An aerospace precision component mechanical property simulation system and method

Abstract

The application provides an aerospace precision component mechanical property simulation system and method, comprising: extracting stress concentration area data from the optimization grid model, the stress concentration area refers to a part with high strength concentration, using a probability simulation method to simulate load conditions in extreme environments, and determining stress distribution rules of the connection area; according to the accurate prediction result, determining a risk index in the design verification stage, the risk index quantifies a potential failure probability, integrating stability requirements through a multi-scale analysis method to obtain a mechanical property evaluation report; extracting key indicators from the mechanical property evaluation report, using a risk assessment algorithm to analyze the influence degree of potential risks, and if the risk index is higher than a preset threshold, updating geometric data through a parameter optimization cycle to obtain an improved component design scheme.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to a simulation system and method for the mechanical properties of precision aerospace components. Background Technology

[0002] As a core pillar of the high-tech industry, the aerospace field places extremely high demands on the performance of precision components. The reliability and stability of their mechanical properties directly affect the safety of aircraft and the success of missions. Especially when dealing with components involving complex geometries, accurately predicting their performance in extreme environments has become a critical challenge that the industry urgently needs to overcome. Research in this field is not only about technological breakthroughs but also has irreplaceable value in ensuring national security and promoting scientific and technological progress.

[0003] However, current analytical methods for the mechanical properties of precision aerospace components often struggle to adapt to the challenges posed by complex geometries. Many existing techniques, when dealing with irregular structures, lack a deep understanding of the relationship between geometric features and mechanical behavior, particularly limiting the accuracy and applicability of analyses for spherical components. This deficiency prevents a comprehensive understanding of the component's true performance under actual operating conditions during the design and verification phases, thereby increasing potential risks.

[0004] Against this backdrop, research on spherical components faces significant technical challenges. The unique curvature of spherical structures makes them prone to stress concentration under load, which further affects the mechanical transfer at the connection points between the component and other parts. In other words, the curvature of a spherical surface not only alters the stress distribution but also makes the stress state in the connection area exceptionally complex and difficult to predict accurately. For example, in the connection between a spherical pressure vessel and external pipelines, due to differences in curvature and uneven stress distribution, the connection often becomes the weakest point where fatigue failure first occurs, seriously threatening the safety of the overall structure.

[0005] Therefore, accurately capturing the stress distribution of spherical components under complex geometric conditions and revealing their mechanical interaction characteristics with adjacent structures at the connection points has become a key problem that this research urgently needs to solve. Solving this problem will directly affect the design accuracy and service life of aerospace precision components, providing more reliable technical support for the industry. Summary of the Invention

[0006] This invention provides a simulation system and method for the mechanical properties of precision aerospace components, mainly including: Geometric and material property data of the spherical component are acquired, and curvature features and connection region parameters are extracted from a pre-established structural design database. The curvature features include surface curvature, and the connection region parameters include interface size indices. Based on the data, numerical analysis methods are used to perform initial mesh generation to obtain a preliminary structural model. Based on the preliminary structural model, a structural optimization algorithm is used to process complex geometries and determine the distribution of the influence of curvature changes on mechanical properties. If the curvature features exceed a preset threshold, the mesh density is adjusted through iterative calculation to obtain an optimized mesh model. Stress concentration region data are extracted from the optimized mesh model. The stress concentration region refers to the part with high intensity concentration. The load conditions under extreme environment are simulated using a probabilistic simulation method to determine the stress distribution law of the connection region. Based on the stress distribution law, the interaction parameters of adjacent structures are obtained. The interaction parameters include the load transfer coefficient. If the interaction parameters do not match the curvature characteristics, an adaptive calibration mechanism is used to correct the mechanical interaction characteristics to obtain a corrected interaction model. Fatigue failure potential point data are extracted from the modified interaction model. The fatigue failure potential points refer to the locations where damage is easily accumulated. The predictive analysis model is used to predict the behavior changes under long-term load. If the prediction results deviate from the actual range, the training data is optimized by sample augmentation technology to obtain accurate prediction results. Based on the accurate prediction results, risk indicators are determined in the design verification stage. These risk indicators quantify the probability of potential failure. Stability requirements are integrated through multi-scale analysis methods to obtain a mechanical performance evaluation report. Key indicators are extracted from the mechanical performance evaluation report, and the impact of potential risks is analyzed using a risk assessment algorithm. If the risk indicators are higher than a preset threshold, the geometric data is updated cyclically through parameter optimization to obtain an improved component design scheme.

[0007] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: This invention discloses a simulation system and method for the mechanical properties of precision aerospace components, proposing a complete solution to the challenge of evaluating the mechanical properties of spherical components under complex geometries and extreme environments in the aerospace field. The invention extracts curvature features and connection region parameters from a structural design database, combines numerical analysis and structural optimization algorithms to construct an optimized mesh model, accurately identifies stress concentration regions, and analyzes stress distribution patterns under extreme loads using probabilistic simulation methods. Simultaneously, the invention corrects mechanical interaction characteristics through an adaptive calibration mechanism, combines predictive analysis models and sample expansion techniques to accurately predict fatigue failure risks under long-term loads, and finally generates improved design schemes through multi-scale analysis and parameter optimization iterations. The core innovation of this invention lies in the seamless integration of geometric data, mechanical properties, and risk assessment, achieving fully automated simulation from initial modeling to design optimization, effectively improving the reliability and stability of component design, and providing strong technical support for the safety of precision aerospace components. Attached Figure Description

[0008] Figure 1 This is a flowchart of a simulation system and method for the mechanical properties of precision aerospace components according to the present invention.

[0009] Figure 2 This is a schematic diagram of a simulation system and method for the mechanical properties of precision aerospace components according to the present invention.

