Nitrogen low temperature brittle fracture monitoring and protection system based on GMS system

Abstract

The present invention provides a nitrogen low-temperature brittle fracture monitoring and protection system based on a GMS system, which relates to the technical field of material brittle fracture monitoring, and includes: a data acquisition module, which is used to install a temperature sensor and a strain sensor on the material, collect the temperature parameters of the material through the temperature sensor of the GMS system, and collect the stress value parameters and strain quantity parameters of N samples at the temperature through the strain sensor of the GMS system; wherein the stress value parameters of the material include a load coefficient, a cross-sectional size coefficient and a time coefficient; the strain quantity parameters of the material include a thermal contraction coefficient and a material embrittlement coefficient; a data processing module, which is used to process the collected load coefficient, cross-sectional size coefficient and time coefficient of the N samples. The present invention can accurately detect potential brittle fracture risks in real time and improve detection accuracy by collecting stress and strain parameters of the material under load, as well as related parameters affecting stress and strain parameters.

Description

Technical Field

[0001] The invention relates to the technical field of material brittle fracture monitoring, and in particular to a nitrogen low-temperature brittle fracture monitoring and protection system based on a GMS system. Background Art

[0002] The GMS-based nitrogen low-temperature brittle fracture monitoring and protection system (GMS-N2 system) is a key system for monitoring and protecting industrial equipment, especially in applications involving nitrogen low-temperature environments. The system uses the characteristics of nitrogen and advanced monitoring technology to prevent brittle fracture failures caused by low temperatures and take timely measures to protect equipment and personnel safety. The GMS system is commonly used to monitor and control gases in industrial environments, including but not limited to oxygen, nitrogen, and methane. In low-temperature environments, especially in scenarios involving liquid nitrogen, the flow and pressure changes of nitrogen may cause brittle fracture of equipment, thereby causing serious safety accidents.

[0003] At present, in industrial production, especially in applications involving nitrogen low-temperature environments, there are safety hazards of brittle fracture failures. In these environments, the characteristics of nitrogen will cause the embrittlement of equipment materials, thereby increasing the risk of brittle fracture of equipment and may cause serious safety accidents. Traditional monitoring systems only monitor temperature or pressure, but do not collect stress and strain parameters, as well as related parameters that affect stress and strain parameters. However, stress and strain parameters are directly related to the prevention of material brittle fracture, so potential brittle fracture risks cannot be detected in real time and accurately.

[0004] Therefore, it is necessary to develop a nitrogen low-temperature brittle fracture monitoring and protection system based on the GMS system to solve the defect that the existing technology cannot accurately detect the brittle fracture risk in real time.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The object of the present invention is to provide a nitrogen low-temperature brittle fracture monitoring and protection system based on the GMS system to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A nitrogen low temperature brittle fracture monitoring and protection system based on the GMS system, comprising:

[0009] The data acquisition module is used to install a temperature sensor and a strain sensor on the material, collect the temperature parameters of the material through the temperature sensor of the GMS system, and collect the stress value parameters and strain value parameters of N samples at the temperature through the strain sensor of the GMS system;

[0010] in,

[0011] The stress value parameters of the material include load factor, section size factor and time factor;

[0012] The material strain parameters include thermal shrinkage coefficient and material embrittlement coefficient;

[0013] A data processing module is used to process the load coefficient, cross-sectional dimension coefficient and time coefficient of the collected N samples to generate the stress value parameters of the material, and to process the thermal shrinkage coefficient and material embrittlement coefficient of the collected N samples to generate the strain value parameters of the material;

[0014] The data analysis module is used to construct the stress-strain curve of the material based on the stress value parameters and strain value parameters of the material, obtain the elastic modulus, yield strength, ultimate strength and elongation at break of the material through the mathematical model, process the yield strength and ultimate strength data to generate the strength coefficient of the material, process the elastic modulus and elongation at break data to generate the deformation coefficient of the material, process the strength coefficient and deformation coefficient of the material data and conduct correlation analysis to generate the brittle fracture index of the material;

[0015] The early warning module is used to judge the brittle fracture risk level of the material according to the interval range of the brittle fracture index of the material, and to issue different early warning signals according to the brittle fracture risk level of the material.

