Method and device for estimating buckling failure time of vertical ignition storage tank, electronic equipment and storage medium

Abstract

The application discloses a kind of vertical ignition storage tank buckling failure time estimation method and device, the method includes the following steps: A, structural modeling is carried out under specific working condition and simulation storage tank fire, and the characteristic parameters of storage tank combustion medium are set to obtain storage tank thermal radiation intensity and peripheral temperature distribution data;B, the peripheral temperature distribution data of storage tank thermal radiation intensity is used as boundary condition, and tank wall temperature distribution data is obtained;C, according to tank wall temperature distribution data and tank wall material attribute parameter, the thermal buckling analysis calculation of storage tank is carried out, and then the critical temperature and time of buckling failure are judged;D, the time data of buckling failure and the different liquid levels of storage tank combustion medium, storage tank size data are used to construct buckling failure time estimation model, and the time of buckling failure of different types of storage tank when the storage tank combustion medium occurs fire is estimated using the estimation model.The application can quickly calculate buckling time, so as to provide important data support for on-site emergency rescue.

Description

Technical Field

[0001] This invention relates to the field of petrochemical safety engineering technology, and in particular to a method, apparatus, electronic device, and storage medium for estimating the buckling failure time of a vertical fire-prone storage tank. Background Technology

[0002] Currently, research on fires involving atmospheric pressure vertical storage tanks mainly focuses on the impact of the fire on the surrounding environment, including parameters such as heat radiation intensity, with a lack of research on the structural safety of the burning tank. Existing technologies for assessing tank failure on fire mostly rely on experimental judgment to determine the failure mode. However, due to cost constraints, it is not feasible to conduct multiple large-scale experiments to build a database of tank failure times and a rapid estimation model.

[0003] For example, Chinese patent application CN106802336A discloses a system and method for studying the failure modes of a burning storage tank. This system includes an experimental apparatus for testing the stress, strain, and tank wall temperature of the burning storage tank, and an apparatus for determining the thermal fatigue characteristics, creep characteristics, and yield strength of components using a thermal simulation testing machine to determine the failure mode of the burning storage tank. By measuring the strain and temperature of the burning storage tank, the strain and temperature of the entire tank are obtained through modeling. The yield strength, elastic modulus, and overall deformation over time curves of the specimen are measured using a thermal simulation testing machine. The failure mode of the storage tank is determined using corresponding criteria and failure mode characteristics. However, this method cannot determine the failure time of the burning storage tank. Furthermore, due to the enormous cost of large-scale experiments, multiple large-scale experiments cannot be conducted, and a rapid estimation model for the failure time of the burning storage tank cannot be obtained.

[0004] At the scene of a storage tank fire, a large amount of water resources are required. Sometimes, due to insufficient water supply, water may be used for firefighting, which may slow down or even stop the cooling of the burning storage tank. Therefore, it is necessary to study the buckling failure time of the burning storage tank in the absence of water spray cooling, so as to provide support for on-site emergency rescue.

[0005] Therefore, there is an urgent need for a method to estimate the buckling failure time of a vertical burning storage tank. This method would calculate the buckling failure time of the burning storage tank under specific working conditions and construct a buckling failure time estimation model to quickly calculate the buckling time, thereby providing data support for on-site emergency rescue.

[0006] The information disclosed in this background section is intended only to enhance the understanding of the overall background of the invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0007] The purpose of this invention is to provide a method for estimating the buckling failure time of a vertical burning storage tank. By calculating the buckling failure time of the burning storage tank under specific working conditions and constructing a buckling failure time estimation model, the buckling time can be calculated quickly, so as to provide important data support for on-site emergency rescue.

[0008] To achieve the above objectives, according to a first aspect of the present invention, the present invention provides a method for estimating the buckling failure time of a vertically burning storage tank, comprising the following steps: A. Performing structural modeling and simulating a storage tank fire under specific operating conditions, and obtaining data on the thermal radiation intensity and surrounding temperature distribution of the storage tank by setting characteristic parameters of the storage tank's combustion medium; B. Using the storage tank's thermal radiation intensity and surrounding temperature distribution data as boundary conditions, obtaining data on the tank wall temperature distribution; C. Performing thermal buckling analysis calculations of the storage tank based on the tank wall temperature distribution data and tank wall material property parameters, thereby determining the critical temperature and time for buckling failure; D. Constructing a buckling failure time estimation model using the buckling failure time data and different liquid levels and tank size data of the storage tank's combustion medium, and using the estimation model to estimate the time for buckling failure of different types of storage tanks when a fire occurs.

[0009] Furthermore, in the above technical solution, the thermal buckling analysis calculation in step C can specifically include the following steps: C1, import the tank wall temperature distribution data into the tank structure model and divide it into meshes, and set the material property parameters of the tank wall material at different temperatures; C2, calculate the buckling modes of the tank and introduce initial defects using the buckling modes, and use the arc length method to calculate the critical temperature and time of buckling failure of the tank; C3, using the critical temperature as the standard, use the static stability method and the explicit dynamic method to calculate the critical temperature of buckling failure and correct the dissipation energy coefficient of the static stability method and the mass scaling factor of the explicit dynamic method; C4, compare the calculation time of the three algorithms—arc length method, static stability method, and explicit dynamic method—and use the algorithm with the shortest calculation time to calculate the buckling failure time for different liquid levels and different tank sizes.