[0010] Figure 3 This is another schematic diagram of a simulation system and method for the mechanical properties of precision aerospace components according to the present invention. Detailed Implementation

[0011] The technical solutions of the embodiments of the present invention will be clearly and thoroughly described below with reference to the accompanying drawings. The described embodiments are merely some embodiments of the present invention.

[0012] like Figures 1-3 This embodiment of a simulation system and method for the mechanical properties of precision aerospace components may specifically include: Step S101: Obtain the geometric data and material property data of the spherical component, extract curvature features and connection region parameters from the pre-established structural design database, the curvature features include surface curvature, and the connection region parameters include interface size indicators, and perform initial mesh generation using numerical analysis methods based on the data to obtain a preliminary structural model.

[0013] Geometric and material property data of spherical components are obtained from a pre-set structural design database. The stored information is categorized and organized using a data extraction module to obtain categorized geometric and material information. For the categorized geometric information, a specialized analytical tool is used to extract curvature features and connectivity parameters. Curvature features include surface curvature, and connectivity parameters include interface dimensions, thus determining a set of feature parameters. Based on this set, numerical analysis methods are used to process the geometric information of the spherical components. Initial mesh generation is employed to create preliminary mesh data. If uneven distribution occurs in local areas of the preliminary mesh data, mesh smoothing techniques are used to adjust these areas, resulting in an optimized mesh structure. Based on the optimized mesh structure and material property data, a property mapping method is used to associate material information with the mesh structure, determining the complete structural model data. For the complete structural model data, a data verification module checks the matching degree between geometric and material information. If the matching degree is lower than a preset threshold, the mapping relationship is readjusted to obtain the final structural model.

[0014] In one possible implementation, the geometric data and material property data of the spherical component are obtained from a pre-defined structural design database.

[0015] For example, for a spherical shell component in the aerospace field, the database stores geometric data including the spherical curvature with a radius of 500 mm and the shell dimensions with a thickness of 10 mm, while material property data covers the aluminum alloy's elastic modulus of 70 GPa and density of 2.7 g / cm³. 3 The data extraction module categorizes and organizes this information, with geometric information grouped into shape parameter group and material information grouped into physical property group. This ensures the efficiency of subsequent processing, avoids calculation errors caused by mixed data, and thus improves the accuracy of model construction.

[0016] Specifically, for the classified geometric information, specialized analytical tools are used to extract curvature features and connectivity parameters.

[0017] For example, the curvature feature is calculated by determining the surface curvature, resulting in an average curvature of 0.002 mm. -1 The connection area parameters are extracted by the interface size indicators, such as the connection hole with a diameter of 100mm. This determination of the characteristic parameter set can highlight the bending characteristics of the spherical component, which helps to improve the accuracy of subsequent mesh generation and brings a better effect in simulating the real stress distribution.

[0018] In one possible implementation, the geometric information of the spherical component is processed using numerical analysis methods based on a set of characteristic parameters.

[0019] For example, curvature changes can be analyzed using a finite element preprocessing algorithm, and then the initial mesh generation technique can be used to complete the initial mesh generation. The resulting mesh data contains approximately 5,000 mesh elements, which can initially capture geometric details and improve the computational efficiency of structural analysis.

[0020] For example, if the initially divided grid data is unevenly distributed in local areas, such as the grid density in the connecting area being too low, resulting in uneven cell sizes ranging from 1mm to 5mm, then the local areas can be adjusted using grid smoothing techniques such as Laplacian smoothing. This will result in an optimized grid structure that is evenly distributed at around 2mm. Such optimization can reduce errors in numerical simulation and enhance the stability of the model.

[0021] Specifically, based on the optimized mesh structure and combined with material property data, the material information is associated with the mesh structure through a property mapping method.

[0022] For example, by uniformly mapping the properties of aluminum alloys to each mesh cell, the complete structural model data can be determined, which can achieve a close integration of materials and geometry and improve the realism of the simulation.

[0023] In one possible implementation, for complete structural model data, the matching degree of geometric information and material information is detected by a data verification module.

[0024] For example, if the matching degree is calculated to be 85%, which is lower than the preset threshold of 90%, the mapping relationship is readjusted, such as refining the material distribution, to obtain the final structural model. This verification can detect mismatch problems early, resulting in higher model reliability and supporting accurate predictions for subsequent engineering applications.

[0025] Step S102: Based on the preliminary structural model, a structural optimization algorithm is used to process complex geometries and determine the distribution of the influence of curvature changes on mechanical properties. If the curvature features exceed a preset threshold, the mesh density is adjusted through iterative calculation to obtain an optimized mesh model.

[0026] By processing the initial data of the structural model, a structural optimization algorithm is used to decompose and analyze the complex geometry, obtaining preliminary curvature variation data. Based on the curvature variation data, the specific impact distribution on mechanical properties is calculated, and stress concentration points in key areas are determined. If the curvature characteristics of key areas exceed a preset threshold, an iterative calculation process is initiated to locally adjust the mesh density, obtaining updated mesh distribution data. Using the updated mesh distribution data, the curvature variation of the geometry is re-analyzed to determine whether the mechanical properties have reached a stable state. If the mechanical properties have not reached a stable state, iterative calculations are continued to refine the mesh density, obtaining further optimized mesh distribution data. After obtaining the further optimized mesh distribution data, the final mesh model is constructed, determining its mechanical property distribution under complex geometries. For the final mesh model, data validation tools are used to comprehensively analyze its curvature characteristics and mechanical properties, obtaining optimized structural model data.

[0027] In one possible implementation, the core of processing the initial data of the structural model lies in using structural optimization algorithms to decompose complex geometries.

