A weather resistance test method based on low-temperature ice and snow environment materials

Abstract

The application discloses a kind of based on low temperature ice and snow environment material weather resistance test method, belong to material testing technical field, it includes according to environmental simulation scenario, generate dynamic environment parameter set, in the preset composite environment stress cabin, apply dynamic change ice and snow load and multi-physical-chemical environmental stress, construct coupling environment field acting on the sample to be tested;Real-time acquisition is obtained in the state of the sample to be tested in coupling environment field, generate multidimensional monitoring data set and compare and analyze with the preset material aging characteristic template, generate the material response data of the current state of the sample to be tested, construct environment parameter adjustment instruction set real-time regulation coupling environment field in composite environment stress cabin.The application adopts the closed-loop feedback control strategy of combination of constructing dynamic coupling environment field and real-time monitoring material response, can realize the intelligent acceleration of test process under the premise of guaranteeing the authenticity of failure mechanism, significantly improve test efficiency and the accuracy of result.

Description

Technical Field

[0001] This invention relates to the field of materials testing technology, and in particular to a method for testing the weather resistance of materials in low-temperature ice and snow environments. Background Technology

[0002] Weather resistance refers to a material's ability to maintain its original physical and chemical properties under the combined effects of natural environmental factors such as sunlight, temperature, wind, rain, and snow. For structural and functional materials widely used in aerospace, transportation, construction engineering, and polar scientific research, their weather resistance in low-temperature and icy environments is a key indicator determining their service life and reliability. Therefore, developing accurate and efficient weather resistance testing methods is of paramount importance for the research, selection, and application of new materials.

[0003] Current methods for testing the weather resistance of materials primarily rely on environmental test chambers or outdoor exposure test sites. In laboratory environments, testing equipment typically simulates single or combined stress conditions such as constant humidity and heat, salt spray, ultraviolet radiation, or simple thermal cycling. For low-temperature, icy environments, some tests create an ice layer on the material surface through cooling and water spraying, followed by periodic freeze-thaw cycles to examine the material's freeze-thaw resistance. These methods can, to some extent, evaluate a material's resistance to specific environmental factors.

[0004] However, existing testing methods often simply superimpose environmental stresses such as temperature, humidity, and light intensity, neglecting the complex physicochemical coupling effects between these factors and ice and snow loads. The environmental parameters are mostly set to constant values ​​or preset simple periodic variations, failing to replicate the nonlinear and dynamic characteristics of parameters in the actual environment. Furthermore, the testing process is typically open-loop, unable to adjust environmental stresses based on the material's real-time response, leading to a disconnect between the testing process and the actual aging process of the material, resulting in low testing efficiency and potentially inducing failure modes that are inconsistent with reality. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides a method for testing the weather resistance of materials in low-temperature ice and snow environments. It employs a closed-loop feedback control strategy that combines the construction of a dynamically coupled environmental field with real-time monitoring of the material response. This approach enables intelligent acceleration of the testing process while ensuring the authenticity of the failure mechanism, significantly improving testing efficiency and the accuracy of the results.

[0006] The above objectives can be achieved through the following approach:

[0007] A method for testing the weather resistance of materials in low-temperature ice and snow environments includes: generating a dynamic environmental parameter set based on an environmental simulation script, wherein the dynamic environmental parameter set includes at least target temperature parameters, target humidity parameters, target light intensity parameters, target de-icing agent concentration parameters, and target ice and snow morphology parameters; applying dynamically changing ice and snow loads and multiple physicochemical environmental stresses in a preset composite environmental stress chamber according to the dynamic environmental parameter set to construct a coupled environmental field acting on the test sample; acquiring the state of the test sample in the coupled environmental field in real time to generate a multi-dimensional monitoring dataset; comparing and analyzing the multi-dimensional monitoring dataset with a preset material aging characteristic template to generate material response data characterizing the current state of the test sample; generating an environmental parameter adjustment instruction set based on the material response data; and adjusting the coupled environmental field in the composite environmental stress chamber in real time according to the environmental parameter adjustment instruction set.

[0008] Optionally, the construction of the coupled environmental field acting on the test sample includes: analyzing the dynamic environmental parameter set to obtain target ice and snow morphology parameters, target temperature parameters, target humidity parameters, target light intensity parameters, and target de-icing agent concentration parameters; dynamically depositing an ice and snow layer on the surface of the test sample according to the target ice and snow morphology parameters; and synchronously adjusting the environmental parameters in the preset composite environmental stress chamber to match the target temperature parameters, the target humidity parameters, the target light intensity parameters, and the target de-icing agent concentration parameters, so that together with the ice and snow layer, they constitute a coupled environmental field.

[0009] Optionally, generating the multi-dimensional monitoring dataset includes: acquiring the surface temperature distribution of the test sample and the interfacial stress and strain data caused by frost heave in the micro-region of the material-ice-snow interface; acquiring ice-snow layer state data characterizing the physical properties of the ice-snow layer; and performing spatiotemporal registration and fusion of the interfacial stress and strain data and the ice-snow layer state data to generate the multi-dimensional monitoring dataset.

[0010] Optionally, generating material response data characterizing the current state of the test sample includes: matching the interface stress-strain data with the material aging feature template to identify material aging state characteristics; comparing the actual cabin environment parameter data in the multi-dimensional monitoring dataset with the dynamic environment parameter set, and identifying environmental simulation deviation characteristics based on the environmental simulation fidelity threshold; and structurally integrating the material aging state characteristics with the environmental simulation deviation characteristics to generate material response data.