[0010] Furthermore, in the above technical solution, the buckling failure time estimation model in step D is a two-variable linear function fitted with buckling failure time as the dependent variable and tank level and tank size as independent variables, as detailed below:

[0011] t = ah + bV + c Formula (1);

[0012] Where t is the buckling failure time; h is the tank liquid level (%); and V is the tank size.

[0013] Furthermore, in the above technical solution, the specific operating conditions in step A may specifically include the type of combustion medium in the storage tank, the liquid level in the storage tank, and the size of the storage tank.

[0014] Furthermore, in the above technical solution, the characteristic parameters of the combustion medium in the storage tank in step A may include the heat of combustion, the CO content in the exhaust gas, the smoke content, and the heat release rate; the heat radiation intensity of the storage tank and the surrounding temperature distribution data can be obtained through virtual sensors set at corresponding locations.

[0015] Furthermore, in the above technical solution, the structural modeling tool in step A can be fire dynamics simulation software.

[0016] Furthermore, in the above technical solution, the acquisition of tank wall temperature distribution data in step B is also related to two factors: air convection inside and outside the tank wall and thermal radiation emitted from inside and outside the tank wall. Air convection is characterized by the heat transfer coefficient, and thermal radiation is characterized by the emissivity.

[0017] Furthermore, in the above technical solution, the tank wall temperature distribution data in step B can be simulated using solid heat transfer analysis software.

[0018] Furthermore, in the above technical solution, the thermal buckling analysis in step C can be performed using structural simulation software.

[0019] Furthermore, in the above technical solution, the material properties of the tank wall in step C may include Poisson's ratio, Young's modulus, coefficient of thermal expansion, specific heat capacity, conductivity, and yield strength.

[0020] According to a second aspect of the present invention, the present invention provides a device for estimating the buckling failure time of a vertical burning storage tank, comprising: a thermal radiation and ambient temperature acquisition module, which is used to perform structural modeling and simulate a storage tank fire under specific working conditions, and obtain the thermal radiation intensity and ambient temperature distribution data of the storage tank by setting characteristic parameters of the storage tank combustion medium; a tank wall temperature distribution data acquisition module, which is used to obtain the tank wall temperature distribution data by using the storage tank thermal radiation intensity and ambient temperature distribution data as boundary conditions; a thermal buckling analysis calculation module, which is used to perform thermal buckling analysis calculation of the storage tank based on the tank wall temperature distribution data and tank wall material property parameters, and then determine the critical temperature and time of buckling failure; and an estimation model construction module, which is used to construct a buckling failure time estimation model by using the buckling failure time data and different liquid levels and storage tank size data of the storage tank combustion medium, and use the estimation model to estimate the time of buckling failure of different types of storage tanks when a fire occurs.

[0021] Furthermore, in the above technical solution, the thermal buckling analysis calculation module may specifically include: a parameter setting submodule, which is used to import the tank wall temperature distribution data into the tank structure model and divide it into meshes, and set the material property parameters of the tank wall material at different temperatures; an arc length method calculation submodule, which is used to calculate the buckling modes of the tank and introduce initial defects using the buckling modes, and calculate the critical temperature and time of buckling failure of the tank using the arc length method; a damping coefficient correction submodule, which is used to calculate the critical temperature of buckling failure using the static stability method and the explicit dynamic method with the critical temperature as the standard, and correct the dissipation energy coefficient of the static stability method and the mass scaling factor of the explicit dynamic method; and an algorithm selection submodule, which is used to compare the calculation time of the three algorithms, namely the arc length method, the static stability method and the explicit dynamic method, and use the algorithm with the shortest calculation time to calculate the buckling failure time for different liquid levels and different tank sizes.

[0022] According to a third aspect of the present invention, an electronic device for estimating the buckling failure time of a vertically burning storage tank is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to cause the at least one processor to perform the aforementioned method for estimating the buckling failure time of a vertically burning storage tank.

[0023] According to a fourth aspect of the present invention, a non-transitory computer-readable storage medium is provided, the non-transitory computer-readable storage medium storing computer-executable instructions for causing the computer to perform the aforementioned method for estimating the buckling failure time of a vertically burning storage tank.

[0024] Compared with the prior art, the present invention has one or more of the following beneficial effects:

[0025] 1) This invention addresses the shortcomings in current research on buckling failure of vertical burning storage tanks by studying the critical temperature of buckling failure of burning storage tanks using the arc length method. Using this as a criterion, the dissipation energy coefficient in the static stability method and the mass scaling factor in the explicit dynamic algorithm are determined through continuous debugging. The fastest algorithm among the three algorithms is then identified. This algorithm is used to calculate the buckling failure time of storage tanks of the same size under different media or liquid levels. While ensuring the accuracy of the calculation results, the calculation efficiency can be effectively improved.