[0028] Specifically, this can be understood as dividing a continuously changing surface on a spherical component into multiple sub-regions with different geometric properties based on its curvature characteristics.

[0029] For example, the initial mesh data for the spherical head of a large storage tank might be generated solely based on the overall dimensions. Algorithmic analysis can identify a gradual curvature change in the central region of the head, while the curvature change increases sharply near the transition zone connecting to the tank body. This decomposition aims to facilitate subsequent targeted analysis of the mechanical effects. Based on the preliminary curvature change data obtained after decomposition, it is necessary to calculate the specific distribution of its impact on mechanical properties.

[0030] Understandably, areas of abrupt curvature change are often where stress tends to concentrate.

[0031] For example, using the static calculation module in finite element analysis, the stress contour map inside the component under standard design pressure can be calculated based on the current mesh model. Analyzing this contour map reveals that in the previously identified transition zone, the maximum stress value may reach 2.5 times the average stress, thus clearly identifying this area as a critical stress concentration point. This step directly links abstract geometric features with specific mechanical responses. If the curvature characteristics of the critical region, such as the local radius of curvature, are less than the minimum allowable value and exceed a preset threshold, an iterative calculation process needs to be initiated. At this point, local adjustment of the mesh density is the core operation.

[0032] In one embodiment, the system first re-subdivides the existing mesh around the identified high stress concentration points, reducing the original mesh size by half, for example, from 20 mm to 10 mm, and then performs mechanical calculations again. Re-analysis of the updated mesh distribution data allows observation of whether the stress peak in the region has significantly decreased or the distribution has become more gradual. If the stress fluctuation is still greater than 5%, the mechanical properties are determined not to have reached a stable state, and the mesh needs to be iteratively refined, possibly further densifying local meshes to 5 mm. After obtaining further optimized mesh distribution data, the final mesh model is constructed. This model can more accurately reflect the true mechanical property distribution under complex geometries. For this final model, comprehensive analysis using data validation tools is crucial.

[0033] For example, the tool compares the curvature continuity and stress gradient indices in key regions before and after iterative optimization. The benefit of the optimized structural model data is that it ensures sufficient mesh density in high-stress regions to capture the true stress state, while maintaining a relatively sparse mesh in low-stress regions. This achieves an optimal balance between computational accuracy and efficiency, providing a reliable foundation for subsequent strength verification and life assessment.

[0034] Step S103: Extract stress concentration region data from the optimized mesh model. The stress concentration region refers to the part with high intensity concentration. Use probabilistic simulation method to simulate load conditions under extreme environment and determine the stress distribution law of the connection region.

[0035] Using an optimized mesh model, raw data of stress concentration areas are acquired. Automated tools are used to classify and organize the data, resulting in a preliminary stress concentration distribution map. Based on this map, the specific locations of high-intensity zones are identified. Probabilistic simulation methods are employed to model load conditions under extreme environments, determining the response characteristics of these zones under different load conditions. From the response characteristics of the high-intensity zones, key data for the connection regions are extracted. Local mesh refinement is then performed on these connection regions to obtain more accurate stress distribution details. Probabilistic simulation methods are used to iteratively calculate the stress distribution details in the connection regions, obtaining the dynamic trend of stress distribution under extreme environments. This dynamic trend is analyzed to understand the stress distribution patterns in the connection regions under different load conditions. If the trend exceeds a preset threshold, the local mesh model is further optimized to identify potential risk areas. Based on the risk area assessment, a detailed mapping of the stress distribution patterns in the connection regions is generated, obtaining the final distribution data for subsequent analysis and processing.

[0036] In one possible implementation, the raw stress concentration data obtained from the optimized mesh model can be categorized by an automated script based on the magnitude of the stress value and its spatial location.

[0037] For example, areas where the set stress threshold exceeds 80% of the material's yield strength are automatically marked as high-strength areas, and two-dimensional or three-dimensional distribution maps with different color gradients representing stress levels are generated to intuitively show the location and severity of stress concentration.

[0038] Specifically, high-intensity areas identified from the initial distribution map, such as a specific curved surface at the wing-fuselage junction, need to have their behavior evaluated under complex loads. Probabilistic simulation methods can be used to construct models for various extreme load conditions, including aerodynamic pressure, maneuvering overload, and gust loads. By applying these random or combined loads to this high-intensity area, the stress response range under different conditions can be simulated and calculated.

[0039] For example, simulations might show that, at a 95% confidence level, the maximum stress fluctuation in this region ranges from 350 MPa to 420 MPa. For the connection regions extracted from the response features, such as around the bolt holes at the aforementioned wing connection, local mesh refinement is required to capture the stress gradient. One embodiment is to refine the mesh size from 5 mm to 1 mm at the hole edges, increasing the element density to more accurately calculate the stress concentration factor at the hole edges. After refinement, the local peak stress might increase from the previously estimated 400 MPa to 450 MPa, revealing more realistic details of the stress distribution. Subsequently, probabilistic simulations are used to perform multiple iterative calculations on the refined connection region.

[0040] For example, cyclic calculations are performed under ten set extreme load combinations, recording the changes in stress distribution in the region during each calculation to obtain its dynamic trend. Analyzing this trend reveals that when the angle between the load direction and the main force transmission path exceeds fifteen degrees, the stress distribution becomes drastically uneven, and its fluctuation amplitude may exceed a preset stability threshold of ten percent. If the trend exceeds the threshold, further optimization of the local mesh model is required.

[0041] For example, along paths with severe stress fluctuations, the mesh is adjusted from isotropic to anisotropic along the principal stress directions to better accommodate stress streamlines. This optimization identifies which sub-regions still exhibit high risk even after optimization, such as a chamfer where stress consistently approaches the allowable limit. Finally, based on the identification of risk areas, a detailed stress distribution mapping of the connecting regions is generated.

