Quantitative Calculation and Dynamic Analysis Methods and Systems for Chloride Ion Transport Mechanism in Marine Concrete
By calculating the first-order partial derivative matrix of chloride ion transport flux and the wet-dry stage integral, the decoupling problem of chloride ion transport mechanism in marine concrete was solved, enabling quantitative assessment of chloride ion intrusion and improving the accuracy of marine structure life prediction.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHONGQING JIAOTONG UNIV
- Filing Date
- 2026-05-08
- Publication Date
- 2026-07-31
AI Technical Summary
Existing technologies fail to effectively decouple diffusion and convection when simulating chloride ion transport in marine concrete, resulting in inaccurate assessment of the accelerated chloride ion intrusion effect under alternating wet and dry conditions, and a lack of quantitative analysis methods.
By calculating the first-order partial derivative matrix of chloride ion transport flux and performing spatiotemporal double integration in conjunction with the dry-wet phase division, quantitative decoupling and dynamic evaluation of diffusion and convection contributions are achieved. Data processing is performed using the finite difference method and the trapezoidal numerical integration method.
It achieves dynamic decomposition of chloride ion transport mechanism at the micro level and total amount decoupling at the macro level, accurately assesses the contribution ratio of chloride ions in alternating dry and wet environments, and improves the accuracy and reliability of service life prediction for marine engineering structures.
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Figure CN122490966A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of numerical simulation and data processing technology for civil engineering durability, and in particular to a quantitative decoupling and dynamic analysis method and system for chloride ion diffusion and convection coupling transport mechanism in marine concrete under alternating wet and dry conditions. Background Technology
[0002] With the rapid development of the global marine economy and the large-scale construction of major marine infrastructure such as cross-sea bridges, subsea tunnels, and port terminals, the durability of marine concrete structures has become increasingly prominent. Among these issues, chloride ions in the environment penetrating the concrete and causing steel corrosion are the primary causes of durability failure and early damage in marine structures.
[0003] In the complex marine environment (especially in tidal and splash zones), marine concrete is constantly subjected to alternating wet and dry conditions. In this environment, chloride ion transport within the concrete is not a single mechanism, but a complex process highly coupled with "diffusion" driven by concentration gradients and "convection" driven by capillary moisture transport. However, existing research and data analysis on chloride ion transport mechanisms still have the following significant shortcomings: First, the oversimplification of theoretical models leads to distorted mechanisms. Existing research on chloride ion transport and engineering life predictions are mostly based on the traditional Fick's second law, which only considers the single "diffusion behavior" under fully saturated conditions, while ignoring the objectively existing "convection" in alternating wet and dry environments. This oversimplification of mechanisms often causes models to underestimate the accelerating effect of moisture transport on chloride ion intrusion.
[0004] Second, decoupling analysis under complex coupling mechanisms is difficult. With the development of numerical simulation technology, advanced finite element simulation software can calculate the overall concentration distribution under coupling effects. However, existing technologies lack an effective data post-processing and quantitative analysis method, and can only obtain the final "total concentration" appearance, failing to accurately separate and decouple the "total concentration change" into "diffusion contribution" and "convection contribution".
[0005] Third, there is a lack of spatiotemporal dynamics and phased assessment methods. Within the "wet period" and "dry period" of a single wet-dry cycle, the dominance of diffusion and convection dynamically shifts. Existing technologies cannot quantitatively assess the dynamic proportion of these two mechanisms over a continuous time axis based on spatiotemporal gradients, nor can they quantify the absolute flux differences between different wet and dry phases, and they cannot mathematically verify the correlation between a single transport mechanism and the overall concentration change rate.
[0006] Therefore, there is an urgent need for a comprehensive quantitative calculation method that can effectively analyze numerical simulation results, enabling both microscopic spatiotemporal evolution analysis and macroscopic decoupling of total amounts in dry and wet phases. Summary of the Invention
[0007] In view of this, the purpose of this invention is to provide a quantitative calculation and dynamic analysis method and system for chloride ion transport mechanism in marine concrete. This method quantifies the diffusion and convection contributions by calculating the negative value of the first-order partial derivative matrix of flux, and performs spatiotemporal double integration by combining dry and wet stage division, thus solving the technical problem that the prior art cannot quantitatively decouple and dynamically evaluate the chloride ion transport coupling mechanism.
