Ultralow-temperature two-way pressure-bearing butterfly valve and multi-layer sealing structure and design method thereof

Abstract

The invention is suitable for the technical field of butterfly valve design, and particularly relates to an ultralow-temperature two-way pressure-bearing butterfly valve and a multi-layer sealing structure and a design method thereof, and the design method of the multi-layer sealing structure of the ultralow-temperature two-way pressure-bearing butterfly valve comprises the steps that a user sealing structure design request and butterfly valve core parameter data are collected; obtaining a sealing structure design environment corresponding relation according to a user sealing structure design request and the butterfly valve core parameter data; based on the sealing structure design environment corresponding relation, determining a design environment where the user sealing structure design task is located; generating an initial multi-layer sealing structure model based on the design environment; and executing data verification on the initial multi-layer sealing structure model, and optimizing the initial multi-layer sealing structure model according to a data verification result and the sealing performance redundancy so as to generate a final multi-layer sealing structure model. According to the method, accurate matching of design performance and working condition requirements is achieved, and the efficiency and reliability of sealing structure design are integrally improved.

Description

Technical Field

[0001] This application belongs to the field of butterfly valve design technology, and in particular relates to an ultra-low temperature bidirectional pressure-bearing butterfly valve and its multi-level sealing structure and design method. Background Technology

[0002] As a key control component in cryogenic fluid transport systems, the cryogenic bidirectional pressure-bearing butterfly valve is widely used in high-end fields such as liquefied natural gas storage and transportation, aerospace cryogenic propellant transportation, and cryogenic experimental devices. Its sealing performance directly determines the safety and reliability of the system operation, especially under cryogenic (e.g., below -162℃) and bidirectional pressure conditions. The sealing structure must simultaneously withstand the brittle fracture damage of the cryogenic medium, bidirectional pressure impact, and thermal expansion and contraction deformation. Therefore, the design of the sealing structure is one of the core technical challenges in the research and development of cryogenic bidirectional pressure-bearing butterfly valves.

[0003] Existing design methods often focus on the selection of sealing materials and the local optimization of the geometry of the sealing surface. They rely heavily on the personal experience of engineers to dominate the design process, which can easily lead to design deviations or resource redundancy. This can result in a decline in sealing performance or even failure, making it impossible to guarantee the long-term stable operation of the system. Summary of the Invention

[0004] This application provides an ultra-low temperature bidirectional pressure-bearing butterfly valve and its multi-layer sealing structure and design method, which can solve the problems of design deviation or resource redundancy that are prone to occur in existing design methods, thus leading to the problem of sealing performance degradation or even failure.

[0005] In a first aspect, embodiments of this application provide a design method for a multi-layer sealing structure of a cryogenic bidirectional pressure-bearing butterfly valve, including: Collect user requests for sealing structure design and butterfly valve core parameter data; wherein, the butterfly valve core parameter data includes nominal diameter, design pressure, cryogenic medium type, sealing surface accuracy class, and bidirectional pressure requirements. Based on the user's sealing structure design request and the butterfly valve's core parameter data, a corresponding relationship between the sealing structure design environment is obtained; wherein, the corresponding relationship between the sealing structure design environment is used to characterize the matching rules between user requirements, butterfly valve core parameters, and the adaptive design environment; Based on the correspondence between the sealing structure design environments, the design environment in which the user's sealing structure design task is located is determined; Based on the design environment, an initial multi-layered sealing structure model is generated; The initial multi-layer sealing structure model is validated, and the model is optimized based on the validation results and sealing performance redundancy to generate the final multi-layer sealing structure model.

[0006] The technical solutions described in this application embodiment have at least the following technical effects: The design method for the multi-layer sealing structure of a cryogenic bidirectional pressure-bearing butterfly valve provided in this application involves first collecting user sealing structure design requests and core butterfly valve parameter data, including nominal diameter, design pressure, cryogenic medium type, sealing surface accuracy level, and bidirectional pressure-bearing requirements. Then, based on the user sealing structure design requests and the core butterfly valve parameter data, a sealing structure design environment correspondence is obtained, representing the matching rules between user requirements, butterfly valve core parameters, and the suitable design environment. Next, based on this correspondence, the design environment in which the user's sealing structure design task is located is determined, achieving a precise pre-positioning of design requirements and the design environment, providing a standardized basis for the subsequent scientific allocation of the design environment. Based on the design environment, an initial multi-layer sealing structure model is generated. By relying on the corresponding technical resources of the environment, the initial multi-layer sealing structure model can initially adapt to the core operating conditions of cryogenic bidirectional pressure-bearing, forming a structural prototype that meets basic sealing performance requirements. Finally, data verification is performed on the initial multi-layer sealing structure model, and based on the data verification results and sealing performance redundancy, the initial multi-layer sealing structure model is optimized to generate the final multi-layer sealing structure model. This method significantly improves the standardization and accuracy of the design process through prior parameter acquisition and environmental matching. Through progressive modeling from initial construction to verification and optimization, it achieves precise matching between design performance and operating condition requirements, solves the core defect of existing designs that emphasize local optimization while neglecting system adaptation, and improves the overall efficiency and reliability of sealing structure design. This enables the transformation of the sealing structure design of cryogenic bidirectional pressure butterfly valves from experience-driven to standardized and systematic.

[0007] Secondly, the embodiments of this application provide a multi-layer sealing structure for a cryogenic bidirectional pressure-bearing butterfly valve, which is designed using the design method for the multi-layer sealing structure of the cryogenic bidirectional pressure-bearing butterfly valve described in the first aspect above.

[0008] Thirdly, embodiments of this application provide a cryogenic bidirectional pressure-bearing butterfly valve, including the multi-layer sealing structure described in the second aspect above.

[0009] Fourthly, embodiments of this application provide a design system 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 method described in the first aspect above.

[0010] Fifthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect above.

[0011] Sixthly, embodiments of this application provide a computer program product that, when run on a design system, causes the design system to execute the design method for the multi-layer sealing structure of the cryogenic bidirectional pressure-bearing butterfly valve described in the first aspect.

[0012] It is understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of this application, 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 of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is a flowchart illustrating the design method of the multi-layer sealing structure of the cryogenic bidirectional pressure-bearing butterfly valve provided in the embodiments of this application; Figure 2 This is a cross-sectional view of the cryogenic bidirectional pressure-bearing butterfly valve provided in the embodiments of this application; Figure 3 yes Figure 2 A magnified view of a portion of point A in the middle; Figure 4 This is a schematic diagram of the structure of the cryogenic bidirectional pressure-bearing butterfly valve provided in the embodiments of this application; Figure 5 This is a schematic diagram of the design system provided in the embodiments of this application.

[0015] The following are the labeling elements in the figure: 100. Ultra-low temperature bidirectional pressure butterfly valve; 10. Multi-layer sealing structure; 11. Valve body; 12. Stainless steel base; 13. Hard alloy weld overlay surface; 14. Flexible graphite layer. Detailed Implementation

[0016] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0017] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0018] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0019] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determination" or "if the described condition or event is detected" may be interpreted, depending on the context, as "once determination," "in response to determination," "once the described condition or event is detected," or "in response to the detection of the described condition or event."

[0020] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0021] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0022] Existing design methods for cryogenic butterfly valve sealing structures still have significant shortcomings, making it difficult to meet the design requirements of high precision and high reliability. On the one hand, existing design methods often focus on the selection of sealing materials and the local optimization of sealing surface geometry, lacking a systematic consideration of operational adaptability. Especially when facing complex operating conditions such as bidirectional pressure, the limited parameter collection often leads to a low degree of matching between the design scheme and actual operating requirements. Furthermore, the design process lacks a unified environmental adaptation standard, relying heavily on the individual experience of engineers, which can easily lead to design deviations or resource redundancy. On the other hand, the performance verification of existing design methods is mostly limited to verifying the compliance of basic sealing indicators, with insufficient consideration for the long-term performance stability under cryogenic conditions. The lack of a targeted performance enhancement design approach makes the sealing structure prone to performance degradation or even failure under harsh conditions such as low-temperature fluctuations and long-term pressure, failing to guarantee the long-term stable operation of the system. Therefore, there is an urgent need for a multi-layered sealing structure design method that is adaptable to cryogenic bidirectional pressure conditions, has a scientific and reasonable design process, and offers high sealing reliability to address the shortcomings of existing technologies.

