Performance prediction method for rubber and graphene composite sealing element

Abstract

The invention provides a rubber graphene composite sealing element performance prediction method, which relates to the technical field of performance prediction, and realizes complete mapping from a bottom layer structure to a surface layer contact behavior by establishing a fine-grained structural unit, extracting node-level stress response data and coding the data into a contact response coding set; on the basis, joint matrix construction and regional response difference analysis are adopted, local response abnormal regions are effectively recognized, and the problem that interface microcosmic changes cannot be accurately captured through a traditional method is solved. Furthermore, a predictive factor sequence is formed through weighted combination of the response volatility and the contact strength, and a historical track is backtracked in a linkage manner, so that accurate early warning of a high-risk unit is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of performance prediction, and more particularly, to a rubber graphene composite seal performance prediction method. BACKGROUND

[0002] With the wide application of sealing technology in key engineering fields such as aerospace, petrochemical industry and automobile industry, the performance prediction and life evaluation of sealing materials have gradually become an important direction of material engineering and structural mechanics research. In recent years, rubber-based composites have been widely used to manufacture sealing components due to their excellent elastic recovery and environmental resistance. In particular, the introduction of two-dimensional nanofillers such as graphene has significantly improved the mechanical strength, thermal stability and wear resistance of rubber materials, and has promoted the performance breakthrough of rubber graphene composite seals. However, rubber graphene composites have strong nonlinearity, anisotropy and micro-interface complexity, and their performance degradation behavior during service is influenced by the coupling of multiple factors such as loading conditions, interface contact state and internal heterogeneous response of materials. Traditional performance prediction methods based on macroscopic material constants or average stress-strain relationship are difficult to accurately reveal the local failure mechanism and performance degradation path.

[0003] The current mainstream sealing performance analysis method mainly relies on finite element simulation and experimental data fitting means, by establishing material constitutive model, applying loading boundary conditions, to evaluate the overall response of the sealing structure. However, such methods generally have three shortcomings: first, the response behavior of the contact interface is not described in detail, making it difficult to capture the multi-scale synergistic effect of the micro-contact zone; second, the response difference of the heterogeneous elements in the composite material in different regions is not modeled, leading to the prediction results often deviating from the average field assumption; third, there is a lack of traceability in performance evolution evaluation, making it difficult to locate the historical response trajectory of high-risk areas. Especially under high-frequency alternating load or complex working conditions, these shortcomings will further amplify the prediction error, reduce the identification ability of the failure precursor, and affect the reliability of the structure safety evaluation. Therefore, how to construct a sealing performance prediction method that is oriented to local response characteristics and can realize spatial positioning and time backtracking has become one of the core challenges in this field.

[0004] Therefore, there is a need for a rubber graphene composite seal performance prediction scheme. SUMMARY

[0005] In order to solve the above technical problems, the present application is proposed. The present application provides a rubber graphene composite seal performance prediction method.

[0006] According to one aspect of the present invention, a method for predicting the performance of a rubber-graphene composite seal is provided, comprising: constructing structural units of the rubber-graphene composite seal; extracting stress response data at each contact surface node; encoding the stress response data into a contact response encoding set according to the node position and contact type; performing joint statistical analysis on the contact response encoding set and constructing a joint matrix of contact behavior; using the joint matrix of contact behavior as an input parameter, forming an input pair group with the response differences of heterogeneous units within the composite material, performing regional heterogeneous response difference matching, and marking a set of locally high-deviation units; generating a prediction factor sequence based on the response volatility and co-responsivity of each unit in the set of locally high-deviation units, and performing a backtracking linkage with the contact response encoding set to generate a final performance prediction result set.

[0007] Furthermore, the construction of the structural unit includes: acquiring the geometric topological structure data of the rubber-graphene composite seal, dividing it into a multi-layer unit mesh structure according to the material interface hierarchy, and generating a set of structural units.

[0008] Furthermore, the generation of the contact response encoding set includes: identifying all boundary elements located in the interface contact area within the structural element set, and numbering all nodes in each boundary element to form a contact node set; applying constant amplitude dynamic loading to each node in the contact node set, collecting node stress response data sequences within the loading period, and simultaneously collecting contact area normal displacement response datasets within the same period; constructing a structural tuple for each node based on the spatial location number, contact type label, and corresponding stress response sequence of the contact node, and generating a contact response encoding set.

[0009] Furthermore, the construction of the joint contact behavior matrix includes: classifying each structural tuple in the contact response encoding set according to the contact region, and constructing a contact region sequence index table; extracting the corresponding structural tuple sequence in each contact region based on the contact response encoding set and the contact region sequence index table, and performing standardized preprocessing on the stress response sequence in each structural tuple sequence; performing cross-regional time window sliding calculation on the corresponding stress response sequence group in each pair of contact regions to obtain the cooperative response degree of the contact region pair in each time window; and constructing the joint contact behavior matrix based on the cooperative response degree sequence.

