the three-dimensional pipeline skeleton data can be imported into SolidWorks. Select the "Extrusion" or "Thicken" tool to perform wall thickness parameterization on the pipeline skeleton. Suppose the wall thickness of the pipeline needs to be set to 5 mm. The wall thickness parameter value of 5 mm can be entered in the property bar of the "Extrusion" tool, and the "Double-sided Extrusion" option can be selected to uniformly generate the wall thickness on both the inner and outer sides of the pipeline skeleton. Next, extract the feature dimensions of the generated pipeline solid model. In SolidWorks, use the "Measure" tool to extract the key feature dimensions of the pipeline solid model, including the outer diameter, inner diameter, wall thickness, length, and bending radius of the pipeline. These feature dimensions will be extracted as the pipeline topology geometric parameter set.

[0039] Step S17: Identify the topological features of the fluid pipeline based on the pipeline topology geometric parameter set to obtain pipeline topology description data.

[0040] Specifically, for the detailed implementation process of this embodiment, please refer to the sub-steps of step S17.

[0041] Through the collection of geometric profile surface features and the arrangement of control points, combined with the assignment of surface curvature node weights, the present invention can accurately capture the geometric features of the fluid pipeline. This can not only accurately reflect the shape and size of the pipeline but also generate high-precision three-dimensional pipeline skeleton data through spline curve fitting and axial scanning modeling. Through the analysis of the pipeline topology geometric parameter set, the topological features of the pipeline can be quickly identified. This can effectively distinguish different regions and connection relationships of the pipeline. By adopting the methods of wall thickness parameterization and feature dimension extraction, the wall thickness and dimension parameters of the pipeline can be flexibly adjusted to meet different design requirements. Through detailed identification of the modeling and topological features, the local features and overall structure of the pipeline can be fully considered, so as to achieve more detailed adjustment and optimization in the optimization process. Through the detailed recording and description of the pipeline geometric features and topological features, complete data support can be provided for the design process. This not only improves the verifiability of the design but also facilitates tracing design changes in the subsequent optimization and verification processes, ensuring the transparency and traceability of the design process. Through geometric modeling and efficient topological feature identification, combined with flexible parameterization adjustment, the overall design quality of the fluid pipeline can be significantly improved.

[0042] Preferably, step S17 includes the following steps:

[0043] Step S171: Statistically analyze the grid density distribution of the fluid pipeline based on the pipeline topology geometric parameter set to obtain pipeline grid density distribution data;

[0044] Specifically, the pipeline topology geometric parameter set can be imported into ANSYS Meshing. These parameters include the outer diameter, inner diameter, wall thickness, length, and bending radius of the pipeline. In ANSYS Meshing, select the "Mesh Generation" function and set the mesh type to tetrahedral mesh (suitable for fluid pipelines with complex geometries). In the mesh generation parameter settings, select the "Mesh Density Distribution Statistics" option. Assuming the pipeline has a length of 2 meters and a wall thickness of 5 millimeters, the mesh density parameter can be set to 10 mesh elements per centimeter. ANSYS Meshing will automatically generate the mesh model of the pipeline based on the geometric parameters of the pipeline and the set mesh density parameter, and statistically analyze the mesh density distribution data. Finally, the obtained pipeline mesh density distribution data includes the number of mesh elements and the distribution density in each region.

[0045] Step S172: Generate boundary layer meshes based on the pipeline mesh density distribution data to obtain pipeline boundary layer mesh data;

[0046] Specifically, the pipeline mesh density distribution data can be imported into ANSYS Meshing. In the software, select the "Boundary Layer Mesh Generation" function. Assuming that boundary layer meshes need to be generated near the wall surface of the pipeline to capture the boundary effects of fluid flow, the number of boundary layer mesh layers can be set to 5 layers, and the thickness of each layer is 0.1 millimeter (a reasonable parameter selected based on fluid flow characteristics and wall boundary layer theory). In the boundary layer mesh generation parameter settings, select the "Boundary Layer Mesh Type" as structured mesh. ANSYS Meshing will automatically generate boundary layer meshes based on the geometric shape of the pipeline and the mesh density distribution data. Finally, the obtained pipeline boundary layer mesh data includes information such as the position, number of layers, and thickness of the boundary layer meshes.

[0047] Step S173: Perform volume meshing on the fluid pipeline based on the pipeline boundary layer mesh data and the pipeline topology geometric parameter set to obtain pipeline volume mesh data;

[0048] Specifically, the boundary layer mesh data and the pipeline topology geometric parameter set can be imported into ANSYS Meshing software. In the software, select the "Volume Mesh" function and set the mesh type to hexahedral mesh (suitable for regular pipeline geometries) or tetrahedral mesh (suitable for pipelines with complex shapes). Assume that the main part of the pipeline uses a hexahedral mesh, while the curved part uses a tetrahedral mesh to adapt to the complex geometry. In the mesh division parameter settings, specify the mesh size parameter. For example, set the mesh size to 10 mm in the straight section of the pipeline and 5 mm in the curved part. In addition, the growth rate parameter of the mesh can also be set. For example, set the growth rate to 1.2 in the transition area from the boundary layer mesh to the main body mesh to ensure a smooth transition of the mesh. ANSYS Meshing will automatically generate the volume mesh of the pipeline according to these parameters. The final pipeline volume mesh data includes information such as the type, quantity, position, and connection relationship of the mesh elements.

[0049] Step S174: Evaluate the mesh quality of the pipeline volume mesh data to obtain the pipeline mesh quality index, and adjust the mesh nodes of the fluid pipeline according to the pipeline mesh quality index to obtain the optimized fluid pipeline mesh data;

[0050] Specifically, the pipeline volume mesh data can be imported into ANSYS Meshing. In the software, select the "Mesh Quality Evaluation" function to check the quality of the volume mesh. ANSYS Meshing provides various mesh quality indicators, such as the Jacobian ratio of the mesh elements, the minimum interior angle and the maximum exterior angle of the mesh elements. The threshold of the Jacobian ratio can be set to 0.5, the threshold of the minimum interior angle to 15 degrees, and the threshold of the maximum exterior angle to 165 degrees. The software will automatically scan the entire mesh model, identify the mesh elements that do not meet the quality standards, and mark these elements as areas that need to be adjusted. Next, select the "Mesh Node Adjustment" function to optimize and adjust these marked mesh elements. For example, use the "Smoothing" algorithm to reposition the mesh nodes to improve the shape and angle of the mesh elements. In addition, the "Remeshing" function can also be used to remesh the local area of the mesh. Finally, the adjusted mesh data will have higher quality and numerical stability, and the optimized fluid pipeline mesh data will be obtained.

[0051] Step S175: Identify the topological connectivity of the optimized fluid pipeline mesh data to obtain the pipeline mesh topological relationship data;

[0052] Specifically, the fluid pipeline grid data can be imported into ANSYS Fluent. In the software, select the "Mesh Analysis" module and enter the "Topological Connectivity Analysis" function. This function can identify the connection relationships between grid cells, such as the shared nodes and edges of adjacent cells. Set the analysis parameters, such as specifying the minimum number of connected nodes as 2. ANSYS Fluent will automatically scan the entire mesh model, identify the connectivity of each grid cell, and generate pipeline grid topology relationship data. These data include the numbers of each grid cell, the numbers of adjacent cells, and the connection node information between them.

[0053] Step S176: Identify the characteristic regions from the pipeline grid topology relationship data to obtain pipeline topology description data.

[0054] Specifically, the pipeline grid topology relationship data can be imported into MATLAB. In MATLAB, utilize its matrix operation and data processing functions to analyze the topology relationship data. For example, different characteristic regions can be identified by calculating the connectivity of each grid cell (i.e., the number of other cells connected to this cell). Set the connectivity threshold. For example, for the straight sections of the pipeline, the connectivity threshold can be set to 4, while for the bent sections or connection points of the pipeline, the connectivity threshold can be set to 6. According to these thresholds, MATLAB can automatically identify different characteristic regions of the pipeline, such as straight sections, bent sections, and connection points. Finally, organize the information of the identified characteristic regions into pipeline topology description data, including the position, range, and type of each characteristic region, etc.