[0023] To address the aforementioned issues, this application provides an ultra-low temperature bidirectional pressure-bearing butterfly valve and its multi-layered sealing structure and design method. The method involves first collecting user sealing structure design requests and butterfly valve core parameter data, including nominal diameter, design pressure, ultra-low temperature medium type, sealing surface accuracy level, and bidirectional pressure-bearing requirements. Then, based on the user sealing structure design requests and the butterfly valve core parameter data, a sealing structure design environment correspondence is obtained, representing the matching rules between user requirements, butterfly valve core parameters, and the suitable design environment. Next, based on this correspondence, the design environment where the user's sealing structure design task is located is determined, achieving precise pre-association of design requirements and design environment, providing a standardized basis for the subsequent scientific allocation of design environments. Based on the design environment, an initial multi-layered sealing structure model is generated. By relying on the corresponding technical resources of the environment, the initial multi-layered sealing structure model can initially adapt to the core operating conditions of ultra-low temperature bidirectional pressure bearing, forming a structural prototype that meets basic sealing performance requirements. Finally, data verification is performed on the initial multi-layered sealing structure model, and based on the data verification results and sealing performance redundancy, the initial multi-layered sealing structure model is optimized to generate the final multi-layered sealing structure model. This method significantly improves the standardization and accuracy of the design process through prior parameter acquisition and environmental matching. Through progressive modeling from initial construction to verification and optimization, it achieves precise matching between design performance and operating condition requirements, solves the core defect of existing designs that emphasize local optimization while neglecting system adaptation, and improves the overall efficiency and reliability of sealing structure design. This enables the transformation of the sealing structure design of cryogenic bidirectional pressure butterfly valves from experience-driven to standardized and systematic.

[0024] To better understand the design method of the multi-layer sealing structure of the cryogenic bidirectional pressure-bearing butterfly valve provided in the embodiments of this application, the specific implementation process of the design method of the multi-layer sealing structure of the cryogenic bidirectional pressure-bearing butterfly valve provided in the embodiments of this application will be described by way of example below.

[0025] Figure 1 A schematic flowchart illustrating the design method of the multi-layer sealing structure of the cryogenic bidirectional pressure-bearing butterfly valve provided in this application embodiment is shown. The design method of the multi-layer sealing structure of the cryogenic bidirectional pressure-bearing butterfly valve includes: S100 collects user requests for sealing structure design and butterfly valve core parameter data; among which, the butterfly valve core parameter data includes nominal diameter, design pressure, cryogenic medium type, sealing surface accuracy level, and bidirectional pressure requirements.

[0026] It is understandable that a user's sealing structure design request refers to a collection of all requirements related to the sealing structure design of an ultra-low temperature bidirectional pressure-bearing butterfly valve, based on their actual application scenario needs. This includes the design purpose of the sealing structure, i.e., clearly defining the application scenario of the butterfly valve sealing structure (such as long-distance liquefied natural gas pipelines, aerospace cryogenic propellant refueling systems, ultra-low temperature laboratory fluid control devices, etc.). Different application scenarios directly determine the core design indicators such as the characteristics of the media the sealing structure must withstand and the range of temperature and pressure fluctuations; quality acceptance standards, i.e., clearly defining the performance acceptance criteria for the sealing structure; and personalized design requests, i.e., special requirements put forward by users based on their own equipment compatibility, operation and maintenance habits, such as the sealing structure needing to adapt to the installation dimensions of the existing butterfly valve body, the sealing material needing to meet specific procurement channel requirements, and the disassembly and assembly methods of the sealing structure, etc. For example, the system can receive user-input sealing design priority tags and butterfly valve operating condition tags, then retrieve historical design resource consumption data corresponding to the butterfly valve operating condition tags based on a historical sealing design database. Based on this historical resource consumption data, resource type tags can be calculated, and finally, the sealing design priority tags and resource type tags can be appended to the user's sealing structure design request. Alternatively, the system can directly receive structured design requirement documents submitted by users, extract the core requirements and key parameters from the documents using natural language processing technology, and automatically convert them into standardized user sealing structure design request information, etc., but is not limited to these methods. The collection of butterfly valve core parameter data can be combined with the special operating conditions of ultra-low temperature bidirectional pressure bearing, and can be obtained through structured questionnaires, API interfaces connected to the user management system, or structured document parsing, etc.

[0027] In one possible implementation, step S100 involves collecting a user's sealing structure design request, including: S110 receives user input of the sealing design priority label and the butterfly valve operating condition label.

[0028] It is understandable that the sealed design priority label is used to indicate the urgency of design cycle and performance requirements, such as P1-urgent (delivery cycle ≤ 7 days), P2-routine (delivery cycle 8-30 days), P3-R&D (no clear delivery cycle, focusing on performance breakthroughs), etc. Users can submit their preferences through drop-down selection or custom input, and the label encoding conversion will be completed automatically. The butterfly valve operating condition labels comprehensively cover key operating condition dimensions that affect sealing design, using a combination label format of medium type, temperature range, pressure rating, and application scenario. For example, liquid nitrogen -196℃ -4.0MPa - vehicle-mounted storage tank transportation, liquid oxygen -183℃ -6.4MPa - aerospace propulsion system. Users can select or supplement the labels based on actual operating conditions. The system has a built-in operating condition label library covering common cryogenic media (liquid nitrogen, liquid oxygen, liquid hydrogen, liquefied natural gas, etc.), temperature range (-269℃ to -100℃), pressure rating (1.6MPa, 2.5MPa, 4.0MPa, 6.4MPa, 10.0MPa), and application scenarios (vehicle-mounted, marine, land-based storage tanks, aerospace, scientific research experiments, etc.).

[0029] S120, based on the historical sealing design database, obtains the historical design resource consumption data corresponding to the butterfly valve operating condition label; the historical sealing design database includes sealing structure parameters, performance verification results and processing technology adaptation data.

[0030] It is understandable that a historical sealing design database refers to a distributed data storage database used to structurally store historical data related to the entire process of sealing structure design for cryogenic bidirectional pressure butterfly valves. Its core value lies in providing data support for current design resource planning by reusing experience data from past mature design projects, thereby improving design efficiency and optimizing resource consumption. The historical sealing design database includes: a sealing structure parameter module, which serves as the core foundation of the database and covers detailed design parameters for all past cryogenic bidirectional pressure-bearing butterfly valve sealing structures. This includes basic operating condition parameters such as nominal diameter, design pressure, and cryogenic medium type, as well as core structural parameters such as sealing surface type (spherical, conical, etc.), sealing layer material type, thickness of each sealing layer (stainless steel substrate, flexible graphite layer, hard alloy weld overlay, etc.), sealing gap, and sealing surface roughness and flatness. It also associates the corresponding design task's operating condition tags, forming a parameter-operating condition binding relationship. The performance verification result module stores performance data for each historical design scheme after experimental or simulation verification. This is a key basis for evaluating the reliability of the design scheme, specifically including sealing pressure resistance test data (maximum withstand pressure under forward / reverse pressure, leakage rate compliance), cryogenic stability test data (sealing performance decay curves under different cryogenic environments, thermal expansion and contraction deformation), and double... The system includes test data on pressure switching (seal integrity after multiple forward-reverse pressure switching), with each data point accompanied by auxiliary information such as the verification environment, verification equipment, and verification standards. The processing technology adaptation data module connects key data from the design and production stages, covering the entire processing flow information matching historical sealing structure designs. This includes processing equipment models (such as welding equipment and precision grinding equipment), core process parameters (welding temperature, welding speed, grinding accuracy parameters), processing time, material loss rate, cost accounting data (material procurement cost, processing time cost), and process adaptability assessment results (such as the process difficulty level and pass rate corresponding to a certain structural design). This data can be obtained through laboratory experiments, on-site measurements and monitoring, and past experience. After acquisition, the collected data is organized, classified, and archived, useful information and patterns are extracted, and the relevant data is saved to a database to form a historical sealing design database.

[0031] When retrieving historical design resource consumption data based on user-input butterfly valve operating condition tags, a retrieval algorithm combining precise matching and fuzzy matching is employed. First, precise matching is performed using medium type-temperature range-pressure rating as the core search terms. If ≥3 matching results are found, the corresponding data is directly extracted. If fewer than 3 precise matching results are found, the search scope is expanded to include fuzzy matching using medium type-pressure rating as the core term, supplementing historical data for similar operating conditions. The acquired historical design resource consumption data can specifically include: manpower costs, simulation computing power consumption, and design cycle during the design phase; material procurement costs and material loss rates during the material selection phase; and equipment occupancy time, processing energy consumption, and number of process adjustments during the processing phase. For example, if the user's operating condition label is "liquid nitrogen -196℃ -4.0MPa - vehicle-mounted storage tank transportation", the resource consumption data of three sets of historical designs under the same operating condition can be obtained by searching. These include design cycles of 12 days, 15 days, and 13 days, material loss rates of 3.2%, 2.8%, and 3.0%, and simulation computing power consumption of 800 GPU·h, 850 GPU·h, and 820 GPU·h, respectively.

[0032] S130, Calculate resource type labels based on historical design resource consumption data; wherein, resource type labels are used to indicate the category and matching level of sealing material selection resources and cryogenic process adaptation resources required in the sealing structure design process.