[0016] Furthermore, the load coefficient, cross-sectional dimension coefficient and time coefficient of the collected N samples are processed to generate the stress value parameters of the material according to the following formula:

[0017]

[0018] Among them, σ(t) is the stress value parameter at time t, α i is the load factor of the ith sample, L i is the cross-sectional size factor of the i-th sample, t i is the time coefficient of the i-th sample, β is the time attenuation coefficient, γ is the exponential weight of the cross-sectional size, T is the normalized time constant, and Z is the normalization coefficient. N is the number of samples.

[0019] Furthermore, the thermal shrinkage coefficient and material embrittlement coefficient of the collected N samples are processed to generate the material strain parameters according to the following formula:

[0020]

[0021] ò is the strain parameter of the material, x is the thermal shrinkage coefficient of the collected sample, y is the material embrittlement coefficient of the collected sample, f(z) is the normalized function, and g(t) is the higher integral function;

[0022] The specific f(z) and g(t) are further defined according to the following auxiliary formulas:

[0023] Normalization function Where z is the set of thermal shrinkage coefficients of all samples, and the advanced integral function

[0024]

[0025] Furthermore, based on the stress value parameters and strain value parameters of the material, the stress-strain curve of the material is constructed, and the elastic modulus, yield strength, ultimate strength and elongation at break of the material are obtained through the mathematical model as follows:

[0026] According to the stress value and strain amount, the stress-strain curve of the material is constructed, with the strain amount on the horizontal axis and the stress value on the vertical axis. Through the stress-strain curve, data points of stress value and strain amount are selected from the curve, and then the linear region is determined. The linear region is usually the initial part of the curve, avoiding exceeding the yield point. Linear regression analysis is used to fit this part of the data, and the slope is the elastic modulus of the material;

[0027] Yield strength refers to the minimum stress at which a material begins to undergo plastic deformation. It is determined using the 0.2% offset method, which is to find the stress value corresponding to the straight line on the stress-strain curve that is parallel to the initial elastic line and passes through the 0.2% permanent strain point. This stress value is the yield strength of the material.

[0028] Ultimate strength refers to the maximum stress that a material can withstand before breaking. The peak value of the stress-strain curve is directly observed from the experimental data, and this peak value is the ultimate strength of the material.

[0029] Elongation at break refers to the percentage of elongation of a material relative to its initial length when it breaks. The strain value corresponding to the material's breaking point is found on the stress-strain curve. This strain value is the elongation at break.

[0030] Furthermore, the yield strength and ultimate strength are processed to generate the strength coefficient of the material according to the following formula:

[0031]

[0032] Q is the strength coefficient of the material, σ u is the ultimate strength of the material, σy is the yield strength of the material, log(σ u +1) is the logarithmic transformation of the ultimate strength, N(x, μ, σ) is the normalization function, where x is σ u , μ and σ are the mean and standard deviation of the data respectively.

[0033] Furthermore, the elastic modulus and elongation at break are processed to generate the deformation coefficient of the material according to the following formula:

[0034]

[0035] δ is the deformation coefficient of the material, E is the elastic modulus, μ E and σ E are the mean and standard deviation of elastic modulus, ò is elongation at break, μ ò and σò are the mean and standard deviation of the elongation at break, respectively, and k and λ are adjustment parameters, where k adjusts the sensitivity of the exponential function and λ adjusts the relative influence of the elongation at break on the deformation coefficient.

[0036] Furthermore, the strength coefficient and deformation coefficient of the material are processed and correlated to generate the brittle fracture index of the material, based on the following formula:

[0037]

[0038] C is the brittle fracture index of the material, a and b are adjustment coefficients, which affect the weight of the strength coefficient and the offset of the logarithmic function respectively, and σ is the exponential adjustment coefficient, 1+e -σ·(k-δ) is the normalization term.

[0039] Furthermore, according to the interval range of the brittle fracture index of the material, the brittle fracture risk level of the material is judged, and according to the brittle fracture risk level of the material, different early warning signals are issued as follows:

[0040] Brittle fracture index>30: high risk range, corresponding to red alert;

[0041] 20≤brittle fracture index≤30: medium risk range, corresponding to yellow alert;