[0055] Through the statistics of the pipeline grid density distribution and the generation of boundary layer grids, the present invention can generate high-quality grid data. This can more accurately reflect the geometric and physical characteristics of the fluid pipeline. The optimized grid data can effectively reduce the errors in numerical calculations and improve the reliability and accuracy of the simulation. Through volume mesh division and the evaluation and adjustment of the mesh quality, grids adapted to different complex geometric shapes can be generated. This not only improves the adaptability of the grids but also can dynamically adjust the grid nodes during the optimization process to adapt to the changes in the pipeline geometric parameters. Through topological connectivity identification and characteristic region identification, the topological structure and connection relationships of the pipeline can be clearly reflected. This helps to achieve more efficient topological adjustment and optimization during the optimization process. This reduces the workload of manual mesh adjustment and topological identification and improves the degree of design automation. By optimizing the mesh quality and the accuracy of the topological description, the uncertainty in the optimization design process can be reduced and the design risk can be lowered. Through mesh processing and topological description, combined with the optimized grid data, the overall design quality of the fluid pipeline can be significantly improved.

[0056] Preferably, step S2 includes the following steps:

[0057] Step S21: Identify the physical field boundary conditions for the pipeline topology description data to obtain the pipeline boundary condition data, and set the fluid property parameters for the fluid pipeline according to the pipeline boundary condition data to obtain the pipeline fluid characteristic data;

[0058] Specifically, the pipeline topology description data can be imported into ANSYS Fluent. In the software, select the "Boundary Condition Settings" module, and identify and define the physical field boundary conditions according to the pipeline topology structure and actual application scenarios. For example, for a fluid pipeline system, assume that the inlet boundary condition of the pipeline is a velocity inlet, and the velocity value is set to 10 m / s; the outlet boundary condition is a pressure outlet, and the pressure value is set to 1 atm. At the same time, for the wall of the pipeline, it is set as a no-slip boundary condition. Next, set the fluid property parameters for the fluid pipeline according to the pipeline boundary condition data. In ANSYS Fluent, select the "Material Properties" module, and set the density of the fluid to 1000 kg / m 3 (assuming the fluid is water), and the dynamic viscosity is 1×10-3 Pa·s. These parameters can be directly selected from the software's material library or manually input. Finally, the obtained pipeline fluid characteristic data includes the density, viscosity, boundary condition type and its specific values of the fluid, etc.

[0059] Step S22: Evaluate the thermodynamic parameters of the fluid pipeline based on the pipeline fluid characteristic data to obtain the pipeline fluid thermodynamic characteristic data, and discretize the pipeline fluid thermodynamic characteristic data by fractional derivative to obtain the pipeline fluid fractional operator data;

[0060] Specifically, the pipeline fluid characteristic data can be imported into MATLAB. Assume that the fluid is water, its specific heat capacity is 4186 J / (kg·K), and its thermal conductivity is 0.6 W / (m·K). In MATLAB, use its numerical analysis toolbox to evaluate the thermodynamic parameters of the fluid. For example, calculate the thermal expansion coefficient of the fluid at different temperatures. Assume that in the range of 20°C to 80°C, the thermal expansion coefficient is 0.00021 / K. Next, discretize the pipeline fluid thermodynamic characteristic data by fractional derivative. Select the order of the fractional derivative to be 0.5, and use the grunwald_letnikov function in MATLAB for discretization. Set the time step of discretization to 0.01 s and the space step to 0.1 m. Through fractional derivative discretization, the pipeline fluid fractional operator data is obtained, and these data can more accurately describe the non-linear thermodynamic behavior of the fluid.

[0061] Step S23: Construct the momentum equation according to the pipeline fluid fractional operator data to obtain the viscous fluid momentum conservation equations, and construct the energy equation for the fluid pipeline based on the viscous fluid momentum conservation equations to obtain the fluid-structure interaction energy conservation equations;

[0062] Specifically, the pipeline fluid fractional-order operator data can be imported into ANSYS Fluent. In the software, select the "Momentum Equation Settings" module and construct the momentum conservation equations for viscous fluids based on the fractional-order operator data. Assume the fluid is water with a dynamic viscosity of 1×10-3 Pa·s and a density of 1000 kg / m3. In ANSYS Fluent, customize the fractional-order derivative term through the UDF function and embed it into the momentum equation. Specifically, use the fractional-order operator data generated by MATLAB, write the UDF code in C language, compile it, and load it into ANSYS Fluent. Next, based on the momentum conservation equations of viscous fluids, construct the energy conservation equations for fluid-structure interaction. In ANSYS Fluent, select the "Energy Equation Settings" module, set the thermal conductivity of the fluid to 0.6 W / (m·K) and the specific heat capacity to 4186 J / (kg·K). At the same time, consider the heat exchange between the fluid and the pipeline wall, and set the thermal conductivity of the wall to 50 W / (m·K). Embed the fractional-order derivative term into the energy equation through the UDF function. Finally, obtain the momentum conservation equations of viscous fluids and the energy conservation equations for fluid-structure interaction.

[0063] Step S24: Construct the mass equation according to the momentum conservation equations of viscous fluids and the energy conservation equations for fluid-structure interaction to obtain the mass conservation equations of the continuum, and identify the coupling terms in the mass conservation equations of the continuum to obtain the pipeline multi-field coupling term data;

[0064] Specifically, the momentum conservation equations of viscous fluids and the energy conservation equations for fluid-structure interaction can be imported into ANSYS Fluent. In the software, select the "Mass Equation Settings" module and construct the mass conservation equations of the continuum based on the physical properties of the fluid (such as density, viscosity, etc.). Assume the fluid is water with a density of 1000 kg / m3. In ANSYS Fluent, set the inter-phase interaction of the fluid through the "Multiphase Flow" module. For example, set the mass transfer coefficient between the fluid and the pipeline wall to 0.01 kg / (m2·s). Next, identify the coupling terms in the mass conservation equations of the continuum. In ANSYS Fluent, identify the coupling terms between the momentum equation, energy equation, and mass equation through the "Equation Coupling" module. For example, identify the coupling terms between the fluid pressure gradient and the velocity field, and between the temperature field and the density. Embed these coupling terms into the corresponding equations through the UDF function. Finally, obtain the pipeline multi-field coupling term data.

[0065] Step S25: Based on the pipeline multi-field coupling term data, construct the basic equations of the pipeline coupling field for the fluid pipeline to obtain the basic equations of the pipeline coupling field;

[0066] Specifically, the multi-field coupling term data of the pipeline can be imported into ANSYS Fluent. This data includes the coupling terms in the momentum, energy, and mass conservation equations of the fluid, such as the coupling between the fluid pressure gradient and the velocity field, the coupling between the temperature field and the density, etc. In ANSYS Fluent, select the "Multi-Physics Settings" module and enter the "Equation System Construction" function. Based on the identified coupling terms, construct the complete basic equation system of the pipeline coupling field. For example, for a fluid pipeline system, it is necessary to consider the momentum conservation equation, energy conservation equation, and mass conservation equation of the fluid simultaneously, and integrate the coupling terms in these equations. Specifically, set the density of the fluid to 1000 kg / m3, the dynamic viscosity to 1×10-3 Pa·s, the specific heat capacity to 4186 J / (kg·K), and the thermal conductivity to 0.6 W / (m·K). In ANSYS Fluent, through the "Multiphase Flow" module and the "Thermodynamics" module, embed these parameters and coupling terms into the corresponding equations. Finally, the obtained basic equation system of the pipeline coupling field includes the momentum conservation equation, energy conservation equation, and mass conservation equation of the fluid. These equations are interconnected through the coupling terms and can comprehensively describe the multi-physics field coupling phenomenon in the fluid pipeline system.

[0067] Step S26: Perform eigenvalue decomposition on the basic equation system of the pipeline coupling field to obtain the decoupled equation system of the pipeline, and perform iterative solution on the decoupled equation system of the pipeline to obtain the multi-physics field coupling solution of the pipeline.

[0068] Specifically, for the detailed implementation process of this embodiment, please refer to the sub-steps of Step S26.

[0069] Through the construction of a multi - physical - field equation set, the present invention can comprehensively consider the fluid dynamics, thermodynamics, and fluid - structure interaction effects in a fluid pipeline. This not only covers the conservation of momentum, energy, and mass of the fluid, but also includes the interaction between different physical fields in the analysis scope through coupling - term identification and equation - set construction, thereby providing a complete multi - physical - field coupling analysis framework. By adopting the eigenvalue decomposition and iterative solution methods, the complex multi - physical - field coupling equation set can be efficiently solved. The eigenvalue decomposition can decouple the coupling equation set into a form that is easier to solve, thus significantly improving the calculation efficiency. At the same time, the iterative solution method can gradually approach the exact solution to ensure the high precision of the calculation results. Through dynamic evaluation and fractional - order derivative discretization, it can better adapt to the dynamic changes under complex working conditions. By boundary - condition identification and fluid - property parameter setting, the construction of the multi - physical - field equation set can be ensured to have a solid physical basis. Through coupling - term identification and equation - set construction, the interaction between each physical field can be systematically analyzed, thereby providing a scientific basis for the optimization design. Through multi - physical - field coupling analysis and efficient solution methods, the uncertainty and risk in the design process can be significantly reduced. By dynamically adapting to complex working conditions, the design changes caused by the change of working conditions can be reduced, further reducing the design risk and cost. Through the detailed record of the construction and solution process of the multi - physical - field equation set, complete data support can be provided for the design process.