[0033] This step, as we understand it, transforms historical resource consumption data into concrete design resource guidelines. By quantifying resource type tags, it achieves precise matching and efficient allocation of design resources. The calculation of resource type tags employs a weighted statistical + hierarchical method. First, the calculation dimensions cover two core dimensions: sealing material selection resources and cryogenic process adaptation resources. Each dimension is further subdivided into specific sub-items. For example, sealing material selection resources can include three sub-items: material performance compatibility, material procurement difficulty, and material cost. Cryogenic process adaptation resources can include three sub-items: process maturity, process equipment compatibility, and process adjustment complexity. Second, a weight is assigned to each sub-item. The weight value is determined using the analytic hierarchy process (AHP) based on the degree of influence of each sub-item on the design results in historical designs. For example, the weight for sealing material performance compatibility is 0.4, for material procurement difficulty is 0.3, for material cost is 0.3, for cryogenic process maturity is 0.4, for process equipment compatibility is 0.3, and for process adjustment complexity is 0.3. Subsequently, based on historical designs… Resource consumption data is used to calculate a quantitative score for each sub-item. For example, the material performance compatibility score is calculated based on the historical sealing performance compliance rate of materials under corresponding working conditions: 100% compliance rate earns 10 points, 90%-99% earns 8 points, 80%-89% earns 6 points, and below 80% earns 4 points. The material procurement difficulty score is calculated based on the historical procurement cycle: procurement cycle ≤ 7 days earns 10 points, 8-15 days earns 8 points, 16-30 days earns 6 points, and more than 30 days earns 4 points. Finally, a weighted summation is used to calculate the comprehensive score for each dimension, and then the matching level is divided according to the comprehensive score. The matching level is divided into A (excellent, comprehensive score ≥ 9 points), B (good, 7-8 points), C (qualified, 5-6 points), and D (needs optimization, ≤ 4 points), which ultimately forms a resource type label. The label format is "material resource level - process resource level", such as "AB", "BC", etc. Taking a specific calculation process as an example, if in the historical design resource consumption data, the performance compliance rate of sealing materials for a certain working condition is 98% (8 points), the procurement cycle is 10 days (8 points), and the material cost is at a normal level (8 points), then the comprehensive score for material resources = 8 × 0.4 + 8 × 0.3 + 8 × 0.3 = 8 points, and the grade is B; the maturity of the cryogenic process is 95% (8 points), the adaptability of the process equipment is 100% (10 points), and the complexity of process adjustment is low (10 points), then the comprehensive score for process resources = 8 × 0.4 + 10 × 0.3 + 10 × 0.3 = 9.2 points, and the grade is A, with the final resource type label being "BA". The core function of this label is to clarify the category and matching level of the resources required for the current design. For example, the label "BA" indicates that the sealing material selection resources need to be matched at a good level, and the cryogenic process adaptability resources need to be matched at an excellent level, providing a clear basis for resource allocation in the subsequent design process.

[0034] S140, attach the sealing design priority label and resource type label to the user's sealing structure design request.

[0035] It is understandable that the appending process employs structured data encapsulation technology to integrate the user's original sealed structure design request, sealed design priority label, and resource type label according to a preset data format, generating a unified design request ID. Specifically, the encapsulated data format can be JSON, containing fields such as basic information (user ID, design request number, submission time), core requirements (design purpose, acceptance criteria, delivery requirements), operating condition label, priority label, resource type label, and core parameter data. Each field has a defined data type and validation rules. For example, the design request number uses the encoding rule "REQ-year-month-serial number," the priority label is limited to "P1 / P2 / P3," and the resource type label is limited to "[A / B / C / D]-[A / B / C / D]," etc. The design request data packet with appended tags will be stored in the design request management database and synchronously pushed to the subsequent design environment matching module. For example, a user's original design request was "to design a cryogenic bidirectional pressure butterfly valve sealing structure for liquefied natural gas transportation, with acceptance standards conforming to GB / T 12238-2022 and a delivery period of 15 days." The additional priority tag was "P2", the resource type tag was "BA", and the core parameter data included "nominal diameter DN200, design pressure 4.0MPa, medium type liquefied natural gas (-162℃), sealing surface accuracy Ra1.6, and bidirectional pressure difference ≤0.5MPa".

[0036] This setup enables precise and standardized processing of user sealed structure design requests by receiving tags, acquiring historical data, calculating resource tags, and additional encapsulation. First, it addresses the shortcomings of existing technologies in collecting general and incomplete user design requirements. By introducing sealing design priority tags and butterfly valve operating condition tags, it accurately captures the core needs and operating conditions of users, providing clear directional guidance for subsequent design. Second, by reusing historical sealing design databases and combining them with weighted statistical algorithms to calculate resource type tags, it achieves quantitative assessment of design resource requirements, reducing the blindness of resource allocation and effectively lowering design costs and resource waste. For example, based on the material resource level determined by historical data, it can directly select sealing materials that are suitable for the operating conditions and have the best cost performance, reducing the trial and error costs of material selection. Third, by encapsulating structured data and integrating various tags with original design requests, it achieves standardized and integrated management of design information, improving the accuracy and efficiency of information transmission in subsequent design stages and reducing design cycle extensions or design errors caused by fragmented information. Fourth, the entire process adopts automated data processing and verification mechanisms, improving the efficiency of design request collection and processing. For example, it automatically matches historical data through retrieval algorithms and automatically verifies data integrity through verification modules, significantly reducing manual intervention costs and improving the automation level of the design process.

[0037] S200: Based on the user's sealing structure design request and the butterfly valve's core parameter data, the corresponding relationship of the sealing structure design environment is obtained; wherein, the corresponding relationship of the sealing structure design environment is used to characterize the matching rules between user requirements, butterfly valve core parameters and the appropriate design environment.

[0038] It is understandable that by deeply analyzing the user's sealing structure design request and the butterfly valve's core parameter data, key influencing factors can be extracted. These factors include sealing design priority, butterfly valve operating condition complexity (based on a comprehensive judgment of medium type, temperature, and pressure), core parameter accuracy requirements, and performance acceptance standard levels. For example, operating condition complexity can be divided into four levels: extremely high (e.g., liquid hydrogen -253℃, high pressure 10MPa), high (e.g., liquid oxygen -183℃, medium pressure 6.4MPa), medium (e.g., liquid nitrogen -196℃, medium pressure 4.0MPa), and low (e.g., liquefied natural gas -162℃, low pressure 1.6MPa). Then, based on the analysis of key influencing factors and the sorted environmental characteristics, matching rules are formulated, and a multi-factor weighted matching algorithm is adopted. Each influencing factor is assigned a weight (e.g., priority weight 0.3, operating condition complexity weight 0.4, accuracy requirement weight 0.2, acceptance standard weight 0.1), and a score (0-10 points) is assigned to the degree of matching between each factor and the environment. The total score is calculated by weighted summation, and the environment with the highest total score is the suitable environment. Finally, a correspondence table of sealed structure design environment can be formed, which clearly shows the correspondence between each combination of key influencing factors and the suitable design environment.

[0039] S300 determines the design environment in which the user's sealing structure design task is located based on the correspondence between the sealing structure design environment and the design environment.

[0040] This step is understandably a practical application of the correspondence between sealing structure design environments. Its core is to accurately allocate user sealing structure design tasks to suitable design environments through a standardized matching process, ensuring the rational use of design resources and the efficient advancement of the design process. For example, this can be achieved by parsing user information from the user's sealing structure design request, then querying the sealing design environment mapping table from the sealing design configuration module, and finally assigning the user's sealing structure design task to the corresponding design environment based on the user information, sealing design priority tags, and the sealing design environment mapping table. Alternatively, it can be done by first combining the butterfly valve's core parameter data (such as cryogenic medium type, design pressure, bidirectional pressure difference, etc.) with user requirements, dividing the design tasks into three levels: simple, medium, and complex. Simple tasks correspond to basic sealing designs for conventional cryogenic media (such as liquid nitrogen) and low pressure (≤2.5MPa), while complex tasks correspond to high-precision sealing designs for special cryogenic media (such as liquid hydrogen) and high pressure (≥6.4MPa). Then, the corresponding design environment is matched based on the task complexity level.

[0041] In one possible implementation, in step S300, based on the correspondence between the sealing structure design environments, the design environment in which the user's sealing structure design task is located is determined, including: S310, parse the user information in the user's sealing structure design request; whereby the user information is used to reflect the type of the design subject.

[0042] It is understandable that the type of design subject includes the industry, business scope, project level, etc.

[0043] S320 assigns user sealing structure design tasks to corresponding design environments based on user information, sealing design priority tags in the correspondence between sealing structure design environments, and sealing design environment mapping tables. The design environment includes a sealing structure draft design unit, a sealing performance simulation preview unit, and a sealing structure production design unit.

[0044] It is understandable that the basic scope of environment adaptation is determined by the design subject type. Then, based on the "Subject Adaptation Type" field of each design environment in the sealing design environment mapping table, a list of basic environments matching the current design subject type is filtered out. Next, sealing design priority tags are introduced to refine the scope. Based on the urgency and performance emphasis of the priority tags, the basic environment list is further filtered. Finally, a comprehensive score is calculated using real-time data from the sealing design environment mapping table, and the environment with the highest comprehensive score is selected as the design environment. For example, if the design subject type of a certain design task is "SCI-Aerospace Research" (research institution subject), the sealing design priority tag is P3 (R&D), and the basic environment scope includes the sealing performance simulation preview unit and the sealing structure draft design unit; through comprehensive scoring, the sealing performance simulation preview unit scores 9 points for environment adaptability, 8 points for resource availability, and 8.5 points for task matching efficiency, resulting in a comprehensive score of 8.65, which is ultimately determined as the target design environment.