[0042] Brittle fracture index <20: belongs to the low-risk range, corresponding to the green alert.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] The present invention collects the temperature parameters of the material through a temperature sensor, collects the stress value parameters and strain quantity parameters of N samples at the temperature through a strain sensor, performs data processing on the load coefficient, cross-sectional size coefficient and time coefficient of the collected N samples to generate the stress value parameter of the material, performs data processing on the thermal shrinkage coefficient and material embrittlement coefficient of the collected N samples to generate the strain quantity parameters of the material, constructs the stress-strain curve of the material based on the stress value parameter and the strain quantity parameters of the material, obtains the elastic modulus, yield strength, ultimate strength and elongation at break of the material through a mathematical model, performs data processing on the yield strength and ultimate strength to generate the strength coefficient of the material, performs data processing on the elastic modulus and elongation at break to generate the deformation coefficient of the material, performs data processing and analysis on the strength coefficient and deformation coefficient of the material to generate the brittle fracture index of the material, therefore, on the original basis, by collecting the stress and strain parameters of the material under the action of load, as well as the relevant parameters affecting the stress and strain parameters, the potential brittle fracture risk can be detected in real time and accurately, thereby improving the detection accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a block diagram of the module composition of the present invention. DETAILED DESCRIPTION

[0046] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0047] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0048] Example:

[0049] See also Figure 1 , the present invention provides a technical solution:

[0050] A nitrogen low temperature brittle fracture monitoring and protection system based on the GMS system, comprising:

[0051] The data acquisition module is used to install a temperature sensor and a strain sensor on the material, collect the temperature parameters of the material through the temperature sensor of the GMS system, and collect the stress value parameters and strain value parameters of N samples at the temperature through the strain sensor of the GMS system;

[0052] in,

[0053] The stress value parameters of the material include load factor, section size factor and time factor;

[0054] The material strain parameters include thermal shrinkage coefficient and material embrittlement coefficient;

[0055] A data processing module is used to process the load coefficient, cross-sectional dimension coefficient and time coefficient of the collected N samples to generate the stress value parameters of the material, and to process the thermal shrinkage coefficient and material embrittlement coefficient of the collected N samples to generate the strain value parameters of the material;

[0056] The data analysis module is used to construct the stress-strain curve of the material based on the stress value parameters and strain value parameters of the material, obtain the elastic modulus, yield strength, ultimate strength and elongation at break of the material through the mathematical model, process the yield strength and ultimate strength data to generate the strength coefficient of the material, process the elastic modulus and elongation at break data to generate the deformation coefficient of the material, process the strength coefficient and deformation coefficient of the material data and conduct correlation analysis to generate the brittle fracture index of the material;

[0057] The early warning module is used to judge the brittle fracture risk level of the material according to the interval range of the brittle fracture index of the material, and to issue different early warning signals according to the brittle fracture risk level of the material.

[0058] In the nitrogen low-temperature brittle fracture monitoring and protection system, it is crucial to use temperature sensors to monitor the temperature of equipment and materials to ensure that the temperature does not drop below the critical temperature that causes the material to fracture. Thermocouples are usually used in such systems to measure the temperature of equipment and materials because thermocouples are suitable for a wide temperature range, especially at very low temperatures. Thermocouples determine the temperature by measuring the thermoelectric potential generated at the junction of two different metals. This type of sensor is particularly suitable for extreme environments because they can operate from very low temperatures (such as liquid nitrogen temperature, close to -196°C) to very high temperatures.

[0059] In the nitrogen low-temperature brittle fracture monitoring and protection system, the principle of using strain sensors to collect the stress value parameters and strain value parameters of the material at this temperature is: the strain sensor usually uses a strain gauge, which is a resistive sensor, mainly used to measure the strain on the surface of an object. The basic working principle of the strain gauge is based on the change of resistance. When the strain gauge is pasted on the surface of a stressed object, as the object deforms, the strain gauge will also stretch or shorten. This deformation causes the resistance of the strain gauge to change, and this change in resistance is proportional to the strain on the surface of the object.

[0060] When the stress value parameter of the material reaches a certain value, the brittle fracture index of the material will exceed its threshold, resulting in brittle fracture of the material. The following are the specific reasons:

[0061] Brittle fracture mode: When brittle materials are subjected to stress, their fracture mode is usually instantaneous fracture along the crack surface or weak point. This is because the internal structure of brittle materials is relatively fragile and cannot withstand large strains, causing stress to concentrate at cracks or defects, ultimately causing fracture.

[0062] Fracture toughness: The fracture toughness of brittle materials is relatively low, that is, the material is difficult to undergo plastic deformation under stress. In contrast, ductile materials can better absorb strain energy and undergo a certain degree of plastic deformation, thereby delaying the occurrence of fracture.