[0008] To achieve the above objectives, the present invention provides the following technical solution: The present invention provides a method for quantitative calculation and dynamic analysis of chloride ion transport mechanism in marine concrete, comprising the following steps: Step S1: Obtain the diffusion flux matrix, convection flux matrix, concentration distribution matrix, and corresponding time series and depth series from the numerical simulation output of chloride ion transport in marine concrete. Step S2: Verify the validity of the acquired data. If the depth sequence of the concentration distribution matrix is inconsistent with the depth sequence of the flux matrix, then perform spatial interpolation alignment on the concentration distribution matrix. Step S3: Using the finite difference method, calculate the first-order partial derivative matrices of the diffusion flux matrix and the convection flux matrix with respect to depth, and take their negative values as the diffusion contribution matrix and the convection contribution matrix; at the same time, calculate the concentration time gradient matrix of the concentration distribution matrix with respect to time. Step S4: Based on the calculated diffusion contribution matrix, convection contribution matrix and concentration time gradient matrix, calculate the Pearson correlation coefficient between diffusion and convection mechanisms and concentration change rate, and calculate the dynamic contribution ratio of diffusion and convection at continuous time nodes. Step S5: Divide the time windows into wet and dry phases according to the time series, and perform spatiotemporal double integration on the diffusion flux matrix and convective flux matrix in the wet and dry phases respectively to calculate the absolute flux and relative contribution rate under different environmental phases. Step S6: Output the analysis results of each stage and generate a visualization chart of the dynamic evolution of the transmission mechanism.
[0009] Furthermore, in step S3, the specific method for calculating the first-order partial derivative matrix and the time gradient is as follows: the central difference scheme is used for internal nodes, and the one-sided difference scheme is used for boundary nodes; wherein, the values of the diffusion contribution matrix and the convection contribution matrix are the negative values of the partial derivatives of diffusion flux and convection flux with respect to spatial depth, respectively.
[0010] Furthermore, in step S4, the method for calculating the dynamic contribution ratio is as follows: At any time node t, the total diffusion contribution is obtained by numerically integrating the absolute value of the diffusion contribution matrix along the depth sequence using the trapezoidal numerical integration method, and the total convection contribution is obtained by numerically integrating the absolute value of the convection contribution matrix along the depth sequence. Diffusion dynamics contribution = Total diffusion contribution / (Total diffusion contribution + Total convection contribution); Convection dynamic contribution = Total convection contribution / (Total diffusion contribution + Total convection contribution).
[0011] Furthermore, in step S4, the Pearson correlation coefficient is calculated as follows: extract the column vector of the first-order partial derivative matrix and the column vector of the time gradient at a specific time node, remove invalid values, and then calculate the Pearson correlation coefficient between the two to characterize the driving correlation of a single transport mechanism on the overall concentration distribution change.
[0012] Furthermore, the specific method for performing spatiotemporal dual integration in step S5 is as follows: Using the trapezoidal numerical integration method, the flux matrices of the wetting and drying stages are first integrated along the time dimension, and then their absolute values are integrated along the spatial depth dimension to obtain the total diffusion flux, total convection flux, total diffusion flux, and total convection flux of the wetting and drying stages. Based on this, the relative contribution percentage of each stage is calculated.
[0013] Furthermore, the diffusion flux matrix is calculated according to the following Fick's law: ; in, Indicates the diffusion flux component; Indicates the diffusion coefficient; Indicates concentration; Indicates depth; The circulation matrix is calculated according to the following formula: ; in, Indicates the quantity of flow; This indicates the convective flow velocity of the pore fluid.
[0014] Furthermore, the diffusion flux matrix, convection flux matrix, and concentration distribution matrix are derived from numerical simulation results of the water transport equation based on the Richards equation and the chloride ion transport equation based on the Nernst-Planck equation.
[0015] Furthermore, the time window for dividing the humidification stage and the drying stage in step S5 is based on a preset wet-dry alternation system, which includes the duration of the wet-dry cycle and the wet-dry ratio.
[0016] Furthermore, the visualization charts generated in step S6 include dynamic evolution curves showing the changes in the proportion of diffusion contribution and the proportion of convection contribution over time, as well as bar charts comparing the absolute fluxes of the humid and dry stages.