[0045] This setup, based on the differentiated needs of different design subject types, precisely matches tasks to suitable design environments (seal structure draft design unit, seal performance simulation preview unit, and seal structure production design unit). It also ensures urgent tasks are handled by combining seal design priority tags. Furthermore, with the support of standardized data from the seal design environment mapping table, the allocation process is automated and standardized, significantly reducing manual intervention costs and matching errors, shortening task allocation time, and improving the utilization rate of design resources. Moreover, the accumulation of matching data during the allocation process can feed back into the optimization of the correspondence between the seal structure design environment and the design, forming a virtuous cycle of precise allocation, data accumulation, and optimization iteration. This provides a reliable guarantee for the efficient and accurate advancement of the sealing structure design task for ultra-low temperature bidirectional pressure butterfly valves.

[0046] S400 generates an initial multi-layered sealing structure model based on the design environment.

[0047] It is understandable that, based on a suitable design environment and combined with the core parameters of the butterfly valve and the requirements of bidirectional pressure bearing conditions at ultra-low temperatures, an initial multi-layer sealing structure model with basic sealing performance can be constructed to provide a structural prototype for subsequent optimization processes. For example, a metal + flexible composite multi-layer sealing three-dimensional structure model can be constructed based on the sealing surface geometric properties and sealing material characteristics in the butterfly valve's core parameter data. Then, the multi-layer sealing three-dimensional structure model can be adjusted based on the type of ultra-low temperature medium in the design environment to generate an initial multi-layer sealing structure model. Alternatively, the corresponding parametric modeling module and ultra-low temperature operating condition database can be activated based on the design environment, and the butterfly valve's core parameters (nominal diameter, design pressure, bidirectional pressure difference, etc.) can be entered into the modeling module. Through the ultra-low temperature sealing structure parameter calculation algorithm, key modeling parameters such as sealing surface width, thickness of each sealing layer, and sealing gap can be automatically generated to directly generate the initial multi-layer sealing structure model, and so on, but not limited to these methods.

[0048] In one possible implementation, in step S400, an initial multi-layered sealing structure model is generated based on the design environment, including: S410, based on the sealing surface geometric properties and sealing material characteristics in the core parameter data of the butterfly valve, constructs a multi-layered three-dimensional sealing structure model of metal + flexible composite; wherein, the metal + flexible composite includes a stainless steel substrate, a flexible graphite layer and a hard alloy weld overlay surface.

[0049] Understandably, this step is the core of the initial model construction. The core logic is to accurately match the geometric properties and material characteristics of the sealing surface, relying on the modeling resources of the design environment, to construct a 3D model with hierarchical structural advantages, providing basic structural support for the ultra-low temperature bidirectional pressure-bearing sealing performance. Specifically: First, anchor the core geometric parameters of the sealing surface. Based on the geometric properties of the sealing surface in the butterfly valve's core parameter data, extract key parameters such as sealing surface type (spherical seals are preferred for ultra-low temperature bidirectional pressure-bearing conditions due to their superior sealing performance and pressure-bearing stability), nominal diameter, spherical radius of curvature, sealing surface angle, surface roughness (Ra0.8-Ra3.2), and flatness. These parameters are then entered into the parametric modeling tools in the design environment (such as SolidWorks or ANSYS). DesignModeler establishes parameter-driven relationships to ensure rapid subsequent adjustments. Secondly, it adapts to the characteristics of each sealing layer material by retrieving corresponding material parameters from the cryogenic material database of the design environment. For the stainless steel substrate (e.g., 316L stainless steel, resistant to -270℃, suitable for cryogenic media corrosion), the flexible graphite layer (e.g., expanded graphite, possessing excellent low-temperature elasticity and sealing compensation capabilities), and the hard alloy weld overlay surface (e.g., Stellite alloy, improving the wear resistance and anti-galling properties of the sealing surface), it assigns characteristic parameters such as density, elastic modulus, coefficient of linear expansion, and allowable contact pressure to ensure material properties match cryogenic operating conditions. Thirdly, it performs layered modeling, based on the stainless steel substrate... The layer sequence of flexible graphite layer and hard alloy weld overlay surface is determined by using modeling tools to independently model each layer. The stainless steel substrate serves as the load-bearing foundation, ensuring structural strength, with its thickness matched to the nominal diameter (8-15mm for DN50-DN200). The flexible graphite layer acts as the sealing core, with a thickness controlled at 2-5mm to balance elasticity and strength. The hard alloy weld overlay surface serves as the contact surface, with a weld overlay thickness of 0.5-1.5mm, ensuring a firm bond with the substrate. Finally, the structure is integrated, and the three layers are precisely assembled using Boolean operations and constraint association functions of the modeling tools, ensuring tight fit and accurate positioning of each layer, forming a complete metal + flexible composite multi-layered sealed three-dimensional structural model.

[0050] S420 adjusts the multi-layer sealing three-dimensional structure model based on the type of cryogenic medium in the design environment to generate an initial multi-layer sealing structure model.

[0051] Understandably, this step is a crucial optimization stage for adapting the initial model to the operating conditions. The core logic is to adjust the 3D model parameters specifically based on the differences in the physicochemical properties of the cryogenic medium, so that the model can initially adapt to the specific medium conditions. For example, based on the type of cryogenic medium in the design environment, an optimal opening and closing trajectory can be generated. Then, based on the optimal opening and closing trajectory, the contact pressure distribution characteristics of the metal + flexible composite can be determined. Finally, based on the contact pressure distribution characteristics, the multi-layer sealing 3D structure model can be adjusted to generate the initial multi-layer sealing structure model. Alternatively, a method of direct adaptation of medium properties + static performance pre-verification can be adopted. That is, first, the core physicochemical properties of the cryogenic medium (such as medium temperature, viscosity, corrosiveness, vapor pressure, and phase change characteristics) are deeply analyzed to clarify the core influence dimensions of the medium on the sealing structure (such as low-temperature embrittlement, thermal expansion and contraction deformation, corrosion damage, and phase change pressure impact). Then, the material and structural parameters of the model are adjusted specifically. Finally, the performance pre-verification is completed through basic static simulation to generate the initial model, and so on, but not limited to these methods.

[0052] This setup, based on the sealing surface geometry and sealing material properties in the butterfly valve's core parameters, constructs a metal-flexible composite 3D model. This allows the stainless steel matrix's load-bearing strength, the flexible graphite layer's sealing compensation capability, and the hard alloy weld overlay's wear resistance and anti-galling performance to synergistically enhance the model's advantages. By specifically adjusting the model parameters according to the type of cryogenic medium in the design environment, it can accurately adapt to the differences in physicochemical properties of different media (such as liquid hydrogen, liquid oxygen, and liquid nitrogen). This reduces the risk of sealing failure caused by insufficient media adaptation, such as cryogenic embrittlement, thermal expansion and contraction deformation, and corrosion damage, ensuring that the initial model initially possesses basic sealing performance suitable for specific operating conditions. Simultaneously, this process leverages design environment resources to achieve efficient collaboration between parametric modeling and targeted adjustments, replacing traditional experience-based modeling methods and significantly improving the generation efficiency and accuracy of the initial model.

[0053] In one possible implementation, in step S420, the multi-layered sealing three-dimensional structure model is adjusted based on the type of cryogenic medium in the design environment to generate an initial multi-layered sealing structure model, including: S421 generates the optimal opening and closing trajectory based on the type of cryogenic medium in the design environment.

[0054] It can be understood that the optimal opening and closing trajectory refers to the butterfly valve sealing surface movement trajectory planned based on the specific physicochemical properties of the cryogenic medium in the design environment (such as viscosity, freezing point, oxidizing properties, vapor pressure, and phase change characteristics), combined with the butterfly valve sealing structure parameters (such as sealing surface type, nominal diameter, and sealing gap). This trajectory minimizes friction and wear on the sealing surface during opening and closing, avoids medium retention and phase change impact, and ensures pressure balance. Its core essence is that the trajectory design must be specifically adapted to the operating conditions of different cryogenic media. For example, for the low viscosity and easy leakage characteristics of liquid hydrogen, the trajectory needs to have high smoothness to reduce dry friction; for the strong oxidizing properties of liquid oxygen, the trajectory needs to avoid local high temperatures caused by opening and closing impacts that trigger oxidation reactions; for the easily vaporized characteristics of liquid nitrogen, the trajectory needs to be adapted to pressure balance nodes to suppress vaporization impacts. In terms of trajectory morphology, a composite trajectory mode of rotation and translation can be adopted. By precisely setting key parameters such as the opening and closing angle range, speed curve (such as an S-shaped curve to avoid start-stop impacts), and start-stop dwell time, a smooth transition in the sealing surface contact process can be achieved, reducing sudden changes in contact stress. The optimal trajectory parameters can be obtained by constructing a multi-objective optimization function with three main optimization objectives: minimizing the friction and wear of the sealing surface, minimizing the phase change impact of the medium, and maximizing the stability of the pressure balance. The optimization algorithm module of the design environment is then used to solve for these parameters. The specific implementation process is as follows: First, define the optimization variables, namely the key parameters of the opening and closing trajectory (angle range, velocity curve coefficient, residence time); second, based on the physicochemical properties of the cryogenic medium and the sealing structure parameters, set the constraint range for each optimization variable (e.g., peak velocity ≤ 10° / s under liquid hydrogen conditions to avoid increased dry friction); then construct the optimization objective function, for example, the friction and wear function is established based on Hertzian contact theory, the phase change impact function is established based on the correlation between the medium vapor pressure and trajectory acceleration, and the pressure balance function is established based on the pressure fluctuation amplitude of the sealing cavity; finally, using the NSGA-Ⅲ multi-objective optimization algorithm, with the support of high-performance computing resources in the design environment, solve for the Pareto optimal solution set, and combine this with the priority of actual operating conditions (e.g., prioritizing "minimum friction and wear" under liquid hydrogen conditions) to select the unique optimal trajectory parameters, generating the complete opening and closing trajectory.