[0063] Fracture energy release: The fracture of brittle materials is usually accompanied by the release of a large amount of energy, which causes the cracks to expand rapidly and quickly destroy the integrity of the material. When the stress reaches a certain value, the cracks inside the material will rapidly expand to the material's failure point due to energy accumulation, leading to brittle fracture.

[0064] Material structure and composition: The structure and composition of a material also affect its brittleness. For example, metal materials with an ordered lattice structure are relatively tough, while amorphous materials and many ceramic materials are more prone to brittle fracture.

[0065] Therefore, when the stress value parameter of the material reaches a certain value, the brittle material is prone to brittle fracture, and the toughness of the material is related to its ability to absorb and consume strain energy.

[0066] Collecting material stress and strain parameters plays a role in monitoring the risk of nitrogen low-temperature brittle fracture of materials:

[0067] By collecting the stress value parameters of the material, the stress state of the material under specific loading conditions can be evaluated, which helps to determine the maximum stress value that the material can withstand and determine its bearing capacity. When the stress value of the material approaches or exceeds its ultimate strength, there is a risk of brittle fracture.

[0068] Strain reflects the degree of deformation of a material under stress. For monitoring the risk of low-temperature brittle fracture in nitrogen, the collection and analysis of strain parameters are also very important. The low-temperature nitrogen environment may cause the deformation performance of the material to decrease. Therefore, understanding the strain characteristics of the material under low-temperature conditions is crucial to judging the risk of brittle fracture.

[0069] The brittle fracture index is an indicator used to evaluate the brittleness of a material. It reflects the tendency of a material to fracture brittlely when subjected to stress. By collecting stress and strain data and performing data processing and analysis, the brittle fracture index of a material can be calculated or measured. Changes in the brittle fracture index of a material can provide early warning of the occurrence of brittle fracture risks.

[0070] In summary, collecting the stress value and strain parameter of the material is crucial for monitoring the risk of low-temperature brittle fracture of the material in nitrogen. Analyzing and monitoring these parameters can help evaluate the brittle fracture tendency of the material and take corresponding preventive measures in advance to ensure the safe use of the material in a low-temperature nitrogen environment.

[0071] The collected load coefficient, cross-sectional size coefficient and time coefficient play a vital role in the collection of material stress value parameters:

[0072] Load coefficient: The stress of a material is directly related to the load it receives. By collecting the load coefficient, the stress value can be associated with the force received. The load coefficient can help evaluate the magnitude of the force applied to the material and provide information on the stress level of the material under different load conditions.

[0073] Section size coefficient: The section size of the material affects its stress distribution. By collecting the section size coefficient, stress correction can be made according to the actual section size of the material. The section size coefficient can more accurately estimate the stress value of the material.

[0074] Time coefficient: The stress value of the material is also affected by time. When the material is loaded or the action time changes, the stress state will change. By collecting the time coefficient, the stress change of the material under different time conditions can be taken into account. The time coefficient can provide the stress value information of the material under long-term loading or static loading.

[0075] In summary, collecting load coefficients, cross-sectional size coefficients and time coefficients is very important for collecting material stress values. The collection of the above parameters can help determine the actual stress state of the material, estimate the stress value of the material more accurately, and provide information about the stress level and changes of the material under different loading conditions.

[0076] The collected thermal shrinkage coefficient and material embrittlement coefficient also play a vital role in the collection of material strain parameters:

[0077] Thermal shrinkage coefficient: Thermal shrinkage is the dimensional change that occurs when a material is subjected to temperature changes. When a material is heated or cooled, it will cause strain due to the thermal expansion and contraction effect. By collecting the thermal shrinkage coefficient, we can understand the thermal expansion and contraction behavior of the material under different temperature conditions, and then calculate the strain. The thermal shrinkage coefficient provides important information for evaluating the strain capacity of the material under temperature changes.

[0078] Material brittleness coefficient: Material brittleness refers to the property of a material that is prone to brittle fracture under specific conditions (such as low temperature, high stress, etc.). The brittleness coefficient describes the tendency and degree of brittle fracture of a material under specific conditions. By collecting the brittleness coefficient, the brittle fracture risk of the material under specific stress or temperature conditions can be evaluated and the corresponding strain can be calculated.

[0079] In summary, the collection of thermal shrinkage coefficient and material brittleness coefficient is of great significance for the evaluation of material strain parameters. The thermal shrinkage coefficient and material brittleness coefficient provide information on the strain of the material under thermal loading or temperature change conditions, and help evaluate the strain capacity and brittle fracture risk of the material under specific stress or temperature conditions.