[0010] Furthermore, the step of performing cross-regional time window sliding calculation to obtain the cooperative responsiveness of the contact region within each time window includes: setting the sliding window length to L; for each sliding window, from the contact region... Extract the stress response values ​​from k to k+L-1 in the stress response sequence between discrete time points, denoted as . Similarly, from the contact area The stress response values of the same time period in the regional stress response sequence of the structure unit are extracted, denoted as The mean values of and are calculated respectively, each is subtracted by the mean value, and then divided by the standard deviation to form a normalized sequence, obtaining and The values of the same position in and are multiplied to obtain L products; the L products are averaged to obtain the synergistic response value in the corresponding time window.

[0011] Further, the execution of the regional heterogeneous response difference matching includes: based on the stress response data sequence of each unit in the structure unit set, calculating the response fluctuation rate sequence of the adjacent time window, and generating the response fluctuation set; selecting the contact area pairs with the synergistic response value greater than the fluctuation threshold in the contact behavior joint matrix, extracting the structure unit subset in the corresponding area according to the index relationship in the contact area sequence index table, and constructing the heterogeneous response pair group; performing difference intensity mapping on each heterogeneous response pair group to generate a comprehensive response deviation value, and judging by the deviation threshold to extract a high deviation unit index set satisfying the comprehensive response deviation value greater than the set deviation threshold; performing set fusion on the high deviation unit index sets extracted in all contact area pairs to generate a local high deviation unit set.

[0012] Further, the calculation of the response fluctuation rate includes: for each sliding window, calculating the difference between the maximum value and the minimum value in the sliding window as the response fluctuation amplitude; dividing the response fluctuation amplitude by the average stress response value in the corresponding sliding window to obtain the normalized response fluctuation rate; the generation of the comprehensive response deviation value includes: based on the space or index correspondence relationship established between the two contact areas, forming a structure unit pair set in each heterogeneous response pair group; in each structure unit pair, the response fluctuation rate sequences of the two units in the structure unit pair are extracted respectively; for each time point, the response fluctuation rates of the corresponding time points are extracted from the response fluctuation rate sequences of the two structure units respectively, and the absolute value of the difference between the two is calculated to obtain the response fluctuation rate difference value at the current time point.

[0013] Further, the generation of the prediction factor sequence includes: for each structure unit in the local high deviation unit set, calculating the average response fluctuation rate in the recent time window to form a response fluctuation rate set; according to the belonging relationship of the spatial position of each structure unit in the contact area sequence index table, the corresponding contact area pair is obtained, and the corresponding correlation strength value is extracted from the contact behavior joint matrix to construct the strength set; the response fluctuation rate and the synergistic response degree of each structure unit are fused in a weighted combination factor manner to calculate the prediction factor and construct the prediction factor sequence.

[0014] Further, the linkage backtracking generates the final performance prediction result set, including: performing contact trajectory level backtracking positioning according to the contact structure tuple of each structural unit corresponding to the prediction factor in the prediction factor sequence in the contact response code set, to generate the final performance prediction result set.

[0015] Compared with the prior art, the rubber graphene composite sealing performance prediction method provided by the application realizes complete mapping from the bottom structure to the surface contact behavior by establishing fine-grained structural units, extracting node level stress response data, and encoding into a contact response code set; on this basis, joint matrix construction and regional response difference analysis are used to effectively identify local response abnormal areas, overcoming the problem that traditional methods cannot accurately capture interface micro changes. Further, through the weighted combination of response volatility and contact strength, a prediction factor sequence is formed, and historical trajectories are backtracked in linkage to realize accurate early warning of high-risk units. In summary, the application realizes intelligent performance prediction from multiple scales and multiple dimensions, improving prediction accuracy and robustness. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor. In the drawings:

[0017] Figure 1 The flowchart of the rubber graphene composite sealing performance prediction method according to the embodiment of the present application.

[0018] Figure 2 The flowchart of the generation of the contact response code set in the rubber graphene composite sealing performance prediction method according to the embodiment of the present application. DETAILED DESCRIPTION

[0019] In the following, the example embodiments according to the present application will be described in detail with reference to the drawings. Obviously, the described embodiments are only some of the embodiments of the present application, not all embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments described herein.

[0020] As described in the above background, the current mainstream sealing performance analysis method mainly relies on finite element simulation and test data fitting means, by establishing material constitutive model, applying loading boundary conditions, to evaluate the overall response of the sealing structure. However, such methods generally have three shortcomings: first, the response behavior of the contact interface is not fine enough, and it is difficult to capture the multi-scale synergistic effect of the microscopic contact area; second, the response difference of the heterogeneous units in the composite material in different regions is not modeled, resulting in the prediction results often deviating from the average field assumption; third, there is a lack of traceability in performance evolution evaluation, making it difficult to locate the historical response trajectory of high-risk areas. Especially under high-frequency alternating load or complex working conditions, these shortcomings will further amplify the prediction error, reduce the identification ability of the failure precursor, and affect the reliability of the structure safety evaluation. Therefore, how to build a sealing performance prediction method that is oriented to local response characteristics and can realize spatial positioning and time backtracking has become one of the core challenges in the field.