[0026] 2) This invention can repeat the above process for storage tanks of different sizes to obtain the buckling failure time of the burning storage tank, and the same method can be used to perform thermal buckling analysis on different types of storage tanks;

[0027] 3) This invention studies the buckling failure time of a burning storage tank under various working conditions through simulation. By using the calculated buckling time data, tank size, and liquid level data, a rapid estimation model for the buckling failure time of the burning storage tank is obtained. This model can quickly calculate the buckling failure time to provide data support for on-site emergency rescue.

[0028] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it according to the contents of the specification, and to make the above and other objects, technical features and advantages of the present invention easier to understand, one or more preferred embodiments are listed below and described in detail with reference to the accompanying drawings. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the method for estimating the buckling failure time of a vertical burning storage tank according to Embodiment 1 of the present invention.

[0030] Figure 2 This is a schematic diagram of the thermal buckling analysis calculation process in the method for estimating the buckling failure time of a vertical burning storage tank according to Embodiment 1 of the present invention.

[0031] Figure 3 This is a schematic diagram of the buckling failure time algorithm in Embodiment 1 of the present invention.

[0032] Figure 4 This is a schematic diagram of the structure of the vertical burning storage tank buckling failure time estimation device according to Embodiment 3 of the present invention.

[0033] Figure 5 This is a schematic diagram of the structure of the electronic device for estimating the buckling failure time of a vertical burning storage tank, which is embodiment 4 of the present invention. Detailed Implementation

[0034] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings, but it should be understood that the scope of protection of the present invention is not limited to the specific embodiments.

[0035] Unless otherwise expressly stated, throughout the specification and claims, the term "comprising" or its variations such as "including" or "comprises" shall be understood to include the stated elements or components without excluding other elements or other components.

[0036] In this document, for ease of description, spatial relative terms such as “below,” “under,” “down,” “above,” “above,” “up,” etc., are used to describe the relationship of one element or feature to another element or feature in the accompanying drawings. It should be understood that spatial relative terms are intended to encompass different orientations of an object in use or operation, in addition to those depicted in the figures. For example, if an object in the figure is flipped, an element described as “below” or “under” another element or feature would be oriented “above” that element or feature. Thus, the exemplary term “below” can encompass both the downward and upward orientations. An object may also have other orientations (rotated 90 degrees or other orientations), and the spatial relative terms used herein should be interpreted accordingly.

[0037] In this document, the terms "first," "second," etc., are used to distinguish two different elements or parts, and are not used to define specific positions or relative relationships. In other words, in some embodiments, the terms "first," "second," etc., can also be used interchangeably.

[0038] The methods, systems, electronic devices, and storage media of the present invention are described in more detail below by way of specific embodiments. It should be understood that the embodiments are merely exemplary and the present invention is not limited thereto.

[0039] This invention uses simulation to study the burning of storage tanks during a fire. By calculating the buckling failure time of the burning storage tank under specific working conditions and constructing a buckling failure time estimation model, the buckling failure time can be calculated quickly to provide data support for on-site emergency rescue.

[0040] Example 1

[0041] like Figures 1 to 3 As shown in the figure, this embodiment provides a method for estimating the failure time of a vertical fire-prone storage tank. The method includes the following steps:

[0042] Step S101: Under specific working conditions, perform structural modeling and simulate tank fire. Obtain the thermal radiation intensity (i.e., the thermal radiation received by the inner surface of the tank wall) and surrounding temperature distribution data by setting the characteristic parameters of the tank combustion medium.

[0043] The specific operating conditions in this step include the type of combustion medium in the storage tank, the liquid level in the tank, and the tank dimensions. For example, a 5000 cubic meter diesel storage tank with a liquid level of 40% can be referred to as 5000chai40. This embodiment analyzes the tank structure from the perspective of tank structure. First, it is necessary to simulate a storage tank fire to obtain the heat radiation intensity received by the storage tank and the temperature distribution around the tank. Specifically, in the event of a full-area fire, the storage tank releases the most heat and is most prone to failure (mainly involving buckling failure). Therefore, this invention uses the operating condition of a full-area fire to study the buckling failure of the burning storage tank. The diameter and height of the 5000 cubic meter storage tank, the physical properties and combustion characteristics of diesel fuel, etc., are obtained from existing data. Using fire dynamics simulation software such as Fluent or FDS, a physical model is constructed, a mesh is generated, boundary conditions and initial conditions are set, and physical property parameters and combustion reactions are set to simulate a full-area fire under the 5000chai40 operating condition. The heat radiation distribution received by the inner surface of the tank wall after combustion has stabilized and the air temperature distribution near the inner surface of the tank wall are obtained. Furthermore, the characteristic parameters of the combustion medium in the storage tank include, but are not limited to, combustion heat, CO content in the exhaust gas, smoke content, and heat release rate. Data on the thermal radiation intensity and surrounding temperature distribution of the storage tank can be obtained through virtual sensors placed at appropriate locations. Specifically, multiple virtual thermal radiation sensors are uniformly arranged on the inner wall of the tank, and multiple temperature sensors are uniformly arranged near the inner wall. After the simulation, the thermal radiation and temperature data from each sensor are obtained. The average value of the stable thermal radiation and temperature data is taken as the thermal radiation and temperature data for that point, thus obtaining the thermal radiation distribution (i.e., the received thermal radiation) on the inner surface of the tank and the temperature distribution data on the inner side.