[0080] On the basis of the above embodiment, the load coefficient, cross-sectional dimension coefficient and time coefficient of the collected N samples are processed to generate the stress value parameter of the material, according to the following formula:

[0081]

[0082] Among them, σ(t) is the stress value parameter at time t, α i is the load factor of the ith sample, L i is the cross-sectional size factor of the i-th sample, t i is the time coefficient of the i-th sample, and β is the time attenuation coefficient, which is used to adjust the time t i The influence on the stress value, its value affects the decay rate of the stress value over time, γ is the exponential weight of the cross-sectional size, which is used to adjust the cross-sectional size coefficient L i The influence degree on the stress value, T is the normalized time constant, which is used to control the time parameter t i Normalization, Z is the normalization coefficient, ensuring that σ(t) is within the preset value range, N is the number of samples.

[0083] The above formula combines the interaction rules of load factor, section size factor and time factor through α i , L i and t i The interaction is expressed as, for example, increasing α i will directly increase σ(t), and increasing t i Will pass The function leads to the attenuation of stress values. In addition, the cross-sectional size factor L i The change of affects the final stress value through the exponent γ, where a larger γ value enhances the influence of the cross-sectional size.

[0084] The range of the above formula is determined by the normalization coefficient Z and the properties of the function. In theory, the maximum value of σ(t) does not exceed 1 (when all t i are close to 0), the minimum value is close to 0 (when all t i are both very large), the value range can be set to [0,1]. Different σ(t) values ​​represent the stress state of the material at different time points, which can be used to evaluate the fatigue degree and possible fracture time of the material.

[0085] The material used in the test is common structural steel, which is placed in a simulated extreme environment (low temperature) and the stress response of the material is evaluated by changing the load factor, section size factor and time.

[0086] Before the test began, two groups of test samples of different specifications were prepared, with three samples in each group. The cross-sectional size and shape of each group of samples were different to adapt to different experimental conditions and load requirements. These samples underwent special surface treatment and heat treatment to simulate the material processing process mentioned in the invention.

[0087] The experimental equipment includes a high-precision stress tester, temperature and pressure control equipment, and a data recording system. During the experiment, the test sample is first fixed on the stress tester, and then the pressure is gradually increased and the temperature is adjusted according to the predetermined program. After each parameter is changed, the stable state is maintained for a period of time to ensure the accuracy of the data. In addition, each group of samples is tested repeatedly at different time points to evaluate the stability and repeatability of material performance.

[0088] According to the functional relationship between the load coefficient, section size coefficient, time coefficient and material stress value parameters, a data table is established. The table content is as follows:

[0089]

[0090] From the data in the above table, it can be seen that under the same cross-sectional size coefficient L i and time coefficient t i Under the condition, as the load factor α i With the increase of the stress value parameter of the material, the stress value parameter of the material is significantly enhanced, indicating that the stress bearing capacity of the material is related to the load coefficient α i Directly proportional.

[0091] Specifically, when the load coefficient of sample A increases from 1.0 to 1.5, its stress response increases from 300MPa to 450MPa, an increase of 50%. This significant enhancement effect demonstrates that the bearing capacity of the material can be effectively improved by optimizing the load coefficient.

[0092] In addition, from the time coefficient t i In terms of the influence on stress value parameters, the stress value changes of sample A and sample B after the extension of time are not significant, which indicates that after the initial load adaptation, the structure and performance of the material remain stable even if it is subjected to the same external conditions for a long time.

[0093] Further analysis shows that although sample B has a larger cross-sectional size coefficient, its stress value under the same load condition is lower than that of sample A. This may be because the increase in cross-sectional size disperses the pressure per unit area, thus showing better compressive resistance. This verifies the influence of cross-sectional size on the stress response of the material. From the overall data analysis, by changing the load coefficient α i , Section size factor L i and time coefficient t i , the changes in material stress values ​​can be comprehensively evaluated.

[0094] On the basis of the above embodiment, the thermal shrinkage coefficient and material embrittlement coefficient of the collected N samples are processed to generate the material strain parameters according to the following formula:

[0095]

[0096] ò is the strain parameter of the material, x is the thermal shrinkage coefficient of the collected sample, y is the material brittleness coefficient of the collected sample, f(z) is the normalization function used to process the data of x, and g(t) is the advanced integral function used to process the data of y.