[0011] Optionally, generating an environmental parameter adjustment instruction set based on the material response data includes: determining whether the material response data exceeds a material state safety threshold set to prevent the test sample from experiencing unreal failure; if so, generating a protective environmental adjustment instruction; if not, calculating and generating an acceleration parameter combination based on the material response data; and integrating the protective environmental adjustment instruction or the acceleration parameter combination into an environmental parameter adjustment instruction set.

[0012] Optionally, the step of calculating and generating the acceleration parameter combination based on the material response data includes: analyzing the material response data based on the material aging characteristic template to identify key stress factors related to the current stage of material aging contribution; calculating the allowable increase in the effect level of the key stress factors under the constraint of maintaining the true failure mechanism of the test sample; increasing the effect level of the key stress factors based on the allowable increase, and coordinating and adjusting the environmental stress factors according to the physical coupling relationship between environmental factors to generate the acceleration parameter combination.

[0013] Optionally, the step of adjusting the coupled environmental field in the composite environmental stress chamber in real time according to the environmental parameter adjustment instruction set includes: parsing the environmental parameter adjustment instruction set, issuing control instructions to the environmental control system, dynamically controlling the deposition characteristics of the ice and snow layer; and synchronously controlling the operating state of the composite environmental stress chamber to reconstruct the multi-physical and chemical environmental stress.

[0014] Optionally, the method further includes: in each test cycle, determining whether the material response data reaches a preset material failure threshold, or determining whether the cumulative test time reaches a preset maximum test cycle; and terminating the test when either determination condition is met.

[0015] Based on the same inventive concept, this invention also provides a weathering resistance testing system for materials in low-temperature ice and snow environments. The system comprises: an environmental data setting module, used to generate a dynamic environmental parameter set according to an environmental simulation script, the dynamic environmental parameter set including at least target temperature parameters, target humidity parameters, target light intensity parameters, target de-icing agent concentration parameters, and target ice and snow morphology parameters; an environmental coupling simulation module, used to apply dynamically changing ice and snow loads and multiple physicochemical environmental stresses in a preset composite environmental stress chamber according to the dynamic environmental parameter set, constructing a coupled environmental field acting on the test sample; a multi-dimensional in-situ monitoring module, used to acquire the state of the test sample in the coupled environmental field in real time, generating a multi-dimensional monitoring dataset; a feedback module, used to compare and analyze the multi-dimensional monitoring dataset with a preset material aging characteristic template, generating material response data characterizing the current state of the test sample; an instruction generation module, used to generate an environmental parameter adjustment instruction set based on the material response data; and an instruction execution module, used to adjust the coupled environmental field in the composite environmental stress chamber in real time according to the environmental parameter adjustment instruction set.

[0016] Compared with the prior art, the present invention has the following advantages:

[0017] 1. This invention constructs a dynamically coupled environmental field, synergistically applying multiple environmental stresses such as target temperature, humidity, light, de-icing agent concentration, and dynamically deposited ice and snow morphology. This highly replicates the dynamic, multi-factor coupling effects encountered by materials in real low-temperature ice and snow service environments, rather than the simple stress superposition or static simulation in traditional tests. This significantly improves the realism of the environmental simulation, enabling the test results to more accurately reflect the actual weather resistance performance of the materials, and enhancing the reliability and engineering guidance value of the test conclusions.

[0018] 2. This invention establishes a closed-loop control mechanism from environmental simulation and state monitoring to feedback regulation. By acquiring multi-dimensional monitoring data of the test sample in real time and comparing it with the material aging characteristic template, the system can dynamically evaluate the current state of the material. Based on this evaluation, the system can intelligently generate environmental parameter adjustment instructions, which can not only prevent non-real failure caused by excessive acceleration, but also identify key aging stress factors and improve their effect level in a targeted manner. Thus, while ensuring the authenticity of the failure mechanism, the test cycle is greatly shortened and the test efficiency is improved.

[0019] 3. This invention employs multi-dimensional in-situ monitoring methods, including interfacial stress and strain monitoring, and combines them with material aging characteristic templates for in-depth analysis. This allows the testing to go beyond simply observing macroscopic performance degradation phenomena, and instead delve into the interface between the material and ice and snow, accurately capturing the micro-stress evolution caused by processes such as frost heave and thawing. In this way, the damage evolution process and intrinsic failure mechanism of materials under complex environments can be revealed more clearly, achieving precise characterization from macroscopic phenomena to microscopic responses, and providing deeper data support for material performance evaluation and improvement.

[0020] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart illustrating a method for testing the weather resistance of materials in a low-temperature ice and snow environment, according to an embodiment of the present invention.

[0023] Figure 2 This is a schematic diagram illustrating the change of dynamic environmental parameter set over time according to an embodiment of the present invention.

[0024] Figure 3 This is a schematic diagram of the decoupling of interface stress and strain data according to an embodiment of the present invention.

[0025] Figure 4 This is a schematic diagram of a weather resistance testing system for materials in a low-temperature ice and snow environment according to an embodiment of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] Reference Figure 1One embodiment of the present invention proposes a method for testing the weather resistance of materials in low-temperature ice and snow environments. It adopts a closed-loop feedback control strategy that combines the construction of a dynamically coupled environmental field with real-time monitoring of material response. This method can achieve intelligent acceleration of the testing process while ensuring the authenticity of the failure mechanism, and significantly improve the testing efficiency and the accuracy of the results.

[0028] The method described in this embodiment specifically includes:

[0029] Based on the environmental simulation script, a dynamic environmental parameter set is generated, which includes at least the target temperature parameter, target humidity parameter, target light intensity parameter, target de-icing agent concentration parameter, and target ice and snow morphology parameter.

