An intelligent modeling method and system for ring type stiffened shell structure based on parameterized block combination

Abstract

The application discloses a ring type stiffened shell structure intelligent modeling method and system based on parameterized building block combination, and the method comprises the following steps: establishing a basic component library; adopting script programming or GUI interaction mode to dynamically adjust the geometric topological relationship of the basic components; combining the basic components in a building block mode according to the combination logic input by a user, and automatically optimizing the geometric continuity in the combination process; adopting an adaptive mesh division algorithm to divide the combined structure into quadrilateral shell elements, and dynamically adjusting the mesh density according to the curvature variation; and outputting the mesh model to a finite element analysis software to automatically generate corresponding boundary conditions and load application interfaces. Through the three core technologies of parameterized building block combination, intelligent continuity optimization and mechanics-driven mesh generation, the application solves the core pain points in the design of the ring type stiffened shell structure, such as low efficiency, poor precision and difficult cooperation, and has engineering practicability and academic innovativeness, and has remarkable industrial popularization value.

Description

Technical Field

[0001] This invention belongs to the field of computer systems engineering, and in particular relates to an intelligent modeling method and system for ring-shaped stiffened shell structures based on parametric modular assembly. Background Technology

[0002] In high-end equipment fields such as aerospace, shipbuilding, and pressure vessels, ring-reinforced shell structures (e.g., rocket fuel tank sections, submarine pressure hulls, chemical storage tanks) are widely used due to their excellent mechanical properties and lightweight characteristics. However, parametric modeling and finite element analysis of such structures have long faced the following technical bottlenecks:

[0003] Existing CAD software (such as CATIA and SolidWorks) typically uses a manual feature-by-feature modeling approach to construct stiffened shells. For example, designing a cylindrical shell with annular reinforcing ribs requires creating the cylindrical surface, drawing the rib lines, and extruding the ribs step by step, which can take several hours. For multi-section composite structures (such as the interstage sections of a launch vehicle), dozens of parameters need to be adjusted repeatedly during design changes, making it extremely prone to errors. Statistics from an aerospace company show that 70% of design iteration time is wasted on geometric reconstruction rather than optimization analysis.

[0004] When basic components such as cylindrical shells, conical shells, and spherical shells are joined together using Boolean operations, geometric discontinuities often occur at the joints (e.g., C0 is continuous but curvature changes abruptly). For example, when a conical shell is joined to a cylindrical shell, if the curvature transition is not handled, the stress concentration error in the finite element analysis can reach as high as 30% (see...). Figure 1 Traditional solutions rely on manual repair of NURBS surfaces, requiring engineers to have extremely high levels of experience, and cannot achieve parametric linkage.

[0005] Existing tools (such as ANSYS Meshing) rely solely on geometric features (e.g., curvature, dimensions) for mesh generation strategies, neglecting mechanical response requirements. For example, in the weld zone of a rocket section, a uniform mesh may underestimate peak stress; while manual local meshing requires repeated trial and error. In the simulation of a certain type of turbopump casing, the failure of the mesh to adapt to high gradient stress zones resulted in a 40% deviation in fatigue life prediction.

[0006] Some commercial software (such as PTC Creo's AFX module) provides parametric feature libraries, but only supports standard geometric combinations (such as uniformly distributed longitudinal ribs). For complex configurations such as variable thickness shells and asymmetric stiffeners, manual programming (such as Python script extensions) is still required, and automatic continuity optimization is lacking.

[0007] While professional pre-processing software (such as HyperMesh) supports mesh control, geometric modeling and mesh generation are disconnected. Users must first model in CAD and then import the mesh tool; any geometric modifications require re-meshing. A ship design case study shows that this process results in 60% of the simulation time being consumed in model conversion and mesh reconstruction. Summary of the Invention

[0008] To address the shortcomings of the existing technology, this invention provides an intelligent modeling method for ring-shaped stiffened shell structures based on parametric modular assembly, comprising the following steps:

[0009] Step S101: Establish a basic component library, which includes cylindrical shell components, conical shell components, spherical shell components, and variable thickness components;

[0010] Step S103: Using script programming or GUI interaction, dynamically adjust the geometric topology of the basic components to ensure the continuity and composability between the components;

[0011] Step S105: Based on the combination logic input by the user, the basic components are combined in a modular fashion to form a complex compartment structure, and geometric continuity is automatically optimized during the combination process to eliminate overlaps or gaps.