[0070] Preferably, step S26 includes the following steps:

[0071] Step S261: Conduct a Jacobi eigenvalue evaluation on the basic equation set of the pipeline coupling field to obtain the eigenvalues of the pipeline characteristic equation;

[0072] Specifically, the discretized form of the basic equation set of the pipeline coupling field can be imported into MATLAB. These equation sets are represented in matrix form. For example, the discretized forms of the momentum equation, energy equation, and mass equation can be combined into a large Jacobi matrix J. In MATLAB, using its numerical - calculation function, the eig function is called to evaluate the eigenvalues of the Jacobi matrix. Assuming the size of the Jacobi matrix J is 100×100, the eigenvalues are calculated through the eig function to obtain a vector containing all the eigenvalues. Finally, the eigenvalues of the pipeline characteristic equation are obtained.

[0073] Step S262: Extract the eigenvectors from the eigenvalues of the pipeline characteristic equation to obtain the orthogonal eigenvectors of the pipeline equation set;

[0074] Specifically, the eigenvalues of the pipeline characteristic equation can be imported into MATLAB. In MATLAB, the eig function is used to not only calculate the eigenvalues but also extract the corresponding eigenvectors simultaneously. Suppose the size of the Jacobian matrix J is 100×100. By using the eig function to calculate the eigenvalues and eigenvectors, a matrix is obtained, where each column corresponds to an eigenvector, and a diagonal matrix whose diagonal elements are the eigenvalues. The orth function is used to orthogonalize the eigenvectors. Finally, the orthogonal eigenvectors of the pipeline equation set are obtained.

[0075] Step S263: Based on the orthogonal eigenvectors of the pipeline equation set, perform equation decoupling transformation on the basic equation set of the pipeline coupling field to obtain the pipeline decoupled equation set;

[0076] Specifically, the orthogonal eigenvectors of the pipeline equation set can be imported into MATLAB. These orthogonal eigenvectors will be used to perform decoupling transformation on the equation set. In MATLAB, using the matrix transformation function, the matrix form A of the basic equation set of the coupling field and the orthogonal eigenvector matrix V are transformed. The specific operation is to perform a similarity transformation on the original equation set A through the eigenvector matrix V, that is, calculate V ―1 AV. Suppose the size of the matrix A of the original equation set is 100×100. Through the matrix operation function of MATLAB, the decoupled equation set matrix D can be obtained, where D is a diagonal matrix whose diagonal elements are the eigenvalues of the original equation set. Finally, the obtained pipeline decoupled equation set will be easier to solve.

[0077] Step S264: Numerically discretize the pipeline decoupled equation set to obtain the pipeline fluid discrete equation set;

[0078] Specifically, the pipeline decoupled equation set can be imported into ANSYS Fluent. In the software, select the "Numerical Discretization" module and set the discretization method. Suppose the decoupled equation set includes the momentum, energy, and mass conservation equations of the fluid. Select the Finite Volume Method (FVM) for discretization. In ANSYS Fluent, set the grid discretization accuracy to second-order accuracy, the time step to 0.01 seconds, and the space step to 0.1 meters. Through the discretization function of the software, the decoupled equation set is converted into a discrete form to obtain the pipeline fluid discrete equation set.

[0079] Step S265: Configure the solver parameters according to the pipeline fluid discrete equation set to obtain the fluid solver configuration data;

[0080] Specifically, the discrete equations of the pipeline fluid can be imported into ANSYS Fluent. In the software, enter the "Solver Settings" module and select an appropriate solver type. For fluid flow and heat transfer problems, the Pressure-Based Solver can be selected. Next, configure the solver parameters, including the iteration accuracy and convergence criteria. For example, set the residual convergence criterion to 10 ―6 . At the same time, select an appropriate numerical format, such as the second-order upwind format, for discretizing the convective terms. In addition, set the time step to 0.01 seconds for unsteady simulations. Through these parameter configurations, the fluid solver configuration data is generated.

[0081] Step S266: Iteratively solve the discrete equations of the pipeline fluid based on the fluid solver configuration data to obtain the coupled solution of the pipeline multi-physical fields.

[0082] Specifically, the fluid solver parameters configured in step S265 can be imported into ANSYS Fluent. In the software, enter the "Solver Run" module and start the iterative solution process. During the solution process, ANSYS Fluent will gradually iteratively solve the discrete equations according to the configured parameters. For example, for a complex fluid pipeline system, hundreds of iterations may be required to reach the set convergence criterion. In each iteration, the software will calculate physical quantities such as the velocity field, pressure field, and temperature field of the fluid and check whether the residuals meet the convergence criterion of 10 ―6 . If the residuals do not reach the standard, the solver will continue to iterate until the convergence condition is met. Finally, the obtained coupled solution of the pipeline multi-physical fields includes information on the velocity distribution, pressure distribution, and temperature distribution of the fluid.

[0083] Through Jacobi eigenvalue evaluation and eigenvector extraction, the present invention can decouple complex coupled field equations into multiple independent sub-equations. The decoupling transformation based on orthogonal eigenvectors can ensure that the decoupled equations have good numerical stability and calculation accuracy, further improving the reliability of the solution. By using numerical methods of eigenvalues and eigenvectors, numerical instability problems that occur during the solution process of coupled equations can be effectively avoided. Through systematic decoupling and discretization of coupled field equations, it can better adapt to the complexity of multi-physical field coupling problems. Through numerical discretization and solver parameter configuration, the discrete fluid equations can be efficiently solved. Through efficient decoupling and solution methods, the calculation time and resource consumption in the optimization design process can be significantly reduced.

[0084] Preferably, step S3 includes the following steps:

[0085] Step S31: Extract state variables from the multi-physics field coupling solution of the pipeline to obtain pipeline coupling field variable distribution data, and construct an objective function based on the pipeline coupling field variable distribution data to obtain the pipeline topology optimization objective function;

[0086] Specifically, the multi-physics field coupling solution of the pipeline can be imported into MATLAB. These solutions include state variables of the velocity field, pressure field, and temperature field of the fluid. In MATLAB, use matrix operations and data processing functions to extract the distribution data of these state variables. For example, the velocity, pressure, and temperature values at each grid point in the pipeline can be extracted and stored in matrix form. Next, construct a pipeline topology optimization objective function based on these distribution data. Assume that the optimization objective is to reduce the pressure loss in the pipeline and improve the heat transfer efficiency. The objective function can be defined as: Objective function = α × Pressure loss + β × Heat transfer efficiency, where α and β are weight coefficients, set to 0.6 and 0.4 respectively, to balance the importance of pressure loss and heat transfer efficiency. Through the symbolic calculation function of MATLAB, substitute the expressions of pressure loss and heat transfer efficiency into the objective function, and simplify and optimize it. Finally, obtain the pipeline topology optimization objective function.

[0087] Step S32: Identify the topology structure design variables of the fluid pipeline based on the pipeline topology optimization objective function to obtain the topology structure design variable data, and identify the parameter perturbations of the topology structure design variable data to obtain the pipeline topology variable perturbation data;

[0088] Specifically, the pipeline topology optimization objective function can be imported into MATLAB. In MATLAB, use the symbolic calculation function to analyze the objective function and identify the key design variables that affect the objective function. For example, assume that the wall thickness, cross-sectional shape, and length of the pipeline are the main design variables. The influence degree of these variables on the objective function can be determined by taking the partial derivatives of the objective function. Next, identify the parameter perturbations of these design variables. Assume that a perturbation is made to the wall thickness, and the perturbation amount is set to ±0.1 cm. Through the numerical simulation function of MATLAB, calculate the change in the objective function value under different wall thicknesses. For example, how the objective function value changes when the wall thickness increases by 0.1 cm; how the objective function value changes when the wall thickness decreases by 0.1 cm. Through these calculations, obtain the pipeline topology variable perturbation data.

[0089] Step S33: Perform adjoint gradient evaluation based on the pipeline topology variable perturbation data to obtain the pipeline topology objective function gradient;

[0090] Specifically, the pipeline topology variable perturbation data can be imported into ANSYS Fluent. This data includes the changes in the objective function values under different perturbation conditions. In ANSYS Fluent, select the "adjoint solver" module and set the objective function and design variables. Assume that the objective function is the pressure loss inside the pipeline, and the design variables are the pipeline wall thickness and cross-sectional shape. Through the adjoint solver, calculate the gradient of each design variable with respect to the objective function. For example, set the perturbation amount of the wall thickness to ±0.1 cm, and by solving the adjoint equation, obtain the gradient value of the pressure loss with respect to the change in wall thickness. Assume that the calculated gradient value is -0.05 (indicating that the pressure loss decreases when the wall thickness increases). Finally, obtain the pipeline topology objective function gradient.

