An assembled multi-layer pressure-resistant shell inflation pressure optimization method and related device

Abstract

The application relates to an assembled multi-layer pressure-resistant shell inner inflation pressure optimization method, which comprises the following steps: establishing a nonlinear contact finite element analysis model of a multi-layer pressure-resistant shell; obtaining inner inflation pressure and critical instability external pressure data pairs; constructing an objective function and performing extreme value optimization; and outputting the optimal inner inflation pressure. The optimization design method can minimize the inflation pressure demand under the limit compression requirement of reaching the target water depth, which not only greatly reduces the inner shell structure weight, but also reduces the harsh requirements on the on-site assembly air source equipment, so that the on-site rapid assembly of the giant modular equipment becomes a reality.

Description

Technical Field

[0001] This invention relates to the field of deep-sea equipment structural design and simulation technology, and in particular to a method and related device for optimizing the internal inflation pressure of a prefabricated multi-layer pressure hull. Background Technology

[0002] For deep-sea / underwater pressure-resistant equipment, traditional integral welded cylindrical pressure hulls suffer from large welding deformation and high residual stress. A novel prefabricated pressure hull employing multi-layer nesting and internal air-filled fastening effectively solves these manufacturing challenges and transfers loads through tight interlayer contact.

[0003] However, in actual engineering design, the internal inflation pressure directly determines the shell's resistance to external pressure instability: too low a pressure cannot form an effective fastener, making the structure extremely prone to instability; too high a pressure will lead to severe degradation of structural stiffness, with internal stress exceeding the material's yield point, increasing the risk of inner shell failure and airtightness. Currently, the industry lacks a quantitative design method for pre-tightening parameters for this highly nonlinear structure of "friction contact + internal inflation pressure," resulting in blind spots in structural design.

[0004] Based on the above-mentioned technical problems, this application proposes a method for optimizing the internal inflation pressure of a prefabricated multi-layer pressure-resistant shell. Summary of the Invention

[0005] The purpose of this invention is to provide a method and related apparatus for optimizing the internal inflation pressure of a prefabricated multi-layer pressure-resistant shell, in order to solve the technical problems mentioned in the background art. This purpose is achieved through the following technical solutions: A method for optimizing the internal inflation pressure of a prefabricated multi-layer pressure-resistant shell includes the following steps: Step S1. Establish a nonlinear contact finite element analysis model for the multi-layer pressure-resistant shell. A three-dimensional model is established, comprising an outer shell, an inner shell, and several independent cavity supports between the outer shell and the inner shell; contact surfaces with friction coefficients are defined between the supports and the inner shell, between the supports and the outer shell, and between two adjacent supports; Step S2. Obtain the internal inflation pressure and critical instability external pressure data. A given internal inflation pressure is applied to the interior of the independent cavity support. While maintaining a constant internal inflation pressure, apply hydrostatic pressure to the outer surface of the shell at a set loading rate. Record the hydrostatic pressure at which the mechanical response of the finite element analysis model diverges or the calculation fails to converge due to large slippage at the contact surface or large deflection and buckling characteristics of the structure. This pressure is denoted as the critical instability pressure. Repeat the above steps to obtain different internal inflation pressures. Corresponding multiple discrete Data pairs; Step S3. Construct the objective function and perform extremum optimization. Multiple discrete sets are fitted using polynomial fitting algorithms or spline interpolation algorithms. The data pairs are smoothed, and an objective function is constructed. ; Calculate the first derivative of the objective function Seeking satisfaction And the second derivative The extreme point, and the x-axis corresponding to this extreme point is the theoretical optimal internal inflation pressure. The corresponding vertical axis represents the maximum critical external pressure for instability under the current structural configuration. ; Step S4. Output the optimal internal inflation pressure Based on the theoretically optimal internal inflation pressure obtained in step S3 , will It can be directly used as the optimal internal inflation pressure output; or, under the premise of meeting the preset target critical instability external pressure threshold, within the range The minimum internal inflation pressure value is selected as the optimal internal inflation pressure and output to guide the inflation, curing and safe assembly of multi-layer pressure-resistant shells in the physical field.