[0030] Based on the dynamic environmental parameter set, dynamically changing ice and snow loads and multiple physical and chemical environmental stresses are applied in a preset composite environmental stress chamber to construct a coupled environmental field acting on the test sample.

[0031] The state of the test sample in the coupled environmental field is acquired in real time, and a multi-dimensional monitoring dataset is generated.

[0032] The multi-dimensional monitoring dataset is compared and analyzed with a preset material aging characteristic template to generate material response data characterizing the current state of the test sample;

[0033] Based on the material response data, a set of environmental parameter adjustment instructions is generated;

[0034] Based on the set of environmental parameter adjustment instructions, the coupled environmental field within the composite environmental stress chamber is controlled in real time.

[0035] Specifically, preset dynamic target parameters simulating a real environment are used as input to construct a multi-physical and chemical stress coupling field within a composite environmental stress chamber. In this environment, in-situ monitoring technology captures real-time state changes of the test sample, and this raw monitoring data is compared and analyzed with a pre-established material aging characteristic knowledge base. This transforms physical signals into structured data characterizing the material's current aging degree and response properties. This response data is then used to drive a decision-making system, generating instructions for adjusting environmental stress. Finally, the system executes these instructions, regulating the coupled environmental field in real time, thus forming a complete closed loop from "environmental application - material response - intelligent assessment - environmental regulation." This allows the entire testing process to dynamically and intelligently self-optimize and adjust based on the material's actual state. It can autonomously judge and adjust the intensity of environmental stress based on the material's real-time aging state, thereby safely and effectively accelerating the aging process without inducing unrealistic failure mechanisms, significantly shortening the testing cycle and improving testing efficiency.

[0036] Optionally, the construction of the coupled environmental field acting on the test sample includes:

[0037] The dynamic environmental parameter set is analyzed to obtain the target ice and snow morphology parameters, target temperature parameters, target humidity parameters, target light intensity parameters, and target de-icing agent concentration parameters;

[0038] An ice and snow layer is dynamically deposited on the surface of the test sample according to the target ice and snow morphology parameters.

[0039] The environmental parameters within the preset composite environmental stress chamber are synchronously adjusted to match the target temperature parameter, the target humidity parameter, the target light intensity parameter, and the target de-icing agent concentration parameter, thus forming a coupled environmental field together with the ice and snow layer.

[0040] Specifically, the system first receives and parses a dynamic environmental parameter set pre-generated based on an environmental simulation script. This parameter set defines the target trajectory of how various environmental parameters dynamically change over time during the test. This dynamic environmental parameter set can be represented as a parameter vector that changes over time, such as... Figure 2 As shown, this is a simulation used to precisely guide the environment:

[0041] ,

[0042] Where t represents the time the test takes; It is the set of dynamic environmental parameters at time t; The target temperature parameter is achieved by controlling the cooling and heating systems within the composite environmental stress chamber, and the unit is degrees Celsius. The target humidity parameter is regulated by the humidification and dehumidification system inside the cabin and expressed as a percentage of relative humidity. The target light intensity parameter is provided by the adjustable power solar simulation lamps or ultraviolet lamp arrays inside the cabin, and the unit is watts per square meter; To determine the target de-icing agent concentration parameter, the de-icing agent solution is sprayed in atomized form onto the ice and snow layer on the surface of the test sample using a precision metering pump, and the concentration is characterized by mass concentration per unit volume or per unit area. The target ice and snow morphology parameter is a composite parameter that defines the physical properties of the ice and snow layer, such as thickness, density, and ice crystal structure. The ice and snow layer is dynamically deposited on the surface of the test sample by controlling parameters such as nozzle flow rate, air pressure, and water temperature in the snowmaking system. After obtaining the specific parameter values ​​obtained above, the control system begins to construct a coupled environmental field within the composite environmental stress chamber. The core operation lies in... The snowmaking or icing system within the chamber is activated and controlled to precisely deposit a layer of ice and snow with the required physical properties onto the surface of the test sample. This process is dynamic, meaning that the thickness and density of the ice and snow layer can be adjusted over time (t). Simultaneously, almost synchronously, other environmental control units within the composite environmental stress chamber begin operation, adjusting the temperature, humidity, and light intensity to precisely match the target temperature, humidity, and light intensity parameters at the current moment. At the same time, the de-icing agent spraying system applies a corresponding concentration of de-icing agent to the formed ice and snow layer based on the target concentration parameters. These four environmental stresses do not act independently but interact and couple with the deposited ice and snow layer, collectively forming a highly simulated multi-physical and chemical coupled environmental field acting on the test sample.

[0043] Optionally, the generation of the multi-dimensional monitoring dataset includes:

[0044] Acquire the surface temperature distribution of the test sample and the interfacial stress and strain data caused by frost heave in the micro-region of the material-ice interface;

[0045] Obtain ice and snow layer state data that characterizes the physical properties of the ice and snow layer;

[0046] The interface stress and strain data are spatiotemporally registered and fused with the ice and snow layer state data to generate the multi-dimensional monitoring dataset.

