A snow and ice environment simulation test method based on multi-field coupling

Abstract

The application relates to the technical field of ice and snow environment simulation, and discloses an ice and snow environment simulation test method based on multi-field coupling. The method collects a dynamic multi-field data set under a target ice and snow environment, covers a periodic temperature change sequence, a relative humidity fluctuation sequence and real-time monitoring data of surface ice layer thickness; carries out multi-physical field coupling numerical simulation processing on the data set, generates coupling field distribution characteristics containing an axial thermal conductivity gradient, a radial fluid velocity cumulative amount and a tangential ice layer stress fluctuation coefficient; calls a preconfigured ice and snow growth simulation model to perform ice layer evolution analysis, obtains an ice layer growth rate prediction value and a high-risk icing area position identifier; uses the correlation characteristics of the relative humidity fluctuation sequence and material heat capacity attributes to dynamically compensate and correct the ice layer growth rate prediction value; and provides scientific guidance for related protection under the ice and snow environment based on the high-risk icing area position identifier.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ice and snow environment simulation, in particular to an ice and snow environment simulation test method based on multi-field coupling. BACKGROUND

[0002] In many fields such as engineering construction, transportation and power transmission in cold regions, the influence of ice and snow environment is always a problem that needs to be addressed. With the increasing frequency of extreme weather, ice and snow coverage, icing and other phenomena pose a significant threat to the safe operation of infrastructure, the normal operation of equipment and the safety of life and property of personnel. For example, ice accumulation on power transmission lines can cause line overload and tower collapse; road icing can cause traffic accidents; aircraft surface icing can seriously affect flight safety. Therefore, accurate simulation and testing of ice and snow environment, and then developing effective anti-icing and de-icing strategies, have become an important direction of research in related fields.

[0003] Traditional ice and snow environment simulation test methods often have certain limitations. Some methods only focus on the influence of a single physical field, such as considering only the effect of temperature change on the icing process, while ignoring the coupling effects of humidity, air flow and other factors. Such single-factor simulation cannot truly reflect the complex situation of actual ice and snow environment, resulting in a large deviation between the simulation results and the actual situation. In addition, some simulation methods lack effective dynamic compensation mechanisms when dealing with dynamically changing environmental parameters. When environmental parameters such as temperature and humidity fluctuate, the accuracy of key indicators such as ice layer growth rate decreases significantly, making it difficult to provide reliable basis for the development of anti-icing treatment schemes. At the same time, traditional methods often rely on empirical judgment or simple numerical calculation to identify high-risk icing areas, which makes it difficult to accurately locate risk areas and reduces the effectiveness of anti-icing measures.

[0004] With the development of technology, multi-physical field coupling simulation has gradually attracted attention, but existing technologies still have deficiencies in extracting and analyzing the distribution characteristics of coupled fields. How to effectively integrate multi-field data such as temperature, humidity and ice layer thickness to accurately generate coupled characteristic parameters reflecting the interaction of each physical field, and how to accurately predict the evolution process of ice layer based on these characteristic parameters, are still problems that need to be solved in the field of ice and snow environment simulation testing. In addition, when generating anti-icing treatment schemes, existing methods are often limited to a single technical means, such as using only heat sources or a single surface de-icing technology, which fails to provide a diversified and targeted scheme set according to different icing conditions and environmental conditions, affecting the maximization of anti-icing effect. SUMMARY

[0005] The present application aims to provide an ice and snow environment simulation test method based on multi-field coupling to solve the problems raised in the background.

[0006] To achieve the above objectives, the present invention provides a method for simulating ice and snow environment testing based on multi-field coupling, the method comprising:

[0007] Collect dynamic multi-field data sets in the target ice and snow environment, wherein the dynamic multi-field data sets include periodic temperature change series, relative humidity fluctuation series and real-time monitoring data of surface ice thickness;

[0008] Performing multi-physics field coupling numerical simulation processing on the dynamic multi-field data set to generate coupled field distribution characteristics of the target ice and snow environment, wherein the coupled field distribution characteristics include an axial heat transfer coefficient gradient, a radial fluid velocity accumulation, and a tangential ice layer stress fluctuation coefficient;

[0009] Calling a preconfigured ice and snow growth simulation model to perform ice layer evolution analysis and processing on the coupled field distribution characteristics, and generating an ice layer growth rate prediction value and a location identifier of a high-risk icing area;

[0010] performing an environmental dynamic compensation correction process on the ice layer growth rate prediction value to generate a corrected ice layer growth rate prediction value, wherein the environmental dynamic compensation correction process is implemented by utilizing the correlation characteristics between the relative humidity fluctuation sequence and the heat capacity property of the material;

[0011] Based on the high-risk icing area location identifier, an anti-icing treatment solution set is generated, and the anti-icing treatment solution set includes a heat source deployment strategy and a surface ice melting technology solution.

[0012] Preferably, performing multi-physics field coupling numerical simulation processing on the dynamic multi-field data set to generate coupling field distribution characteristics of the target ice and snow environment includes:

[0013] Dividing the periodic temperature variation sequence into a plurality of temperature subsequences according to a preset time window, each temperature subsequence corresponding to a simulation period unit;

[0014] For each temperature subsequence, perform the following operations:

[0015] Constructing a three-dimensional ice layer deformation topology model based on the real-time monitoring data of the surface ice layer thickness, wherein the three-dimensional ice layer deformation topology model includes spatial coordinate information of thermal expansion field distribution, fluid velocity field distribution, and stress field distribution;

[0016] Performing field coupling interaction processing on the three-dimensional ice layer deformation topology model and the temperature subsequence to generate a coupled field simulation output of the current simulation period unit;

[0017] Performing cumulative integration calculation on the coupled field simulation outputs of a plurality of consecutive simulation period units to obtain the axial heat transfer coefficient gradient, radial fluid velocity accumulation, and tangential ice layer stress fluctuation coefficient;

[0018] Among them, the axial heat transfer coefficient gradient is the maximum rate of change of the thermal expansion field along the axial direction, the radial fluid velocity accumulation is the total integral of the fluid velocity field in the normal direction of the ice layer contact surface, and the tangential ice layer stress fluctuation coefficient is the coefficient of variation of the stress field distribution over time.

[0019] Preferably, performing field coupling interaction processing on the three-dimensional ice layer deformation topology model and the temperature subsequence to generate a coupled field simulation output of the current simulation period unit includes:

[0020] Based on the corresponding relationship between the heat conduction law and the temperature subsequence, a thermal field-temperature balance equation is established, and an initial distribution function of the heat conduction component is obtained by solving the thermal field-temperature balance equation;

[0021] Based on the correlation between fluid dynamics principles and ice deformation data, a fluid-ice coupling calculation framework is constructed, which integrates dynamic adjustment parameters of fluid viscosity coefficient and ice elastic modulus;

[0022] Combining the surface stress change rate with the friction effect factor, an iterative ice growth prediction process is implemented, which includes a feedback calibration mechanism for the stress increment and ice deformation.

[0023] A spatial field fusion operation is performed on the output results of the initial distribution function, the fluid-ice layer coupling calculation framework, and the ice layer growth iterative prediction process to generate a three-dimensional coupled field distribution data set containing heat conduction, fluid velocity, and ice layer stress components.