[0044] Step S102: Using the thermal radiation intensity and surrounding temperature distribution data of the storage tank obtained in step S101 as boundary conditions, obtain the tank wall temperature distribution data.

[0045] Specifically, this step uses solid heat transfer analysis software (such as Abaqus) to establish a 5000 cubic meter storage tank model. The heat radiation from the inner surface of the tank wall and the air temperature near the inner surface of the tank wall obtained from the tank fire simulation in step S101 are applied as boundary conditions to the tank surface. In addition, the acquisition of tank wall temperature distribution data is also related to two factors: air convection on the inner and outer sides of the tank wall and heat radiation emitted from the inner and outer sides of the tank wall. Air convection can be characterized by the heat transfer coefficient, and heat radiation can be characterized by the emissivity. Therefore, in order to make the simulation results closer to the real state, preferably, but not restrictively, the heat radiation radiated outward from the inner and outer sides of the tank wall and the convection between the inner and outer sides of the tank wall and the air on both sides can also be applied as boundary conditions to simulate the temperature distribution of the burning storage tank wall.

[0046] The heat radiation radiated outward from both the inner and outer sides of the tank wall can be calculated by setting the interaction type to radiation, the emissivity to 1, and the air temperatures on both sides of the tank wall. The temperature on the inner side of the tank wall can be the temperature simulated in the aforementioned fire dynamics simulation software, while the temperature on the outer side can be a normal temperature of 20°C. Convection between the inner and outer sides of the tank wall and the air can be calculated by setting the interaction type to film heat transfer, the film heat transfer coefficient to 25, and the air temperatures on both sides of the tank wall. The temperature on the inner side of the tank wall can be the temperature simulated in the aforementioned fire dynamics simulation software, while the temperature on the outer side can be a normal temperature of 20°C.

[0047] Step S103: Perform thermal buckling analysis calculations on the tank based on the tank wall temperature distribution data and tank wall material property parameters, and then determine the critical temperature and time for buckling failure (the critical temperature and time are in one-to-one correspondence, and the buckling failure time can be obtained by calculating the critical temperature).

[0048] Specifically, in this step, structural simulation software (such as Abaqus) is used to establish a 5000 cubic meter storage tank model for buckling instability analysis. The tank wall temperature obtained in step S102 is imported into the physical model, mesh is generated, and tank wall material property parameters (including but not limited to Poisson's ratio, Young's modulus, coefficient of thermal expansion, specific heat capacity, conductivity, and yield strength) are set. Boundary conditions and initial conditions are set, and thermal buckling failure analysis of the burning storage tank structure under fire is performed. The buckling instability process and critical temperature value of the burning storage tank are obtained, and the time at this time is determined, that is, the critical temperature and buckling failure time t of buckling instability are obtained.

[0049] Furthermore, the thermal buckling analysis in this step specifically includes the following sub-steps:

[0050] Step S1031: Import the tank wall temperature distribution data into the tank structure model and divide it into a mesh, and set the material property parameters of the tank wall material at different temperatures.

[0051] Step S1032: Calculate the buckling mode of the storage tank and introduce initial defects using the buckling mode. Use the arc length method to calculate the critical temperature and time for buckling failure of the storage tank.

[0052] First of all, it should be noted that the buckling failure of a burning storage tank can be calculated using the arc length method, the static stability method, or the explicit dynamic algorithm. Among them, the arc-length method can accurately obtain the critical buckling temperature and the initial post-buckling path, and can capture the critical point, resulting in the most accurate calculation results. However, the arc-length method has a large number of iterations and a long calculation time, especially since the buckling of the burning storage tank involves material and geometric nonlinearities, which consumes huge computational resources. The static stability method can quickly calculate the buckling of the burning storage tank. It avoids non-convergence caused by zero elements or negative eigenvalues ​​in the stiffness matrix by adding artificial damping elements to each node of the element. However, the selection of damping (i.e., dissipation energy coefficient) is crucial. If the dissipation energy coefficient is too large, the solution will be incorrect. If the dissipation energy coefficient is too small, the convergence problem cannot be solved. The explicit dynamic algorithm can analyze the buckling process of the burning storage tank as a quasi-static process. Through the mass scaling method, the calculation time can be greatly reduced. The larger the damping (i.e., mass scaling factor) of this method, the shorter the solution time, but the lower the solution accuracy. Therefore, it is necessary to select an appropriate mass scaling factor.