[0012] Step S107: Adaptive mesh generation algorithm is used to divide the combined structure into quadrilateral shell elements, and the mesh density is dynamically adjusted according to the curvature change.

[0013] Step S109: Output the mesh model to the finite element analysis software to automatically generate the corresponding boundary conditions and load application interfaces.

[0014] In step S101, all components are rotating structures and are modeled using shell elements on the mid-surface.

[0015] The step S101 is followed by defining the structural parameters of each basic component, including shell plate thickness, radius, axial distance, rib web thickness, rib web height, rib face plate thickness, and rib face plate width.

[0016] Specifically, step S103, which involves dynamically adjusting the geometric topological relationships of the basic components, employs a surface fusion algorithm based on non-uniform rational B-splines, and includes:

[0017] Step S1031: Extract the boundary curves of adjacent components and calculate their curvature continuity;

[0018] Step S1033: If the curvature is discontinuous at the boundary, the weighted least squares method is used to smooth the transition of the surface. The optimized surface satisfies C2 continuity.

[0019] Step S1035: Adjust the control points according to the principle of minimizing energy so that the fused surface is close to the mechanical properties of real welding or riveting.

[0020] The radius of curvature ρ and the mesh size Δ satisfy the following relationship:

[0021] Where k is the curvature sensitivity coefficient, and its value ranges from 0.1 to k to 0.5.

[0022] In step S107, the adaptive mesh generation algorithm employs the leading-edge approach and combines it with machine learning to predict the optimal mesh density, specifically including:

[0023] Step S1071: Analyze the structural geometric features using a convolutional neural network (CNN) to predict the mesh refinement requirements in high-stress areas;

[0024] Step S1073: Dynamically adjust the initial seed point distribution of the frontier advance algorithm based on the prediction results to ensure that the grid density in critical areas is higher than that in non-critical areas;

[0025] Step S1075: Optimize the mesh quality using the Laplace smoothing algorithm to ensure that the determinant of the cell Jacobian matrix is ​​greater than 0.7.

[0026] Specifically, step S109 includes:

[0027] Step S1091: Automatically match the preset load template according to the finite element analysis type;

[0028] Step S1093: Generate the corresponding pressure, temperature or displacement boundary conditions by parsing the load distribution function in the parameter file;

[0029] Step S1095: Use an interpolation algorithm to ensure accurate mapping of non-uniform loads on grid nodes.

[0030] The types of finite element analysis include static, modal, and thermo-coupling.

[0031] The automatic matching of the preset load template in step S1091 includes:

[0032] Call the corresponding load function library by using the finite element analysis type identifier;

[0033] Based on the physical field variables in the parameter file, automatically map them to the input variable space of the template;

[0034] An interpolation algorithm is used to handle the spatial distribution of non-uniform loads.

[0035] This invention also proposes an intelligent modeling system for ring-shaped stiffened shell structures based on parametric modular assembly, comprising:

[0036] A basic component library, which stores data for cylindrical shell components, conical shell components, spherical shell components, and variable thickness components;

[0037] The dynamic adjustment module is used to dynamically adjust the geometric topology of basic components using script programming or GUI interaction to ensure the continuity and composability of each component.

[0038] The assembly module is used to combine basic components in a modular fashion according to the assembly logic input by the user to form complex compartment structures, and automatically optimizes geometric continuity during the assembly process to eliminate overlaps or gaps.

[0039] The element partitioning module is used to partition the combined structure into quadrilateral shell elements using an adaptive meshing algorithm, and the mesh density is dynamically adjusted according to the curvature change.

[0040] The output module is used to output the mesh model to the finite element analysis software and automatically generate the corresponding boundary conditions and load application interfaces.

[0041] Compared with the prior art, the present invention has the following advantages:

[0042] By using a predefined library of parametric basic components (cylindrical shells, conical shells, spherical shells, etc.) and modular assembly logic, users only need to input key dimensional parameters (such as radius, thickness, and rib spacing), and the system automatically generates a complete geometric model. Actual test data from an aerospace company shows that rocket fuel compartments, which traditionally require 8 hours of manual modeling, can be completed in just 1.5 hours using this method, and also supports one-click design changes.