[0021] Therefore, there is a need for a rubber graphene composite sealing performance prediction scheme.

[0022] Figure 1 A flowchart of the rubber graphene composite sealing performance prediction method according to the embodiments of the present application. As shown in Figure 1 In the rubber graphene composite sealing performance prediction method, it includes: S1: constructing the structure unit of the rubber graphene composite sealing, extracting the stress response data at each contact surface node, and encoding the node position and contact type into a contact response code set; S2: performing joint statistical analysis on the contact response code set and constructing a contact behavior joint matrix; S3: taking the contact behavior joint matrix as an input parameter, forming an input pair with the response difference of the heterogeneous units in the composite material, performing regional heterogeneous response difference matching, and marking a local high deviation unit set; S4: generating a prediction factor sequence according to the response fluctuation rate and synergistic response degree of each unit in the local high deviation unit set, and performing linkage backtracking with the contact response code set to generate a final performance prediction result set.

[0023] In the embodiments of the present application, as shown in Figure 2 Step S1 specifically includes:

[0024] S1.1: Obtain the geometric topological structure data of the rubber graphene composite sealing, divide it into a multi-layer unit grid structure according to the material interface hierarchical relationship, and generate a structure unit set.

[0025] Specifically, the rubber-graphene composite seal is a typical multi-scale composite material system, which contains a rubber matrix and a graphene reinforcing phase, and has multiple interface connection areas, including a rubber-graphene interface, a graphene-graphene agglomerate layer interface, and a rubber-rubber cross-linking interface. Due to the multi-phase composite characteristics of this structure, it exhibits obvious spatial inhomogeneous characteristics in geometric morphology, including apparent geometric complexity and internal mesoscopic heterogeneous distribution.

[0026] Therefore, to realize the performance prediction of the seal, the primary operation step is to accurately obtain complete geometric topological structure data, including external contour, internal filler distribution, interface topological information, and material distribution layer. The acquisition of this data usually includes methods such as laser scanning, X-ray CT slice reconstruction, or microscopic image modeling.

[0027] In conventional technology, a single structure layer modeling method is often used to simulate such composite parts, ignoring the important differences in interface levels, resulting in large accuracy deviations in subsequent performance modeling. In our invention, a multi-layer identification technology based on interface level relationships is used to divide the entire composite structure into multiple material layer regions that are independent of each other and have different functions, and to construct corresponding finite element grids in a three-dimensional coordinate system. Specifically, the rubber matrix area is expressed by a hexahedral grid, the graphene reinforcing phase is constructed by a local triangularization bounding volume to form a fine particle distribution, and the interface layer is constructed by a continuous deformation body grid with a transition zone structure, finally forming a multi-layer nested element structure.

[0028] S1.2: In the structure element set, identify all boundary elements located in the interface contact area, and number all nodes in each boundary element to form a contact node set.

[0029] Specifically, there are multiple potential contact areas between all element types and material regions involved in the structure element set, and these areas are often key positions for performance degradation and stress concentration. During the operation of the seal, external extrusion, temperature fluctuations, and internal pressure changes first induce micro-cracks, adhesion fatigue, and interlayer peeling in the interface contact area.

[0030] Therefore, to establish a traceable and predictable response model, it is necessary to identify all potential contact areas in advance and take the boundary elements of these areas as high-attention analysis objects.

[0031] In operation, first, by scanning whether there is a normal contact surface (such as rubber-graphene, rubber-rubber, etc.) between any two material layers in the structural unit set, any two nodes in each boundary unit are combined to form a node pair, and the Euclidean distance of the node pair is calculated; when the distance is lower than the set contact interval threshold, the normal vector angle of the two nodes is further judged; if the normal vector angle of the two nodes is less than the set angle threshold, it is determined that the node pair may constitute a contact surface; when the Euclidean distance between the node pairs is lower than the contact threshold, and the normal angle meets the direction condition, if the two nodes are not in the topological chain relationship, but the stress change sequence similarity of the two nodes in the loading response period is greater than the set similarity threshold (such as 0.9), the node pair is also marked as a contact surface primary element. The units corresponding to the boundary surfaces that meet the contact conditions are all marked as boundary units. Each boundary unit contains a plurality of vertex nodes, in order to ensure the uniqueness and traceability of the nodes, the nodes in all boundary units are uniformly numbered, and the numbering mode adopts a combination coding of spatial ordering and contact type suffix. For example, the xyz coordinate grid position + contact type code (such as RG for rubber-graphene) form is used in numbering to distinguish the node characteristics under different contact categories.

[0032] After completing the numbering, all the marked nodes are included in the contact node set, which provides accurate target positioning basis for subsequent loading tests. Compared with the traditional method, the present application avoids full-structure node traversal screening, improves efficiency, and ensures that all contact responses come from physically valid contact boundary areas.

[0033] S1.3: Apply equal amplitude dynamic loading to each node in the contact node set, collect node stress response data sequence in the loading period, and synchronously collect contact area normal displacement response data set in the same period.