[0091] Step S34: Derive the parameter space sensitivity coefficients for the fluid pipeline based on the pipeline topology objective function gradient to obtain the initial sensitivity coefficients in the topology space;

[0092] Specifically, the pipeline topology objective function gradient can be imported into MATLAB. In MATLAB, use the symbolic calculation function to analyze the objective function gradient. Assume that the objective function gradient is -0.05 (indicating that the pressure loss decreases when the wall thickness increases). Through symbolic calculation, derive the sensitivity coefficient of the change in wall thickness with respect to the objective function. For example, assume that the change range of the wall thickness is from 0.1 to 1 cm. Through the numerical analysis function of MATLAB, calculate the sensitivity coefficient of the change in wall thickness with respect to the objective function within this range. Assume that the calculated sensitivity coefficient is -0.05 / 0.1 = -0.5, indicating that for every 0.1 cm increase in wall thickness, the objective function (pressure loss) decreases by 0.5 units. Finally, obtain the initial sensitivity coefficients in the topology space.

[0093] Step S35: Regularize the initial sensitivity coefficients in the topology space to obtain the standard topology field sensitivity, and construct a response surface based on the standard topology field sensitivity to obtain the pipeline physical field sensitivity;

[0094] Specifically, the initial sensitivity coefficients of the topological space can be imported into MATLAB. Assume that these sensitivity coefficients are for design variables such as pipe wall thickness and cross-sectional shape, and their numerical ranges are relatively large, for example, from -1.5 to 0.8. In MATLAB, the normalize function is used to normalize the sensitivity coefficients so that their range is between 0 and 1. For example, for the sensitivity coefficient of the wall thickness, the standardized topological field sensitivity obtained after regularization varies between 0 and 1. Next, based on the regularized standardized topological field sensitivity, a response surface is constructed. In MATLAB, the fit function or the rsm (response surface method) toolbox can be used to construct the response surface. Assume that the response surface model is a quadratic polynomial model, and the following command can be used to construct the response surface: responseSurface = fit(x,y,'poly2'); where x is the wall thickness variable and y is the sensitivity coefficient; through this command, MATLAB will fit a quadratic polynomial response surface model according to the input wall thickness variable x and the corresponding sensitivity coefficient y. The finally obtained physical field sensitivity of the pipe can be intuitively represented by the response surface model. For example, a curve graph of the sensitivity varying with the wall thickness can be plotted.

[0095] Step S36: Conduct a time-domain integral response analysis on the physical field sensitivity of the pipe to obtain the contribution degree of the pipe physical field, and update the topological weight coefficient of the fluid pipe according to the contribution degree of the pipe physical field to obtain the pipe weight configuration data.

[0096] Specifically, for the detailed implementation process of this embodiment, please refer to the sub-steps of step S36.

[0097] Through the extraction of state variables, the present invention can accurately construct the pipe topology optimization objective function. This not only clarifies the optimization objective but also provides a clear direction for subsequent sensitivity analysis and optimization adjustment. By adopting adjoint gradient evaluation and parameter space sensitivity coefficient derivation, it is possible to efficiently identify the topological structure design variables and their influence on the objective function. This can quickly determine which design variables have a significant impact on the optimization objective, thus providing key information for optimization adjustment. Through the time-domain integral response analysis of the physical field sensitivity of the pipe, it is possible to dynamically evaluate the contribution degree of each physical field to the optimization objective and update the topological weight coefficient accordingly. Through regularization processing and response surface construction, the scientificity and reliability of the sensitivity analysis can be ensured. Through the systematic sensitivity analysis and weight update process, the complexity of the optimization process can be significantly reduced. Through objective function construction, efficient sensitivity analysis, and dynamic weight update, the overall optimization quality of the fluid pipe can be significantly improved.

[0098] Preferably, step S36 includes the following steps:

[0099] Step S361: Decompose the pipeline physical field sensitivity in the time domain to obtain the pipeline sensitivity time-domain characteristic data;

[0100] Specifically, the pipeline physical field sensitivity data can be imported into MATLAB. These data are usually sensitivity signals that vary with time. For example, the sensitivity values of the pipeline at different time points. In MATLAB, use the fft (Fast Fourier Transform) function in the signal processing toolbox to perform time-domain analysis on the sensitivity signal. Assume that the sampling frequency of the sensitivity signal is 1000 Hz and the signal length is 1000 data points, representing the sensitivity change within 1 second. Through the fft function, convert the time-domain signal into a frequency-domain signal to obtain the frequency spectrum diagram of the pipeline sensitivity. Then, by analyzing the frequency spectrum diagram, extract the main frequency components and corresponding amplitudes of the sensitivity signal. For example, if it is found that there are obvious peaks at 10 Hz and 50 Hz in the sensitivity signal, these frequency components can be regarded as the pipeline sensitivity time-domain characteristic data.

[0101] Step S362: Perform spectrum identification on the pipeline sensitivity time-domain characteristic data to obtain the pipeline sensitivity frequency-domain response data;

[0102] Specifically, the pipeline sensitivity time-domain characteristic data can be imported into MATLAB. These data include the main frequency components and corresponding amplitudes of the sensitivity signal. In MATLAB, use the pwelch function in the signal processing toolbox for spectrum estimation. Assume that the sampling frequency of the sensitivity signal is 1000 Hz and the signal length is 1000 data points. Through the pwelch function, the power spectral density (PSD) of the sensitivity signal can be calculated. For example, set the window function for spectrum estimation as the Hanning window, the window length as 256 data points, and the overlap length as 128 data points. Through these parameter settings, the pwelch function will output the frequency spectrum diagram of the sensitivity signal, showing the power distribution of different frequency components. By analyzing the frequency spectrum diagram, the main frequency-domain response characteristics of the sensitivity signal can be identified. For example, the power peaks of the sensitivity signal at 10 Hz and 50 Hz, and the relative intensities of these frequency components. Finally, obtain the pipeline sensitivity frequency-domain response data.

[0103] Step S363: Perform Fourier integration on the pipeline sensitivity frequency-domain response data to obtain the pipeline sensitivity cumulative response data;

[0104] Specifically, the pipeline sensitivity frequency-domain response data can be imported into MATLAB. These data include the power spectral density (PSD) of different frequency components. Assume that the frequency range of the frequency-domain response data is from 0 to 500 Hz, and the sampling frequency is 1000 Hz. In MATLAB, the trapz function is used for numerical integration to calculate the cumulative response of the sensitivity frequency-domain response data within a specific frequency range. For example, the frequency-domain response data within the range of 10 Hz to 50 Hz can be integrated to obtain the cumulative response value within this frequency band. By setting the integration range and step size, such as a step size of 1 Hz, the trapz function will return the numerical value of the cumulative response. Finally, the cumulative response data of the pipeline sensitivity is obtained.

[0105] Step S364: Extract the multi-field distribution characteristics of the pipeline sensitivity cumulative response data to obtain the sensitivity energy response characteristic data;

[0106] Specifically, the pipeline sensitivity cumulative response data can be imported into MATLAB. These data represent the cumulative effect of sensitivity within a specific frequency band. In MATLAB, the statistical analysis function is used to extract the characteristics of the cumulative response data. For example, calculate the mean, variance, and energy distribution of the cumulative response data. Assume that the mean of the cumulative response data is 0.5 and the variance is 0.1. The energy response characteristics can be calculated through the following formula: In MATLAB, the mean and var functions can be used to calculate the mean and variance respectively, and then the energy response characteristics are calculated according to the above formula. Finally, the sensitivity energy response characteristic data is obtained.

[0107] Step S365: Evaluate the contribution degree according to the sensitivity energy response characteristic data to obtain the contribution degree of the pipeline physical field;

[0108] Specifically, the sensitivity energy response characteristic data can be imported into MATLAB. These data include the energy response characteristic values under different frequency bands or different physical fields. Assume there are three main physical fields: fluid pressure field, temperature field, and structural stress field, and the corresponding energy response characteristic values are E p = 0.8, E t = 0.5, and E s = 0.3. In MATLAB, these energy response characteristic values are used for contribution degree evaluation. The contribution degree formula can be defined as: where E i represents the energy response characteristic value of the i-th physical field. Through MATLAB calculation, the contribution degree of the fluid pressure field is 0.53, the contribution degree of the temperature field is 0.33, and the contribution degree of the structural stress field is 0.14. These contribution degree values reflect the influence degree of different physical fields on the pipeline performance.