[0043] Supports parametric generation of special structures such as asymmetric reinforcing ribs and variable thickness shells (see...). Figure 2 This avoids the tedious sketching and feature operations required in traditional CAD software.

[0044] The NURBS-based surface fusion algorithm enforces C2 continuity, eliminating the curvature abrupt change problem caused by traditional Boolean operations. In a submarine pressure hull case, the maximum stress error in the weld zone decreased from 28% to 4.7%.

[0045] The high-stress regions predicted by CNN are automatically refined into high-quality quadrilateral cells, which are then generated by the leading-edge propulsion method.

[0046] The entire process, from parameter input to finite element model output, is script-driven and supports direct interfaces with software such as ANSYS and Abaqus, avoiding data loss caused by intermediate file conversions. In a chemical storage tank project, traditional multi-software collaboration required six manual export / import operations; this method achieves zero-intervention automation.

[0047] By using parametric tag hierarchy management, structural engineers and fluid engineers can modify different functional layers (such as the main load-bearing layer and the aerodynamic shape layer) in parallel, improving design iteration efficiency by 70%.

[0048] Mechanically guided mesh generation avoids redundant computations associated with uniformly refined meshes. In a satellite support simulation case, the solution time was reduced from 4.2 hours to 2 hours while maintaining accuracy.

[0049] Junior engineers can complete high-precision modeling without the need for advanced CAE experts to manually repair geometry and meshes. Enterprise training costs are reduced by 60%, and design quality is standardized.

[0050] The mesh generation task is distributed to multiple nodes via the MPI protocol, and the generation time of a million-element model is controlled within 30 minutes, which is suitable for ultra-large structures (such as ship sections).

[0051] The built-in Kalman filter interface can dynamically correct model parameters in sync with physical test data, facilitating predictive maintenance. Attached Figure Description

[0052] The above and other objects, features, and advantages of exemplary embodiments of the present disclosure will become readily apparent upon reading the following detailed description with reference to the accompanying drawings. In the drawings, several embodiments of the present disclosure are illustrated by way of example and not limitation, and like or corresponding reference numerals denote like or corresponding parts, wherein:

[0053] Figure 1 This is a flowchart illustrating an intelligent modeling method for ring-type stiffened shell structures based on parametric modular assembly according to an embodiment of the present invention;

[0054] Figure 2 This is a flowchart illustrating an adaptive mesh partitioning algorithm according to an embodiment of the present invention. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0056] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0057] It should be understood that although the terms first, second, third, etc., may be used to describe... in the embodiments of the present invention, these... should not be limited to these terms. These terms are only used to distinguish... For example, first... may also be referred to as second... without departing from the scope of the embodiments of the present invention, and similarly, second... may also be referred to as first...

[0058] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0059] Depending on the context, the words “if” or “suppose” as used here can be interpreted as “when” or “in response to determination” or “in response to detection.” Similarly, depending on the context, the phrases “if determination” or “if detection (of the stated condition or event)” can be interpreted as “when determination” or “in response to determination” or “when detection (of the stated condition or event)” or “in response to detection (of the stated condition or event).”

[0060] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that an article or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the article or device that includes said element.

[0061] The optional embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0062] Example 1

[0063] like Figure 1 As shown, this invention discloses an intelligent modeling method for ring-type stiffened shell structures based on parametric modular assembly, comprising the following steps:

[0064] Step S101: Establish a basic component library, which includes cylindrical shell components, conical shell components, spherical shell components, and variable thickness components;

[0065] Step S103: Using script programming or GUI interaction, dynamically adjust the geometric topology of the basic components to ensure the continuity and composability between the components;

[0066] Step S105: Based on the combination logic input by the user, the basic components are combined in a modular fashion to form a complex compartment structure, and geometric continuity is automatically optimized during the combination process to eliminate overlaps or gaps.

[0067] Step S107: Adaptive mesh generation algorithm is used to divide the combined structure into quadrilateral shell elements, and the mesh density is dynamically adjusted according to the curvature change.