[0017] The present invention provides a quantitative calculation and dynamic analysis system for chloride ion transport mechanism in marine concrete, used to implement the above method, including: The data parsing and preprocessing module is used to read numerical simulation flux and concentration files, extract spatiotemporal sequences, and perform data verification and spatial interpolation alignment. The spatiotemporal gradient calculation module is used to calculate the first-order partial derivative matrices of diffusion flux and convection flux, as well as the concentration-time gradient matrix of concentration, based on the finite difference algorithm. The micro-dynamic analysis module is used to calculate the Pearson correlation coefficient of the gradient and to use numerical integration to calculate the dynamic contribution ratio of each mechanism at a specific time point. The macroscopic stage decoupling module is used to perform spatiotemporal double integration of the flux matrix according to the divided dry and wet time windows, and quantify the absolute flux and relative contribution rate of different environmental stages. The visualization output module is used to statistically output analysis results and draw dynamic curves showing the evolution of the transmission mechanism.
[0018] The beneficial effects of this invention are as follows: This invention provides a quantitative calculation and dynamic analysis method and system for chloride ion transport mechanisms in marine concrete, belonging to the field of numerical calculation technology for the durability of civil engineering materials. This invention aims to solve the problem that existing technical models struggle to quantitatively distinguish and assess the contribution of diffusion and convection, which jointly drive chloride ion erosion, under complex alternating wet and dry environments. To achieve the above objectives, the method provided by this invention is executed through the following core steps: First, based on the numerical model calculation results of the co-evolution of the moisture saturation field and the chloride ion concentration field, the method decouples the calculated total chloride ion flux into diffusion flux and convection flux components in real time within each time step of the model solution. Finally, this invention innovatively introduces a "dual integral decoupling algorithm," which integrates the decoupled flux components in two dimensions: 1) Continuous integration is performed on the time axis of the entire simulation cycle to calculate the average contribution ratio of the two mechanisms in the global process and generate an intuitive dynamic evolution curve; 2) On a macroscopic scale, according to a preset wet-dry cycle regime (e.g., a 24-hour wet-dry cycle and a wet-dry ratio of 1:1), the flux in the wet and dry stages is integrated piecewise. Through this invention, a series of accurate quantitative analysis results can be obtained. This invention not only allows for the plotting of dynamic evolution curves showing the changing proportions of diffusion and convection contributions over time, revealing their instantaneous fluctuations in wet and dry traffic environments, but also, for the first time, precisely identifies the dominant mechanisms in different physical stages based on data. For example, under a 24-hour wet-dry cycle and a 1:1 wet-dry ratio, the contribution of convection to chloride ion transport during the dry stage can be significantly increased to over 8%. By providing a complete set of quantitative analysis tools from microscopic instantaneous to macroscopic stage-specific approaches, this invention profoundly reveals the physical mechanism behind the "wet in, dry out" phenomenon, fundamentally improving the accuracy and reliability of predicting the service life of concrete structures in marine environments. Specific beneficial effects are as follows: (1) Comprehensive decoupling combining micro and macro levels. This invention breaks through the limitations of traditional methods that only consider the "final concentration". At the micro level, it achieves dynamic decomposition of the mechanism by calculating the spatial partial derivative (spatiotemporal gradient) of flux; at the macro level, it achieves total decoupling of different environmental stages through the division of dry and wet time windows and double integration. A complete evaluation system from process to result is established.
[0019] (2) Accurately reproduces the real evolution of alternating wet and dry environments. This invention can automatically distinguish between "wet period" and "dry period", accurately capture the different driving weights of water transport (convection) on chloride ion intrusion during dry and wet periods, and highly match the real physical boundary conditions of ocean tidal zones and splash zones.
[0020] (3) A reverse quantitative evaluation closed loop of "simulation evolution - mechanism verification" was constructed. This invention innovatively introduces Pearson correlation analysis of flux first-order partial derivative matrix (as driving force term) and concentration time gradient matrix (as evolution response term). It breaks through the limitation of traditional research that relies solely on qualitative experience to determine the dominant mechanism. Through statistical correlation coefficient index, it quantitatively reveals the real driving weight of diffusion and convection mechanisms on overall concentration changes at different spatiotemporal nodes. It can rigorously verify the consistency between the transmission equation setting and physical mechanism in numerical simulation from a purely mathematical perspective, proving that the simulation process is not a simple numerical accumulation, but a real evolution supported by physical logic, which greatly enhances the credibility and scientificity of the conclusions of marine structure durability analysis.
[0021] The above and other objects, advantages, and features of the present invention will be more fully set forth and demonstrated through the following detailed description of specific embodiments in conjunction with the accompanying drawings. Those skilled in the art, upon referring to the following detailed description and the accompanying drawings, will be able to better understand and realize the above advantages of the present invention. Other objects, features, and advantages of the present invention will become clearer after being described in detail in the detailed description section in conjunction with the accompanying drawings. Attached Figure Description
[0022] To make the objectives, technical solutions, and beneficial effects of this invention clearer, the following drawings are provided for illustration.