[0109] Step S366: Construct a topological unit weight function for the fluid pipeline based on the contribution degree of the pipeline physical field to obtain a pipeline topology weight allocation scheme;

[0110] Specifically, the pipeline physical field contribution degree data can be imported into MATLAB. These data include the contribution degree values of the fluid pressure field, temperature field, and structural stress field, which are 0.53, 0.33, and 0.14 respectively. In MATLAB, a topological unit weight function is constructed based on these contribution degree values. Assuming that the pipeline grid is divided into 100 units, the weight function of each unit can be expressed as: W i = ∑(contribution degree j × sensitivity ij ); where the contribution degree j is the contribution degree of the jth physical field, and the sensitivity ij is the sensitivity of the ith unit to the jth physical field. Through the matrix operation function of MATLAB, the weight value of each unit is calculated. For example, for the first unit, its sensitivity to the fluid pressure field is 0.7, its sensitivity to the temperature field is 0.4, and its sensitivity to the structural stress field is 0.2. Then the weight value of this unit is: W 1 = (0.53 × 0.7) + (0.33 × 0.4) + (0.14 × 0.2) = 0.531; Through similar calculations, the weight values of all units are obtained, and these weight values are stored as the weight allocation scheme.

[0111] Step S367: Perform relaxation adjustment on the pipeline topology weight allocation scheme to obtain pipeline weight configuration data.

[0112] Specifically, the pipeline topology weight allocation scheme can be imported into MATLAB. Assuming that the weight allocation scheme contains the weight values of 100 units, these weight values reflect the importance of different units in the optimization process. In MATLAB, the relaxation adjustment function in the optimization toolbox is used to adjust the weight allocation scheme. Assuming that the goal of relaxation adjustment is to make the weight distribution more uniform to avoid the weight of some units being too high or too low during the optimization process. The parameters of relaxation adjustment can be set. For example, the relaxation factor is 0.1, indicating that the weight is allowed to float up and down by 10% based on the original value. Through the optimization algorithm of MATLAB, the weight of each unit is adjusted. For example, for the unit with a weight value of 0.531, the adjusted weight is 0.584 (increased by 10%). Finally, the pipeline weight configuration data is obtained.

[0113] Through time-domain decomposition and spectrum recognition, the present invention can accurately extract the time-domain and frequency-domain characteristics of sensitivity. Further, through Fourier integral and multi-field distribution feature extraction, the cumulative contributions of various physical fields to the pipeline performance can be quantified. By constructing a topological unit weight function, the importance of each physical field under different working conditions can be dynamically reflected. By relaxing and adjusting the weight allocation scheme, the changes in the contribution degrees of physical fields during the optimization process can be flexibly adapted to ensure that the optimization design remains efficient and accurate under complex dynamic working conditions. Through multi-dimensional analysis (time domain, frequency domain, energy response) of sensitivity data, the impacts of various physical fields on the pipeline performance can be more comprehensively understood. Through contribution degree evaluation and dynamic weight configuration, the optimization design can more scientifically adjust the topological structure, thereby improving the credibility and practicality of the design results. By dynamic weight adjustment, unnecessary optimization iterations are reduced, the design cycle is shortened, and thus the design cost is reduced.

[0114] Preferably, step S4 includes the following steps:

[0115] Step S41: Initialize the level set according to the pipeline topology description data to obtain the initial pipeline geometry level set data;

[0116] Specifically, the pipeline topology description data can be imported into MATLAB. These data include the geometric shape, size, and topology structure information of the pipeline. Assume that the initial shape of the pipeline is a simple cylinder with a radius of 0.1 meters and a length of 1 meter. In MATLAB, use the level set method to initialize the geometry of the pipeline. Define a level set function φ(x 1 , y 1 , z), which represents the interface between the inside and outside of the pipeline. For a cylindrical pipeline, the level set function can be defined as: where, when φ < 0, it represents the inside of the pipeline, and when φ > 0, it represents the outside of the pipeline. Next, create a three-dimensional grid in MATLAB with a size of 100×100×100, representing the computational domain of the pipeline. Then, calculate the level set function values at each grid point. Finally, obtain the initial pipeline geometry level set data.

[0117] Step S42: Based on the initial pipeline geometry level set data, construct an evolution equation for the topological evolution region of the fluid pipeline to obtain a discrete topological region evolution equation;

[0118] Specifically, the initial pipeline geometry level set data can be imported into MATLAB. In MATLAB, use the numerical calculation function to construct the evolution equation for the topological evolution region. Assume that the initial value of the level set function φ has been defined, and an evolution equation needs to be constructed to describe the change of the level set function over time. The following form of evolution equation can be adopted: Among them, F is the evolution speed, which is related to the physical fields of the pipeline (such as pressure, velocity). It is assumed that the evolution speed F is proportional to the fluid velocity field u in the pipeline and can be expressed as: F = Cu, where C is a proportionality coefficient and its value is assumed to be 0.1. In MATLAB, the finite difference method is used to discretize the evolution equation. Assuming the time step is 0.01 seconds and the spatial step is 0.01 meters, the level set function values at each time step are obtained through numerical calculation to describe the topological evolution process of the pipeline. Finally, the discrete equation of the topological region evolution is obtained.

[0119] Step S43: Construct a topological evolution time advancement scheme for the fluid pipeline according to the discrete equation of the topological region evolution to obtain topological evolution time advancement data;

[0120] Specifically, the discrete equation of the topological region evolution can be imported into MATLAB. It is assumed that the evolution equation has been discretized by the finite difference method, and the time step Δt = 0.01 seconds and the spatial step Δx 2 = 0.01 meters are defined. In MATLAB, a time advancement scheme is constructed, and the evolution process of the topological structure is simulated by iteratively updating the value of the level set function φ. For example, assuming that the evolution process requires 100 time steps, the numerical calculation function of MATLAB is used to calculate the level set function values at each time step. Specifically, for each time step, the gradient of the level set function is calculated, the value of the level set function is updated according to the evolution speed F, and the level set function values at each time step are stored. In this way, the level set function values at each time step can be obtained to describe the topological evolution process of the pipeline. Finally, the topological evolution time advancement data is obtained.

[0121] Step S44: Develop a fluid interface boundary treatment scheme for the fluid pipeline based on the topological evolution time advancement data to obtain fluid-solid interface boundary treatment data;

[0122] Specifically, the topological evolution time advancement data can be imported into MATLAB. These data include the level set function values at each time step, reflecting the dynamic changes of the pipeline topological structure. In MATLAB, the sign change of the level set function is used to identify the interface between the fluid and the solid. It is assumed that the zero isosurface of the level set function φ represents the interface between the fluid and the solid, and the isosurface function of MATLAB is used to extract the zero isosurface. For each time step, the zero isosurface of the level set function is found and the interface information is saved. Next, according to the extracted interface information, the boundary conditions between the fluid and the solid are set. It is assumed that the no-slip boundary condition needs to be satisfied at the boundary between the fluid and the solid, that is, the fluid velocity is zero. In MATLAB, the no-slip boundary condition is set for the interface at each time step, and the boundary condition data is saved. Finally, the fluid-solid interface boundary treatment data is obtained.

[0123] Step S45: Plan a topological region boundary processing solution for the fluid pipeline based on the fluid-structure interface boundary processing data and the pipeline weight configuration data to obtain pipeline topological boundary processing data;

[0124] Specifically, the fluid-structure interface boundary processing data and the pipeline weight configuration data can be imported into MATLAB. The fluid-structure interface boundary processing data includes the interface position and boundary conditions at each time step, while the pipeline weight configuration data reflects the importance of different topological units. In MATLAB, combining these two types of data, plan the boundary processing solution for the topological region. For example, for units with higher weights, more stringent boundary conditions can be set to ensure their stability during the optimization process; while for units with lower weights, the boundary conditions can be appropriately relaxed to allow for more topological changes. Suppose the weight of a certain unit in the weight configuration data is 0.8, indicating that this unit is very important during the optimization process. Therefore, when processing the boundary, its boundary conditions can be set as fixed boundaries, not allowing any topological changes. For a unit with a weight of 0.2, it can be set as a free boundary, allowing adjustments during the optimization process. In this way, a suitable boundary processing solution can be formulated for each topological unit, and finally the pipeline topological boundary processing data is obtained.