[0042] For example, outputting a data file that includes not only the mean stress at each point, but also its standard deviation, maximum value, and probability of occurrence under different loads, forms the final distribution data for subsequent fatigue or reliability analysis. This provides direct, quantitative input for structural durability design, effectively guiding local strengthening or design modifications, and improving the safety margin of the structure under uncertain loads.

[0043] Step S104: Based on the stress distribution law, obtain the interaction parameters of adjacent structures. The interaction parameters include the load transfer coefficient. If the interaction parameters do not match the curvature characteristics, an adaptive calibration mechanism is used to correct the mechanical interaction characteristics to obtain the corrected interaction model.

[0044] By analyzing stress distribution data, interaction parameters between adjacent structures are obtained. The load transfer coefficient is initially extracted, and outliers are removed using data filtering methods, resulting in a preliminary set of interaction parameters. Based on this preliminary set of parameters and boundary condition data, a fusion process is performed to unify the multi-source data. If the fused parameter values ​​do not match the preset curvature feature range, an anomaly marker is triggered, determining the parameter range requiring calibration. For the anomaly-marked parameter range, an adaptive calibration mechanism is employed, adjusting the weight allocation of parameters one by one based on a pre-established mechanical interaction rule base, resulting in adjusted mechanical interaction characteristic data. Using this adjusted mechanical interaction characteristic data, an intermediate model with adjusted characteristics is constructed. Response data under different load transfer conditions is obtained, and the model's performance is assessed to determine if it meets the preset stability criteria. If the response data meets the stability criteria, the intermediate model is further optimized using a support vector machine algorithm to refine the parameter extraction process, resulting in an optimized set of interaction parameters. Based on the optimized set of interaction parameters, a final corrected model is generated. Multi-scenario simulations are performed on the corrected model to obtain simulation results and determine the model's applicability under different boundary conditions. By extracting key mechanical interaction indicators from the simulation results data and combining them with the feedback data from the calibration mechanism, the output structure of the final corrected model is solidified, resulting in a stable model that can be used for subsequent analysis.

[0045] For example, in the process of obtaining interaction parameters between adjacent structures through stress distribution data, the load transfer coefficient is first extracted, which involves extracting the original data of high-intensity concentrated areas from the optimized mesh model.

[0046] Specifically, assuming the initial load transfer coefficient is 0.75 in the stress analysis of the bridge connection area, outliers are removed by data filtering methods, such as eliminating noise points that exceed twice the average value, to obtain a preliminary set of interaction parameters. This helps to improve the reliability of the data, avoid subsequent calculation deviations, and thus improve the prediction accuracy of the model.

[0047] In one possible implementation, a fusion process is performed based on the initial set of interaction parameters and boundary condition data to unify the multi-source data. If the fused parameter values, such as curvature characteristics, exceed a preset range of 0.5-1.2, an anomaly flag is triggered, determining the parameter range requiring calibration. This mechanism can identify potential risks early and ensure the accuracy of mechanical interactions.

[0048] For example, for the parameter range of the anomaly marker, an adaptive calibration mechanism is adopted to adjust the weight allocation based on the mechanical interaction rule base. For example, the load influence weight is increased from 0.4 to 0.6 to obtain the adjusted mechanical interaction characteristic data. This not only optimizes the stress distribution details of the connection area, but also enhances the model's response adaptability to extreme environmental loads.

[0049] Specifically, by constructing an intermediate model with adjusted characteristics using the adjusted data, response data under different load transfer conditions is obtained, such as a stability of 95% under simulated wind load, to determine whether it meets the preset standard. This step can verify the robustness of the model and reduce the probability of failure in practical applications.

[0050] In one possible implementation, if the response data meets the stability criteria, further optimization is performed based on the intermediate model. The parameter extraction process is refined using the support vector machine algorithm to obtain an optimized set of interaction parameters. For example, the classification accuracy is improved to 98%, which is conducive to generating a more accurate correction model and improving the overall simulation efficiency.

[0051] For example, the final modified model is generated based on the optimized set, and multi-scenario simulation verification is carried out, such as under extreme load conditions in probabilistic simulation. The simulation result data is obtained to determine the applicability of the model. This can provide reliable distribution pattern data by analyzing potential risk areas through dynamic change trends.

[0052] Specifically, key mechanical interaction indicators, such as the peak stress of 2.5 MPa, are extracted from the simulation results data. Combined with calibration feedback, the output structure of the final corrected model is solidified to obtain a stable model. This ensures the accuracy and consistency of subsequent analysis and processing, and effectively supports the long-term durability assessment of the bridge structure.

[0053] Step S105: Extract fatigue failure potential point data from the modified interaction model. The fatigue failure potential points refer to the locations where damage is easily accumulated. Use a predictive analysis model to predict behavior changes under long-term load. If the prediction results deviate from the actual range, optimize the training data through sample augmentation technology to obtain accurate prediction results.

[0054] By modifying the data processing of the interaction model, potential location information related to fatigue failure is obtained. Data filtering techniques are used to extract location data prone to cumulative damage, yielding preliminary damage distribution results. Based on these preliminary results, a predictive analysis model is used to simulate behavioral changes under long-term load conditions, determining the response data of potential locations under different load scenarios. If the response data deviates from the actual range, the training data is supplemented and optimized using sample augmentation techniques to obtain an expanded training dataset. Based on the expanded training dataset, the predictive analysis model is rerun to perform a secondary simulation of behavioral changes under long-term load, obtaining adjusted prediction results. The damage accumulation trend of potential locations is determined using the adjusted prediction results. If the trend exceeds a preset threshold, relevant locations are prioritized to identify high-risk areas. Based on the high-risk area data, a targeted load distribution analysis is generated to obtain dynamic changes in fatigue failure, completing a comprehensive assessment of potential locations.