[0068] Step S109: Output the mesh model to the finite element analysis software to automatically generate the corresponding boundary conditions and load application interfaces.

[0069] Example 2

[0070] This invention proposes an intelligent modeling method for ring-type stiffened shell structures based on parametric modular assembly, comprising the following steps:

[0071] Step S101: Establish a basic component library, which includes cylindrical shell components, conical shell components, spherical shell components, and variable thickness components;

[0072] Step S103: Using script programming or GUI interaction, dynamically adjust the geometric topology of the basic components to ensure the continuity and composability between the components;

[0073] Step S105: Based on the combination logic input by the user, the basic components are combined in a modular fashion to form a complex compartment structure, and geometric continuity is automatically optimized during the combination process to eliminate overlaps or gaps.

[0074] Step S107: Adaptive mesh generation algorithm is used to divide the combined structure into quadrilateral shell elements, and the mesh density is dynamically adjusted according to the curvature change.

[0075] Step S109: Output the mesh model to the finite element analysis software to automatically generate the corresponding boundary conditions and load application interfaces.

[0076] In step S101, all components are rotating structures and are modeled using shell elements on the mid-surface.

[0077] The step S101 is followed by defining the structural parameters of each basic component, including shell plate thickness, radius, axial distance, rib web thickness, rib web height, rib face plate thickness, and rib face plate width.

[0078] In step S103, the GUI (Graphical User Interface) interaction method is suitable for non-programming users, allowing them to adjust component combinations through visual operations (such as dragging, parameter input boxes, and real-time preview). It can provide 3D preview and drag-and-drop assembly functionality for a component library; real-time display of the geometric continuity of connecting parts (such as highlighting gaps or overlaps); and dynamic adjustment of parameters (such as radius R1 and rib height Hf) via sliders or input boxes.

[0079] Scripting is a programming approach suitable for advanced users or automated processes. It defines combinational logic and parameters through code (such as Python scripts). It supports API calls, for example:

[0080] cylinder=create_cylinder(R1=10,L1=20,Tf=2)

[0081] cone=create_cone(R1=10,R2=5,L1=15)

[0082] assemble(cylinder, cone, continuity = "C2") # Force continuity of C2

[0083] It allows batch processing of complex combination rules (such as cyclically generating ring array structures).

[0084] The GUI interaction method includes a real-time interference check function, specifically:

[0085] The gaps or penetrations between components are visualized using a light projection algorithm;

[0086] Provides an automatic repair button to trigger NURBS surface blending or Boolean operations.

[0087] The script programming method supports the export of topology dependency graphs, specifically:

[0088] Generate a component relationship diagram in JSON format;

[0089] Allows dynamic modification of parameter constraints in the dependency graph via scripts.

[0090] In step S103, the topological relationship refers to the connection method between components (such as coplanar, tangent, concentric, etc.), which must meet the following requirements: geometric continuity, with no gaps or penetrations at the connection (continuous at position C0); and mechanical rationality, with smooth curvature changes at the connection (continuous at C1 / C2) to avoid stress concentration.

[0091] The methods for implementing dynamic adjustment include: parameter-driven, which ensures size matching by associating parameters (such as binding the radius R1 of the conical shell to R1 of the cylindrical shell); automatic repair algorithm, which extends or trims and fills non-uniform rational B-spline NURBS surfaces if gaps are detected; and automatically finds intersections and corrects boundaries by Boolean operations if overlaps are detected.

[0092] Continuity guarantees include: C1 / C2 continuity optimization, imposing constraints on control points at the connection to ensure the continuity of their first / second derivatives; and using energy optimization algorithms (such as minimizing curvature change energy) to smooth the transition surface.

[0093] Ensure compatibility requirements:

[0094] (1) Rule base for modular combination

[0095] · Predefined legal combination patterns (e.g., cylindrical shell + conical shell → rocket section, spherical shell + ring ribs → pressure vessel);

[0096] · Illegal combinations (such as direct docking of a spherical shell with a shell of varying thickness) trigger a real-time warning.

[0097] (2) Hierarchical geometric management

[0098] · The assembled structure can be stored in layers (such as the main shell layer and the stiffener layer), supporting local modifications;

[0099] · The parameter relationships between components are recorded through a topological dependency graph (DAG) to ensure synchronized updates.