[0047] Specifically, the surface temperature distribution of the test sample and the interfacial stress-strain data caused by frost heave in the micro-regions of the material-ice interface are first acquired. The operation involves deploying a non-contact high-resolution infrared thermal imager in a composite environmental stress chamber to continuously scan the surface of the test sample, generating a two-dimensional surface temperature distribution map. Simultaneously, a fiber Bragg grating sensor array is pre-deployed on the surface of the test sample; these sensors are attached or embedded in the material surface before the ice layer is deposited. When the ice layer forms and freezes and melts under temperature changes, its volume change, i.e., the frost heave effect, exerts pressure on the micro-regions of the material-ice interface, causing minute deformations on the surface of the test sample. The fiber Bragg grating sensors can accurately capture this deformation, i.e., the interfacial stress-strain data. The sensor's measurement principle is based on the shift of the Bragg wavelength, and the relationship can be characterized by the following formula:

[0048] ,

[0049] Here, It is the amount of wavelength shift of the center reflection of the fiber Bragg grating sensor, which is directly measured by a spectrometer or grating demodulator. It is the mechanical strain on the surface of the test sample caused by frost heave, which is the core interface stress and strain data that needs to be obtained. It is the temperature change at the location of the sensor, which can be obtained through a strain-free grating installed nearby or based on surface temperature distribution data measured by an infrared thermal imager. The strain sensitivity coefficient of the sensor is denoted as . Here, represents the temperature sensitivity coefficient of the sensor; both of these are inherent calibration parameters of the grating sensor. For example... Figure 3 As shown, through decoupling calculations, the drift caused by stress and strain can be accurately separated from the total wavelength drift, thus obtaining the interface stress and strain data. Secondly, ice and snow layer state data characterizing the physical properties of the ice and snow layer are acquired simultaneously. This can be achieved by integrating a laser displacement sensor or a 3D laser scanner into the composite environmental stress chamber to measure the thickness, coverage, and macroscopic morphology of the ice and snow layer on the surface of the test sample in real time. Finally, the collected interface stress and strain data and ice and snow layer state data are spatiotemporally registered and fused. Spatiotemporal registration refers to unifying all sensor data to the same time reference and spatial coordinate system, ensuring that at any given time, the strain data at any point on the surface of the test sample can accurately correspond to the temperature at that point and the state data of the ice and snow layer above it. Data fusion integrates these registered multi-source heterogeneous data into a structured dataset, i.e., a multi-dimensional monitoring dataset, which can comprehensively and dynamically describe the response behavior of the test sample in the coupled environmental field.

[0050] Optionally, the generation of material response data characterizing the current state of the test sample includes:

[0051] The interface stress-strain data is matched with the material aging feature template to identify the material aging state characteristics;

[0052] The actual parameters of the cabin environment in the multi-dimensional monitoring dataset are compared with the dynamic environmental parameter set, and the environmental simulation deviation characteristics are identified based on the environmental simulation fidelity threshold.

[0053] The material aging state characteristics and the environmental simulation deviation characteristics are structurally integrated to generate material response data.

[0054] Specifically, the process of generating material response data characterizing the current state of the test sample involves in-depth analysis and information extraction of the multi-dimensional monitoring dataset generated in the previous step. This process is divided into two parallel analysis streams, which are ultimately integrated. First, the interfacial stress-strain data contained in the multi-dimensional monitoring dataset is matched with a preset material aging feature template to identify the material aging state characteristics. This material aging feature template is a priori knowledge base, established through extensive experimental or theoretical analysis. It stores typical patterns or signature features of the interfacial stress-strain signals of specific materials at different aging stages, such as the correspondence between stress amplitude, stress cycle frequency, strain accumulation rate, etc., and the evolution of material micro-damage, such as microcrack initiation and interfacial debonding. The matching process uses a pattern recognition algorithm to compare the real-time acquired interfacial stress-strain data stream with the features in the template. Once the current data pattern highly matches the signature of a certain aging stage in the template, the system identifies the corresponding material aging state characteristics. Second, the actual parameters of the cabin environment in the multi-dimensional monitoring dataset are compared with the initially set dynamic environmental parameter set to identify environmental simulation deviation characteristics. The actual environmental parameters inside the chamber were obtained from real-time readings collected by various sensors within the composite environmental stress chamber, such as thermometers, hygrometers, and light intensity meters, during the testing process. The comparison process calculated the deviation between the actual environmental parameters and the target parameters. For any given environmental parameter, its normalized deviation can be expressed as:

[0055] ,

[0056] In this formula, The normalization bias is a dimensionless value used for subsequent threshold determination. These are the real-time values ​​of environmental parameters measured by the sensor. The target values ​​set at the same moment for the dynamic environmental parameter set have the same physical dimensions. A preset normalization factor is used for this environmental parameter, such as its range of variation throughout the entire testing period or a standard fluctuation, to eliminate the influence of differences in the dimensions and numerical ranges of different parameters. The system compares the calculated deviation with a preset environmental simulation fidelity threshold. If the deviation exceeds the threshold, it is considered that there is a significant deviation in the environmental simulation, and this is recorded as an environmental simulation deviation feature. Finally, the results obtained from the above two analysis streams, namely the identified material aging state characteristics and the environmental simulation deviation characteristics, are structurally integrated. This integration is not a simple mathematical addition, but rather combines the two as independent but related information fields into a unified data structure, namely material response data. This data structure comprehensively describes what aging state the test sample exhibits under what environmental deviations.

[0057] Optionally, generating an environmental parameter adjustment instruction set based on the material response data includes:

[0058] Determine whether the material response data exceeds the material state safety threshold set to prevent the test sample from experiencing unreal failures;

[0059] If so, then generate a protective environment adjustment command;

[0060] If not, then calculate and generate the acceleration parameter combination based on the material response data;

[0061] The protective environment adjustment instructions or the acceleration parameters are combined and integrated into an environmental parameter adjustment instruction set.