[0024] Preferably, the calling of a preconfigured ice and snow growth simulation model to perform ice layer evolution analysis and processing on the coupled field distribution characteristics to generate an ice layer growth rate prediction value and a high-risk icing area location identifier includes:

[0025] Inputting the axial heat transfer coefficient gradient into the initial characteristic analysis layer of the ice and snow growth simulation model, and determining the distribution coordinates of the heat concentration area and the heat amplitude change trajectory through the heat concentration factor calculation unit;

[0026] Inputting the radial fluid velocity accumulation into the secondary characteristic analysis layer of the ice and snow growth simulation model, performing fluid scour damage accumulation calculation, and generating the melting probability and crack growth rate prediction value of the ice surface;

[0027] Inputting the tangential ice layer stress fluctuation coefficient into the final characteristic analysis layer of the ice and snow growth simulation model, and calculating the material wear depth of the friction surface and the ice layer roughness evolution data based on the surface ice layer degradation model;

[0028] The thermal amplitude variation trajectory, the melting probability, and the material wear depth are integrated to generate a comprehensive evolution index of the target ice and snow environment, and a predicted value of the ice layer growth rate is determined based on a comparison result of the comprehensive evolution index with a preset threshold library;

[0029] Based on the spatial field superposition results of the distribution coordinates, the crack growth rate prediction value and the ice layer roughness evolution data, the geometric position information of the heat concentration area, the crack path and the high-risk wear area is identified.

[0030] Preferably, performing environmental dynamic compensation correction processing on the ice layer growth rate prediction value to generate a corrected ice layer growth rate prediction value includes:

[0031] Extracting the extreme humidity values ​​and humidity change frequencies in the relative humidity fluctuation sequence, and calculating the dynamic correction amount of the material heat capacity property as the humidity fluctuates;

[0032] performing a thermal stress compensation calculation on the axial heat transfer coefficient gradient according to the dynamic correction amount to generate a compensated heat transfer coefficient gradient;

[0033] Based on the correlation between the humidity change frequency and the phase change characteristics of the material, performing phase change damage correction processing on the radial fluid velocity accumulation to generate a corrected fluid velocity accumulation;

[0034] performing surface strength adaptation adjustment processing on the tangential ice layer stress fluctuation coefficient according to the material thermal conductivity variation data under extreme humidity to generate an adjusted ice layer stress fluctuation coefficient;

[0035] The compensated thermal conductivity gradient, the corrected fluid velocity accumulation and the adjusted ice layer stress fluctuation coefficient are input into the ice and snow growth simulation model for recalculation to generate an ice layer growth rate prediction value including environmental factor compensation.

[0036] Preferably, performing thermal stress compensation calculation on the axial thermal conductivity gradient according to the dynamic correction amount to generate a compensated thermal conductivity gradient includes:

[0037] Obtaining the initial heat capacity property of the target ice and snow environment under reference humidity conditions and the dynamic correction amount, and establishing a heat capacity-humidity correlation function;

[0038] Calculating an axial thermal stress compensation value according to the heat capacity-humidity correlation function, wherein the axial thermal stress compensation value is the product of the humidity change amplitude and the heat capacity change amplitude;

[0039] superimposing the axial thermal stress compensation value into the calculation process of the axial thermal conductivity gradient to generate an intermediate thermal conductivity gradient value including thermal stress compensation;

[0040] Performing thermal relaxation effect compensation processing on the thermal conductivity coefficient gradient intermediate value, the thermal relaxation effect compensation processing is realized by using the product factor of the material thermal relaxation curve and the humidity retention time.

[0041] Preferably, based on the high-risk icing area position identification, an anti-icing treatment scheme set is generated, including:

[0042] For the identification information of the heat concentration area, an optimal heat source deployment path is calculated, which is realized by adjusting the power distribution ratio of adjacent heat sources;

[0043] According to the identification information of the crack path, a surface ice melting technical scheme is constructed, which contains the selection of microwave heating area and the optimal configuration of energy parameters;

[0044] Based on the identification information of the high-risk wear area, a lubricant application strategy is generated, which dynamically adjusts the application frequency and application thickness according to the wear rate prediction value;

[0045] The optimal heat source deployment path, the surface ice melting technical scheme and the lubricant application strategy are prioritized to generate an anti-icing treatment scheme set containing execution timing and technical parameters.

[0046] Preferably, the surface ice melting technical scheme is constructed, including:

[0047] Extract the geometric feature data of the crack path, calculate the path curvature radius and the extension direction angle;

[0048] According to the curvature radius, the coverage density parameter of the microwave heating spot is selected, which is in a non-linear inverse proportional relationship with the curvature radius;

[0049] Based on the extension direction angle, the application direction of microwave heating energy is adjusted, so that the energy application direction and the crack path direction form a preset included angle;

[0050] According to the material surface heat conduction characteristic test data, dynamically adjust the microwave heating pulse duration to ensure that the heating energy is below the critical value of the material melting point;

[0051] Generate an ice melting parameter configuration table containing coverage density parameters, application direction and pulse duration.

[0052] Preferably, the method further comprises:

[0053] In a preset verification period, the actual ice layer thickness and crack extension length data of the target ice and snow environment are collected;

[0054] performing deviation analysis on the actual ice thickness and the predicted ice thickness to generate a thickness error correction coefficient;

[0055] Performing a time series comparison process on the crack propagation length and the predicted propagation speed to generate a length error correction coefficient;

[0056] Adjusting the parameter weights of the ice and snow growth simulation model according to the thickness error correction coefficient and the length error correction coefficient to generate an optimized ice and snow growth simulation model;

[0057] The optimized ice and snow growth simulation model is applied to ice and snow environment simulation tasks in subsequent test batches.

[0058] Preferably, after generating the anti-icing treatment solution set, the method further includes:

[0059] Inputting the anti-icing treatment plan set into the environmental simulation control system to perform real-time scheduling processing;

[0060] updating the acquisition parameters of the dynamic multi-field data set according to the scheduling result feedback;

[0061] Restarting the multi-physics field coupling numerical simulation process using the updated acquisition parameters to generate an iterative coupling field distribution feature;

[0062] The anti-icing treatment scheme set is optimized based on the iterative coupling field distribution characteristics, and a final simulation test report is output.

[0063] Compared with the prior art, the present invention has the following beneficial effects:

[0064] This multi-field coupled ice and snow environment simulation test method collects dynamic, multi-field data sets from the target ice and snow environment, encompassing periodic temperature change sequences, relative humidity fluctuation sequences, and real-time monitoring data on surface ice thickness. This method comprehensively captures the multi-dimensional and dynamic information of the ice and snow environment, providing rich and realistic basic data for subsequent simulation analysis. Compared with traditional methods that only consider a single physical field, this multi-field data collection can more comprehensively reflect the complex characteristics of the actual ice and snow environment and avoid simulation bias caused by missing data.

[0065] Applying multi-physics coupled numerical simulations to dynamic multi-field data sets generates coupled field distribution characteristics, including axial heat transfer coefficient gradients, radial fluid velocity accumulations, and tangential ice layer stress fluctuation coefficients. These coupled characteristic parameters can deeply reveal the interaction mechanisms between various physical fields, such as temperature, humidity, and ice layer. These coupled characteristic parameters quantify the coupling effects between multiple fields from different dimensions, providing key clues for understanding the inherent laws of ice and snow formation and development. This makes the simulation process more consistent with actual physical processes and overcomes the shortcomings of traditional methods that lack consideration of multi-field coupling effects.

[0066] A preconfigured ice and snow growth simulation model analyzes the coupled field distribution characteristics to analyze ice evolution, generating predicted ice growth rates and identifying high-risk icing areas. This allows for precise deduction of ice layer dynamics and accurate location of risk areas. The ice and snow growth simulation model analyzes coupled field distribution characteristics and leverages the rich information provided by multi-field coupling, making ice growth rate predictions more scientific and identifying high-risk icing areas more realistic. This addresses the inaccurate positioning issues often associated with traditional methods that rely on empirical judgment or simple calculations.