[0023] Figure 1 Flowchart of a quantitative calculation and dynamic analysis method for chloride ion transport mechanism in marine concrete; Figure 2 Dynamic evolution curves of the contribution ratios of diffusion and convection over time; Figure 3 A schematic diagram comparing the concentration distribution before and after spatial interpolation alignment; Figure 4 A schematic diagram illustrating the calculation of the first-order partial derivative matrix of flux and the definition of the contribution matrix; Figure 5 This is a bar chart comparing the absolute flux during the wet and dry phases. Detailed Implementation
[0024] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention. Example 1
[0025] like Figure 1 As shown in this embodiment, the quantitative calculation and dynamic analysis method for chloride ion transport mechanism in marine concrete includes the following steps: Step S1: Obtain the diffusion flux matrix, convection flux matrix, and concentration distribution matrix, as well as the corresponding time series and depth series, from the numerical simulation output of chloride ion transport in marine concrete. Specifically, obtain the diffusion flux matrix, convection flux matrix, and concentration distribution matrix by reading the numerical simulation output of chloride ion transport in marine concrete, and extract the corresponding time series and depth series from the file.
[0026] Step S2 involves validating the acquired data. If the depth sequence of the concentration distribution matrix is inconsistent with the depth sequence of the flux matrix, spatial interpolation alignment is performed on the concentration distribution matrix. Specifically, for example... Figure 3 As shown, Figure 3 This diagram illustrates the concentration distribution before and after spatial interpolation alignment. The spacing of the original discrete nodes (hollow circles) is not fixed. To ensure computational accuracy, this embodiment uses one-dimensional linear interpolation for spatial interpolation alignment. Figure 3 As shown by the solid line, interpolation generates equidistant interpolation nodes (solid black dots, 2∆x ≠ const). This process ensures that the concentration distribution data is perfectly aligned with the flux data in terms of spatial nodes, eliminating numerical oscillations caused by uneven step sizes in subsequent gradient calculations and ensuring the physical spatial consistency of the data source.
[0027] Step S3: Using the finite difference method, calculate the first-order partial derivative matrices of the diffusion flux matrix and the convection flux matrix with respect to depth, and take their negative values as the diffusion contribution matrix and the convection contribution matrix, respectively; simultaneously, calculate the concentration-time gradient matrix of the concentration distribution matrix with respect to time; specifically, as follows... Figure 4 As shown, Figure 4 This diagram illustrates the calculation of the first-order partial derivative matrix of flux and the definition of the contribution matrix. For the concentration distribution curve C(x, t), this embodiment uses the central difference scheme to calculate the spatial gradient; that is, for the internal node xi, its partial derivative is approximately expressed as... Based on this algorithm, the first-order partial derivative matrices of diffusion flux and convection flux with respect to depth are calculated respectively. Their negative values are defined as the diffusion contribution matrix Jdiff and the convection contribution matrix Jconv, and a structure is constructed as follows: Figure 4 The spatiotemporal dynamic contribution matrix is shown at the bottom; simultaneously, the temporal gradient of the chloride ion concentration matrix at each spatial node is calculated to obtain the concentration temporal gradient matrix. This step transforms the "static" concentration distribution into a "dynamic" mechanism contribution, achieving quantitative decoupling of diffusion and convection under the same dimensions.
[0028] Step S4: Based on the calculated diffusion contribution matrix, convection contribution matrix, and concentration time gradient matrix, calculate the Pearson correlation coefficient between diffusion and convection mechanisms and the concentration change rate, and calculate the dynamic contribution ratio of diffusion and convection at continuous time nodes; specifically, within a set time range, calculate the Pearson correlation coefficient between diffusion contribution, convection contribution, and concentration time change rate at each time node; at the same time, perform spatial numerical integration on the absolute values of diffusion contribution and convection contribution, calculate and output the dynamic contribution ratio of diffusion and convection on the continuous time axis.