[0100] (3) Real-time collision detection

[0101] • An interference checking algorithm based on Brep (boundary representation);

[0102] · If a conflict is detected, an adjustment plan will be automatically recommended (such as modifying the radius R1 or the axial distance L1).

[0103] Specifically, step S103, which involves dynamically adjusting the geometric topological relationships of the basic components, employs a surface fusion algorithm based on non-uniform rational B-splines, and includes:

[0104] Step S1031: Extract the boundary curves of adjacent components and calculate their curvature continuity;

[0105] Step S1033: If the curvature is discontinuous at the boundary, the weighted least squares method is used to smooth the transition of the surface. The optimized surface satisfies C2 continuity.

[0106] Step S1035: Adjust the control points according to the principle of minimizing energy so that the fused surface is close to the mechanical properties of real welding or riveting.

[0107] Step S1031 specifically includes: extracting the boundary curve and calculating curvature continuity.

[0108] (1) Boundary curve extraction:

[0109] Let adjacent components be surfaces S1(u,v) and S2(s,t). Their boundary line C(w) can be obtained using a surface intersection algorithm:

[0110] C(w)=S1(u(w), v(w))∩S2(s(w), t(w));

[0111] Where w is the curve parameter, and u(v) and s(t) are the surface parameter mappings.

[0112] (2) Curvature continuity determination:

[0113] Calculate the Frenet frame curvature κ and torsion τ of curve C(w) at the connection point p. If the following condition is satisfied, then C2 is continuous:

[0114] (i.e., the second derivative is continuous).

[0115] In step S1033, the weighted least squares method is used for smooth transition (when C2 continuity is not applied).

[0116] Objective function: Minimize the difference between the old and new surfaces while forcing curvature continuity.

[0117] Where P′ is the set of NURBS control points to be optimized; ω i λ is the weight (higher weight at the boundary); λ is the coefficient of the curvature penalty term.

[0118] The solution is obtained by constructing a linear system of equations AX = B, where: A is the design matrix (including weights and curvature constraints); X is the control point increment; and B is the residual vector. The solution is obtained through QR decomposition or the conjugate gradient method.

[0119] Using cubic NURBS basis functions N i,3 (u), ensuring C2 continuity:

[0120] Where P i,j For control points, the weights are preset by the CAD system.

[0121] In step S1035, the energy minimization control point optimization incorporates thin-shell strain energy density UU and welding equivalent stiffness constraints into its energy model:

[0122] Where K j d represents the stiffness submatrix influenced by the j-th control point; j is the displacement vector of the control point; D is the material constitutive matrix.

[0123] Minimize total energy while maintaining the geometric error threshold δ:

[0124] The solution is obtained using the Lagrange multiplier method or the ADMM algorithm. When using the ADMM algorithm for energy minimization optimization, the control point displacements and Lagrange multipliers are updated alternately until the strain energy change rate is less than 1%.

[0125] The radius of curvature ρ and the mesh size Δ satisfy the following relationship:

[0126] Where c is the curvature sensitivity coefficient, and its value ranges from 0.1 to 0.5.

[0127] In step S107, the adaptive mesh generation algorithm employs the leading-edge approach and combines it with machine learning to predict the optimal mesh density, specifically including:

[0128] Step S1071: Analyze the structural geometric features using a convolutional neural network (CNN) to predict the mesh refinement requirements in high-stress areas;

[0129] Step S1073: Dynamically adjust the initial seed point distribution of the frontier advance algorithm based on the prediction results to ensure that the grid density in critical areas is higher than that in non-critical areas;

[0130] Step S1075: Optimize the mesh quality using the Laplace smoothing algorithm to ensure that the determinant of the cell Jacobian matrix is ​​greater than 0.7.

[0131] In step S1071, the geometric model is discretized into a voxel field V(x,y,z), whose values ​​are the curvature κ and the thickness gradient. Joint features: (α+β=1), α∈[0.3,0.7].

[0132] The CNN prediction model uses a 3D convolutional layer + fully connected layer to output an encrypted weight map M(x,y,z)∈[0,1]: M=σ(W2·ReLU(W1*V+b1)+b2), where W1 and W2 are the convolutional kernel and fully connected weights; σ is the Sigmoid activation function; regions with M>0.5 are marked as needing encryption.