[0055] For example, in the field of bridge structural analysis, by modifying the data processing of interactive models, potential location information related to fatigue failure can be obtained.

[0056] Specifically, this process uses historical stress distribution data to identify potential fatigue points at beam connections. For example, under a load transfer factor of 0.8, it extracts stress concentration data at beam ends, thereby revealing microcrack initiation points that may be caused by long-term vibration. This method helps to detect potential problems early and improve structural durability.

[0057] For example, data filtering techniques can be used to extract location data of easily accumulated damage to obtain preliminary damage distribution results.

[0058] Specifically, threshold filtering methods are used to remove noisy data, such as screening out points with damage values ​​exceeding 5%, locating 10 high-risk locations on the main beam of the bridge, and forming a distribution map. This can effectively reduce misjudgments and improve assessment accuracy.

[0059] For example, based on the preliminary damage distribution results, a predictive analysis model is used to simulate and calculate the behavioral changes under long-term load conditions to determine the response data of potential locations under different load scenarios.

[0060] Specifically, in a scenario simulating a vehicle load of 50 tons, the strain change rate at the calculated point reaches 2%, which helps predict the fatigue evolution path and ensures that the model matches the actual working conditions.

[0061] For example, if the response data deviates from the actual range, the training data can be supplemented and optimized using sample augmentation techniques to obtain an augmented training dataset.

[0062] Specifically, adding 100 historical payload samples to expand the dataset to 500 can improve the model's generalization ability and reduce prediction errors caused by bias.

[0063] For example, based on the expanded training dataset, the predictive analysis model is rerun to perform a secondary simulation of behavioral changes under long-term loads, resulting in adjusted predictions.

[0064] Specifically, the secondary simulation showed that the damage accumulation rate decreased from the initial 3% to 1.5%, which improved the reliability of the results and supported more accurate maintenance decisions.

[0065] For example, by using the adjusted prediction results, the damage accumulation trend of potential locations can be determined. If the trend exceeds a preset threshold, the relevant locations can be prioritized to identify high-risk area data.

[0066] Specifically, if the trend exceeds the threshold of 2%, the bridge bearing area is ranked as the highest risk, which helps to prioritize resource allocation and prevent sudden damage.

[0067] For example, based on data from high-risk areas, targeted load distribution analyses can be generated to obtain dynamic changes in fatigue failure and complete a comprehensive assessment of potential locations.

[0068] Specifically, the analysis shows that the damage propagation rate under dynamic load is 0.1 mm / cycle, which provides a comprehensive assessment, supports optimized design, and extends the structural life.

[0069] Step S106: Based on the accurate prediction results, determine the risk indicators for the design verification stage. The risk indicators quantify the potential failure probability. Integrate the stability requirements through multi-scale analysis methods to obtain a mechanical performance evaluation report.

[0070] The process begins by acquiring the predicted data, cleaning and formatting it, and extracting key fields relevant to design verification to obtain a preliminary predicted dataset. For this dataset, a preset threshold is used to categorize risk indicators. If a data field exceeds the threshold, it is marked as a high-risk item, thus establishing a potential risk list. Based on this list and the failure probability calculation method, the failure likelihood of each high-risk item is analyzed, yielding a failure probability distribution. Using this distribution, a multi-scale analysis method is applied to decompose the risk influencing factors at different scales, determining the degree of each factor's impact on overall stability. Data on the impact of each factor on stability is then acquired, and combined with stability requirements, a mechanical performance evaluation model is constructed to determine intermediate parameters for performance evaluation. Finally, based on these intermediate parameters, the final mechanical performance evaluation data is generated, completing a comprehensive analysis of the design verification phase.

[0071] For example, after acquiring the prediction data, data cleaning and formatting are crucial. Suppose we are processing fatigue prediction data for a mechanical component under long-term load. The cleaning process removes invalid and outlier values, such as stress data from sensors that exceed reasonable ranges; for instance, outliers with stress values ​​exceeding 1000 MPa will be discarded. Formatting unifies data from different sources into a standard format, such as standardizing timestamps to year-month-day format, ensuring consistency in subsequent analysis. When extracting key fields related to design verification, we focus on data in stress concentration areas, such as strain values ​​and cycle counts at key connection points. The initially processed prediction dataset may contain 1000 data points, of which 50 points have strain values ​​exceeding a preset value of 200 microstrain.

[0072] For example, when classifying risk indicators for the initially compiled prediction dataset, a preset threshold might be set at a strain value of 250 microstrain; values ​​exceeding this are marked as high-risk. Suppose 10 data points exceed this threshold; these points are concentrated in the welded areas of the component, forming a potential risk list. This classification helps quickly identify areas requiring focused attention, reducing the complexity of subsequent analysis.

[0073] For example, when calculating the probability of failure based on a list of potential risks, statistical methods can be used to analyze historical failure data for each high-risk item. Assuming that three out of ten high-risk points in a welded area have exhibited microcracks in past tests, and considering environmental factors such as temperature and humidity, the probability of failure can be estimated at 30%. This probability distribution provides data support for subsequent risk assessments, helping to more accurately identify weak points.

[0074] For example, when applying multi-scale analysis to decompose risk influencing factors, one can start from both macroscopic and microscopic scales. Macroscopically, the load distribution of the overall structure is analyzed, such as the average stress of a component being 50 MPa. Microscopically, the impact of grain defects within the material on fatigue life is considered, such as the potential presence of microcracks at grain boundaries. When assessing the degree of influence of each factor on overall stability, it might be found that the macroscopic load distribution accounts for 60% of the impact, while the microscopic defects account for 40%, thus providing direction for optimized design.