[0055] S422, based on the optimal opening and closing trajectory, determines the contact pressure distribution characteristics of the metal + flexible composite.

[0056] It can be understood that the contact pressure distribution characteristics refer to the distribution pattern and core quantitative indicators of the contact pressure in different areas of the sealing surface during the dynamic contact process of the sealing surface of the metal + flexible composite sealing structure along the optimal opening and closing trajectory to complete the full stroke under ultra-low temperature medium conditions. For example, a finite element simulation model of a metal-flexible composite sealing structure can be established. The viscosity and thermal expansion coefficient of the cryogenic medium type are used as simulation boundary conditions. Then, the key parameters of the optimal opening and closing trajectory are imported into the finite element simulation model to simulate the dynamic contact process of the sealing surface during the entire opening and closing stroke. The pressure values ​​of different contact areas of the sealing surface are obtained through simulation calculation, and a contact pressure cloud map is generated. Finally, the contact pressure distribution characteristics are extracted based on the contact pressure cloud map. Alternatively, a multi-physics coupled model integrating structural mechanics, thermodynamics, and fluid mechanics can be constructed. The physical properties of the cryogenic medium (in addition to viscosity and thermal expansion coefficient, density, latent heat of phase change, etc.) and the optimal opening and closing trajectory parameters (angle, velocity, residence time, etc.) are transformed into multi-field boundary conditions and motion loads of the coupled model, establishing a coupled correlation equation of "trajectory-temperature-pressure". The coupled simulation calculation program is started, and an adaptive step-size solver is used to simulate the dynamic process of the sealing surface moving along the optimal trajectory throughout the entire stroke. The system collects multi-dimensional data such as contact pressure, temperature distribution, and medium flow velocity across the entire sealing surface in real time, forming a spatiotemporal sequence dataset. Next, a machine learning module based on the design environment calls a pre-trained feature extraction model (generated through training on a massive number of cryogenic sealing simulation samples, covering pressure distribution data for different medium types, trajectory parameters, and sealing structures). The spatiotemporal sequence dataset coupled with the simulation output and a visualized pressure cloud map are used as model inputs. A convolutional neural network (CNN) extracts high-order features of the pressure distribution (such as pressure gradient change trends and the spatiotemporal correlation of local pressure abrupt changes). Simultaneously, traditional data analysis algorithms are combined to calculate basic quantitative indicators such as maximum / minimum contact pressure and pressure uniformity. Finally, the program fuses and verifies the high-order features extracted by machine learning with traditional quantitative indicators, eliminating outlier data (such as local pressure distortion caused by simulation step size), and integrating them to form a complete and accurate contact pressure distribution feature set, etc., but not limited to these steps.

[0057] In one possible implementation, in step S422, based on the optimal opening and closing trajectory, the contact pressure distribution characteristics of the metal + flexible composite are determined, including: S4221, establish a finite element simulation model of the metal + flexible composite sealing structure, and use the viscosity and temperature expansion coefficient of the medium corresponding to the cryogenic medium type as the simulation boundary conditions.

[0058] It is understandable that by meshing the multi-layered sealed three-dimensional structural model, using high-precision tetrahedral meshes, the mesh size is refined in the contact area of ​​the sealing surface (2-5mm), while the non-contact area is appropriately simplified (5-10mm). Material constitutive models for each sealing layer are defined, considering the nonlinear characteristics of materials at cryogenic temperatures, an elastoplastic constitutive model is selected. Material parameters (elastic modulus, Poisson's ratio, yield strength, etc.) for materials such as 316L stainless steel, flexible graphite, and Stellite alloy at the corresponding medium temperatures are input. Finally, simulation boundary conditions are set, including the viscosity of the cryogenic medium (e.g., 1.4μPa·s at -196℃ for liquid nitrogen) and the coefficient of thermal expansion (e.g., 12×10⁻⁶ for linear expansion under liquid hydrogen conditions). -6 The core boundary condition is set as / ℃, and the constraint conditions (the stainless steel base is fixed and the butterfly plate moves according to the optimal opening and closing trajectory) and contact conditions (the sealing layers are in bonded contact and the sealing surface and the butterfly plate are in frictional contact, and the friction coefficient is set according to the medium type, such as 0.15 under liquid oxygen conditions) to obtain the finite element simulation model of the metal + flexible composite sealing structure.

[0059] S4222 imports the key parameters of the optimal opening and closing trajectory into the finite element simulation model to simulate the dynamic contact process of the sealing surface during the full opening and closing stroke; among them, the key parameters include the opening and closing angle range, the opening and closing speed curve, and the start and stop dwell time.

[0060] Understandably, the process begins by designing a motion coupling module for an environmental simulation tool. This module transforms the key parameters of the optimal opening and closing trajectory (opening and closing angles of 0°-90°, S-shaped velocity curves, and start / stop dwell times of 0.5s / 1s) into motion loads for the simulation model, defining the motion equations of the butterfly plate. Secondly, the simulation step size and solver are set. An explicit solver can be used, dividing the opening and closing stroke into 18 simulation steps (each step being 5°). Five separate simulation steps are set for the start / stop dwell phase to ensure smooth capture of pressure changes. Finally, the simulation is initiated to simulate the dynamic contact process of the sealing surface throughout its entire stroke from opening to closing, recording data such as contact stress and relative displacement of the sealing surface in real time for each simulation step.

[0061] S4223 obtains the pressure values ​​of different contact areas of the sealing surface through simulation calculation and generates a contact pressure cloud map.

[0062] Understandably, the first step is to extract the sealing surface contact pressure data for each simulation step during the entire opening and closing stroke, and then select the pressure data in the closed state (a key operating condition for sealing performance) as the core analysis object. Then, the pressure data is interpolated, and finally, a color contact pressure cloud map can be generated. Gradient color scales (blue for minimum pressure and red for maximum pressure) are used to intuitively display the pressure distribution in different areas of the sealing surface, while also marking the pressure value range to facilitate quick location of pressure concentration areas (red areas) and low-pressure areas (blue areas).

[0063] S4224, extract contact pressure distribution features based on contact pressure cloud map; among which, contact pressure distribution features include maximum contact pressure value, minimum contact pressure value, pressure uniformity and location of pressure concentration area.

[0064] The process involves: firstly, visually identifying pressure concentration areas (such as the edge of the sealing surface or abrupt changes in spherical curvature) using contact pressure cloud maps, and marking the coordinates and extent of these areas; secondly, using the data analysis function of the simulation post-processing module to calculate the maximum contact pressure value (peak pressure in the red area of ​​the cloud map) and the minimum contact pressure value (valley pressure in the blue area of ​​the cloud map); and finally, calculating the pressure uniformity using the quantitative formula "pressure standard deviation / average pressure," where a smaller standard deviation indicates better uniformity. After extraction, all feature parameters are integrated into a contact pressure distribution feature set.

[0065] This setup, by incorporating the viscosity and thermal expansion coefficient of the cryogenic medium into the simulation boundary conditions, and combining key trajectory parameters such as the opening and closing angle range and velocity curves to simulate the dynamic contact process throughout the entire stroke, effectively overcomes the limitations of traditional static analysis. This makes the simulation model more closely resemble actual working conditions, significantly improving the realism and accuracy of pressure distribution analysis. By generating contact pressure cloud maps, the pressure distribution can be visualized intuitively, allowing for the rapid location of pressure concentration and low-pressure risk areas. Combined with the extraction of quantitative features such as maximum / minimum contact pressure values ​​and pressure uniformity, abstract pressure data is transformed into clear and traceable adjustment criteria, providing precise targets for subsequent sealing structure optimization.

[0066] S423, based on the contact pressure distribution characteristics, adjust the multi-level sealing three-dimensional structure model to generate the initial multi-level sealing structure model.