[0006] Furthermore, step S1 specifically includes: Step S11. Define the geometric parameter space: A three-dimensional model of a multi-layer pressure-resistant shell is established. The three-dimensional model includes an outer shell, a support body, and an inner shell from the outside to the inside. The outer shell and the inner shell are both cylindrical shells. The support body is an independent closed tube bundle. A plurality of the support bodies are arranged in a circumferential array between the outer shell and the inner shell. Step S12. Define the material constitutive relation: Use an isotropic elastoplastic material model, and input the elastic modulus E and Poisson's ratio. and the yield strength of the material Considering the strain hardening effect of the material; Step S13. Constructing frictional contact pairs: Establishing normal contact behavior and tangential contact behavior between the inner wall of the outer shell and the outer wall of the support, between the outer wall of the inner shell and the inner wall of the support, and between the side walls of adjacent supports, respectively; Step S14. Mesh generation: Mesh the multi-layer pressure shell and set at least 5 integration points in the thickness direction to accurately capture the large displacement geometric nonlinearity and cross-sectional progressive plastic yielding behavior of the multi-layer pressure shell during buckling instability. Step S15. Verification of boundary conditions and mesh independence: Apply symmetrical boundary conditions to both ends of the meshed model to restrict the axial displacement of the meshed model; adjust the meshing accuracy until the relative error of the calculation results is less than 2%.

[0007] Furthermore, in step S13, the normal contact behavior adopts the penalty function method, which allows interface pressure transmission but prevents penetration; the tangential contact behavior adopts the Coulomb friction model, defining the static friction coefficient μ. The static friction coefficient μ is determined according to the arithmetic mean deviation roughness Ra of the contact surface profile. The manufacturing process and the value of μ corresponding to the conventional roughness Ra of the metal contact surface of the deep-sea assembled pressure hull meet the engineering quantitative comparison table.

[0008] Furthermore, in step S14, the meshing strategy is a continuous shell element or a four-node reduced integral shell element.

[0009] A computer device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a method for optimizing the inflation pressure inside an assembled multi-layer pressure-resistant shell, as described above.

[0010] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for optimizing the inflation pressure inside an assembled multi-layer pressure-resistant housing as described above.

[0011] A computer program product includes a computer program that, when executed by a processor, implements a method for optimizing the internal inflation pressure of an assembled multi-layer pressure-resistant housing as described above.

[0012] The technical solutions provided in this application have at least the following technical effects or advantages: 1. The method of this invention reveals the nonlinear relationship between the "stiffness softening effect" and the "ultimate bearing capacity improvement" brought about by air pre-tightening. Relying on the nonlinear buckling tracking and optimization evaluation of this method, the multi-layer pressure shell of the modular nested structure can be theoretically proven to be safe and feasible, avoiding the misjudgment of such high-quality structures by traditional evaluation methods; 2. The method of this invention incorporates process parameters such as the roughness of the contact surface (converted into the friction coefficient through an engineering quantitative reference table) and assembly clearance into the optimization algorithm, which can accurately match the safest initial air pressure for modular components with different roughness levels and different manufacturing tolerances, thereby ensuring the safety of physical assembly through computational design. 3. When a multi-layer pressure hull with a nested multi-body structure requires replacement of partially damaged modules during its service life, or when the thickness of the inner and outer shells needs to be modularly interchanged for different diving missions, traditional trial-and-error methods require a lengthy re-evaluation period. The parameterized optimization evaluation system provided by this invention has strong versatility, reducing the maintenance difficulty and redesign cost throughout the entire life cycle.