[0075] For example, when constructing a mechanical performance evaluation model, in conjunction with stability requirements, intermediate parameters can be set as the material's fatigue limit and safety factor. Assuming a fatigue limit of 400 MPa and a safety factor of 1.5, performance evaluation data of the component under the current design can be obtained through model analysis. This method helps to quantify whether the design meets the requirements for long-term use.

[0076] For example, when generating final mechanical performance evaluation data, the above parameters may be combined to determine that the expected life of the component under a specific load is 100,000 cycles. This comprehensive analysis provides a reliable basis for the design verification phase, ensuring that subsequent improvements are targeted. Through the detailed processing of each step, not only can the accuracy of predictions be improved, but scientific guidance can also be provided for the prevention of fatigue failure, extending the service life of components.

[0077] Step S107: Extract key indicators from the mechanical performance evaluation report, and use a risk assessment algorithm to analyze the impact of potential risks. If the risk indicators are higher than a preset threshold, update the geometric data through parameter optimization to obtain an improved component design scheme.

[0078] Initial data is obtained from the mechanical performance evaluation report, and core indicator data is extracted to complete preliminary data processing, resulting in a structured performance dataset. Based on this structured dataset, a risk assessment algorithm is used to quantify potential risks, analyze the correlation between each core indicator and the risk indicator, and determine the risk assessment results. If the risk indicator in the risk assessment results exceeds a preset threshold, a parameter optimization process is triggered, iteratively adjusting the geometric data to obtain an updated set of geometric parameters. Using the updated set of geometric parameters, a preliminary improved component model is generated. Combined with the constraints of mechanical performance, the initial model construction is completed, determining the basic framework of the improved component. The basic framework data of the improved component is obtained, and the key geometric data within the framework are secondary-verified to determine whether they meet the design requirements, resulting in verified component framework data. Based on the verified component framework data, the final design scheme data is generated. Combined with the constraints of the core indicators, the design scheme output is completed, determining the final design result of the improved component.

[0079] For example, when obtaining initial data from a mechanical performance evaluation report, the data can be categorized and organized, with a focus on indicators related to structural strength, stiffness, and fatigue life. Suppose a report contains information such as the maximum stress value, deformation, and fatigue cycle count of a component; in the initial organization, this data would be grouped according to different performance dimensions to form a structured performance dataset. This approach helps to quickly locate key performance parameters, laying the foundation for subsequent analysis.

[0080] For example, when using risk assessment algorithms to quantify potential risks, historical data and industry standards can be used to set weights and thresholds for risk indicators. Assuming that a maximum stress value exceeding 120% of the design standard is marked as a high-risk item, analyzing its correlation with the component failure probability can be done by combining the proportion of failures caused by stress exceeding standards in past cases to determine the current component's risk level. This method can intuitively reflect the impact of each indicator on overall safety.

[0081] For example, when a risk indicator exceeds a preset threshold, triggering a parameter optimization process, stress concentration can be reduced by adjusting the component's geometric data, such as its thickness or cross-sectional shape. Suppose a component in the original design has a thickness of 10 mm; if the stress value exceeds the limit, it can be iteratively adjusted to 12 mm, and simulation analysis can confirm that the stress value has decreased to a safe range. This iterative adjustment helps improve the safety of the component.

[0082] For example, when generating an initial improved component model and incorporating mechanical performance constraints, boundary conditions can be set for the model, such as load distribution and support methods, to ensure the model closely approximates actual working conditions. Assuming the component needs to withstand a uniform load of 1000 Newtons, parameters will be set based on this condition during model construction to ensure the basic framework meets design expectations. This approach provides a reliable basis for subsequent optimization.

[0083] For example, when performing secondary verification of the key geometric data of the basic framework of an improved component, the focus can be on checking whether the component's dimensional proportions meet the design specifications. Suppose the design requires a component length-to-width ratio of 2:1, while the preliminary model is 2.2:1; then adjustments need to be made to meet the requirements. This verification can avoid performance problems caused by dimensional deviations.

[0084] For example, when generating final design data and considering key performance constraints, the improved component data can be compared with the initial performance indicators to ensure all parameters are within acceptable ranges. Assuming a fatigue life of 1 million cycles is required, the final design, after adjusting material and geometric parameters, achieves 1.2 million cycles, exceeding expectations. This comparative analysis helps confirm the reliability of the design.

[0085] For example, when outputting the final improved component design results, detailed design documents can be generated, including geometric parameters, performance data, and risk assessment conclusions. This comprehensive output method facilitates reference in subsequent production and verification stages, ensures that design intent is accurately conveyed, and provides data support for possible further optimization.

[0086] If the technical solution of this application involves personal information, the product using this technical solution has clearly informed the user of the personal information processing rules and obtained the user's voluntary consent before processing the personal information. If the technical solution of this application involves sensitive personal information, the product using this technical solution has obtained the user's separate consent before processing the sensitive personal information, and also meets the requirement of "express consent". For example, at personal information collection devices such as cameras, clear and prominent signs are set up to inform users that they have entered the scope of personal information collection and that personal information will be collected. If an individual voluntarily enters the collection scope, it is deemed that they have agreed to the collection of their personal information; or on the personal information processing device, the personal information processing rules are clearly informed through signs / information, and authorization is obtained through pop-up information or by asking the individual to upload their personal information; wherein, the personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the types of personal information processed.