[0034] Specifically, the nodes included in the contact node set represent the physical interaction points in the interface contact area, and the micro-response behavior plays a decisive role in evaluating the overall sealing performance of the rubber-graphene composite sealing element under multiple load conditions.

[0035] In order to obtain the mechanical response characteristics of the nodes, equal amplitude dynamic loading is applied to each contact node, the loading mode is set to periodic sine form, and the amplitude is kept constant to simulate the equal period mechanical excitation in the actual working environment, such as compression vibration or periodic friction. The loading period is set by the simulation control parameter, and is usually in the range of several milliseconds to seconds, and dynamic loading is implemented through a finite element simulation platform. During the operation process, the equivalent stress, shear stress and contact stress response indexes of the node at each time step in each loading period are automatically collected to form a node stress response data sequence.

[0036] In addition to the stress response, the normal displacement response of the contact area where the node is located needs to be collected synchronously. Since interface damage often first manifests as an increase in normal gap, the change in normal displacement is an important indicator of sealing integrity. To ensure synchronization, the total displacement change in the vertical direction of the contact surface is read at each time step, and interpolation mapping is performed on the region where each node is located to generate a contact area normal displacement response dataset. This dataset will be combined with the node stress response data to describe the physical response of the node, which will be used for subsequent structure component construction and behavior coding.

[0037] S1.4: Construct a structure component for each node according to the spatial position number, contact type label, and corresponding stress response sequence of the contact node, and generate a contact response coding set.

[0038] To achieve large-scale structured modeling and rapid comparative analysis of contact node data, a structure component needs to be constructed for each node, which includes three types of information: spatial position number, contact type label, and stress response sequence. The spatial position number is derived from the node coding constructed in S1.2, ensuring that each node has a unique identifier in the entire structure model.

[0039] The contact type label defines the interface type to which the node belongs, such as "RG" for rubber-graphene interface and "RR" for rubber-rubber interface, etc. This label is used for subsequent analysis to quickly filter the behavior characteristics of the same interface type.

[0040] The stress response sequence contains the complete stress change data recorded during the loading cycle in S1.3 for the node, which is the most direct characteristic parameter reflecting the mechanical property changes of the node. After encapsulating the above three types of information into a structure component, all nodes are traversed and encapsulated to finally construct a contact response coding set that is structured, labeled, and complete in response.

[0041] Unlike the unstructured and scattered storage of node response data in traditional methods, this coding set can support efficient sorting, filtering, categorizing, and matching operations, providing a data foundation for subsequent construction of joint matrices and local response identification.

[0042] S1.5: Classify each structure component in the contact response coding set according to the contact area, and construct a contact area sequence index table.

[0043] In the contact response coding set, since each node is distributed in different interface contact areas, it is necessary to classify the structure element tuples according to the areas to which they belong, so as to construct a contact area sequence index table that can be used for local behavior analysis. The contact area division is determined according to the different interface material combination relationship in the structure model, for example, all the element tuples with the prefix "RG" in the number are classified into the rubber-graphene interface area, and the element tuples with the number "RR" are classified into the rubber-rubber interface area. In the classification process, the system traverses each structure element tuple in the coding set, locates the area to which it belongs according to its contact type label and spatial number, and inserts it into the corresponding contact area subset.

[0044] Subsequently, under each contact area, the node sequence index of the area is constructed in the order of node number. The index table records the number of nodes contained in each area, the number of each node, the loading response time sequence and the corresponding fluctuation characteristics. The sequence index table can be used as an index framework for subsequent joint matrix construction, and can also be used for dynamic window sliding analysis and behavior comparison between areas.

[0045] S1.6: For all structure elements in the contact node set, a spatial adjacency table is constructed, each structure element is paired with its upstream and downstream elements adjacent to it in the grid structure, and the node number and position index are recorded to generate a path node mapping table; the table is used for subsequent behavior path tracing of response data between structure elements on the time axis.

[0046] In the embodiment of the present application, step S2 specifically comprises:

[0047] S2.1: According to the contact response coding set and the contact area sequence index table, the structure element sequence in each contact area is extracted, and the stress response sequence in each structure element sequence is standardized and preprocessed.

[0048] Specifically, according to the structure element and spatial number information contained in the contact response coding set, the corresponding structure element sequence in each contact area is extracted according to the contact area and node number mapping relationship set in the contact area sequence index table, and the stress response sequence is unfolded in turn to construct a node-level response time sequence set;

[0049] In each contact area, all contact nodes under it are time-sequenced. Specifically, at each discrete time point, the stress response values of all contact nodes in the contact area are respectively summarized, the number of nodes is counted, and the arithmetic mean of the stress response values of all nodes is calculated. Repeat the operation at all time points to finally form a stress response time sequence at the contact area level.

[0050] In the above calculation process, in order to eliminate the influence of the difference in the number of nodes in each contact area on the calculation of the average value, a node homogeneous weighting scheme under a fixed time step is adopted, that is, the weight of each node at the same time point is equal, and is set to 1 / N, wherein N represents the total number of effective contact nodes in the current contact area. This processing method can maintain the physical equivalence and statistical stability of the area response, and avoid introducing false response fluctuations due to sparse or uneven distribution of nodes.