[0053] The inventors considered that the arc-length method is relatively slow, requiring a long time and low computational efficiency when analyzing buckling under various working conditions and establishing buckling time calculation models. Therefore, this invention first uses the arc-length method to calculate thermal buckling, and then uses this as a benchmark to correct the damping coefficients (i.e., the dissipation energy coefficient of the static stability method and the mass scaling factor of the explicit dynamic method) of two other algorithms (i.e., the static stability method and the explicit dynamic method). Then, depending on the specific situation, the other two algorithms can be used to calculate thermal buckling (the damping coefficients need to be readjusted for different tank sizes). When calculating the thermal buckling of a burning tank using the arc-length method, structural defects need to be considered. These defects can be achieved by actually measuring the geometric and load defects of the tank, or by performing tank buckling modal analysis, using the first few buckling modes to introduce initial defects. Considering the low operability of actually measuring the defects of each tank, this invention uses the method of introducing defects to simulate structural defects.

[0054] Step S1033: Using the critical temperature calculated by the arc-length method in step S1032 as the standard, the critical temperature for buckling failure is calculated using both the static stabilization method and the explicit kinetic method, and the dissipation energy coefficient of the static stabilization method and the mass scaling factor of the explicit kinetic method are corrected. The correction of the coefficients refers to continuously adjusting the dissipation energy coefficient of the static stabilization method and the mass scaling factor of the explicit kinetic algorithm until the buckling critical temperature calculated using these two methods is consistent with the critical temperature calculated using the arc-length method.

[0055] Step S1034 compares the calculation times of the three algorithms—arc length method, static stability method, and explicit dynamic method—and selects the algorithm with the shortest calculation time to calculate the buckling failure time for different liquid levels and tank sizes. Specifically, using this shortest-time algorithm, for various media such as gasoline, diesel, and crude oil, for various tank sizes such as 1000 cubic meters, 3000 cubic meters, 5000 cubic meters, 10000 cubic meters, and 50000 cubic meters, and for various liquid levels such as 40%, 60%, and 80%, the buckling critical temperature and time of the ignition tank under various operating conditions are calculated.

[0056] Step S104: Construct a buckling failure time estimation model using buckling failure time data, different liquid levels of the burning medium in the storage tank, and storage tank size data. Then, use the estimation model to estimate the time when the burning medium in the storage tank will buckle and fail in different types of storage tanks during a fire.

[0057] Specifically, the buckling failure time estimation model can be constructed using the aforementioned database of buckling failure time t and combustion medium, liquid level, and size. For a specific medium, a relationship between buckling failure time and tank size and liquid level can be established. For example, taking diesel fuel as an example, using data on buckling failure time t and various diesel fuel tank sizes and liquid levels, a linear function with buckling failure time as the dependent variable and size and liquid level as independent variables can be constructed. This function can be fitted using data analysis software such as Excel, Origin, or SPSS. The linear function with buckling failure time t as the dependent variable and tank level and tank size as independent variables is as follows:

[0058] t = ah + bV + c Formula (1);

[0059] Where t is the buckling failure time; h is the tank liquid level (%); and V is the tank size.

[0060] This embodiment addresses the shortcomings in current research on the buckling failure of vertical burning storage tanks. It studies the critical temperature for buckling failure of burning storage tanks using the arc-length method and uses this as a criterion. Through continuous adjustments, it determines the dissipation energy coefficient in the static stability method and the mass scaling factor in the explicit dynamic algorithm, identifying the fastest of the three algorithms. This algorithm is then used to calculate the buckling failure time of storage tanks of the same size under different media or liquid levels. The above process can be repeated for storage tanks of different sizes to obtain the buckling failure time of burning storage tanks. This embodiment also studies the buckling failure time of burning storage tanks under various operating conditions through simulation. Using the above data, a rapid estimation model for the buckling failure time of burning storage tanks is fitted, enabling rapid calculation of buckling failure time and providing data support for on-site emergency rescue.

[0061] Example 2

[0062] This embodiment is a more specific example based on Embodiment 1. The following description, based on the implementation of each step of Embodiment 1 and in conjunction with the specific indicators and data used, is provided (the same content as in Embodiment 1 will not be repeated):

[0063] Following step S101 of Example 1, in the fire simulation stage of the burning storage tank, using FDS software as an example, based on existing data, the following is known: the diameter of the 5000 cubic meter burning storage tank is 21m, the height is 16.5m, and the wall thickness is uniformly taken as 0.01m for modeling; to ensure calculation quality, the calculation domain is taken as 60*60*80m; the calculation grid of the storage tank area is set to 0.5*0.5*0.5; the characteristic parameters of diesel are as follows: heat of combustion is 4.41*10 4 kJ / kg, CO content in exhaust gas is 0.01, smoke content is 0.034; heat release rate is 1800 kW / m³. 2 Set the simulation time to 360 seconds.