[0087] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. The present invention has been described in detail with reference to preferred embodiments. Those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications and substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A simulation system and method for the mechanical properties of precision aerospace components, characterized in that, The method includes: Geometric and material property data of the spherical component are acquired, and curvature features and connection region parameters are extracted from a pre-established structural design database. The curvature features include surface curvature, and the connection region parameters include interface size indices. Based on the data, numerical analysis methods are used to perform initial mesh generation to obtain a preliminary structural model. Based on the preliminary structural model, a structural optimization algorithm is used to process complex geometries and determine the distribution of the influence of curvature changes on mechanical properties. If the curvature features exceed a preset threshold, the mesh density is adjusted through iterative calculation to obtain an optimized mesh model. Stress concentration region data are extracted from the optimized mesh model. The stress concentration region refers to the part with high intensity concentration. The load conditions under extreme environment are simulated using a probabilistic simulation method to determine the stress distribution law of the connection region. Based on the stress distribution law, the interaction parameters of adjacent structures are obtained. The interaction parameters include the load transfer coefficient. If the interaction parameters do not match the curvature characteristics, an adaptive calibration mechanism is used to correct the mechanical interaction characteristics to obtain a corrected interaction model. Fatigue failure potential point data are extracted from the modified interaction model. The fatigue failure potential points refer to the locations where damage is easily accumulated. The predictive analysis model is used to predict the behavior changes under long-term load. If the prediction results deviate from the actual range, the training data is optimized by sample augmentation technology to obtain accurate prediction results. Based on the accurate prediction results, risk indicators are determined in the design verification stage. These risk indicators quantify the probability of potential failure. Stability requirements are integrated through multi-scale analysis methods to obtain a mechanical performance evaluation report. Key indicators are extracted from the mechanical performance evaluation report, and the impact of potential risks is analyzed using a risk assessment algorithm. If the risk indicators are higher than a preset threshold, the geometric data is updated cyclically through parameter optimization to obtain an improved component design scheme.

2. The simulation system and method for mechanical properties of precision aerospace components according to claim 1, characterized in that, The process involves acquiring geometric and material property data of the spherical component, extracting curvature features and connection region parameters from a pre-established structural design database, whereby the curvature features include surface curvature and the connection region parameters include interface size indices. Based on this data, numerical analysis methods are used to perform initial mesh generation to obtain a preliminary structural model, including: Geometric and material property data of spherical components are obtained from a pre-set structural design database. The stored information is then classified and organized by the data extraction module to obtain the classified geometric and material information. For the classified geometric information, a specialized analytical tool is used to extract curvature features and connectivity parameters. The curvature features include surface curvature, and the connectivity parameters include interface size indices, thus determining the set of feature parameters. Based on the set of characteristic parameters, the geometric information of the spherical component is processed using numerical analysis methods, and the initial mesh generation is completed using mesh generation technology to obtain the preliminary mesh data. If the initially divided grid data is unevenly distributed in local areas, grid smoothing technology is used to adjust the local areas to obtain an optimized grid structure. Based on the optimized mesh structure and combined with material property data, the material information is associated with the mesh structure through the property mapping method to determine the complete structural model data; For complete structural model data, the matching degree of geometric information and material information is detected by the data verification module. If the matching degree is lower than the preset threshold, the mapping relationship is readjusted to obtain the final structural model.

3. The simulation system and method for the mechanical properties of precision aerospace components according to claim 1, characterized in that, Based on the preliminary structural model, a structural optimization algorithm is used to process complex geometries, determine the distribution of the influence of curvature changes on mechanical properties, and if the curvature characteristics exceed a preset threshold, the mesh density is adjusted through iterative calculation to obtain an optimized mesh model, including: By processing the initial data of the structural model, a structural optimization algorithm is used to decompose and analyze the complex geometry to obtain preliminary curvature variation data. Based on the curvature variation data, calculate the specific distribution of its impact on mechanical properties and determine the stress concentration points in key areas; If the curvature characteristics of the key region exceed the preset threshold, the iterative calculation process is initiated to make local adjustments to the grid density and obtain updated grid distribution data. By reanalyzing the curvature changes of the geometry using the updated grid distribution data, we can determine whether the mechanical properties have reached a stable state. If the mechanical properties have not reached a stable state, the mesh density will be further refined through iterative calculations to obtain further optimized mesh distribution data. After obtaining further optimized mesh distribution data, the final mesh model is constructed to determine its mechanical property distribution under complex geometries. For the final mesh model, data verification tools are used to comprehensively analyze its curvature characteristics and mechanical properties to obtain optimized structural model data.

4. The simulation system and method for the mechanical properties of precision aerospace components according to claim 1, characterized in that, The process of extracting stress concentration region data from the optimized mesh model, where stress concentration regions refer to areas of high intensity concentration, and using probabilistic simulation methods to simulate load conditions under extreme environments to determine the stress distribution patterns in the connection regions includes: The original data of stress concentration areas were obtained through the optimized mesh model. The data was then classified and organized using automated tools to obtain a preliminary stress concentration distribution map. Based on the preliminary stress concentration distribution map, the specific location of the high-strength zone is identified. The probabilistic simulation method is used to model the load conditions under extreme environments and determine the response characteristics of the high-strength zone under different load conditions. From the response characteristics of the high-intensity region, key data of the connection region are extracted, and local mesh refinement is performed on the connection region to obtain more accurate stress distribution details. The probabilistic simulation method is used to perform multiple iterative calculations on the stress distribution details in the connection area to obtain the dynamic trend of stress distribution under extreme conditions; By analyzing the dynamic trend, the stress distribution law of the connection area under different load conditions is analyzed. If the trend exceeds the preset threshold, the local mesh model is further optimized to identify potential risk areas. Based on the risk area assessment, a detailed mapping of the stress distribution pattern in the connection area is generated to obtain the final distribution pattern data for subsequent analysis and processing.