[0051] For the obtained stress response time series of the contact area, further standardization preprocessing operation is performed: all values in the time series are subtracted by the mean value of the whole sequence, and divided by the standard deviation of the sequence.

[0052] S2.2: For each pair of contact areas, the corresponding stress response sequence group is executed to calculate the cross-area time window sliding correlation, and the cooperative response degree of the contact area pair in each time window is obtained.

[0053] Set the sliding window length L (for example, 5), start from the first time point, construct the first window (1-5), then slide the window by one time point, construct the second window (2-6), and so on.

[0054] For each sliding window, the cooperative response degree of the contact areas and is calculated according to the following steps:

[0055] The stress response values from k to k+L-1 in the stress response sequence between discrete time points of the contact area are extracted, denoted as ; similarly, the stress response values in the same time period from the area stress response sequence of the contact area are extracted, denoted as ; the mean values of and are calculated respectively, and each item is subtracted by the mean value and divided by the standard deviation to form a normalized sequence, obtaining and , which ensures that the response trends of the two areas are compared on the same scale and are not affected by the absolute stress size;

[0056] The values at the same position in and are multiplied to obtain L products; the average of the L products is obtained to obtain the cooperative response degree value in the corresponding window; wherein the cooperative response degree value range is generally-1 to 1:

[0057] Tends to 1: the stress change trends of the two areas in the time period are highly consistent; tends to-1: the two areas present reverse change; tends to 0: the change trends are not related or irregular.

[0058] S2.3: Constructing the contact behavior joint matrix based on the sequence of the synergistic response.

[0059] After the calculation of the sliding window synergistic response between each region pair is completed, the data structure of the current stage has sufficient dimensions to encode and express the synergistic behavior relationship between multiple contact regions in the time window scale. To further improve the organization and analyzability of the data structure, a two-dimensional matrix structure needs to be constructed to uniformly represent the synergistic relationship between all region pairs and its stability characteristics in the time dimension. This structure is defined as the contact behavior joint matrix in this method, which is used to centrally represent and manage the stress response trend consistency between any two contact regions.

[0060] The construction of the contact behavior joint matrix is based on the contact region sequence index table. First, all possible region pair combinations are enumerated in the index table. Assuming that there are n different contact regions, a maximum of n(n−1) / 2 unique region pair combinations can be generated. For each region pair combination, such as region A and region B, the sliding window corresponding synergistic response sequence is regarded as a set of behavior feature vectors of the region pair.

[0061] Subsequently, for ease of storage and retrieval, a cell is located in the contact behavior joint matrix using the region index pair (A, B) as the row and column index, and the complete synergistic response sequence of the region pair is stored in the cell.

[0062] S2.4: According to the synergistic trend stability of different region combinations in the contact behavior joint matrix, extract the set of region pair combinations with significant fluctuations greater than the fluctuation threshold as the input region pairs for subsequent heterogeneous response difference correlation analysis.

[0063] After the construction of the contact behavior joint matrix, the synergistic response sequence corresponding to each contact region pair combination in the matrix contains trend data in multiple sliding time windows, and the change pattern of the sequence reflects whether the stress response of the two regions is stable. If the synergistic response of a region pair is close to a fixed value (such as continuously approaching 1) in most time windows, it can be judged that the synergistic trend of the region pair is stable, which means that the two regions have long-term coupling behavior during the loading process; otherwise, if the synergistic response frequently jumps in the time window, with a large value span, it indicates that the region pair has a significantly fluctuating dynamic response relationship, which is likely to be affected by internal material heterogeneity or loading boundary disturbance.

[0064] To screen out these combinations of significant fluctuation characteristics, it is necessary to perform trend fluctuation judgment on the cooperative response degree sequence stored in each cell of the contact behavior joint matrix. The specific operation includes calculating the variance, maximum jump amplitude, and extreme value fluctuation interval length of the sequence (not uniquely limited by the embodiments of the present application), and setting a global fluctuation threshold as a judgment reference. If the response degree sequence of a certain region pair exceeds the set threshold, the region pair is marked as a significantly fluctuating combination, and enters the region pair candidate set. The set only contains key region pairs that have potential contributions to subsequent performance degradation and heterogeneous behavior.

[0065] In the embodiments of the present application, step S3 specifically includes:

[0066] S3.1: Based on the stress response data sequence of each unit in the structure unit set, the response fluctuation rate sequence of adjacent time windows is calculated, and a response fluctuation set is generated.

[0067] Among them, the calculation of the response fluctuation rate includes: for each sliding window, the difference between the maximum value and the minimum value in the sliding window is calculated as the response fluctuation amplitude; the response fluctuation amplitude is divided by the average stress response value in the corresponding sliding window to obtain the normalized response fluctuation rate.

[0068] For the same structure unit, the sliding calculation will be performed within the entire loading cycle range until all window positions are traversed, and finally a complete response fluctuation rate sequence is generated for the unit. After completing the calculation of all structure units, the fluctuation rate sequence of each structure unit is numbered and archived, and is uniformly stored as a response fluctuation set.