[0067] The ice growth rate predictions are corrected using dynamic environmental compensation, leveraging the correlation between relative humidity fluctuations and material heat capacity. This correction effectively improves the accuracy of the predictions in dynamic environments. This dynamic compensation mechanism allows for timely adjustments to predictions when environmental parameters fluctuate, preventing bias caused by environmental changes. This makes ice growth rate predictions more adaptable to dynamic changes in the actual environment and provides a more reliable reference for developing anti-icing strategies.

[0068] Based on the location of high-risk icing areas, a collection of anti-icing solutions, including heat source deployment strategies and surface ice melting technology solutions, is generated. This provides diverse and targeted solutions for different high-risk areas and icing conditions. This collection of solutions is no longer limited to a single anti-icing method. Instead, it flexibly selects appropriate anti-icing technologies based on specific icing characteristics and environmental conditions. This improves the relevance and effectiveness of anti-icing measures, overcoming the limitations of traditional anti-icing methods, which often rely on a single method and have limited effectiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 This is a working principle diagram of the ice and snow environment simulation test method based on multi-field coupling according to the present invention;

[0070] Figure 2 Flowchart for multi-physics field coupling numerical simulation processing;

[0071] Figure 3 Flowchart for ice layer evolution analysis and processing;

[0072] Figure 4 Construct a flow chart for the surface ice melting technology solution. DETAILED DESCRIPTION

[0073] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0074] See also Figure 1 The present invention provides a method for simulating and testing an ice and snow environment based on multi-field coupling, the method comprising:

[0075] This system collects dynamic multi-field data sets from the target ice and snow environment, including periodic temperature change sequences, relative humidity fluctuation sequences, and real-time monitoring data on surface ice thickness. Multi-physics field coupling numerical simulation is then performed on this dynamic multi-field data set to generate the coupled field distribution characteristics of the target ice and snow environment, including the axial heat transfer coefficient gradient, radial fluid velocity accumulation, and tangential ice stress fluctuation coefficient. A pre-configured ice and snow growth simulation model is then used to perform ice layer evolution analysis on the coupled field distribution characteristics, generating ice layer growth rate predictions and identification of high-risk icing areas. The predicted ice layer growth rate is then corrected for environmental dynamic compensation to generate a corrected ice layer growth rate prediction. Based on the identification of high-risk icing areas, a set of anti-icing solutions is generated, including heat source deployment strategies and surface ice melting technology solutions.

[0076] Example 1: See Figure 2 , a multi-physics coupled numerical simulation process for dynamic multi-field data sets. The process begins with the segmentation of the periodic temperature variation sequence, dividing the continuously collected temperature data into several discrete temperature subsequences according to preset time windows. Each temperature subsequence represents a complete temperature fluctuation cycle and serves as the basic unit for subsequent simulation calculations. The division of the time window is determined by the actual temperature variation characteristics of the target ice and snow environment, and the minimum time span that can reflect the complete freeze-thaw cycle is usually selected.

[0077] For each temperature subsequence, the system first uses real-time monitoring data of the surface ice thickness to construct a three-dimensional ice deformation topology model. This model divides the ice structure into a finite number of calculation units through a spatial discretization method. Each unit records the spatial coordinate information of the thermal expansion field distribution, fluid velocity field distribution, and stress field distribution. The thermal expansion field data is derived from the product relationship between the material thermal expansion coefficient and the local temperature change. The fluid velocity field data is obtained by interpolation of the boundary layer velocity measurement results, and the stress field data is inverted based on the strain monitoring results inside the ice layer. The grid density of the three-dimensional model is automatically adjusted according to the gradient of the ice thickness change, and a finer grid division strategy is adopted in the area of ​​thickness mutation.

[0078] The field-coupling interaction processing stage performs multi-physical field coupling calculation on the three-dimensional ice layer deformation topology model and the current temperature sub-sequence. The establishment of the heat field-temperature equilibrium equation is based on the Fourier heat conduction law, considering the constitutive relationship between temperature gradient and heat flux density. The implicit difference format is adopted in the equation solving process to improve the time step under the premise of ensuring the calculation stability. The initial distribution function of the heat conduction component is obtained by iterative method, and its spatial distribution characteristics reflect the initial thermal influence of the temperature field on the ice layer structure. The fluid-ice layer coupling calculation framework integrates the Navier-Stokes equation and the ice layer elastic deformation equation, and realizes the fluid-structure coupling calculation through the alternating solution strategy. The fluid viscosity coefficient adopts a temperature-dependent dynamic adjustment algorithm, and the ice layer elastic modulus is updated in real time according to the measured stress-strain curve.

[0079] The ice layer growth iterative prediction process introduces the surface stress change rate as the key control parameter, and combines the friction effect factor to build a closed-loop feedback system. The stress increment calculation considers the influence of ice crystal growth orientation and uses an anisotropic material model for description. The feedback calibration of ice layer deformation is realized by comparing the deviation between the simulation value and the measured value, and the parameter adjustment mechanism is automatically triggered when the deviation exceeds the threshold. The spatial field fusion operation performs vector superposition on the heat conduction component, fluid velocity field and ice layer stress field, and uses a weighted average algorithm to process the coupling effect of different physical fields. The weight coefficient is dynamically allocated according to the influence degree of each field on the ice layer evolution, and the heat conduction field has a higher weight in the freezing stage, and the fluid field dominates in the melting stage.

[0080] The cumulative integration calculation of the continuous simulation period unit uses the time series analysis method. The calculation of the axial heat conduction coefficient gradient selects the variation extreme value of the thermal expansion field along the main axis direction, and obtains the maximum value of the spatial variation rate through difference operation. The radial fluid velocity accumulation is obtained by area integration of the normal velocity component, and the integration area covers the entire ice layer contact surface. The calculation of the tangential ice layer stress fluctuation coefficient is based on the statistical characteristics of the time series of the stress field, and the ratio of standard deviation to mean value is used to represent the stress fluctuation intensity. The integration process of multi-period data introduces a decay factor, and the contribution of earlier period data gradually decreases with the simulation time.

[0081] The post-processing of the coupled field simulation output includes data normalization and abnormal value correction. The normalization process converts the dimensions of different physical fields into dimensionless parameters, which facilitates subsequent model calling. The abnormal value correction algorithm detects and eliminates the simulation results that deviate obviously from the normal range, and the missing data is supplemented by interpolation of adjacent nodes. The final generated coupled field distribution characteristic data set contains complete spatial distribution information and time evolution characteristics, providing accurate input parameters for the ice and snow growth simulation model.

[0082] The update mechanism of the three-dimensional ice layer deformation topology model adopts a dynamic adaptive strategy. When the monitored ice layer thickness change exceeds the preset threshold, the system automatically triggers the model reconstruction process. The reconstruction process retains the topological structure of the original model and only updates the node coordinates and physical field parameters. The correction of the thermal expansion field data takes into account the influence of the latent heat of the material phase change, and adopts a special constitutive relationship in the ice-water phase transition temperature range. The update of the fluid velocity field introduces a turbulence model and considers the influence of eddy effects in high flow velocity areas. The stress field data is recalculated using an incremental method, superimposing the current strain increment on the stress state at the previous moment.