5. The simulation system and method for the mechanical properties of precision aerospace components according to claim 1, characterized in that, The interaction parameters of adjacent structures are obtained based on the stress distribution law. These interaction parameters include load transfer coefficients. If the interaction parameters do not match the curvature characteristics, an adaptive calibration mechanism is used to correct the mechanical interaction characteristics, resulting in a corrected interaction model, including: By using stress distribution data, interaction parameters between adjacent structures are obtained. The load transfer coefficient is initially extracted, and outliers are removed using data filtering methods to obtain a preliminary set of interaction parameters. Based on the initial set of interactive parameters and combined with boundary condition data, a fusion processing operation is performed to unify the multi-source data. If the fused parameter values ​​do not match the preset curvature feature range, an anomaly marker is triggered to determine the parameter range that needs to be calibrated. For the parameter range of the anomaly marker, an adaptive calibration mechanism is adopted. Based on the pre-established mechanical interaction rule library, the weight allocation of the parameters is adjusted one by one to obtain the adjusted mechanical interaction characteristic data. By using the adjusted mechanical interaction characteristic data, an intermediate model with adjusted characteristics is constructed, and the response data of the model under different load transmission conditions is obtained to determine whether the response data meets the preset stability standard. If the response data meets the stability criteria, further optimization is performed based on the intermediate model. The support vector machine algorithm is used to refine the parameter extraction process, resulting in an optimized set of interaction parameters. Based on the optimized set of interaction parameters, the final modified model is generated. Multi-scenario simulations are performed on the modified model to obtain simulation result data and determine the applicability of the model under different boundary conditions. By extracting key mechanical interaction indicators from the simulation results data and combining them with the feedback data from the calibration mechanism, the output structure of the final corrected model is solidified, resulting in a stable model that can be used for subsequent analysis.

6. The simulation system and method for the mechanical properties of precision aerospace components according to claim 1, characterized in that, The process involves extracting fatigue failure potential point data from the modified interaction model, where potential fatigue failure points refer to locations prone to accumulated damage. A predictive analysis model is then used to predict behavioral changes under long-term loads. If the prediction results deviate from the actual range, the training data is optimized using sample augmentation techniques to obtain accurate prediction results, including: By modifying the data processing of the interaction model, potential location information related to fatigue failure is obtained, and data filtering technology is used to extract location data of easily accumulated damage to obtain preliminary damage distribution results. Based on the preliminary damage distribution results, a predictive analysis model was used to simulate and calculate the behavioral changes under long-term load conditions to determine the response data of potential locations under different load scenarios. If the response data deviates from the actual range, the training data is supplemented and optimized using sample augmentation techniques to obtain an augmented training dataset. Based on the expanded training dataset, the predictive analysis model is rerun to perform a secondary simulation of behavioral changes under long-term loads, and the adjusted prediction results are obtained. Based on the adjusted prediction results, the damage accumulation trend of potential locations is determined. If the trend exceeds the preset threshold, the relevant locations are prioritized to identify high-risk area data. Based on data from high-risk areas, targeted load distribution analysis is generated to obtain dynamic changes in fatigue failure and to complete a comprehensive assessment of potential locations.

7. The simulation system and method for the mechanical properties of precision aerospace components according to claim 1, characterized in that, Based on the accurate prediction results, risk indicators are determined in the design verification phase. These risk indicators quantify the probability of potential failure. Stability requirements are integrated through multi-scale analysis methods to obtain a mechanical performance evaluation report, including: Obtain the prediction results data, and through data cleaning and formatting, extract the key fields related to design verification to obtain the preliminary prepared prediction dataset; For the preliminary compiled prediction dataset, a preset threshold is used to classify risk indicators. If a data field exceeds the threshold range, it is marked as a high-risk item, and a potential risk list is determined. Based on the potential risk list and the failure probability calculation method, the failure probability distribution of each high-risk item is analyzed. Based on the failure probability distribution results, multi-scale analysis methods are applied to decompose the risk influencing factors at different scales and determine the degree of influence of each factor on the overall stability. Obtain data on the degree of influence of each factor on stability, and construct an evaluation model for mechanical performance based on stability requirements to determine intermediate parameters for performance evaluation. Based on the intermediate parameters of the performance evaluation, the final mechanical performance evaluation data is generated, completing a comprehensive analysis of the design verification phase.

8. The simulation system and method for the mechanical properties of precision aerospace components according to claim 1, characterized in that, The process involves extracting key indicators from the mechanical performance evaluation report, analyzing the impact of potential risks using a risk assessment algorithm, and if the risk indicators exceed a preset threshold, then iteratively updating the geometric data through parameter optimization to obtain an improved component design scheme, including: By obtaining initial data from the mechanical performance evaluation report, extracting the core index data, and completing the preliminary data processing, a structured performance dataset is obtained. Based on the structured performance dataset, a risk assessment algorithm is used to quantify potential risks, analyze the correlation between each core indicator and risk indicator, and determine the risk assessment results. If the risk indicators in the risk assessment results are higher than the preset threshold, the parameter optimization process is triggered to iteratively adjust the geometric data and obtain an updated set of geometric parameters. By using the updated set of geometric parameters, a preliminary improved component model is generated. Combined with the constraints of mechanical properties, the initial construction of the model is completed, and the basic framework of the improved component is determined. Obtain the basic frame data of the improved component, perform secondary verification on the key geometric data in the frame, determine whether it meets the requirements of the design scheme, and obtain the verified component frame data. Based on the verified component framework data, the final design scheme data is generated. Combined with the constraints of the core indicators, the design scheme is output, and the final improved component design result is determined.

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