All-parameterized automatic closed-loop pressure pulsation optimization method for spaceflight high-pressure corrugated pipe

Abstract

The invention relates to a full-parameterization automatic closed-loop pressure pulsation optimization method for a spaceflight high-pressure corrugated pipe. The method comprises the following steps that S1, geometric parameters and optimization instructions of the corrugated pipe are input into a text command interface; s2, the parametric modeling module generates a corrugated pipe geometric model; s3, an automatic simulation analysis module performs grid division and computational fluid dynamics simulation on the generated geometric model, and automatically extracts an index value representing the pressure pulsation intensity from a simulation result; s4, an optimization decision module automatically adjusts the geometric parameters, generated in the step S2, of the corrugated pipe, a new geometric parameter combination is generated with the purpose of minimizing the index value representing the pressure pulsation intensity, the new geometric parameter combination is fed back to a parameterization modeling module for next iteration, and a closed-loop optimization process is formed till a termination condition is met; and S5, outputting the optimal geometric parameter combination by a result output module. According to the method, the operation efficiency is improved, the design period is shortened, and the optimization quality and reliability are fundamentally improved.

Description

Technical Field

[0001] This invention belongs to the field of bellows testing technology, and in particular relates to a method for fully parameterized automatic closed-loop optimization of pressure pulsation in aerospace high-pressure bellows. Background Technology

[0002] In industries such as chemical engineering, oil and gas, energy, and aerospace, high-pressure pipeline systems are widely used in fluid transmission and energy exchange processes. The frequent start-stop cycles of power sources such as pumps and compressors, as well as valves, inevitably generate strong pressure pulsations (or pressure fluctuations). This periodic dynamic load has become one of the leading causes of failure in these systems. High-pressure metal bellows, as a key flexible connecting element, are widely used due to their excellent compensation capabilities. However, while their corrugated geometry provides flexibility, it also makes them exceptionally sensitive to dynamic pressure changes. Under continuous pressure pulsation, bellows are highly susceptible to high-cycle fatigue failure, resonance, and structural instability, leading to leaks or even ruptures. This not only causes significant economic losses due to unplanned downtime but can also trigger serious safety and environmental accidents. Therefore, optimizing bellows for pressure pulsation is crucial.

[0003] Currently, similar technical solutions for optimizing bellows pressure pulsation can be mainly divided into the following categories: 1. The manual optimization method based on traditional experience and trial and error is the most basic approach, relying entirely on the engineer's personal experience and intuition. Designers initially determine the structural parameters of the bellows (such as wave height, wave pitch, wall thickness, etc.) based on experience, and then create physical samples or establish simple computational models for testing. Based on the test results (such as whether resonance occurs, whether the stress is too high), the parameters are manually adjusted, and the test is repeated, in a cyclical manner, until a seemingly "feasible" or "relatively optimal" design is obtained.

[0004] 2. Semi-automated optimization based on commercial simulation software (GUI platform): This method utilizes mature commercial software platforms (such as ANSYS Workbench, COMSOL Multiphysics, etc.) and their built-in or integrated optimization modules. The main workflow is as follows: Parametric modeling: Users manually create a bellows model in the software's graphical interface (GUI) and set key dimensions as parameters. Simulation settings: Simulation conditions (boundary conditions, loads, mesh generation, etc.) are manually set in the GUI. Optimization settings: In the optimization module's GUI, an optimization algorithm (such as genetic algorithm, response surface methodology, etc.) is selected, and optimization objectives (such as minimizing the root mean square value (RMS) of outlet pressure pulsation) and constraints (such as maximum stress limits) are manually set. Operation and monitoring: The optimization process is started, and the software automatically performs several iterations. Users need to monitor the process and sometimes need to manually intervene (such as adjusting parameter ranges or restarting failed simulations). Result analysis: After optimization, users view the results in the GUI, manually determine whether they are satisfactory, and may need to adjust and re-optimize if necessary.