[0083] The adaptive time step adjustment algorithm dynamically optimizes the time step based on computational convergence. When increasing nonlinear effects are detected, the time step is automatically reduced to ensure computational accuracy; during periods of gradual field changes, the step size is appropriately increased to improve computational efficiency. The step size adjustment strategy comprehensively considers multiple factors, including temperature change rate, flow velocity gradient, and stress fluctuation amplitude, and implements intelligent control through a fuzzy logic algorithm. This dynamic time management approach significantly improves overall simulation efficiency while maintaining computational accuracy.

[0084] The core of multi-physics field coupling numerical simulation lies in the precise description of the interaction mechanisms between the various physical fields. The coupling of the thermal field and the flow field is achieved through the Businesq approximation, taking into account the buoyancy effect caused by temperature changes. The coupling of the flow field and the stress field uses the arbitrary Lagrangian-Euler method to accurately capture the mechanical effects of fluid loads on the ice structure. The coupling of the thermal field and the stress field introduces the theory of thermoelasticity to analyze the thermal stress distribution caused by temperature gradients. The solution to the fully coupled three-field problem adopts a step-by-step iterative strategy, gradually approaching the true solution through alternating calculations between the fields.

[0085] Example 2: See Figure 3 , the ice and snow growth simulation model analyzes and processes the evolution of the ice layer based on the coupled field distribution characteristics. This process uses the coupled field distribution characteristics generated in Example 1 as input data, and realizes the accurate prediction of the dynamic evolution of the ice layer through a multi-level feature analysis mechanism. The axial thermal conductivity coefficient gradient is first input into the initial feature analysis layer for processing. The analysis layer contains a heat concentration factor calculation unit, and a spatial gradient analysis method is used to identify abnormal heat conduction areas. The determination of the heat concentration area is based on the local extreme value characteristics of the heat conductivity coefficient gradient. The entire calculation domain is scanned by a sliding window algorithm, and the spatial coordinates where the gradient change exceeds the threshold are marked. The tracking of the thermal amplitude change trajectory adopts a time correlation algorithm to correlate and match the coordinates of the heat concentration area in consecutive time steps to construct the development path of the heat affected area.

[0086] The secondary feature analysis layer processes radial fluid velocity accumulation data and includes a module for calculating fluid scour damage accumulation. The module's operating mechanism considers the interaction between fluid velocity and the ice surface, with the normal component of the velocity vector used to calculate the local scour intensity. The calculation of the melt probability incorporates a phase transition dynamics model to analyze the microstructural changes on the ice surface under fluid scour. The crack propagation rate prediction utilizes the principles of fracture mechanics, based on the interaction between fluid pressure fluctuations and inherent defects in the ice layer. The calculation considers the stress intensity factor at the crack tip and, combined with the material's fracture toughness parameters, derives the dynamic crack propagation law. The output of the secondary analysis layer includes a spatially distributed melt probability field and a crack propagation velocity field, reflecting the erosion effect of fluid action on the ice structure.

[0087] The final feature analysis layer processes the stress fluctuation coefficient of the tangential ice layer and integrates the surface ice degradation model. The calculation framework of the degradation model includes a wear depth prediction algorithm and a roughness evolution analysis module. The calculation of wear depth is based on the cumulative damage theory, which correlates the historical data of stress fluctuations with the fatigue properties of the material. The roughness evolution analysis uses surface morphology reconstruction technology to calculate the surface contour changes through the micro-plastic deformation caused by stress fluctuations. The analysis process considers the influence of ice crystal orientation on wear characteristics and adopts an anisotropic material response model to improve prediction accuracy. The output of the final analysis layer includes a wear depth distribution map and a roughness evolution curve, which characterizes the long-term effect of mechanical stress on the ice surface.

[0088] The generation of the comprehensive evolution index utilizes a multi-field coupling weighted algorithm, spatially superimposing the thermal amplitude variation trajectory of the initial characteristic resolution layer, the melting probability of the secondary characteristic resolution layer, and the material wear depth of the final characteristic resolution layer. The weighting mechanism considers the relative contributions of different physical fields to the evolution of the ice layer. The thermal field weight automatically increases in low-temperature environments, while the fluid field weight increases accordingly during the melting phase. A preset threshold library contains reference data for a variety of typical ice and snow environmental conditions. A pattern matching algorithm is used to determine the threshold combination that best matches the current environmental state. The comparison process utilizes fuzzy logic reasoning to address the nonlinear relationship between the index and the threshold. Probabilistic statistical methods are used to determine the final predicted value of the ice growth rate, calculating the probability distribution of different growth rates.

[0089] The identification process of high-risk areas uses spatial clustering analysis technology. The distribution coordinates of the heat concentration area are first converted into three-dimensional point cloud data, and the critical heat-affected zone is identified through a density clustering algorithm. The predicted results of the crack path are spatially correlated with the fluid velocity field to mark the dangerous sections where the flow velocity is consistent with the crack propagation direction. The determination of high-risk areas for wear is based on the gradient change of wear depth, and an edge detection algorithm is used to identify the boundary areas of sudden wear changes. The integration of geometric position information uses a spatial overlay analysis method to project areas of different risk types into a unified coordinate system. The output of the identification results includes a risk level distribution map and three-dimensional spatial position data, which supports the precise positioning of subsequent anti-icing measures.

[0090] An adaptive learning algorithm is used to optimize the parameters of the feature resolution layer. The heat concentration threshold of the initial feature resolution layer is dynamically adjusted based on the ambient temperature, with a more sensitive threshold setting used under low-temperature conditions. The fluid erosion model parameters of the secondary feature resolution layer are automatically updated as the flow rate changes, accounting for turbulence effects at high flow rates. The wear calculation coefficient of the final feature resolution layer is modified in real time based on the material state, taking into account the impact of changes in ice density on wear characteristics. The optimization process utilizes a closed-loop feedback mechanism, comparing predicted results with actual observed data and automatically adjusting model parameters to reduce prediction bias.

[0091] The time-marching mechanism for ice layer evolution analysis utilizes a variable step-size control strategy. During periods of drastic environmental parameter changes, a smaller time step is used to ensure accurate analysis of transient processes. During steady-state evolution, the time step is appropriately increased to improve computational efficiency. A step-size adjustment algorithm monitors the rate of change of each physical field and automatically triggers a step-size reduction when a sudden change in field intensity gradient is detected. A hybrid implicit-explicit time discretization scheme is used, with implicit discretization used for rapidly changing quantities to ensure stability and explicit discretization used for slowly changing quantities to improve computational efficiency.

[0092] Adaptive meshing technology is used to manage spatial resolution. The computational grid is automatically refined in areas of heat concentration and near crack propagation paths to improve local feature resolution. A coarser mesh is used in low-gradient regions to optimize computational resource allocation. The mesh refinement criterion is based on the gradient change rate of the field variables, triggering local refinement when the spatial derivative exceeds a threshold. Dynamic adjustment of the mesh topology utilizes an octree data structure, supporting fast spatial queries and neighborhood searches.

[0093] Multi-source data fusion utilizes a Bayesian inference framework. Numerical simulation results are probabilistically fused with real-time monitoring data to address measurement errors and model uncertainties. The data assimilation process considers the reliability differences between different data sources, assigning greater weight to high-precision measurement data. The confidence level of the fusion results is expressed through a posterior probability distribution, supporting reliability assessment for risk-based decision-making.

[0094] The risk visualization system uses augmented reality technology to present analysis results. Heat concentration areas are displayed as a red heat map overlay, crack paths are marked with blue wireframes, and high-risk wear areas are rendered with yellow semi-transparent volumetric rendering. Visualization parameters can be adjusted interactively, supporting multi-level observation from overall distribution to local details. The roaming function of the 3D scene allows users to view the three-dimensional distribution of risk areas from any perspective, assisting in the planning and deployment of anti-icing measures.