[0069] S3.2: Select the contact region pairs with significant cooperative fluctuation in the contact behavior joint matrix, extract the structure unit subset in the corresponding region according to the index relationship in the contact region sequence index table, and construct a heterogeneous response pair group.

[0070] In the contact behavior joint matrix constructed in the previous step, all region pair combinations record the cooperative response degree sequence under the sliding time window, which reflects the degree of convergence of the stress response trend of two regions in the same time period. When the cooperative response degree sequence shows significant fluctuation in multiple windows, it means that there is dynamic coupling or disturbance instability behavior between the two regions. On this basis, the current operation step screens out all region pair combinations that show cooperative fluctuation amplitude greater than the set threshold from the joint matrix, as the candidate region group for subsequent in-depth response difference analysis.

[0071] To extract the corresponding structural unit of the region pair, the operation starts from each contact region in the region pair combination, queries the node number set corresponding to the contact region in the contact region sequence index table, and locates the belonging unit number in the structural unit set with each number in the node set as an anchor point.

[0072] The process is repeated for two contact regions respectively, that is, a pair of structural unit subsets is extracted, and the number of members in the two subsets may be different, but a one-to-one correspondence relationship can be established through spatial position or node topology mapping for subsequent response comparison.

[0073] Finally, each region combination showing significant cooperative fluctuation is mapped to a set of structural unit pair subsets, which are collectively referred to as heterogeneous response pair groups after combination, and are used to carry the differences in material organization structure and mechanical response mechanism between the two regions.

[0074] S3.3: Perform difference intensity mapping on each heterogeneous response pair group, generate a comprehensive response deviation value, and determine whether the high deviation unit index set meets the set deviation threshold value.

[0075] On the basis of the construction of the heterogeneous response pair group in the previous step, the operation goal of the current stage is to evaluate the response difference of each structural unit pair and determine whether it belongs to the high response deviation unit. In the specific operation, first, based on the spatial or index correspondence relationship established between the two contact regions, a set of structural unit pairs is formed in each heterogeneous response pair group, that is, the unit number pairing items with contrast in the two regions.

[0076] Subsequently, in each structural unit pair, the response fluctuation rate sequence of the two units in the pair is extracted, which has been calculated in step S3.1 and included in the response fluctuation set.

[0077] In the response difference calculation process, for each time point, the fluctuation rate value of the corresponding time point is extracted from the fluctuation rate sequence of the two units, and the absolute value of the difference between the two is calculated to obtain the response fluctuation rate difference value at the current time point. While calculating the response fluctuation rate difference of the structural unit pair, the self-response fluctuation rate of each structural unit in the time period is recorded. If the self-fluctuation rate of a structural unit is greater than the region fluctuation index of the region it belongs to, and the difference with the adjacent unit does not exceed the set threshold value, the unit is also included in the high deviation unit set for identifying the internal local unstable area under cooperative response.

[0078] The above operation is repeated at all time points in the entire loading period to form a fluctuation difference sequence of the structural unit pair.

[0079] Finally, an average calculation operation is performed on the difference sequence to obtain a comprehensive response deviation value of the unit pair in the whole cycle range.

[0080] To screen high-response difference units, a response deviation determination threshold is set by the system, and if the comprehensive response deviation value of a unit pair is greater than the threshold, the numbers of the two units in the unit pair are both added to the high-deviation unit index set.

[0081] S3.4: Perform a union fusion on the high-deviation unit index sets extracted in all contact area pairs to generate a local high-deviation unit set as the positioning basis of the prediction factor in the next step.

[0082] The high-deviation unit index set is a local response difference significant unit number set extracted from each heterogeneous response pair group, and the set has the composite characteristics of regionality, heterogeneity and difference. Since the unit number may appear repeatedly in multiple area pairs, in order to fully merge all response difference significant units, the union fusion operation without repetition is performed on the index sets extracted in all area pairs in the current step to generate a complete local high-deviation unit set as the core input index set for the positioning and behavior tracking of the prediction factor.

[0083] During the operation, the system converts each structural unit number into a unique identifier and arranges them in order to form a data index table that can be directly mapped to the original structural unit set. After the fusion is completed, the local high-deviation unit set formed represents the local area units in the entire structural model that exhibit persistent response inconsistency, coupling imbalance, and cooperative distortion. The set has irregularity and cross-regional extension in spatial structure, which exactly reflects the heterogeneous mechanical behavior characteristics caused by uneven distribution of graphene, abnormal cross-linking structure or local stress concentration in the composite sealing structure.

[0084] In the embodiment of the application, step S4 specifically comprises:

[0085] S4.1: For each structural unit in the local high-deviation unit set, calculate the average response fluctuation rate in the recent time window to form a response fluctuation rate set.

[0086] In specific operation, for each high-deviation structural unit, the fluctuation rate sequence of the unit is directly called from the response fluctuation set, and the fluctuation rate values corresponding to the last L (such as 5) time points are intercepted to constitute a short-time response fluctuation window. Then, an arithmetic average is performed on all the fluctuation rate values in the window to obtain the average response fluctuation rate of the unit in the recent time window.