[0005] Existing bellows design platforms suffer from problems such as complex operation, low automation, and insufficient multi-objective optimization capabilities. Specifically, these problems manifest in the following aspects: Operational complexity: The existing platform's graphical user interface (GUI) is complex to operate, requiring users to possess extensive software knowledge, resulting in low operational efficiency. Low automation: It is difficult to achieve a fully automated workflow from design, simulation, optimization, and post-processing; manual intervention is required, leading to low design efficiency. Insufficient multi-objective optimization capabilities: Existing platforms offer limited support for multi-objective optimization, making it difficult to balance multiple performance metrics and achieve comprehensive optimization of multi-objective performance. Lack of pressure pulsation optimization methods: Currently, pressure pulsation optimization mainly relies on manual optimization algorithm attempts, failing to achieve fully automated optimization and resulting in low optimization efficiency. Summary of the Invention

[0006] To address the aforementioned technical problems, the present invention aims to provide a method for fully parameterized automatic closed-loop optimization of pressure pulsation in aerospace high-pressure bellows. This method significantly improves operational efficiency, shortens the design cycle by orders of magnitude, greatly enhances the utilization of computational resources, ensures optimization depth, and fundamentally improves optimization quality and reliability, approaching the global optimum.

[0007] To achieve the objectives of the invention described above, the present invention adopts the following technical solution: A method for fully parameterized automatic closed-loop optimization of pressure pulsation in aerospace high-pressure bellows is proposed. This method employs an automated closed-loop optimization system, which includes a text command interface, a parametric modeling module, an automatic simulation analysis module, an optimization decision-making module, and a result output module. The specific steps are as follows: S1. The user inputs the geometric parameters and optimization instructions of the designed bellows into the text command interface; S2. The parametric modeling module responds to the instructions of the text command interface and automatically generates a bellows geometric model with variable geometric parameters. S3. The automatic simulation analysis module automatically performs mesh generation and computational fluid dynamics simulation on the generated geometric model, and automatically extracts index values ​​characterizing the intensity of pressure pulsation from the simulation results; S4. The optimization decision module uses an optimization algorithm to automatically adjust the geometric parameters of the bellows generated in step S2, with the goal of minimizing the index value that characterizes the intensity of pressure pulsation, to generate a new combination of geometric parameters, and feeds it back to the parameterized modeling module for the next iteration, forming a closed-loop optimization process until the termination condition is met. S5. The result output module will ultimately output the optimal combination of geometric parameters that minimizes the index value representing the intensity of pressure pulsation.

[0008] As a preferred embodiment, the geometric parameters of the bellows include the total length L1 of the bellows, the length of the bellows outlet section L2, the inner diameter d, the peak height C, the trough height T, and the number of waves N.

[0009] As a preferred option, the index characterizing the pressure pulsation intensity is the root mean square value of the pressure at the bellows outlet section, i.e., the RMS value.

[0010] As a preferred option, the workflow of the optimization decision module in step S4 includes: first, using the Latin hypercube sampling method to efficiently explore the entire parameter space with a small sample size and quickly locate the parameter region with better performance; then, using the genetic algorithm and / or the NSGA-III advanced optimization algorithm, which treats the parameter combination as a "chromosome" and simulates natural evolution through "selection, crossover, and mutation" to guide the search direction and find the global optimal solution.

[0011] As a preferred option: the automatic simulation analysis module in step S3 performs fluid dynamics simulation by calling the script interface of external commercial FEA / CFD software, calculates the internal flow field and pressure distribution of the bellows under specific working conditions, and the post-processing process automatically extracts the key quantitative index of pressure pulsation intensity - the root mean square value of the pressure at the bellows outlet section.

[0012] As a preferred embodiment, the entire closed-loop optimization process in step S4 is controlled by multiple independent script programs in series through a high-level programming language. These scripts correspond to parametric modeling, mesh generation, simulation settings, and solution control, respectively.