[0029] Step S5: Divide the time series into wet and dry phases, and perform spatiotemporal double integration on the diffusion flux matrix and convective flux matrix within the wet and dry phases respectively to calculate the absolute flux and relative contribution rate under different environmental phases; specifically, as follows... Figure 5 As shown, Figure 5 A bar chart comparing the absolute fluxes during the wet and dry phases is presented. Based on a defined wet-dry cycle, time windows for the wet and dry phases are divided. The diffusion flux and convection flux matrices for both the wet and dry phases are integrated using the trapezoidal rule for both time and space dimensions to quantify the absolute fluxes and relative contributions of each phase. Figure 5 As shown in the bar chart comparison, this method can quantify the differences in the strength of the transport mechanism under different environmental stages, revealing that the convection effect induced by water evaporation in the dry stage is significantly improved compared with the wet stage, providing accurate data support for the prediction of concrete life under alternating dry and wet environments.
[0030] Step S6: Output the analysis results of each stage and generate a visualization chart of the dynamic evolution of the transport mechanism; specifically, output the micro-dynamic Pearson correlation coefficient, the average dynamic contribution ratio, and the decoupling contribution rate of the macro-dry and wet stages, and draw a dynamic evolution curve of the contribution ratio of diffusion and convection over time.
[0031] In step S2 of this embodiment, a one-dimensional linear interpolation method is used to perform spatial interpolation alignment of the concentration distribution matrix along the spatial dimension using an extrapolation mode.
[0032] In step S3 of this embodiment, the specific method for calculating the first-order partial derivative matrix and the time gradient is as follows: the central difference scheme is used for internal nodes, and the one-sided difference scheme is used for boundary nodes; wherein, the values of the diffusion contribution matrix and the convection contribution matrix are the negative values of the partial derivatives of diffusion flux and convection flux with respect to spatial depth, respectively.
[0033] In step S4 of this embodiment, the method for calculating the dynamic contribution ratio is as follows: At any time node t, the total diffusion contribution is obtained by numerically integrating the absolute value of the diffusion contribution matrix along the depth sequence using the trapezoidal numerical integration method, and the total convection contribution is obtained by numerically integrating the absolute value of the convection contribution matrix along the depth sequence. Diffusion dynamics contribution = Total diffusion contribution / (Total diffusion contribution + Total convection contribution); Convection dynamic contribution = Total convection contribution / (Total diffusion contribution + Total convection contribution).
[0034] In step S4 of this embodiment, the Pearson correlation coefficient is calculated as follows: extract the column vector of the first-order partial derivative matrix and the column vector of the time gradient at a specific time node, remove invalid values, and then calculate the Pearson correlation coefficient between the two to characterize the driving correlation of a single transport mechanism on the overall concentration distribution change.
[0035] In step S5 of this embodiment, the specific method for performing spatiotemporal double integration is as follows: Using the trapezoidal numerical integration method, the flux matrices of the wetting and drying stages are first integrated along the time dimension, and then their absolute values are integrated along the spatial depth dimension to obtain the total diffusion flux, total convection flux, total diffusion flux, and total convection flux of the wetting and drying stages. Based on this, the relative contribution percentage of each stage is calculated.
[0036] The diffusion flux matrix described in this embodiment is calculated according to the following Fick's law: ; in, Indicates the diffusion flux component; Indicates the diffusion coefficient; Indicates concentration; Indicates depth; The circulation matrix is calculated according to the following formula: ; ; in, Indicates the quantity of flow; Indicates convective velocity; Indicates water head; The diffusion flux matrix, convection flux matrix, and concentration distribution matrix described in this embodiment are derived from numerical simulation results of the water transport equation based on the Richards equation and the chloride ion transport equation based on the Nernst-Planck equation.
[0037] In this embodiment, the time window for dividing the humidification stage and the drying stage in step S5 is based on a preset wet-dry alternation system, which includes the duration of the wet-dry cycle and the wet-dry ratio.
[0038] The visualization charts generated in step S6 of this embodiment include dynamic evolution curves showing the changes in the proportion of diffusion contribution and the proportion of convection contribution over time, as well as bar charts comparing the absolute fluxes of the humid and dry stages.