[0087] To enhance the wide adaptability of stability evaluation, the system performs normalization operation on the average value, unified to the range of 0~1, and performs boundary compression processing on abnormal values (such as extremely low or extremely high), to avoid subsequent index imbalance.

[0088] Finally, by performing the above calculation on all local high-bias structure units, a set of response volatility sets containing the short-term average volatility of all target structure units is formed.

[0089] S4.2: According to the belonging relationship of each structure unit in the contact area sequence index table, the corresponding contact area pair is obtained, and the corresponding correlation strength value is extracted from the contact behavior joint matrix to construct the strength set.

[0090] The specific operation is as follows: First, the system traverses each structure unit in the local high-bias unit set, reads its spatial coordinate data in the original structure unit set, and finds its contact area number through the area index mapping table. If the structure unit is located at the overlapping boundary of two areas, the main contact surface with higher node density is matched preferentially. After obtaining the contact area number, further search the area pair combination it participates in in the contact area sequence index table, and locate the matrix cell corresponding to the area pair in the contact behavior joint matrix, extract the mean or median response strength value of the overall cooperative response degree from the cell, as the strength index representing the coupling of the participation behavior.

[0091] This operation introduces a spatial context level of cooperative behavior parameters for each high-bias structure unit under the premise of ensuring that the structure unit body information remains unchanged, that is, the size of the cooperation between the region it is located in and the adjacent region in the response process of the complex.

[0092] The finally constructed strength set is the behavior coupling strength value set corresponding to each structure unit.

[0093] S4.3: Fuse the response volatility and cooperative response degree of each structure unit in a weighted combination factor manner, calculate the prediction factor, and construct the prediction factor sequence.

[0094] In the specific operation, first extract the corresponding response volatility from the response volatility set for each structure unit, and extract the cooperative response degree of the participating contact area pair. Then set the weighted combination factors a and β of the two (for example, a=0.6, β=0.4), perform weighted combination operation, and get the prediction factor of the structure unit.

[0095] After repeating the above operation on all structure units, the prediction factor sequence is formed, that is, a numerical set corresponding to each local high-bias structure unit, which collectively reflects the behavior volatility degree and regional cooperative deficiency level in the recent time window.

[0096] S4.4: performing contact trajectory level backtracking positioning according to the contact structure tuple of each structure unit corresponding to the prediction factor in the prediction factor sequence in the contact response encoding set, to generate a final performance prediction result set.

[0097] In operation, first, according to the unit number in the prediction factor sequence, the corresponding contact structure tuple in the contact response encoding set is searched, and the stress response sequence and the spatial number in the whole period are extracted. Then, the response sequence is trend fitted, the change amplitude jump point, the saturation region and the oscillation section are identified, and these feature sections are classified and weighted in combination with the value of the prediction factor, wherein the value of each structure unit in the prediction factor sequence is divided into three intervals (the interval division is according to the actual setting), and whether the unit is in the performance degradation, load migration or stable transition state is judged.

[0098] For the structure tuple determined as the degradation section, the upstream nodes in the contact path are further backtracked according to the spatial number, the behavior propagation path is tracked through the path node mapping table, and the spatial interpolation is performed in combination with the prediction factor value of the adjacent unit to expand the degradation influence range.

[0099] Among them, the adjacent unit refers to the unit having a direct spatial connection relationship with the current structure unit in the path node mapping table; its prediction factor value can be obtained from the prediction factor sequence according to the spatial number index, which is used to calculate the local prediction value gradient in the backtracking process to perform interpolation judgment of the degradation propagation trend.

[0100] Finally, the structure tuple set that has completed backtracking and path level fusion constitutes the performance prediction result set, which can be directly used for performance level marking, repair priority evaluation or design risk prompt, and has engineering deployability and prediction universality.

[0101] In summary, the performance prediction method of the rubber graphene composite sealing piece based on the embodiment of the application is illustrated, and the performance prediction method of the rubber graphene composite sealing piece provided by the application realizes complete mapping from the bottom structure to the surface contact behavior by establishing a fine-grained structure unit, extracting node level stress response data, and encoding into a contact response encoding set. On this basis, the joint matrix construction and the regional response difference analysis are adopted to effectively identify the local response abnormal region, which overcomes the problem that the traditional method cannot accurately capture the micro changes of the interface. Further, through the weighted combination of response fluctuation rate and contact strength, a prediction factor sequence is formed, and the historical trajectory is backtracked in linkage, to realize accurate early warning of high-risk units. In summary, the application realizes intelligent performance prediction from multiple scales and multiple dimensions, and improves the prediction accuracy and robustness.

Claims

1. A method of predicting the performance of a rubber graphene composite seal, characterised in that, The method comprises the following steps: Constructing the structural units of the rubber graphene composite seal, extracting the stress response data at each contact surface node, and encoding the node position and contact type into a contact response code set; Joint statistical analysis of the contact response code set and construction of a contact behavior joint matrix; Taking the contact behavior joint matrix as an input parameter, forming an input pair group with the response difference of the heterogeneous units inside the composite material, performing regional heterogeneous response difference matching, and marking a local high deviation unit set; According to the response fluctuation rate and synergistic response degree of each unit in the local high deviation unit set, a prediction factor sequence is generated, and a final performance prediction result set is generated through linkage backtracking with the contact response code set.