[0013] As a preferred option, the automated closed-loop optimization system can automatically perform simulations under various different boundary conditions in sequence, and use the comprehensive performance of the index values ​​characterizing the intensity of pressure pulsation under all operating conditions as the optimization target.

[0014] As a preferred embodiment: In step S4, new parameters are obtained through algorithm optimization. These new optimized parameters are used to automatically reconstruct the model. The new model is then used to draw the corrugated pipe mesh using a mesh drawing script, and simulated using a simulation solution script. All data results are analyzed, and the parameter corresponding to the minimum RMS value is extracted. The formula for calculating the RMS value is as follows. In the formula, N is the number of samples; Pi is the pressure value at the outlet section of the bellows.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention employs a Text-Based User Interface (TUI). After fully automating the process, the time required for manual intervention in subsequent iterations approaches zero after initial setup. A single start command is all it takes for the system to run automatically. The platform enables continuous 24 / 7 operation, reducing the total project cycle of 100 iterations to 1-2 working days. Compared to traditional methods, this represents an 80%-90% reduction in project cycle time and an order-of-magnitude improvement in design efficiency.

[0016] This invention's method directly calls the solver kernel in batch mode, bypassing the resource-intensive GUI interface and allowing computational resources to focus more on the simulation itself, improving single-cycle efficiency by approximately 10-20%. The automated process enables back-to-back task submission and even parallel computation, bringing computational resource utilization close to 100%. The extremely low marginal cost makes hundreds or even thousands of iterations possible, allowing for a more thorough and in-depth exploration of the parameter space. Compared to traditional methods, this invention improves computational resource utilization by over 50% and increases the number of effective iterations by more than 10 times under the same hardware conditions.

[0017] This invention employs a strategy of "intelligent sampling + global optimization algorithm" specifically designed to escape local optima. The massive number of iterations provides the necessary condition for finding the global optimum. The platform can easily achieve multi-condition optimization, finding a "robust optimal solution" with robust pressure pulsation performance under various operating conditions. The optimal pressure pulsation index (RMS value) obtained by this invention can be further optimized by 5%-20% compared to traditional methods. In high-end industrial fields, this translates to a significant improvement in product lifespan and reliability. Simultaneously, the performance fluctuation range (variance) of the design scheme under varying operating conditions can be reduced by more than 30%, significantly enhancing reliability. Attached Figure Description

[0018] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute a limitation thereof.

[0019] Figure 1 This is a schematic diagram of the overall architecture of the platform using the method of the present invention; Figure 2 This is a schematic diagram illustrating the specific implementation process of the method of the present invention; Figure 3 This is a modeling diagram and a magnified schematic diagram of a partial structure of the bellows in the method of this invention; Figure 4 This is a schematic diagram of the Latin hypercube sampling method used in the present invention; Figure 5 This is a schematic diagram of the specific optimized process in the method of the present invention. Detailed Implementation

[0020] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0021] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0022] Furthermore, in the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0023] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more, unless explicitly defined otherwise.

[0024] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0025] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can include direct contact between the first and second features, or contact between the first and second features through another feature between them. Furthermore, "above," "over," and "on top" of the second feature includes the first feature directly above or diagonally above the second feature, or simply indicates that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature includes the first feature directly below or diagonally below the second feature, or simply indicates that the first feature is at a lower horizontal level than the second feature.

[0026] The present invention will be further described below with reference to the accompanying drawings and embodiments: like Figure 1 and Figure 2 As shown, the method for fully parameterized automatic closed-loop optimization of pressure pulsation in aerospace high-pressure bellows employs an automated closed-loop optimization system. This system includes a text command interface, a parameterized modeling module, an automatic simulation analysis module, an optimization decision module, and a result output module. The specific steps are as follows: S1. The user inputs the geometric parameters and optimization instructions of the designed bellows into the text command interface; the geometric parameters of the bellows include the total length of the bellows L1, the length of the bellows outlet section L2, the inner diameter d, the height of the crest C, the height of the trough T, and the number of waves N (as shown in Table 1). By simply modifying the parameter values ​​in the script, a brand-new geometric model can be automatically generated, laying the foundation for large-scale parameter scanning.