[0039] The quantitative calculation and dynamic analysis system for chloride ion transport mechanism in marine concrete provided in this embodiment is used to implement the method described in any one of claims 1-6, characterized in that it includes: The data parsing and preprocessing module is used to read numerical simulation flux and concentration files, extract spatiotemporal sequences, and perform data verification and spatial interpolation alignment. The spatiotemporal gradient calculation module is used to calculate the first-order partial derivative matrices of diffusion flux and convection flux, as well as the concentration-time gradient matrix of concentration, based on the finite difference algorithm. The micro-dynamic analysis module is used to calculate the Pearson correlation coefficient of the gradient and to use numerical integration to calculate the dynamic contribution ratio of each mechanism at a specific time point. The macroscopic stage decoupling module is used to perform spatiotemporal double integration of the flux matrix according to the divided dry and wet time windows, and quantify the absolute flux and relative contribution rate of different environmental stages. The visualization output module is used to statistically output analysis results and draw dynamic curves showing the evolution of the transmission mechanism. Example 2
[0040] This embodiment further illustrates the method with specific illustrations and implementation details. The core of the dynamic decoupling analysis method for chloride ion transport mechanisms in concrete under alternating wet and dry conditions provided in this embodiment lies in using numerical simulation to quantitatively distinguish and evaluate the contributions of diffusion and convection to the chloride ion erosion process at different dry and wet stages on a macroscopic scale. The specific implementation steps of this method are as follows: Step S1: Establish physical model and initialize parameters A one-dimensional unsaturated porous medium model is constructed to represent the concrete structure. Boundary conditions for the model are defined, including defining a wet-dry cycle (e.g., 1:1), such as a 24-hour wet-dry cycle (12 hours of wetting, 12 hours of drying). Material parameters such as initial porosity, permeability, and diffusion coefficient of the concrete are set based on standard tests or references.
[0041] Step S2: Solve the coupled transmission control equations Using the finite difference method or finite element method, at each time step (Δt) and spatial step (Δx), the moisture transport equation based on the Richards equation and the Nernst-Planck chloride ion transport equation considering convection-diffusion are solved simultaneously. This step will yield the humidity field h(x,t) and chloride ion concentration field C(x,t) at each time step and location.
[0042] Step S3: Dynamic decoupling and integration of transmission flux The total chloride ion transport flux J within each computation time step total Real-time decoupling is performed, separating the diffusion flux component J. d and the flow component J c : Diffusion flux: Regarding circulation: The water flow velocity u is determined by the humidity gradient. The calculation yielded the result.
[0043] This method performs double integration on the two components mentioned above along the time axis: ① Global contribution integral: Accumulated over the entire simulation period (e.g., 60 days) | | and | The total amount of | is used to calculate the average total contribution percentage of the two mechanisms.
[0044] ② Phased contribution points: The point accumulation process is grouped according to a preset wet-dry cycle. Accumulation is only performed during the wet phase (e.g., the first 12 hours of a 24-hour cycle). | and | |; Similarly, | accumulates only during the drying phase (e.g., the last 12 hours of each 24-hour cycle). | and | |
[0045] Step S4: Contribution Ratio and Correlation Analysis Based on the integral results of step S3, a quantitative analysis is performed: ① Calculate the dynamic contribution ratio: at each time point, calculate... and And drawn as attached Figure 2 The dynamic evolution curve shown is shown below. Among them, Indicates the percentage of diffusion contribution, % Indicates the percentage of convection contribution, % ② Calculate the average total contribution percentage: Using the global integration results, calculate the average contribution of diffusion and convection over the entire service life.
[0046] ③ Calculate the contribution ratio of each stage: Use the results of the stage integration to accurately calculate the contribution ratio of each mechanism during the wet and dry periods.
[0047] ④ Calculate the Pearson correlation coefficient: Calculate the diffusion mechanism separately ( ) and convection mechanism ( ) and the rate of change of chloride ion concentration inside concrete The Pearson correlation coefficient is used to statistically determine which mechanism is the dominant factor causing the concentration change.
[0048] Through the above steps, this invention can deeply analyze and quantify the effects of different physical mechanisms at both macroscopic (dry / wet stage) and microscopic (instantaneous fluctuation) scales. Example 3
[0049] This embodiment aims to illustrate how to apply the above method to analyze the chloride ion erosion mechanism of marine concrete under specific working conditions.
[0050] 1. Simulation parameters Wet-dry regime: A 24-hour wet-dry alternation cycle is adopted (12 hours of wetting followed by 12 hours of drying).
[0051] Simulated duration: Total exposure time is 60 days.