[0064] Combining step S103 of Example 1, the buckling mode is introduced as an initial defect in Abaqus (using the first four buckling modes, scaling factor t / 10 = 0.001, where t is the tank wall thickness). The critical buckling temperature of the burning storage tank is calculated using the arc length method. Using the static stability method, different dissipation energy coefficients are selected to calculate the corresponding critical buckling temperature. A functional relationship between the critical buckling temperature and the dissipation energy coefficient is constructed using the data of the corresponding dissipation energy coefficient and the critical buckling temperature. The dissipation energy coefficient is calculated (i.e., continuously adjusted) to be 0.0002 using the critical buckling temperature obtained by the arc length method. Using the explicit dynamic method, different mass scaling factors are selected to calculate the corresponding critical buckling temperature. A functional relationship between the critical buckling temperature and the dissipation energy coefficient is constructed using the data of the corresponding mass scaling factors and the critical buckling temperature. The mass scaling factor was calculated using the arc-length method to obtain the critical buckling temperature, and the mass scaling factor was continuously adjusted to 121. Using the dissipation energy coefficient of 0.0002 and the mass scaling factor of 121, the critical buckling temperature was calculated using both the static stability method and the explicit dynamic method. The calculation time was recorded and compared with that of the arc-length method to find the method with the shortest calculation time. Using the method with the shortest calculation time, the critical buckling temperature and time of the ignition tank were calculated under various operating conditions for a 5000 cubic meter storage tank, with multiple media such as gasoline, diesel, and crude oil, and multiple liquid levels such as 40%, 60%, and 80%. Using the same method, the critical buckling temperature and time of the ignition tank were calculated for other sizes of storage tanks under different media and liquid levels.

[0065] Referring to step S104 of Example 1, taking diesel as an example, the buckling failure times of a 1000 cubic meter storage tank at 40%, 60%, and 80% liquid levels are t1, t2, and t3, respectively; the buckling failure times of a 3000 cubic meter storage tank at 40%, 60%, and 80% liquid levels are t4, t5, and t6, respectively; and the buckling failure times of a 5000 cubic meter storage tank at 40%, 60%, and 80% liquid levels are t1, t2, and t3, respectively. 7, t8, t9; the buckling failure times of a 10,000 cubic meter storage tank under 40%, 60%, and 80% liquid level conditions are t10, t11, t12, respectively; the buckling failure times of a 50,000 cubic meter storage tank under 40%, 60%, and 80% liquid level conditions are t13, t14, t15, respectively; the buckling failure time t is established as a function of the storage tank size V and liquid level h using Origin, see the aforementioned formula (1).

[0066] For diesel fuel, by fitting formula (1) with the aforementioned sets of known independent and dependent variable data, we obtain a = -14, b = -0.001, c = 422, i.e., t = -14h - 0.0001V + 422. This rapid estimation model can quickly calculate the time when buckling failure may occur in various types of diesel fuel storage tanks without cooling spray protection during a fire.

[0067] Similarly, for gasoline, crude oil, and other types of oil products, a similar principle can be used to construct a rapid estimation model for the buckling failure time, size, and liquid level of a burning storage tank. For gasoline, the estimation model of this invention is t = -16h - 0.001V + 398; for crude oil, the estimation model of this invention is t = -13h - 0.00085V + 432.

[0068] Example 3

[0069] like Figure 4As shown, this embodiment provides a device for estimating the buckling failure time of a vertical burning storage tank, including a thermal radiation and ambient temperature acquisition module 201, a tank wall temperature distribution data acquisition module 202, a thermal buckling analysis and calculation module 203, and an estimation model construction module 204. The thermal radiation and ambient temperature acquisition module 201 is used to model the structure and simulate a storage tank fire under specific operating conditions, obtaining the tank's thermal radiation intensity and ambient temperature distribution data by setting characteristic parameters of the tank's combustion medium. The tank wall temperature distribution data acquisition module 202 is used to obtain the tank wall temperature distribution data by using the tank's thermal radiation intensity and ambient temperature distribution data as boundary conditions. The thermal buckling analysis and calculation module 203 is used to perform thermal buckling analysis and calculation of the storage tank based on the tank wall temperature distribution data and tank wall material property parameters, thereby determining the critical temperature and time for buckling failure. The estimation model construction module 204 is used to construct a buckling failure time estimation model using the buckling failure time data and different liquid levels and tank size data of the tank's combustion medium, and uses the estimation model to estimate the time for buckling failure of different types of storage tanks during a fire.

[0070] Preferably, but not limitingly, the thermal buckling analysis calculation module 203 may specifically include a parameter setting submodule 2031, an arc-length method calculation submodule 2032, a damping coefficient correction submodule 2033, and an algorithm selection submodule 2034. Specifically, the parameter setting submodule 2031 is used to import tank wall temperature distribution data into the tank structure model and mesh it, setting the material property parameters of the tank wall material at different temperatures; the arc-length method calculation submodule 2032 is used to calculate the buckling modes of the tank and introduce initial defects using the buckling modes, and calculate the critical temperature and time for buckling failure of the tank using the arc-length method; the damping coefficient correction submodule 2033 is used to calculate the critical temperature for buckling failure using the static stability method and the explicit dynamic method, based on the critical temperature as a standard, and correct the dissipation energy coefficient of the static stability method and the mass scaling factor of the explicit dynamic method; the algorithm selection submodule 2034 is used to compare the calculation times of the three algorithms—arc-length method, static stability method, and explicit dynamic method—and use the algorithm with the shortest calculation time to calculate the buckling failure time for different liquid levels and different tank sizes.