[0067] It is understandable that this step is to specifically optimize the sealing structure parameters for abnormal indicators in the contact pressure distribution characteristics, so as to ensure that the adjusted model contact pressure distribution meets the requirements of ultra-low temperature bidirectional pressure bearing conditions. For example, the contact pressure distribution characteristics can be compared with the corresponding preset thresholds. If the pressure uniformity is lower than the first preset threshold, the thickness of the flexible graphite layer is adjusted at the location of the minimum contact pressure value. If the maximum contact pressure value is greater than the second preset threshold and is located in the pressure concentration area, the curvature of the hard alloy weld overlay surface is optimized. Then, the adjusted sealing layer parameters are imported into the multi-layer sealing three-dimensional structure model, the contact pressure cloud map is regenerated and verified until the contact pressure distribution characteristics meet the preset threshold requirements, thus obtaining the initial multi-layer sealing structure model. Alternatively, a parameterized optimization model of the sealing structure can be constructed based on the design environment. The core structural parameters affecting the contact pressure distribution (in addition to the thickness of the flexible graphite layer and the curvature of the hard alloy weld overlay surface, the sealing surface contact angle, the pre-compression of the flexible graphite layer, and the stiffness parameters of the stainless steel matrix support are added) are included in the set of optimization variables. At the same time, the constraints under ultra-low temperature conditions are clarified (such as the material's low-temperature mechanical property limits, the size adaptation requirements of the sealing structure and the valve body, and the structural strength threshold under bidirectional pressure). Secondly, the contact pressure is used as the basis for optimization. The preset threshold for the distribution characteristics is the objective function (i.e., maximizing pressure uniformity, controlling the maximum contact pressure to be less than or equal to the second preset threshold, and the minimum contact pressure to be greater than or equal to the critical sealing pressure). A multi-objective optimization algorithm (such as a genetic algorithm) is called in the design environment. The current contact pressure distribution characteristic data and the parameterized optimization model are input. The algorithm generates multiple sets of candidate parameter combinations through iterative calculation (each set of combinations contains the specific values ​​of all optimization variables). Subsequently, each candidate parameter combination is imported into the multi-level sealing three-dimensional structure model one by one. The cryogenic dynamic simulation module is called simultaneously to simulate the contact pressure distribution process under the optimal opening and closing trajectory, generating contact pressure cloud maps and distribution characteristic data corresponding to each set of parameters, and completing the working condition adaptation verification of the candidate parameters. Finally, the verification results of each candidate parameter combination are comprehensively evaluated through the data analysis module of the design environment. The optimal parameter combination that meets the preset threshold and adapts to cryogenic working conditions (such as maintaining stable pressure distribution after cryogenic thermal deformation) is selected and solidified into the three-dimensional structure model to directly generate the initial multi-level sealing structure model.

[0068] This setup, based on the type of cryogenic medium, customizes the optimal opening and closing trajectory, avoiding problems such as sealing surface wear and medium phase change impact caused by the mismatch between the general trajectory and the physicochemical properties of the cryogenic medium (such as low viscosity, strong oxidizing properties, and easy phase change) from the perspective of motion mechanism, thus improving the adaptability of the trajectory to the working conditions. By determining the contact pressure distribution characteristics through the correlation of the optimal opening and closing trajectory, it breaks through the limitations of traditional static pressure analysis, and can accurately capture the pressure distribution law and abnormal areas of the sealing surface throughout the entire stroke under cryogenic dynamic working conditions, providing a quantitative basis for model adjustment. Finally, the model is adjusted in a targeted manner based on the quantitative characteristics, reducing the blindness of empirical adjustments, so that the contact pressure distribution of the initial model meets the bidirectional pressure bearing requirements of cryogenics. At the same time, relying on the design environment, it realizes efficient coordination of trajectory generation, pressure analysis and model adjustment, which significantly shortens the generation cycle of the initial model and improves the model's adaptability to working conditions and sealing reliability.

[0069] In one possible implementation, in step S423, the multi-layer sealing three-dimensional structure model is adjusted based on the contact pressure distribution characteristics to generate an initial multi-layer sealing structure model, including: S4231, compare the contact pressure distribution characteristics with the corresponding preset threshold. If the pressure uniformity is lower than the first preset threshold, adjust the thickness of the flexible graphite layer at the location of the minimum contact pressure value. If the maximum contact pressure value is greater than the second preset threshold and is located in the pressure concentration area, optimize the curvature of the hard alloy weld overlay surface.

[0070] It is understandable that both the first and second preset thresholds are pre-set values, which can be manually entered or retrieved from a database, etc. Insufficient pressure uniformity means an imbalance of force on the sealing surface, and the minimum contact pressure area may be lower than the "critical sealing pressure" under cryogenic conditions, easily leading to media leakage (cryo-cryo media have low viscosity and are easily permeable; even a small pressure gap can cause leakage). Secondly, the flexible graphite layer is the core of elastic compensation in the composite seal, possessing excellent low-temperature elasticity and compressibility. It can maintain good deformation compensation capability even in cryogenic environments. Thickening the flexible graphite layer at the minimum contact pressure location can actively increase the local contact pressure through its elastic deformation, precisely compensating for pressure deficiencies. Simultaneously, the locally adjustable characteristics of flexible graphite do not compromise the stability of the overall sealing structure, and can quickly improve the pressure distribution uniformity of the sealing surface through "targeted compensation."

[0071] Excessive maximum contact pressure can cause brittle fracture and wear on the hard alloy weld overlay surface in the pressure concentration area at ultra-low temperatures (the toughness of metal materials decreases at ultra-low temperatures, and excessive local stress easily leads to failure). On the other hand, the hard alloy weld overlay surface is the wear-resistant load-bearing layer of the sealing surface, and its curvature directly determines the contact shape between the sealing surface and the disc. Pressure concentration often stems from irregular contact shapes (such as abrupt changes in curvature or excessively small contact area). Optimizing the curvature of the weld overlay surface (such as increasing the radius of curvature and smoothing the transition) can expand the contact area, dispersing the concentrated pressure to a larger area and reducing the local maximum pressure to within the threshold. At the same time, the high strength of hard alloy can ensure the wear resistance of the optimized sealing surface, adapting to the dynamic opening and closing friction requirements of bidirectional pressure at ultra-low temperatures.

[0072] S4232, import the adjusted sealing layer parameters into the multi-layer sealing three-dimensional structure model, regenerate the contact pressure cloud map and verify it until the contact pressure distribution characteristics meet the preset threshold requirements, and obtain the initial multi-layer sealing structure model.

[0073] It is understandable that the adjusted parameters in S4231 (the increase in the thickness of the flexible graphite layer and the curvature of the hard alloy weld surface) are imported into the parametric 3D model to update the model structure. Secondly, the simulation process of S4221-S4223 is repeated to rebuild the finite element simulation model, import the opening and closing trajectory parameters, and generate a new contact pressure cloud map. Thirdly, the new contact pressure distribution characteristics are extracted and compared with the preset threshold for verification. If the threshold requirements are still not met, the process of S4231-S4232 is repeated to fine-tune the adjustment parameters (such as continuing to increase the thickness of the flexible graphite layer by 0.1-0.2 mm) until all contact pressure distribution characteristics meet the preset threshold. Finally, the model that meets the requirements is determined as the initial multi-layer sealing structure model.

[0074] This setup enables efficient collaboration between digital simulation and parametric modeling tools throughout the process, significantly shortening the initial model optimization cycle, improving the accuracy and reliability of model adjustments, laying a high-quality structural foundation for subsequent sealing performance optimization, and effectively ensuring the leakage protection capability and service life of the sealing structure under ultra-low temperature bidirectional pressure conditions.

[0075] S500 performs data verification on the initial multi-level sealing structure model and optimizes the initial multi-level sealing structure model based on the data verification results and sealing performance redundancy to generate the final multi-level sealing structure model.

[0076] For example, key sealing nodes of the initial multi-layer sealing structure model can be extracted, a set of sealing coefficients can be calculated based on the key sealing nodes, and then a sealing performance verification algorithm can be used to perform data verification on the set of sealing coefficients. The data verification results and sealing performance redundancy are output, and the optimization priority is determined based on the data verification results and sealing performance redundancy. Finally, an iterative optimization program is executed based on the optimization priority to generate the final multi-layer sealing structure model. Alternatively, an ultra-low temperature bidirectional pressure multi-condition scenario library can be constructed, and the initial multi-layer sealing structure model can be imported into the multi-physics field coupled simulation module of the design environment. Various conditions in the scenario library can be loaded in sequence to simulate the dynamic response of the sealing structure under different conditions. The sealing performance redundancy and cross-condition redundancy stability under each condition can be calculated. The sealing gap, the pre-compression of the flexible graphite layer, the sealing surface accuracy parameters, and the thermal expansion and contraction compensation can be used as optimization variables, and constraints can be set (limits of mechanical properties of ultra-low temperature materials, structural dimensions and valve body adaptation requirements, and manufacturing process tolerance range). The design environment's intelligent optimization algorithm (such as particle swarm optimization) is invoked. The optimization function and initial model parameters are input. The algorithm generates the optimal parameter combination that satisfies all objectives through multiple rounds of iterative calculation. The optimal parameter combination is then imported into the initial model, and multi-condition coupled simulation verification is re-executed. The focus is on verifying the performance compliance of single-condition operation, cross-condition consistency, and redundancy stability until all requirements are met, generating the final multi-level sealing structure model, and so on, but not limited to this.