[0052] 2. Simulation Results and Analysis Input the above parameters into the MATLAB program constructed based on the method of this invention, and after running it, obtain the attached... Figure 2 The dynamic evolution curves shown below and the following quantitative data are presented: [Microscopic Dynamic Analysis (0-60 days)] The average Pearson correlation coefficient between diffusion mechanism and concentration change rate was 0.421. The average Pearson correlation coefficient between convection mechanism and concentration change rate was 0.082. The average total contribution of diffusion mechanisms was 91.12%. The average total contribution of convection mechanisms: 8.88%; [Macro-level dry / wet phase decoupling analysis] During the wetting period - diffusion contribution: 97.11% | convection contribution: 2.89%; Drying period - Diffusion contribution: 97.79% | Convection contribution: 2.21%; ① Overall contribution analysis: like Figure 2As shown, under conditions of a 24-hour wet-dry cycle and a 1:1 wet-dry ratio, over a 60-day exposure period, the contribution of chloride ion diffusion within the concrete (blue curve) consistently fluctuated at a high level of around 90%, while the contribution of convection (red curve) fluctuated at a low level of around 10%. Calculations showed that the average total contribution of the diffusion mechanism was 91.12%, and the average total contribution of the convection mechanism was 8.88%. Furthermore, the average Pearson correlation coefficient between the diffusion mechanism and the concentration change rate was 0.421, significantly higher than the 0.082 for the convection mechanism. These data consistently indicate that, under alternating wet and dry conditions, despite the presence of a convection effect, the accumulation of chloride ions within the concrete is generally dominated by the diffusion mechanism.
[0053] ② Decoupling analysis of macroscopic dry and wet stages: The core decoupling function of this invention reveals a deeper physical process. During the wetting stage, water permeates from the outside in, with diffusion contributing as much as 97.11%, while convection contributes only 2.89%. This indicates that during the water absorption stage, chloride ions mainly penetrate into the concrete along the gradient from high to low concentration. However, during the drying stage, as surface moisture evaporates, capillary action "pumps" the internal chloride ion solution back to the surface convection zone. At this point, the convection contribution decreases to 2.21%, while the diffusion contribution increases to 91.79%. In summary, this result accurately quantifies the role of different mechanisms in the "wet in, dry out" phenomenon. It reveals that although convection accounts for a small proportion of the total contribution, it is a significant driving force for surface chloride ion enrichment and accelerated erosion during the drying stage.
[0054] ③ Dynamic feature analysis: Figure 2 Both curves exhibit dense, high-frequency "sawtooth" fluctuations, the frequency of which corresponds perfectly to the 24-hour wet-dry cycle. This demonstrates that the method of this invention possesses extremely high temporal resolution, capable of capturing and reflecting the instantaneous response of the transmission mechanism triggered by each minute wet-dry cycle.
[0055] In summary, this embodiment demonstrates, through specific numerical values and graphs, that the method of the present invention can successfully perform dynamic and quantitative decoupling analysis of the complex process of chloride ion transport in concrete, providing a reliable scientific basis for accurately assessing structural durability and predicting service life. The above-described embodiments are merely preferred embodiments provided to fully illustrate the present invention, and the scope of protection of the present invention is not limited thereto. Equivalent substitutions or modifications made by those skilled in the art based on the present invention are all within the scope of protection of the present invention. The scope of protection of the present invention is defined by the claims.
Claims
1. A method for quantitative calculation and dynamic analysis of chloride ion transport mechanism in marine concrete, characterized in that, Includes the following steps: Step S1: Obtain the diffusion flux matrix, convection flux matrix, concentration distribution matrix, and corresponding time series and depth series from the numerical simulation output of chloride ion transport in marine concrete. Step S2: Verify the validity of the acquired data. If the depth sequence of the concentration distribution matrix is inconsistent with the depth sequence of the flux matrix, then perform spatial interpolation alignment on the concentration distribution matrix. Step S3: Using the finite difference method, calculate the first-order partial derivative matrices of the diffusion flux matrix and the convection flux matrix with respect to depth, and take their negative values as the diffusion contribution matrix and the convection contribution matrix; at the same time, calculate the concentration time gradient matrix of the concentration distribution matrix with respect to time. Step S4: Based on the calculated diffusion contribution matrix, convection contribution matrix and concentration time gradient matrix, calculate the Pearson correlation coefficient between diffusion and convection mechanisms and concentration change rate, and calculate the dynamic contribution ratio of diffusion and convection at continuous time nodes. Step S5: Divide the time windows into wet and dry phases according to the time series, and perform spatiotemporal double integration on the diffusion flux matrix and convective flux matrix in the wet and dry phases respectively to calculate the absolute flux and relative contribution rate under different environmental phases. Step S6: Output the analysis results of each stage and generate a visualization chart of the dynamic evolution of the transmission mechanism.