[0071] The apparatus in this embodiment corresponds to the methods in Embodiments 1 and 2, and can achieve the same technical effect.

[0072] Example 4

[0073] Figure 5 This is a schematic diagram of the hardware structure of the electronic device for estimating the buckling failure time of a vertically burning storage tank according to this embodiment. The device (e.g., a terminal, server, etc.) includes one or more processors 610 and a memory 620. Taking one processor 610 as an example, the device may further include an input device 630 and an output device 640.

[0074] The processor 610, memory 620, input device 630 and output device 640 can be connected by a bus or other means.

[0075] The memory 620, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules. The processor 610 executes various functional applications and data processing of the electronic device by running the non-transitory software programs, instructions, and modules stored in the memory 620, thereby implementing the processing method of the above-described method embodiments.

[0076] The memory 620 may include a program storage area and a data storage area, wherein the program storage area may store the operating system and applications required for at least one function; the data storage area may store data, etc. Furthermore, the memory 620 may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 620 may optionally include memory remotely located relative to the processor 610, and these remote memories may be connected to the processing device via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0077] Input device 630 can receive input digital or character information and generate signal input. Output device 640 may include display devices such as a display screen.

[0078] The one or more modules are stored in the memory 620. When executed by the one or more processors 610, the following steps are performed: A. Structural modeling and simulation of a tank fire are performed under specific operating conditions. The thermal radiation intensity and surrounding temperature distribution data of the tank are obtained by setting the characteristic parameters of the tank combustion medium; B. The thermal radiation intensity and surrounding temperature distribution data of the tank are used as boundary conditions to obtain the tank wall temperature distribution data; C. The thermal buckling analysis of the tank is performed based on the tank wall temperature distribution data and the tank wall material property parameters to determine the critical temperature and time for buckling failure; D. A buckling failure time estimation model is constructed using the buckling failure time data and the different liquid levels and tank size data of the tank combustion medium. The estimation model is then used to estimate the time when different types of tanks experience buckling failure during a fire.

[0079] The aforementioned electronic device can execute the methods provided in the embodiments of the present invention, and possesses the corresponding functional modules and beneficial effects for executing the methods. Technical details not described in detail in this embodiment can be found in the methods provided in other embodiments of the present invention.

[0080] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0081] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented using software plus a general-purpose hardware platform, or of course, using hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0082] Example 5

[0083] This embodiment provides a non-transitory computer-readable storage medium storing computer-executable instructions. These instructions are used to cause a computer to execute the following method for estimating the buckling failure time of a vertically burning storage tank: A. Performing structural modeling and simulating a storage tank fire under specific operating conditions, obtaining data on the tank's thermal radiation intensity and surrounding temperature distribution by setting characteristic parameters of the tank's combustion medium; B. Using the tank's thermal radiation intensity and surrounding temperature distribution data as boundary conditions, obtaining tank wall temperature distribution data; C. Performing thermal buckling analysis calculations on the tank based on the tank wall temperature distribution data and tank wall material property parameters, thereby determining the critical temperature and time for buckling failure; D. Constructing a buckling failure time estimation model using buckling failure time data, different liquid levels of the tank's combustion medium, and tank size data, and using the estimation model to estimate the time for buckling failure of different types of storage tanks during a fire.

[0084] The foregoing description of specific exemplary embodiments of the present invention is for illustrative and explanatory purposes. These descriptions are not intended to limit the invention to the precise forms disclosed, and it will be apparent that many changes and variations can be made in accordance with the foregoing teachings. The exemplary embodiments were chosen and described in order to explain the specific principles of the invention and its practical application, thereby enabling those skilled in the art to implement and utilize various different exemplary embodiments of the invention, as well as various different choices and variations. Any simple modifications, equivalent changes, and alterations made to the foregoing exemplary embodiments should fall within the scope of protection of the present invention.