[0062] Specifically, a crucial safety assessment is first performed on the material response data. The system extracts key indicators characterizing the material's aging state from the material response data, such as the maximum interfacial stress or strain accumulation rate calculated from the interfacial stress-strain data, and compares them with a preset material state safety threshold. This threshold is designed to prevent the test sample from exhibiting failure modes inconsistent with actual service environments under excessive accelerated stress, i.e., non-real failure. This judgment process can be expressed as: ,in, This represents a quantitative indicator of the current material state extracted from the material response data, such as the maximum interfacial stress value, which is updated in real time. This is the material condition safety threshold pre-set for the test sample, and its dimensions are... The system maintains consistency. If the judgment result is yes, meaning the current material state is approaching or exceeding the safety limit, the system will determine that the test sample is at risk of non-realistic failure. At this time, the system will immediately generate a set of protective environmental adjustment instructions. The goal of this set of instructions is to reduce the stress level of the coupled environmental field, such as instructing the composite environmental stress chamber to reduce the cooling rate, reduce the light intensity, or suspend the application of de-icing agents, thereby bringing the state of the test sample back to a safe range. If the judgment result is no, it indicates that the state of the test sample is stable, and the test can continue or even be accelerated. At this time, the system will calculate and generate a set of acceleration parameter combinations based on the current material response data. This calculation process will comprehensively analyze the current aging rate of the material and the fidelity of the environmental simulation, aiming to find a new set of environmental parameter settings that can effectively improve the aging rate, shorten the test cycle, and ensure that non-realistic failure is not triggered. Finally, both the generated protective environmental adjustment instructions and the calculated acceleration parameter combinations will be integrated into a standardized data package, namely the environmental parameter adjustment instruction set, to guide the next step of real-time control of the coupled environmental field.

[0063] Optionally, the step of calculating and generating the acceleration parameter combination based on the material response data includes:

[0064] Based on the material aging characteristic template, the material response data is analyzed to identify the key stress factors related to the material aging contribution at the current stage.

[0065] Under the constraint of maintaining the true failure mechanism of the test sample, calculate the allowable increase in the level of the key stress factor.

[0066] Based on the allowable increase, the effect level of the key stress factor is increased, and according to the physical coupling relationship between environmental factors, the environmental stress factor is coordinated and adjusted to generate the acceleration parameter combination.

[0067] Specifically, the calculation and generation of accelerated parameter combinations based on material response data begins with in-depth analysis of the data. The aim is to identify the key stress factors that dominate material aging at the current testing stage. This identification process is based on a pre-defined material aging characteristic template, a knowledge base containing aging behavior patterns of materials under different environmental stresses. The system compares and analyzes the material aging state characteristics contained in the material response data, such as the amplitude, frequency, and cumulative damage rate of interfacial stress and strain, with the feature signatures in the template. Through correlation analysis or pattern recognition algorithms, the system can determine which environmental stresses, such as sudden temperature drops, ultraviolet radiation, or salt spray corrosion, are most closely associated with the current aging phenomenon, thus identifying these environmental stresses as the key stress factors at the current stage. After identifying the key stress factors, the next step is to calculate the allowable increase in the effect level of these key stress factors while maintaining the constraints of the true failure mechanism of the test sample. The true failure mechanism refers to the progressive damage process that occurs in the material in the actual service environment, consistent with its physicochemical nature. To ensure the validity of the test, the accelerated process cannot introduce unnatural failure modes, such as excessively high temperatures causing material melting instead of aging. Therefore, the system needs to set a dynamic upper limit for each key stress factor based on the failure boundaries defined in the materials science principles and the material aging characteristic template. The calculation of the allowable increase can be expressed as:

[0068] ,

[0069] in, It is the allowable increase in the critical stress factor k, and it is the target value to be determined. This represents the current level of the key stress factor within the composite environmental stress chamber, which can be directly obtained from system status monitoring. This represents a quantitative indicator of the current material state extracted from the material response data. In the current material state The upper limit is the maximum achievable level of the critical stress factor k; exceeding this level may induce unrealistic failure. This upper limit is a function dependent on the current health state of the material, provided by the material aging characteristic template. Under this constraint, the system uses an optimization algorithm to calculate a value that significantly accelerates aging while ensuring safety. The value. Finally, the system is based on the calculated allowable increase. This enhances the effectiveness of key stress factors and, based on the physical coupling relationships between environmental factors, coordinates and adjusts other environmental stress factors to ultimately generate a combination of acceleration parameters. For example, if the key stress factor is temperature, the system increases its effectiveness. Subsequently, the partial pressure of water vapor inside the chamber must be adjusted accordingly based on physical laws, such as the saturated vapor pressure curve, to maintain the accuracy of the target humidity parameter. This series of coordinated adjustments to the new environmental parameter settings together constitutes the acceleration parameter combination used for the next stage of testing.

[0070] Optionally, the step of adjusting the coupled environmental field within the composite environmental stress chamber in real time according to the environmental parameter adjustment instruction set includes:

[0071] The environmental parameter adjustment instruction set is analyzed, and control instructions are sent to the environmental control system to dynamically control the deposition characteristics of the ice and snow layer.

[0072] The operating status of the composite environmental stress chamber is synchronously controlled to reconstruct the multi-physical and chemical environmental stress.