[0095] Example 3: See Figure 4 This process uses dynamic environmental compensation to correct ice growth rate predictions. This process establishes a compensation mechanism for the effects of humidity fluctuations on material properties in icy environments, improving prediction accuracy through a multi-stage correction algorithm. Preprocessing of relative humidity fluctuation sequences includes extreme value detection and frequency analysis. A sliding time window statistical method is used to extract extreme humidity values ​​from the sequence, and Fourier transform decomposition is used to obtain the characteristic frequencies of humidity variations. The dynamic correction calculation for the material's heat capacity properties considers the nonlinear relationship between humidity and heat capacity, establishing the following compensation relationship:

[0096]

[0097] in, represents the heat capacity correction, is the humidity change amplitude, is the basic heat capacity coefficient of the material, is the humidity sensitivity factor, These parameters are obtained through material property experimental calibration and have different values ​​for different types of ice and snow covered surfaces.

[0098] Thermal stress compensation calculations utilize an incremental superposition method to convert heat capacity corrections into compensation values ​​for the axial thermal conductivity. The calculation considers the coupled effects of temperature gradients and thermal expansion coefficients, introducing an additional source term into the heat conduction equation to account for the effects of humidity changes. The compensated thermal conductivity gradient is solved iteratively, with each iteration updating the humidity-related material parameters. A time delay factor is introduced to compensate for thermal relaxation effects, accounting for the hysteresis in the material's response to humidity changes. This delay time is calculated based on the material's thermal diffusion characteristics and multiplied with the humidity hold time to correct for thermal conductivity.

[0099] Phase change damage correction compensates for the effects of humidity on fluid velocity accumulation. A model correlating humidity change frequency with material phase change properties is constructed based on phase change kinetics theory, analyzing the degree of completeness of the ice-water phase change under different humidity fluctuation cycles. A correction algorithm detects regions of incomplete phase change and adjusts the release rate of latent heat during fluid calculations. The phase change front is tracked using the level set method, with the signed distance function describing the position of the phase change interface. The distribution of corrections to the fluid velocity field is correlated with the geometric characteristics of the phase change interface, with a greater correction weight applied to regions near the interface.

[0100] Surface strength adaptation adjustment processing analyzes the change of material thermal conductivity under extreme humidity conditions. The adjustment algorithm establishes a humidity-thermal conductivity correlation database, and obtains the equivalent thermal conductivity parameter under the current humidity state through interpolation method. The recalculation of stress fluctuation coefficient considers the thermal stress redistribution caused by thermal conductivity inhomogeneity, and uses the equivalent homogenization method to process local parameter mutation. The adjusted stress field realizes parameter continuous transition through virtual node technology, avoiding the instability of numerical calculation.

[0101] The ice layer growth rate prediction with environmental factor compensation adopts a multi-field coupling iterative calculation framework. The compensated thermal conductivity coefficient gradient, the corrected fluid velocity cumulative amount, and the adjusted ice layer stress fluctuation coefficient are used as input parameters to re-solve the ice and snow growth simulation model. The iterative process monitors the convergence of key physical quantities, and terminates the calculation when the difference between the prediction results of adjacent two iterations is less than a threshold value. The final output of the ice layer growth rate prediction value includes the humidity compensation effect, reflecting the actual influence of dynamic changes of environmental humidity on ice layer evolution.

[0102] The adaptive adjustment mechanism of humidity sensitive factors is dynamically optimized according to environmental monitoring data. The adjustment algorithm analyzes the corresponding relationship between historical humidity data and material response, and updates the sensitive factor parameters through regression analysis method. The optimization process considers seasonal variation factors and establishes differentiated models for humidity influence in different time periods. The sensitive factor value is lower under dry winter conditions, and a more sensitive adjustment strategy is used during the high humidity fluctuation period in spring and autumn.

[0103] The spatial and temporal distribution processing of phase change latent heat correction adopts a non-uniform compensation method. In the spatial dimension, different correction strengths are allocated according to the local humidity change amplitude, and in the time dimension, the duration of the correction effect is adjusted according to the humidity fluctuation frequency. This four-dimensional compensation strategy accurately captures the spatial heterogeneity and temporal dynamics of the phase change process, avoiding the error accumulation of traditional uniform compensation methods.

[0104] The construction of material parameter database adopts a hierarchical storage structure. The basic material parameters are stored in the core layer, including inherent properties such as thermal capacity coefficient and thermal conductivity coefficient. The environmental compensation parameters are stored in the intermediate layer, recording the parameter correction rules under different humidity conditions. Temporary calculation parameters are stored in the cache layer, saving the intermediate results of the current simulation state. The database update mechanism adopts a version control method, preserving historical parameter sets for backtracking analysis.

[0105] The stability control of thermal force coupling calculation adopts a step relaxation technique. The humidity compensation amount is divided into multiple increments and applied gradually, and each increment step is calculated separately for thermal force balance. The relaxation factor is dynamically adjusted according to the convergence speed, and the increment step is automatically reduced when the calculation divergence risk is high. This gradual compensation method effectively handles strong nonlinear problems and ensures the stability of calculations under complex humidity conditions.

[0106] The Extreme Conditions Identification and Processing module monitors for abnormal humidity fluctuations. When humidity fluctuations exceeding safety thresholds are detected, the system automatically switches to enhanced compensation mode. This mode employs a finer time step and spatial grid, and utilizes higher-order numerical formats in critical areas. The duration of the abnormal event is recorded in a log for subsequent optimization of the compensation model.

[0107] The effectiveness of humidity compensation is verified using a dual validation method. Theoretical validation uses energy conservation analysis to check the energy balance in the compensation calculation. Numerical validation uses a grid convergence test to analyze the consistency of compensation results at different spatial resolutions. Deviations discovered during the validation process trigger automatic fine-tuning of compensation parameters, forming an adaptive error correction loop.

[0108] Assimilation of real-time monitoring data incorporates the latest humidity observations into compensation calculations. The assimilation algorithm uses an optimal interpolation method to update field variables while ensuring computational continuity. The data assimilation interval automatically adjusts based on the rate of humidity change, with a higher assimilation frequency during periods of rapid fluctuation. This dynamic data fusion mechanism ensures that compensation calculations consistently track actual environmental conditions.

[0109] The output of compensation calculations contains complete field distribution information and statistical characteristics. Spatially distributed data records the physical field state after compensation, while statistical characteristics extract the variation range and trend indicators of key parameters. The output format supports direct access to various post-processing tools, facilitating further analysis and visualization.

[0110] Example 4: This article focuses on the generation process of a set of anti-icing solutions, using specific examples to illustrate the complete process from identifying high-risk icing areas to finalizing the solution. Using the snow and ice disaster prevention and control of an overhead high-voltage transmission line as an application scenario, the system first receives the risk area identification data from Example 2, which includes three typical risk areas: the heat concentration area at the upper end of the insulator string hardware, the crack path between the composite insulator sheds, and the high-risk wear area at the conductor suspension point.

[0111] The optimal heat source deployment path for the heat-concentrated area at the upper end of the insulator string hardware was calculated based on the following parameters: the hardware was made of cast aluminum alloy, the surface temperature fluctuated periodically between -12°C and -5°C, and the spacing between adjacent heating elements was constrained to within 15 cm. The system-generated heat source configuration adopted a staggered layout. Table 1 shows the heat source deployment parameters for this area.