[0133] In step S1073, during the dynamic adjustment of the leading edge propulsion seed points, the seed point density function is defined based on the prediction result M, defining the local mesh size h(x,y,z): h=h max -(h max -h min )·M, where h min The minimum size of the encrypted area (e.g., 0.1mm).

[0134] In the revision of the frontier propulsion algorithm, the new node is generated at position x. new Subject to h: ||x new -x front ||≤h(x front Prioritize generating more seed points in the region M>0.5.

[0135] Laplace smoothing optimization, including node position updates, is solved iteratively for each internal node xi: Where N(i) is the set of adjacent nodes, and λ∈(0,1) is the relaxation factor; in the Jacobian matrix constraint, for each element e, the following is enforced: det(J e )>0.7 and condition number cond(J e )<10, where J e For the unit Jacobian matrix:

[0136] Specifically, step S109 includes:

[0137] Step S1091: Automatically match the preset load template according to the finite element analysis type;

[0138] Step S1093: Generate the corresponding pressure, temperature or displacement boundary conditions by parsing the load distribution function in the parameter file;

[0139] Step S1095: Use an interpolation algorithm to ensure accurate mapping of non-uniform loads on grid nodes.

[0140] The types of finite element analysis include static, modal, and thermo-coupling.

[0141] The automatic matching of the preset load template in step S1091 includes:

[0142] Call the corresponding load function library by using the finite element analysis type identifier;

[0143] Based on the physical field variables in the parameter file, automatically map them to the input variable space of the template;

[0144] An interpolation algorithm is used to handle the spatial distribution of non-uniform loads.

[0145] The method employs a cloud computing architecture to implement distributed modeling, specifically including:

[0146] The meshing task is decomposed into subtasks and distributed to computing nodes via the MPI protocol;

[0147] Use Redis to cache intermediate data to reduce the overhead of repeated calculations.

[0148] The basic component library supports user-defined cross-sectional shapes, specifically including:

[0149] Arbitrary cross-sectional profiles are defined using B-spline curves, and the corresponding shell element mid-surface models are automatically generated.

[0150] An isoparametric transformation algorithm is used to topologically associate custom cross-sections with standard components, ensuring geometric compatibility during assembly.

[0151] The method further includes a multi-level geometric set generation module, specifically comprising:

[0152] The layers are divided according to structural function, including the main load-bearing layer, the stiffening layer, and the connecting layer;

[0153] Assign independent parameterized labels to each level to support partial updates for subsequent design changes.

[0154] Example 3

[0155] This invention also proposes an intelligent modeling system for ring-shaped stiffened shell structures based on parametric modular assembly, comprising:

[0156] A basic component library, which stores data for cylindrical shell components, conical shell components, spherical shell components, and variable thickness components;

[0157] The dynamic adjustment module is used to dynamically adjust the geometric topology of basic components using script programming or GUI interaction to ensure the continuity and composability of each component.

[0158] The assembly module is used to combine basic components in a modular fashion according to the assembly logic input by the user to form complex compartment structures, and automatically optimizes geometric continuity during the assembly process to eliminate overlaps or gaps.

[0159] The element partitioning module is used to partition the combined structure into quadrilateral shell elements using an adaptive meshing algorithm, and the mesh density is dynamically adjusted according to the curvature change.

[0160] The output module is used to output the mesh model to the finite element analysis software and automatically generate the corresponding boundary conditions and load application interfaces.

[0161] Example 5

[0162] This disclosure provides a non-volatile computer storage medium storing computer-executable instructions that can perform the steps described in the above embodiments.

[0163] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0164] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0165] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (AN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0166] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0167] The units described in the embodiments of this disclosure can be implemented in software or hardware. The names of the units are not, in some cases, intended to limit the specific unit.

[0168] The preferred embodiments of the present invention have been described above to make the spirit of the present invention clearer and easier to understand, and are not intended to limit the present invention. All modifications, substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope summarized by the appended claims.