[0097] The specific f(z) and g(t) can be further defined according to the following auxiliary formulas:

[0098] Normalization function Where z is the set of thermal shrinkage coefficients of all samples, and the advanced integral function

[0099]

[0100] In this way, we can calculate the material strain ò based on the collected values ​​of thermal shrinkage coefficient x and material brittleness coefficient y.

[0101] Five groups of material samples were selected for testing, and the thermal shrinkage coefficient x and material embrittlement coefficient y were measured for each group of materials.

[0102] According to the functional relationship between the thermal shrinkage coefficient x, the material embrittlement coefficient y and the material strain parameter ò, a data table is established. The table content is as follows:

[0103]

[0104]

[0105] Through the above table, we can observe the relationship between the strain parameter ò obtained after applying the formula for various materials and the thermal shrinkage coefficient x and the material embrittlement coefficient y of the material.

[0106] It can be seen from the data table that as the material embrittlement coefficient y increases, the integral value of g(y) also increases, which is consistent with the expected influence of the material embrittlement coefficient, that is, the higher the embrittlement coefficient of the material, the weaker its fracture resistance when subjected to the same external force; at the same time, through the relationship between the normalized f(x) and the material strain parameter ò, the influence of the thermal shrinkage coefficient on material deformation can be analyzed.

[0107] On the basis of the above embodiment, based on the stress value parameters and strain value parameters of the material, the stress-strain curve of the material is constructed, and the elastic modulus, yield strength, ultimate strength and elongation at break of the material are obtained through a mathematical model as follows:

[0108] According to the stress value and strain amount, the stress-strain curve of the material is constructed, with the strain amount on the horizontal axis and the stress value on the vertical axis. Through the stress-strain curve, data points of stress value and strain amount are selected from the curve, and then the linear region is determined. The linear region is usually the initial part of the curve, avoiding exceeding the yield point. Linear regression analysis is used to fit this part of the data, and the slope is the elastic modulus of the material.

[0109] Yield strength refers to the minimum stress at which a material begins to undergo plastic deformation. It is usually determined using the 0.2% offset method, which is to find the stress value corresponding to the straight line on the stress-strain curve that is parallel to the initial elastic line and passes through the 0.2% permanent strain point. This stress value is the yield strength of the material.

[0110] The specific steps are: move the strain from the origin to the right to 0.2% (ie 0.002), draw a line segment parallel to the straight line in the elastic region, and calculate the stress value at the intersection, which is the yield strength of the material.

[0111] Ultimate strength refers to the maximum stress that a material can withstand before breaking. The peak value of the stress-strain curve can be directly observed from the experimental data, and this peak value is the ultimate strength of the material.

[0112] Elongation at break refers to the percentage of elongation of a material relative to its initial length when it breaks. The strain value corresponding to the material's breaking point is found on the stress-strain curve. This strain value is the elongation at break.

[0113] Based on the above embodiment, the yield strength and ultimate strength are processed to generate the strength coefficient of the material according to the following formula:

[0114]

[0115]

[0116] Q is the strength coefficient of the material, σ u is the ultimate strength of the material, σ y is the yield strength of the material, log(σ u +1) is the logarithmic transformation of the ultimate strength, N(x, μ, σ) is the normalization function, where x is σ u , μ and σ are the mean and standard deviation of the data respectively.

[0117] The value range of the result Q of the above formula is [0,1], because log(σ u +1) is at the maximum value of σ u When it increases to infinity, the ultimate strength of the material has an upper limit in actual operation. Therefore, the output of this logarithmic function will be limited to the range of [0,1] by the normalization function N(x, μ, σ). Different Q values ​​reflect the performance of the material under different strength conditions. A higher K value indicates that the material has a stronger ability to maintain stability and reliability at higher strengths.

[0118] On the basis of the above embodiment, the elastic modulus and elongation at break are processed to generate the deformation coefficient of the material according to the following formula:

[0119]

[0120] δ is the deformation coefficient of the material, E is the elastic modulus, μ E and σ E are the mean and standard deviation of elastic modulus, ò is elongation at break, μ ò and σò are the mean and standard deviation of the elongation at break, respectively, and k and λ are adjustment parameters, where k adjusts the sensitivity of the exponential function and λ adjusts the relative influence of the elongation at break on the deformation coefficient.