[0112] Table 1: Parameters of heat source deployment at the top of insulator string.

[0113] Heat source number Installation position (cm from the top) Rated power (W) Working cycle (min) Temperature trigger threshold (°C) HS-01 3.2 120 8 -8.5 HS-02 18.7 80 12 -9.2 HS-03 34.5 150 6 -7.8 HS-04 50.0 100 10 -8.0

[0114] Microwave ice melting technology is used to treat the crack path between the sheds of composite insulators. Systematic analysis of the crack geometry shows that the main crack path extends in a spiral shape, with a curvature radius varying in the range of 2.1-5.3 cm, and the extension direction forms an average angle of 22° with the shed axis. The coverage density of the microwave heating spots is dynamically adjusted according to the curvature radius. At the minimum curvature radius, 3 heating points are set per square centimeter, and the larger curvature area is reduced to 1.5 heating points per square centimeter. The energy application direction is set to a 15° deflection angle with the crack path. This angle design can effectively block the crack extension and avoid excessive concentration of energy in a single direction. The pulse duration is controlled between 200-350 milliseconds. The specific value is dynamically adjusted according to the real-time surface temperature of the silicone rubber material to always maintain the heating temperature below the critical softening point of the material.

[0115] A lubricant application strategy is adopted in the high-risk wear area of ​​the conductor suspension point. System analysis shows that there are two wear modes in this area: periodic vibration wear in the vertical direction and breeze vibration wear in the horizontal direction. The lubricant formula uses low-temperature silicon-based composite materials, and a correlation model is established between the application frequency and the conductor vibration amplitude. When the vibration acceleration exceeds 0.5g, the application interval is automatically shortened from the standard 4 hours to 2 hours. The application thickness is controlled within the range of 0.1-0.3mm, and the upper limit value is used in extremely cold weather conditions to enhance the protection effect. The movement trajectory of the lubricant nozzle is optimized according to the wear distribution map, and a spiral covering path is used in key areas to ensure a uniform and complete protective layer.

[0116] Prioritizing options utilizes a multi-criteria decision-making approach, taking into account key factors: the hazard level of the risk area, the immediate effectiveness of the treatment measures, implementation costs, and ease of maintenance. In the case of transmission lines, the system prioritizes heat concentration in insulator strings, as they could potentially trigger insulation failure. Crack path treatment was ranked second, and conductor suspension point lubrication, a routine protective measure, ranked third. The implementation schedule is as follows: heat source deployment is completed 24 hours before the onset of the cold wave, the microwave de-icing system is activated when the temperature drops to -5°C, and lubricant application is automatically applied according to a pre-set cycle.

[0117] Optimal configuration of technical parameters is achieved through a feedback loop. Heat source power allocation uses an adaptive algorithm, with initial settings calculated based on a standard heat conduction model. After actual operation, adjustments are made dynamically based on infrared temperature measurement results. Monitoring revealed a 5°C temperature deviation in the area covered by the HS-03 heat source. The system automatically increased its power from the initial 150W to 170W, while simultaneously reducing the HS-02 power by 10W to maintain overall balance. Adjustment of microwave heating parameters is more refined, with the energy output of each heating spot independently controllable. By monitoring changes in the crack tip strain rate in real time, the pulse parameters at each point are dynamically optimized.

[0118] Material compatibility testing is conducted throughout the entire project development process. Material contact corrosion testing is performed before the installation of the heat source to confirm the chemical compatibility of the heating element with the cast aluminum alloy. The microwave frequency is selected to be 2.45 GHz to avoid overlapping with the molecular resonance frequency of the composite insulator. The lubricant formulation is tested for 48 hours of low-temperature aging to confirm that it still maintains good adhesion in an environment of -30°C. All material selections are based on the bulk material database, which contains the characteristic parameters of more than 200 commonly used materials in power equipment.

[0119] The differential treatment of micro-environmental factors reflects the fine features of the scheme. Different orientations of the same tower use different heat source configurations: the sunny side considers the auxiliary heating effect of solar radiation, and the power setting is reduced by 15%; the shady side increases the power margin by 10% to cope with continuous low temperatures. Altitude correction factors are applied to all heating parameters, with a 1.2% increase in power for every 100 meters of altitude. Local wind speed influences the adjustment of microwave heating direction, and when the wind speed exceeds 5 m / s, the energy application direction is deflected 8° upwind to compensate for the wind cooling effect.

[0120] The layout and processing scheme of the state monitoring network are designed synchronously. Temperature sensors are placed at the boundary of the heat source action radius, and wireless networking technology is used to realize real-time data transmission. High-frequency acoustic emission sensor arrays are used for crack monitoring to capture stress wave signals generated by crack propagation. Wear monitoring uses resistance wear detection sheets to quantify the degree of wear by impedance changes. Monitoring data is sampled at 1-minute intervals, and real-time analysis and processing are performed through edge computing nodes to provide data support for scheme adjustment.

[0121] The cost-benefit analysis model evaluates the economic efficiency of different configuration schemes. The heat source deployment scheme compares three technical routes: resistance heating, induction heating, and photoelectric heating, and finally selects the resistance heating scheme with the best operation and maintenance cost. The energy consumption of the microwave ice melting system is quantitatively related to the ice prevention effect to determine the optimal energy consumption control curve. The lubricant usage prediction model considers weather trends and equipment operation history to minimize material consumption. All economic analysis is based on life cycle cost calculation, including initial investment, operating energy consumption, and maintenance costs.

[0122] Emergency handling plans for abnormal situations are embedded in the main scheme. When the heat source fails, the compensation mode of the adjacent heat source is automatically started and a maintenance alarm is issued. When the microwave system fails, switch to the backup heating mode and use local hot air compensation. In the case of lubricant supply interruption, activate the mechanical ice scraping device as a temporary replacement. These plans are pre-designed through fault tree analysis and cover more than 90% of typical failure scenarios.

[0123] Solution version management records all adjustments and decision-making processes. Each parameter modification generates a new solution version, documenting the reason for the change and the expected results. A version comparison tool visually displays technical parameter differences between solutions and enables quick rollback to historical versions. This strict version control ensures transparency and traceability throughout the solution evolution process.

[0124] The operator interface is designed to align with engineering practices. Heat source installation steps are broken down into detailed process cards, including key parameters such as torque values ​​and wiring sequences. The microwave equipment commissioning interface offers a simple wizard mode to guide operators through the calibration process. The lubricant filling system features voice prompts, providing real-time guidance on filling volume and position control. These user-friendly features reduce the need for specialized operator skills and improve the accuracy and efficiency of solution implementation.

[0125] Example 5: Closed-loop optimization and iterative update mechanism for the ice and snow environment simulation test system. This process is based on cross-validation of previous simulation test results and actual monitoring data, and achieves continuous improvement of model parameters through dynamic feedback adjustment. The system collects measured ice data in the target area during a typical cold wave weather cycle, including ice thickness distribution on the insulator surface, cross-sectional morphology of ice-covered conductors, and ice growth at hardware connections. This measured data is acquired using 3D laser scanning and image recognition technology, with a spatial resolution of millimeters and a temporal sampling interval of 30 minutes.

[0126] Ice thickness deviation analysis utilizes a spatial grid comparison method. The three-dimensional data field output by the predicted ice thickness model is aligned with the measured point cloud data, and the relative error percentage is calculated within each grid cell. The error distribution diagram shows that the prediction deviation in the insulator shed groove area is relatively large, reaching an average of 12.3%, while the prediction accuracy in flat surface areas is higher, with an error within 5%. This spatial difference in the system prompts the system to focus on optimizing the ice thickness calculation algorithm for complex geometric structures. The generation of the error correction coefficient takes into account the influence of local microenvironmental factors, and an additional air entrapment factor is introduced in the shed groove area with poor ventilation.