[0027] Table 1 Variable name Variable Description <![CDATA[ L 2 ]]> Export section length / mm Corrugated pipe inner diameter / mm <![CDATA[ L 1 ]]> Total length of corrugated pipe / mm Crest height / mm Valley height / mm Number / Rounded down S2. The parametric modeling module responds to instructions from the text command interface and automatically generates a bellows geometric model with variable geometric parameters (e.g., Figure 3 ); S3. The automatic simulation analysis module automatically performs mesh generation and computational fluid dynamics simulation on the generated geometric model. The automatic simulation analysis module performs fluid dynamics simulation by calling the script interface of external commercial FEA / CFD software, calculating the internal flow field and pressure distribution of the bellows under specific working conditions. The post-processing process automatically extracts the key quantitative index of pressure pulsation intensity - the root mean square value of the pressure at the bellows outlet section, i.e., the RMS value.

[0028] The indicator characterizing the intensity of pressure pulsation is the root mean square (RMS) value of the pressure at the bellows outlet section. The RMS value is a core indicator for measuring the intensity of pressure fluctuations; a smaller RMS value indicates smoother pressure pulsation and better performance. This process is completed automatically by a script, eliminating the need for manual viewing of contour plots or charts.

[0029] S4. The optimization decision module uses an optimization algorithm to automatically adjust the geometric parameters of the bellows generated in step S2, with the goal of minimizing the index value that characterizes the intensity of pressure pulsation, to generate a new combination of geometric parameters, and feeds it back to the parameterized modeling module for the next iteration, forming a closed-loop optimization process until the termination condition is met.

[0030] The entire closed-loop optimization process is controlled by multiple independent scripts linked together using a high-level programming language. There are four TUI scripts in total: a parameter script controlling the bellows geometric model, an optimization algorithm script controlling the algorithm search, a mesh generation script controlling mesh creation, and a simulation setup script for numerical simulation. These four TUI scripts are independent, and their code logic corresponds to their own software. Finally, Python is used to link the software together to achieve automated closed-loop optimization. For example, in the bellows parameter script, modifying the number of bellows only requires modifying the code for the number; the specific code is shown below. rapple_munber.setValue(50) You only need to modify 50 of them, and the newly generated model will be automatically modified accordingly.

[0031] For example, if you want to adjust the number of iterations and the number of steps per iteration in the bellows simulation settings, you only need to modify the code for the number of iterations, as shown below. / solve / dual-time-iterate 1760 10 Simply modify 1760 and 10, and the new simulation settings will be modified according to the set values.

[0032] All scripts are line-by-line code, allowing the entire closed-loop optimization process to be simulated and optimized without calling the user interface. This reduces the GPU load, improves computational efficiency, and reduces computation time.

[0033] When optimizing the parameters of a parameterized model using an algorithm, the optimization process is controlled by an optimization script, which mainly consists of two parts: intelligent sampling and optimizing the solution space. Intelligent sampling incorporates various sampling methods, such as Latin hypercube sampling, see... Figure 4 It can cover the entire parameter space and estimate the system's output distribution more efficiently and accurately with a relatively small sample size. The optimized solution space is designed with various built-in optimization algorithms, such as using a genetic algorithm (GA) to optimize parameters. It analogizes the problem to be solved to an "environment," encodes potential solutions (usually a set of parameters) into a "chromosome," and then allows a group of chromosomes (a population) to continuously "evolve" (iterate) in this environment. Through selection, crossover, mutation, and other operations, it ultimately evolves individuals that are best adapted to the environment (optimal solution). The specific optimization process is as follows: Figure 5 As shown.

[0034] In step S4, new parameters are obtained through algorithm optimization. These new optimized parameters are used to automatically reconstruct the model. The new model is then used to draw the corrugated pipe mesh using a mesh drawing script, and simulated using a simulation solution script. All data results are analyzed, and the parameter corresponding to the minimum RMS value is extracted. The formula for calculating the RMS value is as follows. In the formula, N is the number of samples; Pi is the pressure value at the outlet section of the bellows.