2. The method for quantitative calculation and dynamic analysis of chloride ion transport mechanism in marine concrete as described in claim 1, characterized in that, In step S3, the specific method for calculating the first-order partial derivative matrix and the time gradient is as follows: the central difference scheme is used for internal nodes, and the one-sided difference scheme is used for boundary nodes; wherein, the values of the diffusion contribution matrix and the convection contribution matrix are the negative values of the partial derivatives of diffusion flux and convection flux with respect to spatial depth, respectively.
3. The method for quantitative calculation and dynamic analysis of chloride ion transport mechanism in marine concrete as described in claim 1, characterized in that, In step S4, the dynamic contribution ratio is calculated as follows: At any time node t, the total diffusion contribution is obtained by numerically integrating the absolute value of the diffusion contribution matrix along the depth sequence using the trapezoidal numerical integration method, and the total convection contribution is obtained by numerically integrating the absolute value of the convection contribution matrix along the depth sequence. Diffusion dynamics contribution = Total diffusion contribution / (Total diffusion contribution + Total convection contribution); Convection dynamic contribution = Total convection contribution / (Total diffusion contribution + Total convection contribution).
4. The method for quantitative calculation and dynamic analysis of chloride ion transport mechanism in marine concrete as described in claim 1, characterized in that, In step S4, the Pearson correlation coefficient is calculated as follows: extract the column vector of the first-order partial derivative matrix and the column vector of the time gradient at a specific time node, remove invalid values, and then calculate the Pearson correlation coefficient between the two to characterize the driving correlation of a single transport mechanism on the overall concentration distribution change.
5. The method for quantitative calculation and dynamic analysis of chloride ion transport mechanism in marine concrete as described in claim 1, characterized in that, In step S5, the specific method for performing spatiotemporal double integration is as follows: Using the trapezoidal numerical integration method, the flux matrices of the wetting and drying stages are first integrated along the time dimension, and then their absolute values are integrated along the spatial depth dimension to obtain the total diffusion flux, total convection flux, total diffusion flux, and total convection flux of the wetting and drying stages. Based on this, the relative contribution percentage of each stage is calculated.
6. The method for quantitative calculation and dynamic analysis of chloride ion transport mechanism in marine concrete as described in claim 1, characterized in that, The diffusion flux matrix is calculated according to the following Fick's law: ； in, Indicates the diffusion flux component; Indicates the diffusion coefficient; Indicates concentration; Indicates depth; The circulation matrix is calculated according to the following formula: ； in, Indicates the quantity of flow; This indicates the convective flow velocity of the pore fluid.
7. The method for quantitative calculation and dynamic analysis of chloride ion transport mechanism in marine concrete as described in claim 1, characterized in that, The diffusion flux matrix, convection flux matrix, and concentration distribution matrix are derived from numerical simulation results of the water transport equation based on the Richards equation and the chloride ion transport equation based on the Nernst-Planck equation.
8. The method for quantitative calculation and dynamic analysis of chloride ion transport mechanism in marine concrete as described in claim 1, characterized in that, The time window for dividing the humidification stage and the drying stage in step S5 is based on a preset wet-dry alternation system, which includes the duration of the wet-dry cycle and the wet-dry ratio.
9. The method for quantitative calculation and dynamic analysis of chloride ion transport mechanism in marine concrete as described in claim 1, characterized in that, The visualization charts generated in step S6 include dynamic evolution curves showing the changes in the proportion of diffusion contribution and the proportion of convection contribution over time, as well as bar charts comparing the absolute fluxes of the wet and dry stages.
10. A quantitative calculation and dynamic analysis system for chloride ion transport mechanism in marine concrete, used to implement the method of any one of claims 1-9, characterized in that, include: The data parsing and preprocessing module is used to read numerical simulation flux and concentration files, extract spatiotemporal sequences, and perform data verification and spatial interpolation alignment. The spatiotemporal gradient calculation module is used to calculate the first-order partial derivative matrices of diffusion flux and convection flux, as well as the concentration-time gradient matrix of concentration, based on the finite difference algorithm. The micro-dynamic analysis module is used to calculate the Pearson correlation coefficient of the gradient and to use numerical integration to calculate the dynamic contribution ratio of each mechanism at a specific time point. The macroscopic stage decoupling module is used to perform spatiotemporal double integration of the flux matrix according to the divided dry and wet time windows, and quantify the absolute flux and relative contribution rate of different environmental stages. The visualization output module is used to statistically output analysis results and draw dynamic curves showing the evolution of the transmission mechanism.