Claims

1. A method for estimating the buckling failure time of a vertical fire-prone storage tank, characterized in that, Includes the following steps: A. Under specific working conditions, perform structural modeling and simulate tank fire, and obtain data on the thermal radiation intensity and surrounding temperature distribution of the tank by setting the characteristic parameters of the combustion medium in the tank. B. Using the thermal radiation intensity and surrounding temperature distribution data of the storage tank as boundary conditions, obtain the tank wall temperature distribution data; C. Based on the tank wall temperature distribution data and tank wall material property parameters, perform thermal buckling analysis calculations on the storage tank to determine the critical temperature and time for buckling failure. D. Construct a buckling failure time estimation model using the buckling failure time data and the different liquid levels and tank size data of the combustion medium in the tank, and use the estimation model to estimate the time when the combustion medium in the tank will buckle and fail in different types of tanks during a fire. The thermal buckling analysis calculation in step C specifically includes the following steps: C1, importing the tank wall temperature distribution data into the tank structure model and dividing it into meshes, and setting the material property parameters of the tank wall material at different temperatures; C2, calculating the buckling modes of the tank and introducing initial defects using the buckling modes, and using the arc length method to calculate the critical temperature and time for buckling failure of the tank; C3. Using the critical temperature as the standard, calculate the critical temperature of buckling failure using the static stabilization method and the explicit dynamic method, and correct the dissipation energy coefficient of the static stabilization method and the mass scaling factor of the explicit dynamic method; C4. Compare the calculation time of the three algorithms, namely the arc length method, the static stabilization method and the explicit dynamic method, and use the algorithm with the shortest calculation time to calculate the buckling failure time for different liquid levels and different tank sizes. The buckling failure time estimation model in step D is a two-variable linear function fitted with buckling failure time as the dependent variable and tank level and tank size as independent variables, as detailed below: t = ah + bV + c Formula (1); Where t is the buckling failure time; h is the tank level, %; and V is the tank size. The specific operating conditions in step A include the type of combustion medium in the storage tank, the liquid level in the storage tank, and the size of the storage tank.

2. The method for estimating the buckling failure time of a vertical fire-prone storage tank according to claim 1, characterized in that, The characteristic parameters of the combustion medium in the storage tank in step A include the heat of combustion, CO content in the exhaust gas, smoke content, and heat release rate; the heat radiation intensity of the storage tank and the surrounding temperature distribution data are obtained through virtual sensors set at corresponding locations.

3. The method for estimating the buckling failure time of a vertical fire-prone storage tank according to claim 1, characterized in that, The structural modeling tool used in step A is fire dynamics simulation software.

4. The method for estimating the buckling failure time of a vertical fire-prone storage tank according to claim 1, characterized in that, The acquisition of tank wall temperature distribution data in step B is also related to two factors: air convection inside and outside the tank wall and thermal radiation emitted from inside and outside the tank wall. Air convection is characterized by the heat transfer coefficient, and thermal radiation is characterized by the emissivity.

5. The method for estimating the buckling failure time of a vertical fire-prone storage tank according to claim 1, characterized in that, The tank wall temperature distribution data in step B is simulated using solid heat transfer analysis software.

6. The method for estimating the buckling failure time of a vertical fire-prone storage tank according to claim 1, characterized in that, The thermal buckling analysis in step C is performed using structural simulation software.

7. The method for estimating the buckling failure time of a vertical fire-prone storage tank according to claim 1, characterized in that, The material properties of the tank wall in step C include Poisson's ratio, Young's modulus, coefficient of thermal expansion, specific heat capacity, conductivity, and yield strength.

8. A device for estimating the buckling failure time of a vertical burning storage tank, characterized in that, The method described in any one of claims 1 to 7 includes: The thermal radiation and ambient temperature acquisition module is used to perform structural modeling and simulate tank fire under specific working conditions. It obtains data on the thermal radiation intensity and ambient temperature distribution of the tank by setting the characteristic parameters of the combustion medium in the tank. The tank wall temperature distribution data acquisition module is used to acquire tank wall temperature distribution data by using the thermal radiation intensity of the storage tank and the surrounding temperature distribution data as boundary conditions. The thermal buckling analysis and calculation module is used to perform thermal buckling analysis and calculation of the storage tank based on the tank wall temperature distribution data and tank wall material property parameters, and then determine the critical temperature and time for buckling failure. The estimation model construction module is used to construct a buckling failure time estimation model using the buckling failure time data and the different liquid levels and tank size data of the burning medium in the tank, and to use the estimation model to estimate the time when the burning medium in the tank will buckle and fail in different types of tanks during a fire.

9. The device for estimating the buckling failure time of a vertical burning storage tank according to claim 8, characterized in that, The thermal buckling analysis and calculation module specifically includes: The parameter setting submodule is used to import the tank wall temperature distribution data into the tank structure model and divide it into a mesh, and set the material property parameters of the tank wall material at different temperatures; An arc-length method calculation submodule is used to calculate the buckling modes of the storage tank and introduce initial defects using the buckling modes, and to calculate the critical temperature and time for buckling failure of the storage tank using the arc-length method. The damping coefficient correction submodule is used to calculate the critical temperature of buckling failure using the critical temperature as a standard, employing both the static stability method and the explicit dynamic method, and to correct the dissipation energy coefficient of the static stability method and the mass scaling factor of the explicit dynamic method. The algorithm selection submodule is used to compare the computation time of the three algorithms, namely the arc length method, the static stability method and the explicit dynamic method, and to use the algorithm with the shortest computation time to calculate the buckling failure time for different liquid levels and different tank sizes.

10. An electronic device for estimating the buckling failure time of a vertically burning storage tank, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to cause the at least one processor to perform the method for estimating the buckling failure time of a vertically burning storage tank as described in any one of claims 1 to 7.

11. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores computer-executable instructions for causing the computer to perform the buckling failure time estimation method for a vertically burning storage tank as described in any one of claims 1 to 7.

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