[0125] Step S46: Construct a re-initialization solution for the pipeline topological boundary processing data to obtain interface re-initialization data, and apply topological unit constraints to the fluid pipeline according to the interface re-initialization data to obtain a basic model for pipeline topological optimization;

[0126] Specifically, the pipeline topological boundary processing data can be imported into MATLAB. This data includes the boundary processing solutions for each topological unit. In MATLAB, use the level set method to re-initialize this boundary processing data. The purpose of re-initialization is to ensure that the level set function always maintains good numerical properties and avoid numerical instability during the evolution process. For example, the Sussman re-initialization method can be used. This method adjusts the level set function by solving a specific partial differential equation to make it remain a distance function. Suppose the initial value of the level set function is φ 0 , after re-initialization by the Sussman method, a new level set function φ new is obtained. Next, according to the re-initialized interface data, apply constraints to the topological units of the fluid pipeline. For example, for units with fixed boundaries, rigid constraints can be applied to ensure their invariance during the optimization process; while for units with free boundaries, flexible constraints can be applied to allow them to be adjusted according to the optimization objective. Finally, the basic model for pipeline topological optimization obtained will contain the constraint information of all topological units.

[0127] Step S47: Solve the improved MMA algorithm for the pipeline topology optimization basic model to obtain the optimized pipeline topology data.

[0128] Specifically, for the detailed implementation process of this embodiment, please refer to the sub-steps of Step S47.

[0129] Through the initialization of the level set method and the construction of the topology evolution equation, the present invention can flexibly handle the topological structure changes of fluid pipelines. The level set method allows for the dynamic adjustment of the pipeline's geometry and topological connections during the optimization process, while the topology evolution time advancement scheme ensures the continuity and stability of the structure update. By adopting the fluid interface boundary treatment scheme and the topological region boundary treatment scheme, the fluid-solid interface and topological boundaries can be precisely managed. By solving the topology optimization basic model with the improved MMA algorithm, the efficiency and convergence of the optimization process can be significantly improved. By combining the level set method and the improved MMA algorithm, the quality of the optimized design of fluid pipelines can be significantly enhanced. Through the topology optimization process, the uncertainty and risk in the design process can be significantly reduced. Through interface treatment and boundary management, the risk of design failure caused by geometric discontinuity or numerical instability is reduced, further improving the economy and feasibility of the design.

[0130] Preferably, Step S47 includes the following steps:

[0131] Step S471: Construct a topology optimization sub-problem for the pipeline topology optimization basic model to obtain topology optimization sub-problem data;

[0132] Specifically, the pipeline topology optimization basic model can be imported into MATLAB. This model contains the constraint information and boundary conditions of the topology units. In MATLAB, use the optimization toolbox to construct the topology optimization sub-problem. Assume that the optimization goal is to maximize the stiffness of the pipeline while minimizing the material usage. The objective function of the optimization sub-problem can be defined as: Objective function = λ × material usage - μ × stiffness where λ and μ are weight coefficients, set to 0.5 and 0.5 respectively, to balance the importance of material usage and stiffness. Next, define the design variable as the density of each topology unit, and the constraint conditions include the minimum and maximum density values of the unit, as well as the volume constraint of the overall structure. For example, set the range of the unit density to 0.1 to 1.0, and the volume of the overall structure does not exceed 50% of the initial volume. Through the symbolic calculation function of MATLAB, combine the objective function and the constraint conditions into an optimization sub-problem. Finally, the obtained topology optimization sub-problem data will be the specific forms of the objective function, design variable, and constraint conditions.

[0133] Step S472: Set the convergence criterion of the improved MMA algorithm according to the topology optimization sub-problem data to obtain topology optimization convergence criterion data;

[0134] Specifically, the topological optimization sub-problem data can be imported into MATLAB. This data includes the objective function, design variables, and constraints. In MATLAB, the improved MMA algorithm in the optimization toolbox is used for optimization. The improved MMA algorithm is an efficient sequential approximation optimization method suitable for complex topological optimization problems. To ensure the convergence of the optimization process, appropriate convergence criteria need to be set. Assume the convergence criteria include that the change in the objective function is less than 10 ―6 and the change in the design variables is less than 10 ―4 . Through the optimization option setting function of MATLAB, these convergence criteria are defined. Specifically, the fmincon function is selected as the optimization solver, and its algorithm is set to sqp (sequential quadratic programming). Then, the tolerance of the objective function is set to 10 ―6 , and the tolerance of the design variables is set to 10 ―4 . These settings ensure that the improved MMA algorithm stops iterating when the change in the objective function is less than 10 ―6 or the change in the design variables is less than 10 ―4 . Finally, the obtained topological optimization convergence criterion data will include the specific values of these convergence criteria.

[0135] Step S473: Iteratively solve the topological optimization convergence criterion data to obtain the pipeline topology iterative calculation data;

[0136] Specifically, the topological optimization convergence criterion data can be imported into MATLAB. This data includes the objective function, design variables, constraints, and convergence criteria. In MATLAB, the fmincon function in the optimization toolbox is used for iterative solution. Assume the goal of the optimization problem is to maximize the stiffness of the pipeline while minimizing the material usage. The design variable is the density of each topological unit. The constraints include the range of the unit density and the volume constraint of the overall structure. During the iterative solution process, the fmincon function will gradually adjust the design variables according to the set convergence criteria (the change in the objective function is less than 10 ―6 , the change in the design variables is less than 10 ―4 ) to optimize the objective function. For example, in each iteration, the fmincon function will calculate the gradient of the objective function, update the design variables, and check whether the convergence criteria are met. If not, the iteration will continue; if so, the iteration will stop. Finally, the obtained pipeline topology iterative calculation data will include the objective function value, design variable value, and status information indicating whether the convergence criteria are met for each iteration.

[0137] Step S474: Identify the convergence based on the pipeline topology iterative calculation data to obtain the topological optimization calculation data;

[0138] Specifically, the pipeline topology iterative calculation data can be imported into MATLAB. These data include the objective function values, design variable values, and status information on whether the convergence criterion is met for each iteration. In MATLAB, a script is written to analyze these data to determine whether the optimization process has converged. For example, check whether the change in the objective function is less than 10 ―6 , and whether the change in the design variables is less than 10 ―4 . If in a certain iteration, the change in the objective function is 9×10 ―7 , and the change in the design variables is 8×10 ―5 , then it can be considered that the optimization process has converged. In MATLAB, this function can be implemented through logical judgment statements. Finally, the obtained topology optimization calculation data will include a clear result on whether the optimization process has converged, as well as the design variable values and objective function values at the time of convergence.

[0139] Step S475: Identify the iterative step size adjustment factor based on the topology optimization calculation data to obtain the topology optimization step size factor data, and update the topology structure optimization step size of the pipeline topology optimization basic model according to the topology optimization step size factor data to obtain the optimized pipeline topology data.

[0140] Specifically, the topology optimization calculation data can be imported into MATLAB. These data include a clear result on whether the optimization process has converged, as well as the design variable values and objective function values at the time of convergence. In MATLAB, analyze these data to identify the iterative step size adjustment factor. Assume that the optimization process has converged, but the step size needs to be further adjusted to ensure the stability of the optimization result. The initial value of the step size adjustment factor can be set to 0.5, indicating that in each iteration, the update step size of the design variables will be multiplied by this factor. By analyzing the change trend of the objective function, if the objective function changes slowly in several consecutive iterations, the step size adjustment factor can be appropriately reduced, for example, adjusted to 0.3; if the objective function changes rapidly, the step size adjustment factor can be appropriately increased, for example, adjusted to 0.7. Finally, the obtained topology optimization step size factor data will include the step size adjustment factor values for each design variable. Next, update the topology structure optimization step size of the pipeline topology optimization basic model according to the topology optimization step size factor data. In MATLAB, use the fmincon function in the optimization toolbox, combined with the updated step size adjustment factor, to re-perform the optimization iteration. For example, in each iteration, the update formula for the design variables can be expressed as: x new = x old + step size adjustment factor × Δx, where x old is the current design variable value, and Δx is the update amount of the design variables. In this way, the design variables are gradually updated until the optimization goal is met. Finally, the obtained optimized pipeline topology data will include the optimized design variable values and objective function values.

[0141] By constructing a topology optimization sub-problem and setting the convergence criterion of the improved MMA algorithm, the present invention can efficiently perform iterative solutions. Through the convergence identification of the topology optimization iterative calculation data, it can accurately judge whether the optimization process reaches the convergence condition. Through the identification of the iteration step adjustment factor and the dynamic update of the optimization step, the stability and accuracy of the optimization process can be further improved, ensuring the reliability of the optimization results. Through the convergence identification and dynamic optimization step adjustment, the optimization design quality of the fluid pipeline can be significantly improved. By dynamically adjusting the optimization strategy, the risk of design failure caused by the instability of the optimization process is reduced, and the economy and feasibility of the design are further improved.