[0077] In one possible implementation, in step S500, data verification is performed on the initial multi-layer sealing structure model, and the initial multi-layer sealing structure model is optimized based on the data verification results and the sealing performance redundancy to generate the final multi-layer sealing structure model, including: S510, extract the key sealing nodes of the initial multi-layer sealing structure model, and calculate the sealing coefficient set based on the key sealing nodes; the sealing coefficient set includes the sealing pressure resistance coefficient, the sealing leakage rate coefficient, the ultra-low temperature adaptation stability coefficient, and the reverse pressure sealing stability coefficient.

[0078] It is understandable that by combining the core stress mechanism of the ultra-low temperature bidirectional pressure-bearing seal and the high-incidence area of ​​seal failure, the key sealing nodes can be accurately located. Specifically, these include the annular sealing strip where the sealing surface contacts the butterfly plate (core sealing area), the bonding interface between the flexible graphite layer and the stainless steel substrate (key area for structural stability), and the stress concentration node during bidirectional pressure switching (key area for bidirectional seal balance). At the same time, the three-dimensional coordinates and associated structural parameters (such as sealing strip width, interface bonding area, etc.) of each node are marked. By constructing a standardized sealing coefficient calculation system: Sealing pressure resistance coefficient = maximum pressure withstandd by model simulation / design pressure (e.g., qualified threshold ≥ 1.2, reserving sufficient pressure resistance redundancy to cope with pressure fluctuations in cryogenic media); Sealing leakage rate coefficient = model simulation leakage rate / allowable leakage rate under operating conditions (e.g., qualified threshold ≤ 0.8, strictly controlling leakage risk, adapting to the characteristics of cryogenic media that are easy to vaporize and leak); Cryogenic adaptability stability coefficient = room temperature sealing performance / cryogenic sealing performance (e.g., qualified threshold ≥ 1.1, i.e., performance degradation rate ≤ 9% under cryogenic conditions, ensuring the stability of sealing performance under low temperature environment); Reverse pressure sealing stability coefficient = reverse pressure sealing performance / forward pressure sealing performance (e.g., qualified threshold ≥ 0.95, ensuring balanced sealing performance under bidirectional pressure conditions and reducing failure under reverse pressure). The calculation process can rely on the cryogenic operating performance data output by multiphysics simulation tools in the design environment (such as ANSYS Fluent, ABAQUS), combined with the material's cryogenic mechanical parameters to complete accurate calculations, ultimately forming a standardized sealing coefficient set containing the values ​​of each coefficient, calculation basis, and related node parameters.

[0079] S520 uses a sealing performance verification algorithm to perform data verification on the sealing coefficient set, and outputs the data verification results and sealing performance redundancy; the data verification results are used to reflect whether each data in the sealing coefficient set is qualified.

[0080] The core logic of the sealing performance verification algorithm is as follows: a multi-index weighted verification algorithm is adopted, combined with the performance priority of the cryogenic bidirectional pressure-bearing seal, and differentiated weights are set for each sealing coefficient (the sealing pressure resistance coefficient has a weight of 0.3, the sealing leakage rate coefficient has a weight of 0.3, and the two directly determine the core function of the seal; the cryogenic adaptability stability coefficient has a weight of 0.2, and the reverse pressure sealing stability coefficient has a weight of 0.2, to ensure adaptability to special working conditions). The comprehensive verification score is calculated by weighted summation (the pass line is ≥0.9, ensuring that the overall performance meets the standards). The process involves a tiered verification workflow: First, individual verification is conducted, comparing each sealing coefficient with its corresponding pass threshold to determine the pass / fail status of each individual indicator (e.g., a sealing leakage rate coefficient of 0.75 < 0.8 is considered unqualified). Second, comprehensive verification is performed, calculating a comprehensive score based on weights to determine whether the overall performance is qualified. Third, the sealing performance redundancy is calculated using the quantification logic of (actual coefficient value - pass threshold) / pass threshold × 100%. The redundancy directly reflects the performance margin (e.g., a sealing pressure resistance coefficient of 1.26, a pass threshold of 1.2, and a redundancy of 5% indicate that the performance margin just meets the standard; a redundancy of < 5% requires close monitoring). After verification, a standardized verification report is output, clearly indicating the individual verification results, comprehensive verification conclusions, specific values ​​of each coefficient, and redundancy.

[0081] S530 determines the optimization priority based on data verification results and sealing performance redundancy.

[0082] The prioritization follows the principle of addressing critical defects first, then addressing performance shortcomings, and finally improving performance redundancy. Specifically, it is divided into three levels: First Priority (Urgent Optimization): Indicators with substandard single sealing coefficients. These indicators directly lead to substandard sealing performance and are core critical defects, requiring priority resolution (e.g., sealing leakage rate coefficient 0.7 < 0.8). If multiple indicators are substandard, they should be ranked according to their coefficient weights (e.g., sealing pressure resistance coefficient and sealing leakage rate coefficient have higher weights and should be optimized first). Second Priority (Focus Optimization): Indicators that pass comprehensive verification but have a single redundancy of <5%. These indicators have insufficient performance margins and are prone to failure under ultra-low temperature fluctuations or slight material aging, requiring supplementary performance margins (e.g., sealing pressure resistance coefficient 1.24, redundancy 3.33%). Third Priority (Optimization and Improvement): Indicators with redundancy ≥5% but can be further optimized. The core objective is to improve overall sealing performance stability or reduce manufacturing costs (e.g., ultra-low temperature adaptability stability coefficient 1.18, redundancy 7.27%, parameters can be fine-tuned to balance stability and material costs). Once the priorities are determined, a standardized optimization priority list is generated, which clarifies the optimization order, core issues, and optimization objectives for each indicator (e.g., "First priority: sealing leakage rate coefficient, objective: increase to ≤0.8, redundancy ≥5%").

[0083] S540 executes an iterative optimization procedure based on optimization priorities to generate the final multi-layer sealing structure model; the optimization procedure includes sealing surface accuracy optimization, sealing gap optimization, and pre-compression optimization of the ultra-low temperature thermal expansion and contraction compensation structure.

[0084] Understandably, for the first priority (such as an unqualified sealing leakage rate coefficient), the core optimization is to improve the sealing surface accuracy by using parametric modeling tools in the design environment to improve the surface roughness level (e.g., from Ra0.8 to Ra0.4) and tighten the flatness tolerance (e.g., from 0.02mm to 0.01mm) to reduce leakage channels. For the second priority (such as insufficient redundancy in the sealing pressure resistance coefficient), the sealing gap is optimized (e.g., reduced from 0.3mm to 0.2mm) and the pre-compression of the flexible graphite layer is increased (e.g., increased from 1.5mm to 1.8mm) to improve the sealing specific pressure. For the third priority (such as optimization of the ultra-low temperature adaptability stability coefficient), the thermal expansion and contraction compensation structure parameters are optimized (e.g., adding elastic compensation gaskets and adjusting the compensation amount) to offset the effects of ultra-low temperature deformation. The iterative optimization process is as follows: First, adjust the corresponding structural parameters according to priority and update the initial model. Second, import the updated model into the cryogenic simulation module of the design environment and recalculate the sealing coefficient set. Third, compare the new coefficients with the acceptable threshold and redundancy requirements to determine if they meet the standards. If not, repeat the parameter adjustment-simulation verification process until all indicators meet the requirements. Finally, the model that meets the standards is determined as the final multi-layer sealing structure model.

[0085] This setup, by constructing a multi-dimensional sealing coefficient quantification system encompassing sealing pressure resistance, leakage rate, cryogenic adaptability stability, and reverse pressure sealing stability, comprehensively covers the core evaluation dimensions of sealing performance under cryogenic bidirectional pressure conditions. It overcomes the shortcomings of traditional design, such as single evaluation indicators and strong subjectivity, achieving an objective and accurate assessment of sealing performance. Utilizing sealing performance verification algorithms and redundancy quantification analysis, it can accurately pinpoint sealing performance weaknesses. Combined with optimization priority allocation, it allows limited optimization resources to focus on core defects (such as non-compliance coefficients) and critical weaknesses (such as insufficient redundancy), improving optimization efficiency. Targeted optimization measures for priority matching of sealing surface accuracy, sealing gap, and thermal expansion and contraction compensation pre-compression, coupled with iterative optimization loops, ensure that the final generated sealing structure model can stably adapt to harsh cryogenic bidirectional pressure conditions. Simultaneously, it leverages automated design environment resources to digitize the process, improving design efficiency and the stability of the final product's sealing performance.

[0086] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0087] This application also provides a multi-layer sealing structure 10 for a cryogenic bidirectional pressure-bearing butterfly valve, such as... Figure 2 and Figure 3 As shown, the multi-layer sealing structure of the cryogenic bidirectional pressure-bearing butterfly valve described in any of the above embodiments was designed.

[0088] This application also provides an ultra-low temperature bidirectional pressure-bearing butterfly valve 100, such as... Figure 2 and Figure 4 As shown, the multi-layer sealing structure 10 described in the above embodiments includes a valve body 11, a stainless steel substrate 12, a hard alloy weld overlay surface 13, and a flexible graphite layer 14. The stainless steel substrate 12 is connected to the flexible graphite layer 14 on the side closest to the valve body 11, and the hard alloy weld overlay surface 13 is located on the side of the flexible graphite layer closest to the valve body, simultaneously connecting to both the stainless steel substrate 12 and the flexible graphite layer 14.