[0121] The value range of the deformation coefficient δ of the material is (0,1). When δ is close to 0, it means that the material has a low deformation capacity and is suitable for applications requiring high rigidity; when δ is close to 1, it means that the material has a high deformation capacity and is suitable for applications requiring good toughness and elongation.

[0122] On the basis of the above embodiment, the strength coefficient and deformation coefficient of the material are subjected to data processing and correlation analysis to generate the brittle fracture index of the material, according to the following formula:

[0123]

[0124] C is the brittle fracture index of the material, a and b are adjustment coefficients, which affect the weight of the strength coefficient and the offset of the logarithmic function respectively, σ is the exponential adjustment coefficient, which is used to adjust the relative influence strength between the strength coefficient and the deformation coefficient, log(δ+b) is used to process the deformation coefficient, and the offset b is added to avoid the situation where the internal value of the logarithmic function is zero, 1+e -σ·(k-δ) It is a normalization term, which ensures that the brittle fracture index C of the material is within a reasonable range and deals with the influence of the relative sizes of the strength coefficient Q and the deformation coefficient δ on the results.

[0125] According to the functional relationship between the material's strength coefficient Q and deformation coefficient δ and the material's brittle fracture index C, a data table is established. The table content is as follows:

[0126]

[0127] According to the above table, the range of the material's brittle fracture index can be obtained, and the material's brittle fracture risk level can be determined. According to the material's brittle fracture risk level, the process of issuing different warning signals is as follows:

[0128] Brittle fracture index (C)>30: It belongs to the high-risk range, which means that the material may break quickly under high load and emergency protective measures need to be taken, corresponding to the red alert.

[0129] 20≤Brittle Fracture Index (C)≤30: Belongs to the medium risk range. The material may fracture brittlely under certain circumstances and requires continuous monitoring, corresponding to the yellow alert.

[0130] Brittle fracture index (C) <20: belongs to the low-risk range, the material performance is stable, corresponding to the green alert.

[0131] Therefore, the higher the brittle fracture index (C), the higher the brittle fracture risk level of the material.

[0132] The specific values ​​of β, γ, a, b and σ in the formula are generally determined by technical personnel in this field according to actual conditions. The essence of this formula is a comprehensive analysis based on weighted summation. Technical personnel in this field collect multiple groups of sample data, and set corresponding preset proportional coefficients for each group of sample data. The preset proportional coefficients and the collected sample data are substituted into the formula. Through repeated experiments and parameter adjustments, the accuracy of the model output and the rationality of the results are observed, and these factor coefficients are gradually adjusted. The performance and effect of the model under different parameter settings are compared to find the optimal coefficient combination. The calculated factor coefficients are screened and averaged to obtain the values ​​of β, γ, a, b and σ.

[0133] In addition, the size of the preset factor coefficient is to quantify each parameter to obtain a specific value. In order to facilitate subsequent comparison, the size of the coefficient depends on the amount of sample data and the preset proportional coefficient initially set by technical personnel in this field for each set of sample data. It is not unique as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0134] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0135] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0136] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0137] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A nitrogen low temperature brittle fracture monitoring and protection system based on the GMS system, characterized in that: Specifically include: The data acquisition module is used to install a temperature sensor and a strain sensor on the material, collect the temperature parameters of the material through the temperature sensor of the GMS system, and collect the stress value parameters and strain value parameters of N samples at the temperature through the strain sensor of the GMS system; in, The stress value parameters of the material include load factor, section size factor and time factor; The material strain parameters include thermal shrinkage coefficient and material embrittlement coefficient; A data processing module is used to process the load coefficient, cross-sectional dimension coefficient and time coefficient of the collected N samples to generate the stress value of the material, and to process the thermal shrinkage coefficient and material embrittlement coefficient of the collected N samples to generate the strain value of the material; The data analysis module is used to construct the stress-strain curve of the material based on the stress value and strain of the material, obtain the elastic modulus, yield strength, ultimate strength and elongation at break of the material through a mathematical model, process the yield strength and ultimate strength data to generate the strength coefficient of the material, process the elastic modulus and elongation at break data to generate the deformation coefficient of the material, process the strength coefficient and deformation coefficient of the material data and conduct correlation analysis to generate the brittle fracture index of the material; The early warning module is used to judge the brittle fracture risk level of the material according to the interval range of the brittle fracture index of the material, and issue different early warning signals according to the brittle fracture risk level of the material; The load coefficient, cross-sectional dimension coefficient and time coefficient of the collected N samples are processed to generate the stress value of the material according to the following formula: Among them, σ(t) is the stress value at time t, α i is the load factor of the ith sample, L i is the cross-sectional size factor of the i-th sample, t i is the time coefficient of the i-th sample, β is the time decay coefficient, γ is the exponential weight of the cross-sectional size coefficient, T is the normalized time constant, Z is the normalization coefficient, N is the number of samples; The thermal shrinkage coefficient and material embrittlement coefficient of the collected N samples are processed to generate the material strain, based on the following formula: ò is the strain of the material, x is the thermal shrinkage coefficient of the sample, y is the material embrittlement coefficient of the sample, f(z) is the normalized function, and g(t) is the higher integral function; The specific f(z) and g(t) are further defined according to the following auxiliary formulas: Normalization function Where z is the set of thermal shrinkage coefficients of all samples, and the advanced integral function t is equivalent to the material embrittlement coefficient.