Claims

1. A method for intelligent modeling of ring-shaped stiffened shell structures based on parametric modular assembly, characterized in that, Includes the following steps: Step S101: Establish a basic component library, which includes parameterized cylindrical shell components, conical shell components, spherical shell components, and variable thickness components. Step S103: Using script programming or GUI interaction, dynamically adjust the geometric topological relationship of at least two basic components selected from the basic component library to ensure the continuity and composability between the components. Step S105: According to the combination logic input by the user, the at least two basic components are combined in a modular manner to form a complex compartment structure, and geometric continuity is automatically optimized during the combination process to eliminate overlaps or gaps. Step S107: Adaptive mesh generation algorithm is used to divide the combined structure into quadrilateral shell elements, and the mesh density is dynamically adjusted according to the curvature change. Step S109: Output the mesh model to the finite element analysis software to automatically generate the corresponding boundary conditions and load application interfaces.

2. The method as described in claim 1, characterized in that, In step S101, all components are rotating structures and are modeled using shell elements on the mid-surface.

3. The method as described in claim 1, characterized in that, The step S101 is followed by defining the structural parameters of each basic component, including shell plate thickness, radius, axial distance, rib web thickness, rib web height, rib face plate thickness, and rib face plate width.

4. The method as described in claim 1, characterized in that, The dynamic adjustment of the geometric topology of the basic components in step S103 employs a surface fusion algorithm based on non-uniform rational B-splines, specifically including: Step S1031: Extract the boundary curves of adjacent components and calculate their curvature continuity; Step S1033: If the curvature is discontinuous at the boundary, the weighted least squares method is used to smooth the transition of the surface. The optimized surface satisfies C2 continuity. Step S1035: Adjust the control points according to the principle of minimizing energy so that the fused surface is close to the mechanical properties of real welding or riveting.

5. The method as described in claim 4, characterized in that, The radius of curvature ρ and the mesh size Δ satisfy the following relationship: Where k is the curvature sensitivity coefficient, and its value ranges from 0.1 to k to 0.

5.

6. The method as described in claim 5, characterized in that, In step S107, the adaptive mesh generation algorithm employs the leading-edge method and combines it with machine learning to predict the optimal mesh density, specifically including: Step S1071: Analyze the structural geometric features using a convolutional neural network (CNN) to predict the mesh refinement requirements in high-stress areas; Step S1073: Dynamically adjust the initial seed point distribution of the frontier advance algorithm based on the prediction results to ensure that the grid density in critical areas is higher than that in non-critical areas; Step S1075: Optimize the mesh quality using the Laplace smoothing algorithm to ensure that the determinant of the cell Jacobian matrix is ​​greater than 0.

7.

7. The method as described in claim 1, characterized in that, Step S109 specifically includes: Step S1091: Automatically match the preset load template according to the finite element analysis type; Step S1093: Generate the corresponding pressure, temperature or displacement boundary conditions by parsing the load distribution function in the parameter file; Step S1095: Use an interpolation algorithm to ensure accurate mapping of non-uniform loads on grid nodes.

8. The method as described in claim 7, characterized in that, The types of finite element analysis include static, modal, and thermo-coupling.

9. The method as described in claim 7, characterized in that, The automatic matching of the preset load template in step S1091 includes: Call the corresponding load function library by using the finite element analysis type identifier; Based on the physical field variables in the parameter file, automatically map them to the input variable space of the template; An interpolation algorithm is used to handle the spatial distribution of non-uniform loads.

10. An intelligent modeling system for ring-shaped stiffened shell structures based on parametric modular assembly, comprising: A basic component library, which stores data for parameterized defined cylindrical shell components, conical shell components, spherical shell components, and variable thickness components; The dynamic adjustment module is used to dynamically adjust the geometric topological relationship of at least two basic components selected from the basic component library using script programming or GUI interaction, so as to ensure the continuity and composability between the components. The combination module is used to combine the at least two basic components in a modular fashion according to the combination logic input by the user to form a complex compartment structure, and to automatically optimize the geometric continuity during the combination process to eliminate overlaps or gaps. The element partitioning module is used to partition the combined structure into quadrilateral shell elements using an adaptive meshing algorithm, and the mesh density is dynamically adjusted according to the curvature change. The output module is used to output the mesh model to the finite element analysis software and automatically generate the corresponding boundary conditions and load application interfaces.

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