[0127] A dynamic time warping algorithm was used to compare the crack growth data in time series. This method aligns the measured and predicted crack length curves, even when nonlinear deformations exist on the time axis. Analysis revealed that during periods of rapid temperature fluctuations, the prediction model's response to accelerated crack growth experienced a lag of approximately two hours. This lag was compensated for by adjusting the material response time constant within the model. The new parameter settings reduced the phase difference between the predicted curve and the measured data to less than 30 minutes. The length error correction factor was calculated segmentally based on the different rates of temperature change, with a greater correction weight applied during periods of rapid temperature drops.

[0128] The model parameter weights are adjusted using a sensitivity-guided optimization strategy. The system automatically tests the marginal impact of each input parameter on the output, identifying that thermal conductivity and surface roughness parameters contribute most significantly to ice thickness prediction. The weight update algorithm maintains the integrity of core physical relationships and adjusts only the relative importance of minor parameters. While maintaining the underlying thermodynamic framework, the optimized model improves prediction accuracy for insulator sheds by approximately 8 percentage points. This constrained parameter optimization avoids overfitting and ensures the model's generalization capabilities under unknown environmental conditions.

[0129] The optimized model was validated using a cross-validation method. Historical data was divided into a training set and a test set. The training set was used for parameter adjustment, and the test set was used to evaluate the effectiveness of the improvements. The validation process monitored the model's improvements in spatial resolution and temporal response speed. The new model demonstrated greater adaptability to sudden snowfall events and was able to more quickly capture changing trends in snow accumulation patterns. Validation results also showed that the optimized model had a stronger ability to distinguish between different types of ice and snow mixtures, and was able to more accurately identify different types of ice accumulation, such as rime and hoarfrost.

[0130] The real-time scheduling of the environmental simulation control system utilizes an event-driven architecture. The anti-icing solution is converted into an executable sequence of control instructions, including heater start and stop timing, de-icing robot motion trajectory, and chemical spray dosage control. These instructions are distributed to field devices via the Industrial Internet of Things (IIoT) platform, with execution status transmitted back in real time via the 4G / 5G network. The scheduling algorithm dynamically adjusts instruction parameters based on device feedback. If a power anomaly is detected in a heating unit, heating tasks are automatically reallocated to surrounding units to compensate for thermal field distribution.

[0131] Data collection parameters are updated based on the principle of maximizing information entropy. The system analyzes the coverage completeness of the current dataset and prioritizes increasing monitoring density in areas of space and time where information is scarce. For example, high-speed camera monitoring points are added mid-span, where conductors frequently vibrate, and the sampling frequency is doubled during sunrise, when temperature gradients fluctuate dramatically. The new data collection solution is implemented through the dynamic deployment of a wireless sensor network, with some monitoring nodes employing mobile inspections to fill in blind spots left by fixed monitoring.

[0132] The trigger for iterative simulations is based on the rate of change of environmental conditions. When the monitored temperature change rate exceeds 3°C / h or the wind speed increases by 5m / s², the system automatically initiates a new round of multiphysics coupling calculations. The iterative process reuses some intermediate results from previous calculations, employing an incremental update method to improve computational efficiency. The newly generated coupled field characteristics of each iteration are analyzed for differences with the previous results, highlighting areas of significant physical quantity distribution changes.

[0133] Anti-icing solution optimization utilizes a multi-objective Pareto frontier search method. The system systematically explores the effectiveness of various parameter combinations, seeking the optimal balance between de-icing efficiency, energy costs, and equipment safety. The optimization process retains multiple non-inferior solutions for operators to choose from, each accompanied by a comprehensive technical and economic evaluation report. The final output solution is expected to reduce energy consumption by approximately 15% and equipment wear by 20% while maintaining core anti-icing performance.

[0134] Simulation test reports are generated using automated structured document layout technology. The report content is logically organized, including sections such as an overview of environmental conditions, simulation method description, results analysis, discussion of deviations, and improvement suggestions. Key data is presented interactively, allowing readers to dynamically explore interesting data details. A report version management system records the content and reasons for each modification and supports comparison of differences between different versions.

[0135] The system's self-diagnostic capabilities continuously monitor the health of the entire workflow. Metrics such as computing resource utilization, data transmission integrity, and model convergence are analyzed in real time. When localized performance degradation is detected, the system automatically initiates fault isolation and recovery procedures. This self-healing capability significantly improves reliability during extended unattended operation, which is particularly important in extreme weather conditions.

[0136] A knowledge accumulation mechanism transforms the experience gained from each iteration into system intelligence. Solutions for typical scenarios are abstracted into case studies and stored in a knowledge base, enabling rapid recall of historical best practices under similar circumstances. Parameter adjustment records form a decision tree model, helping the system make more reasonable initial choices when faced with new circumstances. This continuous learning capability enables the system's simulation and prediction capabilities to improve over time.

[0137] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0138] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for simulating ice and snow environment testing based on multi-field coupling, characterized in that: The method comprises: Collect dynamic multi-field data sets in the target ice and snow environment, wherein the dynamic multi-field data sets include periodic temperature change series, relative humidity fluctuation series and real-time monitoring data of surface ice thickness; Performing multi-physics field coupling numerical simulation processing on the dynamic multi-field data set to generate coupled field distribution characteristics of the target ice and snow environment, wherein the coupled field distribution characteristics include an axial heat transfer coefficient gradient, a radial fluid velocity accumulation, and a tangential ice layer stress fluctuation coefficient; Calling a preconfigured ice and snow growth simulation model to perform ice layer evolution analysis and processing on the coupled field distribution characteristics, and generating an ice layer growth rate prediction value and a location identifier of a high-risk icing area; performing an environmental dynamic compensation correction process on the ice layer growth rate prediction value to generate a corrected ice layer growth rate prediction value, wherein the environmental dynamic compensation correction process is implemented by utilizing the correlation characteristics between the relative humidity fluctuation sequence and the heat capacity property of the material; generating an anti-icing treatment plan set based on the high-risk icing area location identifier, the anti-icing treatment plan set including a heat source deployment strategy and a surface ice melting technology plan; The calling of a preconfigured ice and snow growth simulation model to perform ice layer evolution analysis and processing on the coupled field distribution characteristics to generate an ice layer growth rate prediction value and a high-risk icing area location identifier includes: Inputting the axial heat transfer coefficient gradient into the initial characteristic analysis layer of the ice and snow growth simulation model, and determining the distribution coordinates of the heat concentration area and the heat amplitude change trajectory through the heat concentration factor calculation unit; Inputting the radial fluid velocity accumulation into the secondary characteristic analysis layer of the ice and snow growth simulation model, performing fluid scour damage accumulation calculation, and generating the melting probability and crack growth rate prediction value of the ice surface; Inputting the tangential ice layer stress fluctuation coefficient into the final characteristic analysis layer of the ice and snow growth simulation model, and calculating the material wear depth of the friction surface and the ice layer roughness evolution data based on the surface ice layer degradation model; The thermal amplitude variation trajectory, the melting probability, and the material wear depth are integrated to generate a comprehensive evolution index of the target ice and snow environment, and a predicted value of the ice layer growth rate is determined based on a comparison result of the comprehensive evolution index with a preset threshold library; Based on the spatial field superposition results of the distribution coordinates, the crack growth rate prediction value and the ice layer roughness evolution data, the geometric position information of the heat concentration area, the crack path and the high-risk wear area is identified.