[0035] The aforementioned automated closed-loop optimization process can automatically perform simulations under various different boundary conditions, and use the comprehensive performance of the index values ​​characterizing the intensity of pressure pulsation under all operating conditions as the optimization objective.

[0036] S5. The system compares the calculation results (RMS values) of all iterative schemes and automatically selects the parameter combination that minimizes the RMS value (such as Case 25 in Table 2) as the final optimal design scheme. This ensures that the results are objective and data-driven. The results output module will output the optimal geometric parameter combination that minimizes the index value based on the intensity of pressure pulsation.

[0037] Table 2 Case 1 2 4 25 27616056 2 2.5 3.12 27 27853174 3 2.12 4.32 29 27476110 4 1.02 2.23 20 26688411 5 2.32 4.43 25 27216056 6 2.83 2.32 20 27085990 7 1.76 2.43 20 26866913 8 2.78 2.23 20 26815167 9 2.84 2.42 20 26985990 10 1.93 2.15 20 26933266 11 1.91 2.35 20 26771870 12 1.94 2.25 20 26736631 · · · · · · · · · · · · · · · 25 1.04 5.81 21 26534543 26 1.44 5.52 28 26641650 27 1.54 2.86 25 27202638 28 1.64 2.76 29 27476786 29 2.46 2.72 23 27411315 30 3 6 20 27284065 This invention revolutionizes the interaction method by employing TUI to simplify operation, and its design philosophy focuses on automation and modularity, offering greater flexibility and scalability. Furthermore, this invention prioritizes optimization of existing design processes, resulting in superior feasibility and practicality.

[0038] The method of this invention is primarily applied in the field of bellows design and manufacturing. This method improves design efficiency and shortens the design cycle: by automating the design process, the design cycle is reduced, product launch speed is accelerated, and enterprise competitiveness is enhanced. Furthermore, it optimizes product performance and improves product quality: through multi-objective optimization, bellows products with superior performance can be designed, for example, achieving higher elastic modulus, longer fatigue life, and higher pressure resistance within the same volume. This significantly improves product quality and reliability. Simultaneously, it reduces design costs and enhances profitability: lowering design costs and reducing trial-and-error costs. Improved product performance enhances product competitiveness, thereby increasing enterprise profitability. In addition, the method of this invention promotes technological innovation: providing bellows design engineers with a more powerful tool, making innovative designs easier and driving the development of bellows technology.

[0039] The method of this invention can also be extended to other similar fields of fluid machinery and structural design, such as elastic elements, elastic connectors, shock absorbers, and expansion joints. For example, in piping systems, the design of expansion joints can be optimized to reduce stress and extend service life while meeting temperature compensation requirements. In shock absorption systems, the design of shock absorbers can be optimized to reduce size and weight while maintaining shock absorption effectiveness. In elastic connectors, the design can be optimized to provide a degree of flexibility while bearing loads. The method of this invention extends optimization techniques to a wider range of fields, promoting the application of optimization techniques in fluid machinery and structural design and improving design standards.

[0040] The method of this invention can also be combined with automated design and intelligent manufacturing. For example, it can be integrated with CAD / CAM systems: automatically generating CAD models and CAM programs to achieve integrated design and manufacturing. It can also interface with manufacturing equipment: directly transmitting optimized design parameters to manufacturing equipment to achieve automated production. Furthermore, it can utilize digital modeling and simulation: establishing a digital bellows model to achieve full lifecycle design, simulation, manufacturing, and maintenance management. This invention's method can drive digital transformation, facilitate the digital transformation of design and manufacturing, and realize intelligent production.

[0041] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0042] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention without departing from the principles and spirit of the present invention. Any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention shall still fall within the scope of the technical solution of the present invention.