[0142] Preferably, step S51 includes the following steps:

[0143] Step S51: Collect experimental measurement data of the optimized pipeline topology data to obtain pipeline physical measurement characteristic data, and compare the numerical simulation results according to the pipeline physical measurement characteristic data to obtain pipeline system difference data;

[0144] Specifically, the optimized pipeline topology data can be imported into MATLAB to generate a pipeline model for experiments. Then, a laser Doppler vibrometer is used to measure the vibration response of the pipeline under specific working conditions, and a pressure sensor is used to measure the pressure distribution inside the pipeline. Assume that the vibration response frequency range measured experimentally is from 0 to 500 Hz, and the measurement accuracy of the pressure sensor is 0.1%. The vibration response and pressure distribution data obtained from the experimental measurement are imported into MATLAB. Next, these experimental data are compared with the numerical simulation results. The numerical simulation results are obtained in ANSYS Fluent and include the vibration modes and pressure distribution of the pipeline. In MATLAB, the difference between the experimental data and the numerical simulation results is calculated. For example, the difference is quantified by calculating the root mean square error between the two. Finally, the obtained pipeline system difference data will include the error values of the vibration response and pressure distribution.

[0145] Step S52: Identify the error distribution characteristics of the pipeline system difference data to obtain pipeline spatial error distribution data;

[0146] Specifically, the pipeline system difference data can be imported into MATLAB. These data include the error values of vibration response and pressure distribution. In MATLAB, the statistical analysis function is used to process the error data. For example, calculate the mean, variance, and standard deviation of the errors to identify the distribution characteristics of the errors. Assuming that the error data follows a normal distribution, the confidence interval of the errors can be calculated through the normfit function in MATLAB. In addition, the histfit function in MATLAB can be used to plot the histogram of the errors and fit the normal distribution curve. Finally, the obtained pipeline spatial error distribution data will include the mean, variance, standard deviation, and distribution curve of the errors.

[0147] Step S53: Perform Kriging interpolation on the pipeline spatial error distribution data to obtain the pipeline grid error correction data;

[0148] Specifically, the pipeline spatial error distribution data can be imported into MATLAB. These data include statistical information such as the mean, variance, and standard deviation of the errors, as well as the spatial distribution of the errors. In MATLAB, the Kriging interpolation method is used to perform spatial interpolation on the error data. Assuming that the spatial distribution of the error data has a certain autocorrelation, the fitrgp function (Gaussian process regression) in MATLAB can be used to implement Kriging interpolation. Specifically, set the kernel function of the Gaussian process to the squared exponential kernel, and the noise variance to 0.01, indicating a low noise level of the error data. Through the fitrgp function, the error data is fitted to obtain the spatial distribution model of the errors. Then, this model is used to predict the errors at each point on the pipeline grid to obtain the pipeline grid error correction data.

[0149] Step S54: Perform Bayesian parameter estimation on the pipeline grid error correction data to obtain the pipeline structure probability distribution data, and perform maximum likelihood estimation based on the pipeline structure probability distribution data to obtain the topological parameter iterative correction data;

[0150] Specifically, the pipeline grid error correction data can be imported into MATLAB. These data include the error correction values of each grid point. In MATLAB, the Bayesian parameter estimation method is used to analyze the error correction data to obtain the probability distribution data of the pipeline structure. Assuming that the error correction values follow a normal distribution, the fitdist function in MATLAB can be used to fit the error correction data to obtain the mean and variance of the error correction values. Specifically, set the fitting distribution type to normal distribution, and through the fitdist function, obtain the probability distribution model of the error correction values. Next, maximum likelihood estimation is performed based on the pipeline structure probability distribution data. In MATLAB, the mle function is used to perform maximum likelihood estimation on the probability distribution model of the error correction values to obtain the iterative correction data of the topological parameters. Assuming that the mean of the probability distribution model of the error correction values is 0.05 and the variance is 0.002, the corrected values of the topological parameters are obtained through maximum likelihood estimation.

[0151] Step S55: Update the pipeline topology geometric parameter set according to the iterative correction data of the topological parameters to obtain the optimized pipeline system parameter set.

[0152] Specifically, the iterative correction data of the topological parameters can be imported into MATLAB. These data include the corrected topological parameter values obtained through maximum likelihood estimation, such as the corrected values of parameters such as pipeline wall thickness and cross-sectional shape. In MATLAB, these correction data are used to update the pipeline topology geometric parameter set. Assuming that the wall thickness in the original pipeline topology geometric parameter set is 0.1 m, the corrected value obtained through maximum likelihood estimation is 0.05 m, the cross-sectional radius is 0.5 m, and the corrected value is 0.48 m. In MATLAB, through the update operation, the wall thickness is adjusted from 0.1 m to 0.05 m, and the cross-sectional radius is adjusted from 0.5 m to 0.48 m. Specifically, the matrix operation function of MATLAB can be used to apply the corrected values to the original parameter set to generate a new pipeline topology geometric parameter set. Finally, the obtained optimized pipeline system parameter set will include updated parameters such as wall thickness and cross-sectional shape.

[0153] Through experimental measurement data acquisition and comparison with numerical simulation results, the present invention can directly evaluate the differences between the optimized design and the actual working conditions. By adopting the error distribution feature recognition method, the differential data of the pipeline system can be accurately analyzed. Through Kriging interpolation and Bayesian parameter estimation, the pipeline grid error correction data can be efficiently generated, and the iterative correction data of the topological parameters can be obtained based on the maximum likelihood estimation. This can not only quickly generate the error correction scheme, but also dynamically update and optimize the pipeline topology geometric parameter set to further improve the design accuracy. The error correction mechanism based on experimental data can effectively cope with the uncertain factors in the actual working conditions. By dynamically updating and optimizing the design parameters, the design results can better adapt to the changes in the actual operating environment. Through experimental verification and error correction, the overall design quality of the fluid pipeline can be significantly improved.

[0154] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application document are intended to be encompassed within the present invention.

[0155] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. A fluid pipeline topology optimization design method based on a parameter simulation model, characterized in that: The following steps are involved: Step S1: parametrically modeling the fluid pipeline to obtain a pipeline topology geometry parameter set; and obtaining pipeline topology description data according to topological feature recognition of the pipeline topology geometry parameter set; Step S2: construct a multi-physics field equation group according to the pipeline topology description data to obtain a pipeline coupling field basic equation group; perform characteristic decomposition on the pipeline coupling field basic equation group to obtain a pipeline decoupling equation group; perform iterative solution on the pipeline decoupling equation group to obtain a pipeline multi-physics field coupling solution; Step S3: Evaluate the sensitivity coefficient according to the pipeline multi-physics field coupling solution to obtain the pipeline physical field sensitivity; perform time domain integral response analysis on the pipeline physical field sensitivity to obtain the pipeline physical field contribution; The topological weight coefficient of the fluid pipeline is updated according to the contribution of the pipeline physical field to obtain pipeline weight configuration data; Step S4: performing level set modeling on the pipeline topology description data based on the pipeline weight configuration data to obtain a pipeline topology optimization basic model; performing an improved MMA algorithm to solve the pipeline topology optimization basic model to obtain optimized pipeline topology data; Step S5: Perform experimental data comparison analysis on the optimized pipeline topology data to obtain pipeline spatial error distribution data; perform spatial interpolation on the pipeline spatial error distribution data to obtain pipeline error correction data; update the pipeline topology geometric parameter set according to the pipeline error correction data to obtain the optimized pipeline topology geometric parameter set.

2. The fluid pipeline topology optimization design method based on parameter simulation model according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: Collecting geometric contour surface features of the fluid pipeline to obtain pipeline contour feature data; Step S12: Arranging control points of the fluid pipeline according to the pipeline profile feature data to obtain initial control point distribution data; Step S13: performing pipeline surface curvature node weight distribution on the fluid pipeline based on the initial control point distribution data to obtain a pipeline surface curvature weight coefficient; Step S14: performing control point spline curve fitting on the fluid pipeline according to the pipeline surface curvature weight coefficient to obtain pipeline cross-sectional profile data; Step S15: performing axial scanning modeling on the fluid pipeline based on the pipeline cross-sectional profile data to obtain three-dimensional pipeline skeleton data; Step S16: parameterizing the wall thickness of the three-dimensional pipeline skeleton data to obtain a pipeline solid model, and extracting characteristic dimensions of the pipeline solid model to obtain a pipeline topological geometric parameter set; Step S17: performing topological feature recognition on the fluid pipeline according to the pipeline topological geometric parameter set to obtain pipeline topological description data.