[0013] 4. The optimization design method of the present invention can minimize the inflation pressure requirement while achieving the ultimate pressure resistance requirement of the target water depth. This not only significantly reduces the weight of the inner shell structure, but also reduces the stringent requirements on the on-site assembly air source equipment, making the rapid on-site assembly of giant modular equipment a reality. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a flowchart of the optimization method in an embodiment of this application; Figure 2 This is a three-dimensional structural diagram of an embodiment of this application; Figure 3 The inflation pressure in the embodiments of this application external pressure at critical instability of the pressure vessel The impact curve.

[0016] Reference numerals: 1. Outer shell; 2. Support; 3. Inner shell. Detailed Implementation

[0017] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0018] This embodiment takes the assembled multi-layer pressure hull of a certain 50-meter-class underwater operating equipment as an example, such as Figure 1 The method for optimizing the internal inflation pressure of a prefabricated multi-layer pressure-resistant shell, as shown, includes: Step S1. Establish a nonlinear contact finite element analysis model for the multi-layer pressure-resistant shell. S11. Define the geometric parameter space like Figure 2 As shown, a three-dimensional model of a multi-layered pressure-resistant shell is established. From the outside in, the three-dimensional model includes an outer shell 1, a support body 2, and an inner shell 3. Both the outer shell 1 and the inner shell 3 are cylindrical shells. The support body 2 is an independent, closed tubular bundle, and multiple support bodies 2 are arranged in a circumferential array between the outer shell 1 and the inner shell 3. In this embodiment, the thickness of the outer shell 1 is... The thickness of the inner shell 3 is 2 mm. The wall thickness of support 2 is 2mm. It is 2 mm.

[0019] S12. Define the constitutive relation of materials. An isotropic elastoplastic material model is used, with the elastic modulus E and Poisson's ratio as input. and the yield strength of the material The strain hardening effect of the material is also considered. In this embodiment, stainless steel NL is selected, with an elastic modulus E = 193 GPa and a Poisson's ratio of... Yield strength =205 MPa, using a bilinear isotropic hardening model.

[0020] S13. Constructing frictional contact pairs Normal and tangential contact behaviors are established between the inner wall of the outer shell and the outer wall of the support, between the outer wall of the inner shell and the inner wall of the support, and between the side walls of adjacent supports, respectively. The normal contact behavior employs the penalty function method, allowing interface pressure transmission but preventing penetration; the tangential contact behavior uses the Coulomb friction model, defining a static friction coefficient μ. The static friction coefficient μ is determined based on the arithmetic mean deviation roughness Ra of the contact surface profile. The manufacturing process and μ value corresponding to the conventional roughness Ra of the metal contact surface of the deep-sea assembled pressure hull satisfy the following engineering quantitative comparison table.

[0021] In this embodiment, the friction coefficient μ of all contact areas is set to 0.2, corresponding to a contact surface with a surface roughness Ra = 6.3 μm, matching the value range of the engineering quantitative reference table of roughness Ra-friction coefficient μ.

[0022] S14. Mesh Generation A multi-layered pressure shell was meshed with at least five integration points along the thickness direction to accurately capture the large displacement geometric nonlinearity and progressive plastic yielding behavior of the multi-layered pressure shell during buckling instability. The meshing strategy employed either continuous shell elements or four-node reduced integral shell elements. In this embodiment, SHELL181 four-node reduced integral shell elements were used for meshing in ANSYS Mechanical, with five integration points along the thickness direction.

[0023] S15. Verification of boundary conditions and mesh independence Symmetrical boundary conditions were applied to both ends of the meshed model to restrict the axial displacement of the model. The meshing accuracy was adjusted until the relative error of the calculation results was less than 2%. In this embodiment, symmetric constraints (i.e., zero axial displacement) were applied to both ends of the model, and mesh independence was verified: global mesh sizes of 5 mm, 3 mm, and 2 mm were used respectively, and the relative error of the critical instability external pressure was less than 1.5%. Finally, a global mesh size of 3 mm was adopted.