[0073] Specifically, adjusting the coupled environmental field within the composite environmental stress chamber in real time based on the generated environmental parameter adjustment instruction set is the execution link for achieving closed-loop feedback control of the testing process. This process begins with the control system parsing the environmental parameter adjustment instruction set. This parsing refers to the control system reading and decoding the structured data contained in the instruction set, extracting specific target parameter values ​​and rates of change for each environmental control subsystem, such as new target temperature parameters, target humidity parameters, target light intensity parameters, target snow melting agent concentration parameters, and target ice and snow morphology parameters. After parsing, the central controller converts these high-level instructions into low-level control instructions that can be directly executed by hardware and sends them to each environmental control system. A core operation is the dynamic control of the deposition characteristics of the ice and snow layer. Based on the parsed new target ice and snow morphology parameters, the control system issues precise instructions to the snowmaking or ice-making system, which may include adjusting water flow rate, nozzle air pressure, water temperature, etc., to change the density, crystal form, or deposition rate of the newly formed ice and snow, thereby directly adjusting the physical load applied to the test sample. Simultaneously, the control system coordinates and controls the overall operating status of the composite environmental stress chamber. This means that the cabin's cooling and heating systems, humidification and dehumidification systems, adjustable lighting systems, and de-icing agent spraying systems will simultaneously adjust their output power or operating modes according to the new target values ​​in the instruction set, in order to reconstruct the multi-physical and chemical environmental stresses. For example, when executing an acceleration parameter combination, the cooling system may cool down at a faster rate, while the lighting system increases the ultraviolet output intensity. The key to the entire process lies in synchronization and coordination, ensuring that all changes in environmental stresses are coordinated and consistent, jointly forming a new, instantaneously stable coupled environmental field that meets the instruction requirements.

[0074] Optionally, the method further includes:

[0075] In each test cycle, it is determined whether the material response data reaches the preset material failure threshold, or whether the cumulative test time reaches the preset maximum test cycle.

[0076] The test terminates when any of the judgment conditions are met.

[0077] Specifically, in each test cycle, the system performs two independent judgments in parallel. The first judgment is based on the actual state of the test sample. The system extracts key performance degradation indicators that can quantify the degree of cumulative material damage or performance degradation from the latest material response data and compares them with a preset material failure threshold. This judgment can be expressed as:

[0078] ,

[0079] in, It is a key performance degradation index obtained from material response data analysis, such as the cumulative damage variable calculated from interface stress and strain data or the propagation length of surface cracks in the material. This index reflects the health status of the sample under test in real time. It is a pre-defined critical value, representing the state in which a material is considered to have completely failed in engineering applications. Its dimensions are similar to... Maintaining consistency. The second judgment is based on the test duration. The system queries the internal timer to obtain the cumulative test time since the start of the test and compares it with the preset maximum test period. This judgment can be expressed as:

[0080] ,

[0081] in, It is the cumulative time from the start of the test to the current moment, provided directly by the system clock. This is the maximum permissible time set for this test, a practical constraint based on experimental economy or project cycle. The system combines these two judgment conditions with a logical OR relationship. This means that whether the performance degradation of the test sample reaches the failure criterion or the test duration reaches the preset upper limit, as long as either condition is met first, the system will immediately generate a termination command. This command will stop all environmental simulation activities in the composite environmental stress chamber and end the entire weathering resistance test process.

[0082] Based on the same inventive concept, such as Figure 4 As shown, the present invention also provides a weathering resistance testing system for materials in low-temperature ice and snow environments, characterized in that the system comprises:

[0083] The environmental data setting module is used to generate a dynamic environmental parameter set based on the environmental simulation script. The dynamic environmental parameter set includes at least the target temperature parameter, target humidity parameter, target light intensity parameter, target de-icing agent concentration parameter, and target ice and snow morphology parameter.

[0084] The environmental coupling simulation module is used to apply dynamically changing ice and snow loads and multiple physical and chemical environmental stresses in a preset composite environmental stress chamber based on the dynamic environmental parameter set, thereby constructing a coupled environmental field acting on the test sample.

[0085] A multi-dimensional in-situ monitoring module is used to acquire the state of the test sample in the coupled environmental field in real time and generate a multi-dimensional monitoring dataset.

[0086] The feedback module is used to compare and analyze the multi-dimensional monitoring dataset with the preset material aging feature template to generate material response data that characterizes the current state of the test sample.

[0087] The instruction generation module is used to generate a set of environmental parameter adjustment instructions based on the material response data;

[0088] The instruction execution module is used to adjust the instruction set according to the environmental parameters and to control the coupled environmental field in the composite environmental stress chamber in real time.

[0089] To verify the feasibility, efficiency, and high fidelity of this invention in practice, it was applied to the weathering resistance testing of a novel aerospace composite material. This composite material is intended for manufacturing the wings of unmanned aerial vehicles (UAVs) deployed in high-altitude and cold regions, where it must withstand the coupled effects of various environmental stresses during service, including harsh low temperatures, ice and snow adhesion, freeze-thaw cycles, and solar radiation. Traditional weathering resistance testing methods typically employ constant or simple cyclic stress conditions, which cannot realistically reproduce the dynamic and coupled natural environment, resulting in long testing cycles and significant deviations between the results and actual service conditions. This invention aims to solve this problem by providing a rapid and accurate weathering resistance assessment scheme.

[0090] To verify the effectiveness of this invention, we selected a 30cm*30cm sample of the novel aerospace composite material as the test specimen and conducted a 500-hour accelerated weathering test. The test process fully recorded the dynamic control of environmental parameters, the real-time response of the material, and the closed-loop feedback decision-making of the system.

[0091] Before the test began, the system first generated a typical "blizzard-sunny freeze-thaw" environmental simulation scenario for 72 hours using historical meteorological data of the target service area, namely a high-altitude airport, through the environmental data setting module. This scenario was then compiled into a dynamic environmental parameter set. This provides the target trajectory for the entire test.

[0092] After the test started, the environment coupling simulation module began working. During the first hour of the test, the system based on... The parameters were as follows: the snowmaking system inside the command module deposited a 5mm thick layer with a density of 0.2 g / cm³ on the surface of the test sample. 3 Dry snow. Meanwhile, the cabin temperature is maintained according to... The temperature dropped rapidly from 10°C to -15°C. Over the next 12 hours, the system simulated continuous snowfall and cooling, with the ice and snow layer gradually increasing in thickness to 20 mm.