2. The method of claim 1, wherein, The construction of the structural units comprises: Obtaining the geometric topological structure data of the rubber graphene composite seal, dividing it into a multi-layer unit grid structure according to the material interface hierarchical relationship, and generating a structural unit set.

3. The method of claim 2, wherein the rubber graphene composite seal performance prediction method is characterized by, The generation of the contact response code set comprises: In the structural unit set, identify all boundary units located in the interface contact area, and number all nodes in each boundary unit to form a contact node set; Applying equal amplitude dynamic loading to each node in the contact node set, collecting node stress response data sequence in the loading cycle, and synchronously collecting normal displacement response data set of the contact area in the same cycle; According to the spatial position number, contact type label and corresponding stress response sequence of the contact node, a structure tuple is constructed for each node, and a contact response code set is generated.

4. The method of claim 1, wherein the rubber graphene composite seal performance prediction method is characterized by, The construction of the contact behavior joint matrix comprises: Classifying each structure tuple in the contact response code set according to the contact area, and constructing a contact area sequence index table; According to the contact response code set and the contact area sequence index table, the structure tuple sequence belonging to each contact area is extracted, and the stress response sequence in each structure tuple sequence is standardized and pretreated; Performing cross-regional time window sliding calculation on the corresponding stress response sequence group in each pair of contact areas to obtain the synergistic response degree of the contact area pair in each time window; Based on the synergistic response degree sequence, a contact behavior joint matrix is constructed.

5. The method of claim 4, wherein the rubber graphene composite seal performance prediction method is characterized by, The cross-regional time window sliding calculation comprises: Set the sliding window length to L; For each sliding window, the stress response values from the contact area are extracted from the stress response sequence between discrete time points k and k+L-1, denoted as ; similarly, the stress response values from the contact area are extracted from the stress response sequence between the same time points k and k+L-1, denoted as ; The mean of and are calculated respectively, and each is subtracted from the mean and divided by the standard deviation to form a normalized series, resulting in and ; Will and Multiplying the values ​​at the same position in the matrix yields L products; Average L products to obtain the synergistic response degree value in the corresponding time window.

6. The method of claim 1, wherein, The regional heterogeneous response difference matching comprises: Based on the stress response data sequence of each unit in the structural unit set, the response fluctuation rate sequence of adjacent time windows is calculated, and a response fluctuation set is generated; In the contact behavior joint matrix, select the contact area pairs with synergistic response degree values greater than the fluctuation threshold, extract the structural unit subset in the corresponding area according to the index relationship in the contact area sequence index table, and construct a heterogeneous response pair group; Performing difference intensity mapping on each heterogeneous response pair group, generating a comprehensive response deviation value, and determining whether the comprehensive response deviation value is greater than the set deviation threshold to extract a high deviation unit index set; Performing set fusion on all the high deviation unit index sets extracted from the contact area pairs to generate a local high deviation unit set.

7. The method of claim 6, wherein the rubber graphene composite seal performance prediction method is characterized by, The calculation of the response fluctuation rate comprises: For each sliding window, the difference between the maximum value and the minimum value in the sliding window is calculated as the response fluctuation amplitude; The response fluctuation amplitude is divided by the average stress response value in the corresponding sliding window to obtain the normalized response fluctuation rate; The generation of the comprehensive response deviation value comprises: Based on the spatial or index correspondence relationship established between the two contact areas, a set of structural unit pairs is formed in each heterogeneous response pair group; In each structural unit pair, the response fluctuation rate sequence of the two units in the structural unit pair is extracted respectively; For each time point, the response fluctuation rate of the corresponding time point is extracted from the response fluctuation rate sequence of the two structural units respectively, and the absolute value of the difference between the two is calculated to obtain the response fluctuation rate difference value at the current time point.

8. The method of claim 1, wherein the rubber graphene composite seal performance prediction method is characterized by, The generation of the prediction factor sequence comprises: For each structural unit in the local high deviation unit set, the average response fluctuation rate in the recent time window is calculated to form a response fluctuation rate set; According to the belonging relationship of the spatial position of each structural unit in the contact area sequence index table, the corresponding contact area pair is obtained, and the corresponding correlation strength value is extracted from the contact behavior joint matrix to construct a strength set; The response fluctuation rate and the cooperative response degree of each structural unit are fused in a weighted combination factor manner to calculate the prediction factor and construct the prediction factor sequence.

9. The method of claim 8, wherein the rubber graphene composite seal performance prediction method is characterized by, The linkage backtracking generates a final performance prediction result set, which comprises: according to the contact structure tuple of the structural unit corresponding to each prediction factor in the prediction factor sequence in the contact response encoding set, performing contact trajectory level backtracking positioning to generate a final performance prediction result set.

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