2. The ice and snow environment simulation test method based on multi-field coupling according to claim 1 is characterized in that: The performing multi-physics field coupling numerical simulation processing on the dynamic multi-field data set to generate coupling field distribution characteristics of the target ice and snow environment includes: Dividing the periodic temperature variation sequence into a plurality of temperature subsequences according to a preset time window, each temperature subsequence corresponding to a simulation period unit; For each temperature subsequence, perform the following operations: Constructing a three-dimensional ice layer deformation topology model based on the real-time monitoring data of the surface ice layer thickness, wherein the three-dimensional ice layer deformation topology model includes spatial coordinate information of thermal expansion field distribution, fluid velocity field distribution, and stress field distribution; Performing field coupling interaction processing on the three-dimensional ice layer deformation topology model and the temperature subsequence to generate a coupled field simulation output of the current simulation period unit; Performing cumulative integration calculation on the coupled field simulation output of multiple consecutive simulation period units to obtain the axial heat transfer coefficient gradient, radial fluid velocity accumulation and tangential ice layer stress fluctuation coefficient; wherein, The axial heat transfer coefficient gradient is the maximum rate of change of the thermal expansion field along the axial direction. The radial fluid velocity accumulation is the total integral of the fluid velocity field in the normal direction of the ice layer contact surface. The tangential ice layer stress fluctuation coefficient is the coefficient of variation of the stress field distribution over time.

3. The ice and snow environment simulation test method based on multi-field coupling according to claim 2 is characterized in that: The performing field coupling interaction processing on the three-dimensional ice layer deformation topology model and the temperature subsequence to generate a coupled field simulation output of the current simulation period unit includes: Based on the corresponding relationship between the heat conduction law and the temperature subsequence, a thermal field-temperature balance equation is established, and an initial distribution function of the heat conduction component is obtained by solving the thermal field-temperature balance equation; Based on the correlation between fluid dynamics principles and ice deformation data, a fluid-ice coupling calculation framework is constructed, which integrates dynamic adjustment parameters of fluid viscosity coefficient and ice elastic modulus; Combining the surface stress change rate with the friction effect factor, an iterative ice growth prediction process is implemented, which includes a feedback calibration mechanism for the stress increment and ice deformation. A spatial field fusion operation is performed on the output results of the initial distribution function, the fluid-ice layer coupling calculation framework, and the ice layer growth iterative prediction process to generate a three-dimensional coupled field distribution data set containing heat conduction, fluid velocity, and ice layer stress components.

4. The ice and snow environment simulation test method based on multi-field coupling according to claim 1 is characterized in that: The performing of environmental dynamic compensation correction processing on the ice layer growth rate prediction value to generate a corrected ice layer growth rate prediction value includes: Extracting the extreme humidity values ​​and humidity change frequencies in the relative humidity fluctuation sequence, and calculating the dynamic correction amount of the material heat capacity property as the humidity fluctuates; performing a thermal stress compensation calculation on the axial heat transfer coefficient gradient according to the dynamic correction amount to generate a compensated heat transfer coefficient gradient; Based on the correlation between the humidity change frequency and the phase change characteristics of the material, performing phase change damage correction processing on the radial fluid velocity accumulation to generate a corrected fluid velocity accumulation; performing surface strength adaptation adjustment processing on the tangential ice layer stress fluctuation coefficient according to the material thermal conductivity variation data under extreme humidity to generate an adjusted ice layer stress fluctuation coefficient; The compensated thermal conductivity gradient, the corrected fluid velocity accumulation and the adjusted ice layer stress fluctuation coefficient are input into the ice and snow growth simulation model for recalculation to generate an ice layer growth rate prediction value including environmental factor compensation.

5. The ice and snow environment simulation test method based on multi-field coupling according to claim 4 is characterized in that: The performing of thermal stress compensation calculation on the axial heat transfer coefficient gradient according to the dynamic correction amount to generate a compensated heat transfer coefficient gradient includes: Obtaining the initial heat capacity property of the target ice and snow environment under reference humidity conditions and the dynamic correction amount, and establishing a heat capacity-humidity correlation function; Calculating an axial thermal stress compensation value according to the heat capacity-humidity correlation function, wherein the axial thermal stress compensation value is the product of the humidity change amplitude and the heat capacity change amplitude; superimposing the axial thermal stress compensation value into the calculation process of the axial thermal conductivity gradient to generate an intermediate thermal conductivity gradient value including thermal stress compensation; A thermal relaxation effect compensation process is performed on the intermediate value of the thermal conductivity gradient, wherein the thermal relaxation effect compensation process is implemented by using a multiplication factor of a material thermal relaxation curve and a humidity retention time.

6. The ice and snow environment simulation test method based on multi-field coupling according to claim 1 is characterized in that: The generating of an anti-icing treatment solution set based on the high-risk icing area location identifier includes: Calculating an optimal heat source deployment path based on the identification information of the heat concentration area, wherein the optimal heat source deployment path is achieved by adjusting the power distribution ratio of adjacent heat sources; Constructing a surface ice melting technical solution based on the identification information of the crack path, wherein the surface ice melting technical solution includes the selection of a microwave heating area and the optimization configuration of energy parameters; generating a lubricant application strategy based on the identification information of the high-risk wear area, wherein the lubricant application strategy dynamically adjusts the application frequency and application thickness according to the wear rate prediction value; The optimal heat source deployment path, the surface ice melting technical solution and the lubricant application strategy are prioritized to generate an anti-icing solution set including execution timing and technical parameters.

7. The ice and snow environment simulation test method based on multi-field coupling according to claim 6 is characterized in that: The surface ice melting technology solution includes: Extracting geometric characteristic data of the crack path and calculating the path curvature radius and expansion direction angle; selecting a coverage density parameter of the microwave heating spot according to the curvature radius, wherein the coverage density parameter is in a nonlinear inverse proportional relationship with the curvature radius; Adjusting the direction of application of microwave heating energy based on the expansion direction angle so that the energy application direction forms a preset angle with the crack path direction; Dynamically adjust the duration of microwave heating pulses based on the test data of the material's surface thermal conductivity to ensure that the heating energy is below the critical value of the material's melting point; Generate an ice melting parameter configuration table including coverage density parameters, application direction and pulse duration.

8. The ice and snow environment simulation test method based on multi-field coupling according to claim 1 is characterized in that: The method further comprises: Collecting actual ice thickness and crack propagation length data of the target ice and snow environment within a preset verification period; performing deviation analysis on the actual ice thickness and the predicted ice thickness to generate a thickness error correction coefficient; Performing a time series comparison process on the crack propagation length and the predicted propagation speed to generate a length error correction coefficient; Adjusting the parameter weights of the ice and snow growth simulation model according to the thickness error correction coefficient and the length error correction coefficient to generate an optimized ice and snow growth simulation model; The optimized ice and snow growth simulation model is applied to ice and snow environment simulation tasks in subsequent test batches.

9. The ice and snow environment simulation test method based on multi-field coupling according to claim 1 is characterized in that: After generating the anti-icing treatment solution set, the method further includes: Inputting the anti-icing treatment plan set into the environmental simulation control system to perform real-time scheduling processing; updating the acquisition parameters of the dynamic multi-field data set according to the scheduling result feedback; Restarting the multi-physics field coupling numerical simulation process using the updated acquisition parameters to generate an iterative coupling field distribution feature; The anti-icing treatment scheme set is optimized based on the iterative coupling field distribution characteristics, and a final simulation test report is output.

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