[0093] In the 13th hour of testing, the environmental scenario entered the "sunny day freeze-thaw" phase. The solar simulation lights inside the parameter command cabin are turned on, and the light intensity reaches 800 W / m². 2 Simulates daytime solar radiation. The parameters commanded the cabin temperature to slowly rise from -15°C to 2°C. At this point, the multi-dimensional in-situ monitoring module played a crucial role. A fiber Bragg grating sensor array embedded in the composite material surface monitored the stress changes at the material-ice interface in real time. Data showed that as the bottom of the ice layer began to melt, the interfacial stress rapidly decreased from its peak of 8 MPa in the frozen state. However, due to the "ice wedge" effect caused by the refreezing of meltwater, localized stress concentration occurred in the micropores of the material.

[0094] The feedback module received and analyzed this multi-dimensional monitoring dataset in real time. The system compared the monitored 8 MPa interface stress with a preset material aging characteristic template, identifying this stress level as a key driving force for the initiation of microcracks in the material matrix, but not exceeding the 15 MPa material state safety threshold set to prevent unrealistic failures (such as material yielding). Simultaneously, the system comparison revealed that the actual temperature inside the chamber was 2.2°C, deviating from the target value of 2°C by 0.2°C, but this deviation was within the preset ±0.5°C environmental simulation fidelity threshold. This information was integrated into a material response dataset.

[0095] Based on this material response data, the instruction generation module determined that the test sample was stable and could be safely subjected to accelerated aging. The system initiated the calculation of acceleration parameters and concluded that the "temperature change rate" and "ultraviolet light intensity" were the main contributors to the accelerated microcrack propagation at the current stage, i.e., the key stress factors. Under the constraint of maintaining the true failure mechanism (fatigue crack propagation rather than thermal melting), the system calculated that the cooling rate could be increased from -5°C / h to -8°C / h, and the ultraviolet light intensity could be increased from 800 W / m 2 Increased to 1000 W / m 2 The system generates a combination of acceleration parameters that includes the new parameters mentioned above, and adjusts the humidity parameters accordingly to match the new temperature curve, ultimately forming a complete set of environmental parameter adjustment instructions.

[0096] The instruction execution module parses the instruction set and sends control commands to the control system of the composite environmental stress chamber. The cooling system power is increased to achieve a cooling rate of -8°C / h; the power of the solar simulation lamps is enhanced, and the ultraviolet output is increased. The coupled environmental field is reconstructed in real time into a more challenging accelerated testing environment.

[0097] The entire test operated within a continuous closed loop of "monitoring-analysis-decision-control". At the 482nd hour of the test, the monitoring system detected a sharp increase in the propagation rate of a main crack on the material surface, and the calculated cumulative damage variable reached the preset material failure threshold. The system automatically determined the test was complete and saved all test data.

[0098] To highlight the advantages of this invention, the test results were compared with those obtained using the traditional constant low-temperature cycling method. The traditional method takes 2000 hours and can only simulate a single freeze-thaw fatigue.

[0099] Table 1 Real-time control data of dynamic environmental parameters

[0100]

[0101] Table 2 Material Response and System Dynamic Adjustment Data

[0102]

[0103] Table 3 Comparison of test results between the present invention and traditional methods

[0104]

[0105] As can be seen from the data in Tables 1-3 above, the method of this invention exhibits significant advantages. Table 1 demonstrates the system's high-precision dynamic tracking capability for complex environmental parameters. Table 2 clearly illustrates the system's intelligent decision-making and closed-loop feedback adjustment process based on real-time material response, achieving a "personalized" testing process for each material, ensuring both safety and efficiency. The comparative results in Table 3 show that the testing time of this invention is shortened by approximately 75%, but through intelligent acceleration, its estimated equivalent service life is longer, and the observed failure modes are more complex and closer to real-world conditions, including multiple coupled damages. This provides far more accurate and comprehensive data support for material life prediction and reliability design than traditional methods. These data results fully demonstrate the high efficiency, high fidelity, and intelligent advantages of this invention in material weathering resistance testing.

[0106] It should be noted that the formulas described above, through the principle of dimensional consistency and mathematical standardization methods (such as normalization, dimensionless parameter conversion, or unit system unification), can translate physical quantities with different properties into unitless standard values ​​or parameters that can be superimposed in the same dimension. This eliminates the interference of different dimensions on the computational logic, allowing the formulas to retain the original data distribution characteristics while possessing mathematical rationality and adaptability to objective laws. These are conventional technical methods and will not be elaborated further. The electrical connections between the various units described above do not necessarily represent direct or indirect connections; any indirect connection method is applicable to the embodiments of this invention as long as it achieves the purpose of this invention. The above descriptions are merely exemplary embodiments of this invention and should not be construed as limiting the scope of this invention.

[0107] All equivalent changes and modifications made in accordance with the teachings of this invention are still within the scope of this invention. Those skilled in the art will readily conceive of other embodiments of this invention upon considering the specification and the disclosure of practical truth. This application is intended to cover any variations, uses, or adaptations of this invention that follow the general principles of this invention and include common knowledge or conventional techniques in the art not described herein.