3. The fluid pipeline topology optimization design method based on parameter simulation model according to claim 2 is characterized in that: Step S17 includes the following steps: Step S171: performing grid density distribution statistics on the fluid pipeline based on the pipeline topology geometry parameter set to obtain pipeline grid density distribution data; Step S172: Generate boundary layer grids according to pipeline grid density distribution data to obtain pipeline boundary layer grid data; Step S173: performing volume mesh division on the fluid pipeline based on the pipeline boundary layer mesh data and the pipeline topology geometry parameter set to obtain pipeline volume mesh data; Step S174: performing mesh quality evaluation on the pipeline body mesh data to obtain a pipeline mesh quality index, and adjusting the mesh nodes of the fluid pipeline according to the pipeline mesh quality index to obtain optimized fluid pipeline mesh data; Step S175: performing topological connectivity identification on the optimized fluid pipeline grid data to obtain pipeline grid topological relationship data; Step S176: performing feature area recognition on the pipeline grid topology relationship data to obtain pipeline topology description data.

4. The fluid pipeline topology optimization design method based on parameter simulation model according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: performing physical field boundary condition identification on the pipeline topology description data to obtain pipeline boundary condition data, and setting fluid physical property parameters for the fluid pipeline according to the pipeline boundary condition data to obtain pipeline fluid characteristic data; Step S22: performing thermodynamic parameter evaluation on the fluid pipeline based on the pipeline fluid characteristic data to obtain the pipeline fluid thermodynamic characteristic data, and performing fractional-order derivative discretization on the pipeline fluid thermodynamic characteristic data to obtain pipeline fluid fractional-order operator data; Step S23: constructing momentum equations according to the fractional-order operator data of the pipeline fluid to obtain momentum conservation equations for the viscous fluid, and constructing energy equations for the fluid pipeline based on the momentum conservation equations for the viscous fluid to obtain energy conservation equations for fluid-solid coupling; Step S24: constructing a mass equation according to the viscous fluid momentum conservation equations and the fluid-solid coupling energy conservation equations to obtain a continuous medium mass conservation equations, and identifying coupling terms of the continuous medium mass conservation equations to obtain pipeline multi-field coupling term data; Step S25: constructing a basic equation group of pipeline coupling field for the fluid pipeline based on the pipeline multi-field coupling term data to obtain a basic equation group of pipeline coupling field; Step S26: Perform characteristic decomposition on the pipeline coupling field basic equation group to obtain the pipeline decoupling equation group, and iteratively solve the pipeline decoupling equation group to obtain the pipeline multi-physics field coupling solution.

5. The method for fluid pipeline topology optimization design based on parameter simulation model according to claim 4 is characterized in that: Step S26 includes the following steps: Step S261: performing Jacobian eigenvalue evaluation on the basic equations of the pipeline coupling field to obtain eigenvalues ​​of the pipeline characteristic equation; Step S262: extracting characteristic vectors from characteristic values ​​of pipeline characteristic equations to obtain orthogonal characteristic vectors of pipeline equations; Step S263: performing equation decoupling transformation on the pipeline coupling field basic equations based on the orthogonal eigenvectors of the pipeline equations to obtain the pipeline decoupling equations; Step S264: numerically discretizing the pipeline decoupling equations to obtain pipeline fluid discrete equations; Step S265: configuring solver parameters according to the pipeline fluid discrete equation group to obtain fluid solver configuration data; Step S266: Iteratively solve the pipeline fluid discrete equations based on the fluid solver configuration data to obtain a pipeline multi-physics field coupling solution.

6. The method for fluid pipeline topology optimization design based on parameter simulation model according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: extracting state variables from the pipeline multi-physics field coupling solution to obtain pipeline coupling field variable distribution data, and constructing an objective function based on the pipeline coupling field variable distribution data to obtain a pipeline topology optimization objective function; Step S32: performing topological structure design variable identification on the fluid pipeline based on the pipeline topology optimization objective function to obtain topological structure design variable data, and performing parameter disturbance identification on the topological structure design variable data to obtain pipeline topological variable disturbance data; Step S33: performing adjoint gradient evaluation according to the pipeline topology variable disturbance data to obtain the pipeline topology objective function gradient; Step S34: deriving the parameter space sensitivity coefficient of the fluid pipeline based on the pipeline topology objective function gradient to obtain the topology space initial sensitivity coefficient; Step S35: regularizing the initial sensitivity coefficient of the topological space to obtain the standard topological field sensitivity, and constructing the response surface according to the standard topological field sensitivity to obtain the pipeline physical field sensitivity; Step S36: Perform time domain integral response analysis on the pipeline physical field sensitivity to obtain the pipeline physical field contribution, and update the topological weight coefficient of the fluid pipeline according to the pipeline physical field contribution to obtain pipeline weight configuration data.

7. The method for fluid pipeline topology optimization design based on parameter simulation model according to claim 6 is characterized in that: Step S36 includes the following steps: Step S361: performing time domain decomposition on the pipeline physical field sensitivity to obtain pipeline sensitivity time domain characteristic data; Step S362: performing spectrum recognition on the pipeline sensitivity time domain characteristic data to obtain pipeline sensitivity frequency domain response data; Step S363: Perform Fourier integration on the pipeline sensitivity frequency domain response data to obtain pipeline sensitivity cumulative response data; Step S364: extracting multi-field distribution features from the pipeline sensitivity cumulative response data to obtain sensitivity energy response feature data; Step S365: Evaluate the contribution according to the sensitivity energy response characteristic data to obtain the pipeline physical field contribution; Step S366: constructing a topological unit weight function for the fluid pipeline based on the pipeline physical field contribution, and obtaining a pipeline topology weight distribution scheme; Step S367: performing loose adjustment on the pipeline topology weight distribution scheme to obtain pipeline weight configuration data.

8. The method for fluid pipeline topology optimization design based on parameter simulation model according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: Initialize the level set according to the pipeline topology description data to obtain the initial level set data of the pipeline geometry; Step S42: constructing a topological evolution regional evolution equation for the fluid pipeline based on the initial level set data of the pipeline geometry to obtain a topological regional evolution discrete equation; Step S43: constructing a topological evolution time advancement scheme for the fluid pipeline according to the topological region evolution discrete equation to obtain topological evolution time advancement data; Step S44: formulating a fluid interface boundary processing scheme for the fluid pipeline based on the topological evolution time advancement data to obtain fluid-solid interface boundary processing data; Step S45: planning a topological region boundary processing scheme for the fluid pipeline based on the fluid-solid interface boundary processing data and the pipeline weight configuration data to obtain pipeline topological boundary processing data; Step S46: constructing a reinitialization scheme for the pipeline topology boundary processing data to obtain interface reinitialization data, and applying topological unit constraints to the fluid pipeline according to the interface reinitialization data to obtain a pipeline topology optimization basic model; Step S47: The improved MMA algorithm is used to solve the pipeline topology optimization basic model to obtain optimized pipeline topology data.

9. The method for fluid pipeline topology optimization design based on parameter simulation model according to claim 8, characterized in that: Step S47 includes the following steps: Step S471: constructing a topology optimization sub-problem for the pipeline topology optimization basic model to obtain topology optimization sub-problem data; Step S472: setting the convergence criterion of the improved MMA algorithm according to the topology optimization sub-problem data to obtain the topology optimization convergence criterion data; Step S473: iteratively solving the topology optimization convergence criterion data to obtain pipeline topology iterative calculation data; Step S474: performing convergence identification according to the pipeline topology iterative calculation data to obtain topology optimization calculation data; Step S475: Identify the iterative step adjustment factor according to the topology optimization calculation data to obtain the topology optimization step factor data, and update the topology structure optimization step of the pipeline topology optimization basic model according to the topology optimization step factor data to obtain the optimized pipeline topology data.

10. The method for fluid pipeline topology optimization design based on parameter simulation model according to claim 1, characterized in that: Step S51 includes the following steps: Step S51: performing experimental measurement data collection on the optimized pipeline topology data to obtain pipeline physical measurement characteristic data, and performing numerical simulation result comparison based on the pipeline physical measurement characteristic data to obtain pipeline system difference data; Step S52: performing error distribution feature recognition on the pipeline system difference data to obtain pipeline space error distribution data; Step S53: performing Kriging interpolation on the pipeline spatial error distribution data to obtain pipeline grid error correction data; Step S54: performing Bayesian parameter estimation on the pipeline grid error correction data to obtain pipeline structure probability distribution data, and performing maximum likelihood estimation based on the pipeline structure probability distribution data to obtain topology parameter iterative correction data; Step S55: updating the pipeline topology geometry parameter set according to the topology parameter iteration correction data to obtain an optimized pipeline system parameter set.

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