[0024] Step S2. Obtain the internal inflation pressure and critical instability external pressure data. A given internal inflation pressure is applied inside the independent cavity support 2. Keeping the internal inflation pressure constant, apply hydrostatic pressure to the outer surface of the outer shell 1 at the set loading rate. Record the hydrostatic pressure at which the finite element analysis model fails to converge due to contact surface slippage or structural buckling, and denote it as the critical instability pressure. Repeat the above steps to obtain different results. Corresponding multiple discrete ( , Data pairs.

[0025] In this embodiment, a nonlinear multi-load step solution is performed: different initial internal inflation pressures are input to the model respectively. Then, an equivalent external pressure loading rate of 1 MPa / s is applied to hydrostatic pressure until the calculation fails to converge. Different... Corresponding critical instability external pressure The following 7 sets of discrete data pairs were obtained: when =0 MPa =0.1806 MPa; when When =0.25 MPa, =0.3180 MPa; when When =0.50 MPa, =0.3707 MPa; when When =0.75 MPa, =0.4098 MPa; when =1.00 MPa, =0.4566 MPa; when =1.25 MPa, =0.5016 MPa; when =1.50 MPa, =0.4931 MPa.

[0026] like Figure 3 As shown, trend analysis of the above 7 sets of discrete data clearly reveals an inverted U-shaped nonlinear response: with the initial internal inflation pressure... As the pressure gradually increases, the critical instability external pressure of the assembled multi-layer pressure shell... It shows a trend of first rising significantly and then falling back.

[0027] when Within the 0–1.25 MPa range, the expansion preload generated by the internal high-pressure gas effectively enhanced the contact friction at the multi-body nested interface, making the radial support structure more compact and successfully suppressing the initial buckling deformation of the shell, thus reducing the system's critical instability load. The ultimate compressive strength was increased by approximately 177% from 0.1806 MPa without pre-compression to 0.5016 MPa.

[0028] However, when When the pressure is further increased to 1.50 MPa, PcrPcr shows a decrease. This indicates that the excessively high internal inflation pressure causes the inner thin shell to bear an excessive inward compressive load, and the leading factor for system failure changes from "instability caused by external hydrostatic pressure" to "premature instability of the inner shell caused by internal pre-inflation high pressure", resulting in a decrease in the overall external pressure bearing capacity instead of an increase.

[0029] Step S3. Construct the objective function and perform extremum optimization. Multiple discrete sets of data are fitted using polynomial fitting algorithms or spline interpolation algorithms. , The data pairs are smoothed, and an objective function is constructed. ; Calculate the first derivative f′ of the objective function. ), find the expression f′( ) = 0 and the second derivative f′′( The extreme point where ) < 0, the x-axis corresponding to this extreme point is the theoretical optimal internal inflation pressure. The corresponding vertical axis represents the maximum critical external pressure for instability under the current structural configuration. .

[0030] In this embodiment, a fourth-order polynomial is used to fit the above seven sets of discrete data to obtain the objective function: =f( )=-0.1287 +0.5246 -0.7562 +0.5931 +0.1806 Calculate its first derivative and solve for f′(Pin)=0. Two stationary points are obtained in the interval [0, 1.5], where the second derivative f′′( The maximum point corresponding to )<0 =1.247 MPa, at this time =0.5021 MPa, which is highly consistent with the 1.25 MPa / 0.5016 MPa obtained from the finite element calculation, meaning that the theoretical optimal internal inflation pressure is approximately 1.25 MPa.

[0031] Step S4. Output the optimal internal inflation pressure Based on the theoretically optimal internal inflation pressure obtained in step S3 , will It can be directly used as the optimal internal inflation pressure output; or, under the premise of meeting the preset target critical instability external pressure threshold, within the range The minimum internal inflation pressure value is selected as the optimal internal inflation pressure and output to guide the inflation, curing and safe assembly of multi-layer pressure-resistant shells in the physical field.