[0089] This application also provides a design system. Figure 5 This is a schematic diagram of the structure of a design system 6 provided in one embodiment of this application. For example... Figure 5 As shown, the design system 6 of this embodiment includes: at least one processor 60 ( Figure 5 Only one is shown in the image), at least one memory 61 ( Figure 5 (Only one is shown in the image) and a computer program 62 stored in the at least one memory 61 and executable on the at least one processor 60, wherein when the processor 60 executes the computer program 62, it causes the design system 6 to implement the steps in the design method embodiments of the multi-layer sealing structure of any of the above-described cryogenic bidirectional pressure butterfly valves.

[0090] For example, the computer program 62 may be divided into one or more modules / units, which are stored in the memory 61 and executed by the processor 60 to complete this application. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program 62 in the design system 6.

[0091] The design system 6 can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. The design system 6 may include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art will understand that... Figure 5 This is merely an example of designing System 6 and does not constitute a limitation on the design of System 6. It may include more or fewer components than shown in the figure, or combine certain components, or different components, such as input / output devices, network access devices, buses, etc.

[0092] The processor 60 can be a Central Processing Unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0093] In some embodiments, the memory 61 may be an internal storage unit of the design system 6, such as a hard disk or memory of the design system 6. In other embodiments, the memory 61 may be an external storage device of the design system 6, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the design system 6. Furthermore, the memory 61 may include both internal storage units and external storage devices of the design system 6. The memory 61 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory 61 can also be used to temporarily store data that has been output or will be output.

[0094] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in any of the above method embodiments.

[0095] This application provides a computer program product that, when run on a design system 6, enables the design system 6 to implement the steps in any of the above method embodiments.

[0096] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to the design system 6, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include a USB flash drive, a portable hard drive, a magnetic disk, or an optical disk.

[0097] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0098] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0099] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A design method for a multi-layer sealing structure of an ultra-low temperature bidirectional pressure-bearing butterfly valve, characterized in that, include: Collect user requests for sealing structure design and butterfly valve core parameter data; wherein, the butterfly valve core parameter data includes nominal diameter, design pressure, cryogenic medium type, sealing surface accuracy class, and bidirectional pressure requirements. Based on the user's sealing structure design request and the butterfly valve's core parameter data, a corresponding relationship between the sealing structure design environment is obtained; wherein, the corresponding relationship between the sealing structure design environment is used to characterize the matching rules between user requirements, butterfly valve core parameters, and the adaptive design environment; Based on the correspondence between the sealing structure design environments, the design environment in which the user's sealing structure design task is located is determined; Based on the design environment, an initial multi-layered sealing structure model is generated; The initial multi-layer sealing structure model is validated, and the model is optimized based on the validation results and sealing performance redundancy to generate the final multi-layer sealing structure model.

2. The design method for the multi-layer sealing structure of the cryogenic bidirectional pressure-bearing butterfly valve as described in claim 1, characterized in that, The collection of user sealing structure design requests includes: Receive user input for sealing design priority labels and butterfly valve operating condition labels; Based on the historical sealing design database, the historical design resource consumption data corresponding to the butterfly valve operating condition label is obtained; wherein, the historical sealing design database includes sealing structure parameters, performance verification results and processing technology adaptation data; Based on the historical design resource consumption data, a resource type label is calculated; wherein, the resource type label is used to indicate the category and matching level of the sealing material selection resources and the ultra-low temperature process adaptation resources required in the sealing structure design process; Attach the sealing design priority tag and resource type tag to the user's sealing structure design request.

3. The design method for the multi-layer sealing structure of the cryogenic bidirectional pressure-bearing butterfly valve as described in claim 2, characterized in that, The process of determining the design environment in which the user's sealing structure design task resides based on the correspondence between the sealing structure design environments includes: Parse the user information in the user's sealing structure design request; wherein, the user information is used to reflect the type of the design subject; Based on the user information, the sealing design priority tag in the sealing structure design environment correspondence, and the sealing design environment mapping table, the user's sealing structure design task is assigned to the corresponding design environment; wherein, the design environment includes a sealing structure draft design unit, a sealing performance simulation preview unit, and a sealing structure production design unit.

4. The design method for the multi-layer sealing structure of the cryogenic bidirectional pressure-bearing butterfly valve as described in claim 1, characterized in that, The generation of an initial multi-layered sealing structure model based on the design environment includes: Based on the sealing surface geometric properties and sealing material characteristics in the core parameter data of the butterfly valve, a multi-layered three-dimensional sealing structure model of metal + flexible composite is constructed; wherein, the metal + flexible composite includes a stainless steel substrate, a flexible graphite layer and a hard alloy weld overlay surface. The multi-layer sealing three-dimensional structure model is adjusted based on the type of cryogenic medium in the design environment to generate an initial multi-layer sealing structure model.

5. The design method for the multi-layer sealing structure of the cryogenic bidirectional pressure-bearing butterfly valve as described in claim 4, characterized in that, The adjustment of the multi-layered sealing three-dimensional structure model based on the type of cryogenic medium in the design environment to generate an initial multi-layered sealing structure model includes: Based on the type of cryogenic medium in the design environment, the optimal opening and closing trajectory is generated. Based on the optimal opening and closing trajectory, the contact pressure distribution characteristics of the metal + flexible composite are determined. The multi-layer sealing three-dimensional structure model is adjusted based on the contact pressure distribution characteristics to generate an initial multi-layer sealing structure model.

6. The design method for the multi-layer sealing structure of the cryogenic bidirectional pressure-bearing butterfly valve as described in claim 5, characterized in that, The determination of the contact pressure distribution characteristics of the metal + flexible composite based on the optimal opening and closing trajectory includes: A finite element simulation model of a metal-flexible composite sealing structure was established, and the viscosity and temperature expansion coefficient of the cryogenic medium were used as simulation boundary conditions. The key parameters of the optimal opening and closing trajectory are imported into the finite element simulation model to simulate the dynamic contact process of the sealing surface during the entire opening and closing stroke; wherein, the key parameters include the opening and closing angle range, the opening and closing speed curve, and the start and stop dwell time. The pressure values ​​of different contact areas of the sealing surface are obtained through simulation calculation, and a contact pressure cloud map is generated. Contact pressure distribution features are extracted based on contact pressure cloud maps; wherein, the contact pressure distribution features include the maximum contact pressure value, the minimum contact pressure value, pressure uniformity, and the location of pressure concentration areas.

7. The design method for the multi-layer sealing structure of the cryogenic bidirectional pressure-bearing butterfly valve as described in claim 6, characterized in that, The adjustment of the multi-layer sealing three-dimensional structure model based on the contact pressure distribution characteristics to generate an initial multi-layer sealing structure model includes: The contact pressure distribution characteristics are compared with the corresponding preset thresholds. If the pressure uniformity is lower than the first preset threshold, the thickness of the flexible graphite layer is adjusted at the location of the minimum contact pressure value. If the maximum contact pressure value is greater than the second preset threshold and is located in the pressure concentration area, the curvature of the cemented carbide weld surface is optimized. The adjusted sealing layer parameters are imported into the multi-layer sealing three-dimensional structure model, the contact pressure cloud map is regenerated and verified until the contact pressure distribution characteristics meet the preset threshold requirements, and the initial multi-layer sealing structure model is obtained.

8. The design method for the multi-layer sealing structure of the cryogenic bidirectional pressure-bearing butterfly valve as described in claim 1, characterized in that, The step of performing data verification on the initial multi-layer sealing structure model and optimizing the initial multi-layer sealing structure model based on the data verification results and sealing performance redundancy to generate the final multi-layer sealing structure model includes: Key sealing nodes are extracted from the initial multi-layer sealing structure model, and a set of sealing coefficients is calculated based on the key sealing nodes; wherein, the set of sealing coefficients includes sealing pressure resistance coefficient, sealing leakage rate coefficient, cryogenic adaptation stability coefficient, and reverse pressure sealing stability coefficient. A sealing performance verification algorithm is used to perform data verification on the sealing coefficient set, and the data verification result and sealing performance redundancy are output; wherein, the data verification result is used to reflect whether each data in the sealing coefficient set is qualified; The optimization priority is determined based on the data verification results and the sealing performance redundancy. An iterative optimization procedure is executed based on the optimization priority to generate the final multi-layer sealing structure model; wherein, the optimization procedure includes sealing surface accuracy optimization, sealing gap optimization, and pre-compression optimization of the ultra-low temperature thermal expansion and contraction compensation structure.

9. A multi-layer sealing structure for an ultra-low temperature bidirectional pressure-bearing butterfly valve, characterized in that, The design method of the multi-layer sealing structure of the cryogenic bidirectional pressure-bearing butterfly valve according to any one of claims 1 to 8 is used to design the valve.

10. A cryogenic bidirectional pressure-bearing butterfly valve, characterized in that, It includes the multi-layer sealing structure as described in claim 9.

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