2. The nitrogen low-temperature brittle fracture monitoring and protection system based on the GMS system according to claim 1 is characterized in that: Based on the stress value and strain of the material, the stress-strain curve of the material is constructed. Through the mathematical model, the elastic modulus, yield strength, ultimate strength and elongation at break of the material are obtained as follows: According to the stress value and strain amount, the stress-strain curve of the material is constructed, with the strain amount on the horizontal axis and the stress value on the vertical axis. Through the stress-strain curve, data points of stress value and strain amount are selected from the curve, and then the linear region is determined. The linear region is the initial part of the curve, avoiding exceeding the yield point. Linear regression analysis is used to fit this part of the data, and the slope is the elastic modulus of the material; Yield strength refers to the minimum stress at which a material begins to undergo plastic deformation. It is determined using the 0.2% offset method, which is to find the stress value corresponding to the straight line on the stress-strain curve that is parallel to the initial elastic line and passes through the 0.2% permanent strain point. This stress value is the yield strength of the material. Ultimate strength refers to the maximum stress that a material can withstand before breaking. The peak value of the stress-strain curve is directly observed from the experimental data, and this peak value is the ultimate strength of the material. Elongation at break refers to the percentage of elongation of a material relative to its initial length when it breaks. The strain value corresponding to the material's breaking point is found on the stress-strain curve. This strain value is the elongation at break.

3. The nitrogen low-temperature brittle fracture monitoring and protection system based on the GMS system according to claim 1 is characterized in that: The yield strength and ultimate strength are processed to generate the strength coefficient of the material, based on the following formula: Q is the strength coefficient of the material, σ u is the ultimate strength of the material, σ y is the yield strength of the material, log(σ u +1) is the logarithmic transformation of the ultimate strength, N(x, μ, σ) is the normalization function, where x is σ u , μ and σ are the mean and standard deviation of the data respectively.

4. The nitrogen low-temperature brittle fracture monitoring and protection system based on the GMS system according to claim 1 is characterized in that: The elastic modulus and elongation at break are processed to generate the deformation coefficient of the material, based on the following formula: δ is the deformation coefficient of the material, E is the elastic modulus, μ E and σ E are the mean and standard deviation of elastic modulus, ò is elongation at break, μ 蝌 and σ are the mean and standard deviation of the elongation at break, respectively, and k and λ are adjustment parameters, where k adjusts the sensitivity of the exponential function and λ adjusts the relative influence of the elongation at break on the deformation coefficient.

5. The nitrogen low temperature brittle fracture monitoring and protection system based on the GMS system according to claim 1 is characterized in that: The strength coefficient and deformation coefficient of the material are processed and correlated to generate the brittle fracture index of the material. The formula is as follows: C is the brittle fracture index of the material, a and b are adjustment coefficients, which affect the weight of the strength coefficient and the offset of the logarithmic function respectively, and σ is the exponential adjustment coefficient, 1+e -σ·(Q-δ) is a normalized term, Q is the strength coefficient of the material, and δ is the deformation coefficient of the material.

6. The nitrogen low-temperature brittle fracture monitoring and protection system based on the GMS system according to claim 1 is characterized in that: The process of judging the brittle fracture risk level of the material according to the range of the brittle fracture index of the material and issuing different warning signals according to the brittle fracture risk level of the material is as follows: Brittle fracture index>30: high risk range, corresponding to red alert; 20≤brittle fracture index≤30: medium risk range, corresponding to yellow alert; Brittle fracture index <20: belongs to the low-risk range, corresponding to the green alert.

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