[0032] In this embodiment, the theoretically optimal internal inflation pressure is 1.247 MPa. Considering the control accuracy of the actual inflation device (±0.02 MPa), the pressure tolerance of the sealing structure, and the lightweight requirement (the pressure on the inner shell should not exceed its yield strength), the optimal internal inflation pressure was ultimately selected as 1.25 MPa. This value achieves an optimal balance between the structural bearing capacity (0.5016 MPa) and the compressive load on the inner shell, while also satisfying manufacturing feasibility and safety margin.

[0033] The optimal internal inflation pressure of 1.25 MPa is output to guide the on-site inflation and curing of the prefabricated multi-layer pressure shell support and subsequent safe assembly.

[0034] In one exemplary embodiment, a computer device is also provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps in the above method embodiments.

[0035] In one exemplary embodiment, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the steps in the above method embodiments.

[0036] In one exemplary embodiment, a computer program product is also provided, including a computer program that, when executed by a processor, implements the steps in the method embodiments described above.

[0037] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0038] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0039] In summary, this application has the following technical effects: 1. This invention unlocks the feasibility of modular assembly: Traditional design evaluation methods based on rigid welded structures use "radial stiffness" as the core indicator for measuring compressive performance. However, the method of this invention reveals the nonlinear relationship between the "stiffness softening effect" and the "increased ultimate bearing capacity" brought about by air pre-tightening. Relying on the nonlinear buckling tracking and optimization evaluation of this method, the modular nested structure, which "overcomes rigidity with flexibility," can be theoretically proven to be safe and feasible, avoiding the misjudgment of such high-quality structures by traditional evaluation methods.

[0040] 2. Ensuring "Weld-Free" Assembly Through Algorithms, Unlocking Manufacturing and Inspection Benefits: The elimination of welding and modularization of underwater equipment (offering significant advantages such as eliminating residual stress and enabling internal non-destructive testing) are industry pain points. However, the ultimate pressure resistance of non-welded assemblies is highly dependent on interfacial friction and pre-tightening conditions. This invention incorporates process parameters such as the roughness of the contact surface (converted to the friction coefficient through an engineering quantitative reference table) and assembly clearance into the optimization algorithm. This enables precise matching of the safest initial air pressure for modular components with different roughness levels and manufacturing tolerances, thereby ensuring the safety of physical assembly through computational design and unlocking the engineering benefits of modular manufacturing.

[0041] 3. Enhances product iteration efficiency and local fault-tolerant design capabilities: When a multi-body nested structure requires replacement of partially damaged modules during its service life, or modular interchange of inner and outer shell thicknesses for different deep-dive missions, traditional trial-and-error methods require lengthy re-evaluation cycles. The parametric optimization evaluation system provided by this invention has strong versatility, reducing the maintenance difficulty and redesign costs throughout the entire life cycle.

[0042] 4. Avoiding excessive redundancy and achieving system lightweighting and equipment degradation: The optimization design method of this invention helps avoid blindly increasing the tightening air pressure in engineering. High internal inflation not only forces the inner shell material to accumulate thickness due to enormous prestress, but also requires the mother ship to be equipped with extremely high-pressure inflation pump sets. Based on the principle of "marginal benefit" and extreme value tracking to lock in the optimal air pressure point, this invention minimizes the inflation pressure requirement while achieving the ultimate pressure resistance requirement at the target water depth. This not only significantly reduces the weight of the inner shell structure but also lowers the stringent requirements for on-site assembly air source equipment, making rapid on-site assembly of giant modular equipment a reality.