Claims

1. A method for testing the weather resistance of materials in low-temperature ice and snow environments, characterized in that, The method includes: Based on the environmental simulation script, a dynamic environmental parameter set is generated, which includes at least the target temperature parameter, target humidity parameter, target light intensity parameter, target de-icing agent concentration parameter, and target ice and snow morphology parameter. Based on the dynamic environmental parameter set, dynamically changing ice and snow loads and multiple physical and chemical environmental stresses are applied in a preset composite environmental stress chamber to construct a coupled environmental field acting on the test sample. The state of the test sample in the coupled environmental field is acquired in real time to generate a multi-dimensional monitoring dataset; including: acquiring the surface temperature distribution of the test sample and the interfacial stress and strain data caused by frost heave in the micro-region of the material-ice-snow interface; acquiring ice-snow layer state data characterizing the physical properties of the ice-snow layer; and performing spatiotemporal registration and fusion of the interfacial stress and strain data and the ice-snow layer state data to generate the multi-dimensional monitoring dataset. The multi-dimensional monitoring dataset is compared and analyzed with a preset material aging feature template to generate material response data characterizing the current state of the test sample. This includes: matching the interface stress and strain data with the material aging feature template to identify material aging state characteristics; comparing the actual parameters of the cabin environment in the multi-dimensional monitoring dataset with the dynamic environment parameter set, and identifying environmental simulation deviation characteristics based on the environmental simulation fidelity threshold; and structurally integrating the material aging state characteristics with the environmental simulation deviation characteristics to generate material response data. Based on the material response data, a set of environmental parameter adjustment instructions is generated; Based on the set of environmental parameter adjustment instructions, the coupled environmental field within the composite environmental stress chamber is controlled in real time.

2. The method for testing the weather resistance of materials in low-temperature ice and snow environments according to claim 1, characterized in that, The construction of the coupled environmental field acting on the test sample includes: The dynamic environmental parameter set is analyzed to obtain the target ice and snow morphology parameters, target temperature parameters, target humidity parameters, target light intensity parameters, and target de-icing agent concentration parameters; An ice and snow layer is dynamically deposited on the surface of the test sample according to the target ice and snow morphology parameters. The environmental parameters within the preset composite environmental stress chamber are synchronously adjusted to match the target temperature parameter, the target humidity parameter, the target light intensity parameter, and the target de-icing agent concentration parameter, thus forming a coupled environmental field together with the ice and snow layer.

3. The method for testing the weather resistance of materials in low-temperature ice and snow environments according to claim 1, characterized in that, The generation of an environmental parameter adjustment instruction set based on the material response data includes: Determine whether the material response data exceeds the material state safety threshold set to prevent the test sample from experiencing unreal failures; If so, then generate a protective environment adjustment command; If not, then calculate and generate the acceleration parameter combination based on the material response data; The protective environment adjustment instructions or the acceleration parameters are combined and integrated into an environmental parameter adjustment instruction set.

4. The method for testing the weather resistance of materials in low-temperature ice and snow environments according to claim 3, characterized in that, The calculation and generation of acceleration parameter combinations based on the material response data includes: Based on the material aging characteristic template, the material response data is analyzed to identify the key stress factors related to the material aging contribution at the current stage. Under the constraint of maintaining the true failure mechanism of the test sample, calculate the allowable increase in the level of the key stress factor. Based on the allowable increase, the effect level of the key stress factor is increased, and according to the physical coupling relationship between environmental factors, the environmental stress factor is coordinated and adjusted to generate the acceleration parameter combination.

5. The method for testing the weather resistance of materials in a low-temperature ice and snow environment according to claim 4, characterized in that, The step of adjusting the coupled environmental field within the composite environmental stress chamber in real time according to the environmental parameter adjustment instruction set includes: The environmental parameter adjustment instruction set is analyzed, and control instructions are sent to the environmental control system to dynamically control the deposition characteristics of the ice and snow layer. The operating status of the composite environmental stress chamber is synchronously controlled to reconstruct the multi-physical and chemical environmental stress.

6. The method for testing the weather resistance of materials in a low-temperature ice and snow environment according to claim 5, characterized in that, The method further includes: In each test cycle, it is determined whether the material response data reaches the preset material failure threshold, or whether the cumulative test time reaches the preset maximum test cycle. The test terminates when any of the judgment conditions are met.

7. A weathering resistance testing system for materials in low-temperature ice and snow environments, characterized in that, The system includes: The environmental data setting module is used to generate a dynamic environmental parameter set based on the environmental simulation script. The dynamic environmental parameter set includes at least the target temperature parameter, target humidity parameter, target light intensity parameter, target de-icing agent concentration parameter, and target ice and snow morphology parameter. The environmental coupling simulation module is used to apply dynamically changing ice and snow loads and multiple physical and chemical environmental stresses in a preset composite environmental stress chamber based on the dynamic environmental parameter set, thereby constructing a coupled environmental field acting on the test sample. A multi-dimensional in-situ monitoring module is used to acquire the state of the test sample in the coupled environmental field in real time and generate a multi-dimensional monitoring dataset; including: acquiring the surface temperature distribution of the test sample and the interfacial stress and strain data caused by frost heave in the micro-region of the material-ice-snow interface; acquiring ice-snow layer state data characterizing the physical properties of the ice-snow layer; and performing spatiotemporal registration and fusion of the interfacial stress and strain data and the ice-snow layer state data to generate the multi-dimensional monitoring dataset; The feedback module is used to compare and analyze the multi-dimensional monitoring dataset with a preset material aging feature template to generate material response data characterizing the current state of the test sample. This includes: matching the interface stress-strain data with the material aging feature template to identify material aging state characteristics; comparing the actual cabin environment parameter data in the multi-dimensional monitoring dataset with the dynamic environment parameter set, and identifying environmental simulation deviation characteristics based on the environmental simulation fidelity threshold; and structurally integrating the material aging state characteristics with the environmental simulation deviation characteristics to generate material response data. The instruction generation module is used to generate a set of environmental parameter adjustment instructions based on the material response data; The instruction execution module is used to adjust the instruction set according to the environmental parameters and to control the coupled environmental field in the composite environmental stress chamber in real time.

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