Claims

1. A method for fully parameterized automatic closed-loop optimization of pressure pulsation in aerospace high-pressure bellows, characterized in that, An automated closed-loop optimization system is adopted, which includes a text command interface, a parametric modeling module, an automatic simulation analysis module, an optimization decision module, and a result output module. The specific steps are as follows: S1. The user inputs the geometric parameters and optimization instructions of the designed bellows into the text command interface; S2. The parametric modeling module responds to the instructions of the text command interface and automatically generates a bellows geometric model with variable geometric parameters. S3. The automatic simulation analysis module automatically performs mesh generation and computational fluid dynamics simulation on the generated geometric model, and automatically extracts index values ​​characterizing the intensity of pressure pulsation from the simulation results; S4. The optimization decision module uses an optimization algorithm to automatically adjust the geometric parameters of the bellows generated in step S2, with the goal of minimizing the index value that characterizes the intensity of pressure pulsation, to generate a new combination of geometric parameters, and feeds it back to the parameterized modeling module for the next iteration, forming a closed-loop optimization process until the termination condition is met. S5. The result output module will ultimately output the optimal combination of geometric parameters that minimizes the index value representing the intensity of pressure pulsation.

2. The method for fully parameterized automatic closed-loop optimization of pressure pulsation in aerospace high-pressure bellows according to claim 1, characterized in that: The geometric parameters of the bellows include the total length L1, the length of the bellows outlet section L2, the inner diameter d, the peak height C, the trough height T, and the number of waves N.

3. The method for fully parameterized automatic closed-loop optimization of pressure pulsation in aerospace high-pressure bellows according to claim 1, characterized in that: The index characterizing the intensity of pressure pulsation is the root mean square value (RMS) of the pressure at the bellows outlet section.

4. The method for fully parameterized automatic closed-loop optimization of pressure pulsation in aerospace high-pressure bellows according to claim 1, characterized in that: The workflow of the optimization decision module in step S4 includes: first, using the Latin hypercube sampling method to efficiently explore the entire parameter space with a small sample size and quickly locate the parameter region with better performance; then, using the genetic algorithm and / or the NSGA-III advanced optimization algorithm, which treats the parameter combination as a "chromosome" and simulates natural evolution through "selection, crossover, and mutation" to guide the search direction and find the global optimal solution.

5. The method for fully parameterized automatic closed-loop optimization of pressure pulsation in aerospace high-pressure bellows according to claim 1, characterized in that: The automatic simulation analysis module in step S3 performs fluid dynamics simulation by calling the script interface of external commercial FEA / CFD software, calculates the internal flow field and pressure distribution of the bellows under specific working conditions, and the post-processing process automatically extracts the key quantitative index of pressure pulsation intensity—the root mean square value of the pressure at the bellows outlet section.

6. The method for fully parameterized automatic closed-loop optimization of pressure pulsation in aerospace high-pressure bellows according to claim 1, characterized in that: The entire closed-loop optimization process in step S4 is controlled by multiple independent scripts connected in series using a high-level programming language. These scripts correspond to parametric modeling, mesh generation, simulation settings, and solution control, respectively.

7. The method for fully parameterized automatic closed-loop optimization of pressure pulsation in aerospace high-pressure bellows according to claim 1, characterized in that: The automated closed-loop optimization system can automatically perform simulations under various different boundary conditions, and use the comprehensive performance of the index values ​​characterizing the intensity of pressure pulsation under all operating conditions as the optimization target.

8. The method for fully parameterized automatic closed-loop optimization of pressure pulsation in aerospace high-pressure bellows according to claim 1, characterized in that: In step S4, new parameters are obtained through algorithm optimization. These new optimized parameters are used to automatically reconstruct the model. The new model is then used to draw the corrugated pipe mesh using a mesh drawing script, and simulated using a simulation solution script. All data results are analyzed, and the parameter corresponding to the minimum RMS value is extracted. The formula for calculating the RMS value is as follows. In the formula, N is the number of samples; Pi is the pressure value at the outlet section of the bellows.

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