[0043] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for optimizing the internal inflation pressure of a prefabricated multi-layer pressure-resistant shell, characterized in that, Includes the following steps: Step S1. Establish a nonlinear contact finite element analysis model for the multi-layer pressure-resistant shell. A three-dimensional model is established, comprising an outer shell, an inner shell, and several independent cavity supports between the outer shell and the inner shell; contact surfaces with friction coefficients are defined between the supports and the inner shell, between the supports and the outer shell, and between two adjacent supports; Step S2. Obtain the internal inflation pressure and critical instability external pressure data. A given internal inflation pressure is applied to the interior of the independent cavity support. While maintaining a constant internal inflation pressure, apply hydrostatic pressure to the outer surface of the shell at a set loading rate. Record the hydrostatic pressure at which the mechanical response of the finite element analysis model diverges or the calculation fails to converge due to large slippage at the contact surface or large deflection and buckling characteristics of the structure. This pressure is denoted as the critical instability pressure. Repeat the above steps to obtain different internal inflation pressures. Corresponding multiple discrete Data pairs; Step S3. Construct the objective function and perform extremum optimization. Multiple discrete sets are fitted using polynomial fitting algorithms or spline interpolation algorithms. The data pairs are smoothed, and an objective function is constructed. ; Calculate the first derivative of the objective function Seeking satisfaction And the second derivative The extreme point, and the x-axis corresponding to this extreme point is the theoretical optimal internal inflation pressure. The corresponding vertical axis represents the maximum critical external pressure for instability under the current structural configuration. ; Step S4. Output the optimal internal inflation pressure Based on the theoretically optimal internal inflation pressure obtained in step S3 , will It can be directly used as the optimal internal inflation pressure output; or, under the premise of meeting the preset target critical instability external pressure threshold, within the range The minimum internal inflation pressure value is selected as the optimal internal inflation pressure and output to guide the inflation, curing and safe assembly of multi-layer pressure-resistant shells in the physical field.

2. The method for optimizing the internal inflation pressure of a prefabricated multi-layer pressure-resistant shell according to claim 1, characterized in that, Step S1 specifically includes: Step S11. Define the geometric parameter space: A three-dimensional model of a multi-layer pressure-resistant shell is established. The three-dimensional model includes an outer shell, a support body, and an inner shell from the outside to the inside. The outer shell and the inner shell are both cylindrical shells. The support body is an independent closed tube bundle. A plurality of the support bodies are arranged in a circumferential array between the outer shell and the inner shell. Step S12. Define the material constitutive relation: Use an isotropic elastoplastic material model, and input the elastic modulus E and Poisson's ratio. and the yield strength of the material Considering the strain hardening effect of the material; Step S13. Constructing frictional contact pairs: Establishing normal contact behavior and tangential contact behavior between the inner wall of the outer shell and the outer wall of the support, between the outer wall of the inner shell and the inner wall of the support, and between the side walls of adjacent supports, respectively; Step S14. Mesh generation: Mesh the multi-layer pressure shell and set at least 5 integration points in the thickness direction to accurately capture the large displacement geometric nonlinearity and cross-sectional progressive plastic yielding behavior of the multi-layer pressure shell during buckling instability. Step S15. Verification of boundary conditions and mesh independence: Apply symmetrical boundary conditions to both ends of the meshed model to restrict the axial displacement of the meshed model; adjust the meshing accuracy until the relative error of the calculation results is less than 2%.

3. The method for optimizing the internal inflation pressure of a prefabricated multi-layer pressure-resistant shell according to claim 2, characterized in that, In step S13, the normal contact behavior adopts the penalty function method, which allows interface pressure transmission but prevents penetration; the tangential contact behavior adopts the Coulomb friction model and defines the static friction coefficient μ; the static friction coefficient μ is selected according to the arithmetic mean deviation roughness Ra of the contact surface profile, Ra is taken as the conventional manufacturing grade of deep-sea assembled pressure hull, and the correspondence between Ra and μ conforms to the engineering quantitative reference table.

4. The method for optimizing the internal inflation pressure of a prefabricated multi-layer pressure-resistant shell according to claim 2, characterized in that, In step S14, the meshing strategy is either continuous shell elements or four-node reduced integral shell elements.

5. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement a method for optimizing the internal inflation pressure of an assembled multi-layer pressure-resistant shell according to any one of claims 1-4.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for optimizing the internal inflation pressure of an assembled multi-layer pressure-resistant shell as described in any one of claims 1-4.

7. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for optimizing the internal inflation pressure of an assembled multi-layer pressure-resistant shell as described in any one of claims 1-4.

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