Modeling Method for Simulating High-Temperature Performance of UHPCFST Members

Abstract

The present invention provides a modeling method for simulating the high-temperature performance of UHPCFST members, including: First, establish a UHPC double-random mesoscopic geometric model, endow corresponding material properties, perform unit mesh segmentation processing and local homogenization calculation to obtain a discretized distribution field of the equivalent high-temperature material properties of UHPC. Then, conduct probability statistics on the discretized field to form a mathematical model of the mesoscopic equivalent probability model. Next, generate an equivalent high-temperature material property random field through a random field generation algorithm according to the mathematical model, and assign the random field to the grid cells of the core UHPC to form a finite element equivalent mesoscopic model of heterogeneous UHPC. Then, combine it with the macroscopic steel pipe model to establish a macro-mesoscopic high-temperature numerical model of UHPCFST members. Finally, perform numerical solution of the model to obtain the simulation results of the temperature field and thermo-mechanical coupling of the members. The present invention can better simulate the failure mode, thermodynamic response and discreteness characteristics of UHPCFST members, and at the same time has a lower computational cost, and is applicable to the simulation at the member level.

Description

Technical Field

[0001] The present invention belongs to the technical field of simulation numerical analysis of concrete components, and particularly relates to a method for constructing a macro-meso scale high-temperature numerical model of UHPCFST components. Background Art

[0002] Ultra-High Performance Concrete Filled Steel Tube (UHPCFST) is a new type of high-performance component, which is formed by pouring Ultra-High Performance Concrete (UHPC) into a perforated steel tube. It can give full play to the advantages of high strength of UHPC, and at the same time effectively solve the problems of poor ductility and easy explosion at high temperature of UHPC, and has broad application prospects. At the same time, the fire resistance design of structures and the assessment after fire have become an important part of the whole life cycle design of engineering structures, and the high-temperature performance and deterioration mechanism of UHPCFST are the important basis for the safety assessment of such structures under and after fire. Since UHPC is a typical heterogeneous composite material, the random distribution of its internal pores, steel fibers, coarse aggregates and other micro-meso scale components makes the performance of such structures have significant discreteness, and the existing conventional macroscopic research methods cannot deeply reveal the discreteness of test results and the internal essence of high-temperature deterioration. Therefore, it is necessary to develop a model of UHPCFST components to explore the internal essence of its discreteness and high-temperature deterioration to provide a theoretical basis for its application. Summary of the Invention

[0003] The purpose of the present invention is to provide a method for constructing a macro-meso scale high-temperature numerical model of UHPCFST components in view of the deficiencies of the prior art. This method reflects the influence of the heterogeneity and randomness of the core UHPC material of the components on the discreteness of the cross-section temperature field and the mechanical response of the components, and can simulate the high-temperature thermodynamics performance of real UHPCFST components.

[0004] To solve the above technical problems, the present invention adopts the following technical solutions:

[0005] A modeling method for simulating the high-temperature performance of UHPCFST components, comprising the following steps:

[0006] Step 1: Establish a double random geometric model of UHPC according to the material properties of Ultra-High Performance Concrete UHPC, and divide it into multiple equal-sized units;

[0007] Step 2: Assign corresponding material properties to the components contained in each unit. Calculate the macroscopic equivalent thermal conductivity, specific heat, density, and equivalent elastic modulus corresponding to each unit according to the assigned material properties, and then assign the calculation results to the coordinates of the corresponding unit center point in sequence to form the discrete field distribution result of the material properties;

[0008] Step 3: Conduct probability statistics on the amplitude sizes of the discrete fields of different material properties obtained in Step 2, and use the Weibull distribution to obtain the probability density distribution of each material property through regression analysis, and obtain the correlation length and autocorrelation distribution of each material property according to the autocorrelation function;

[0009] Step 4: Conduct regression on the values of each material property to obtain the conversion relationship between the property values of different materials at room temperature and the evolution law of different material properties with temperature. Then, combined with the probability density distribution and correlation length in Step 3, construct an equivalent probability mathematical model for each high-temperature material property;

[0010] Step 5: Generate a two-dimensional random field of high-temperature physical parameters based on the equivalent probability mathematical model in Step 4 through a random field generation algorithm, and then expand the two-dimensional random field to a three-dimensional random field;

[0011] Step 6: Establish a core UHPC solid model, divide the core UHPC solid model into multiple equal-sized grid units, and then assign the three-dimensional random field in Step 5 to the grid units of the core UHPC solid model according to the spatial position, so as to form a finite element equivalent mesoscopic model of heterogeneous UHPC. Then, establish a macroscopic steel pipe solid model, and combine the macroscopic steel pipe solid model and the finite element equivalent mesoscopic model of heterogeneous UHPC to obtain a macro-mesoscopic high-temperature numerical model of UHPCFST columns;

[0012] Step 7: Apply the initial conditions and boundary conditions related to temperature load and mechanical load to the macro-mesoscopic high-temperature numerical model of UHPCFST columns, so as to establish a macro-mesoscopic high-temperature numerical model of UHPCFST components. Then, numerically solve the macro-mesoscopic high-temperature numerical model of UHPCFST components through a general finite element solver to obtain the simulation results of the temperature field and thermo-mechanical coupling of UHPCFST components.

[0013] Further, when constructing the UHPC double random geometry model in Step 1, UHPC is regarded as a heterogeneous material including mortar and inclusion phases. The mortar part is characterized by porosity to represent its heterogeneity, and the inclusion phases randomly generate geometric shapes and are randomly placed according to the set gradation and content.

[0014] Further, the material properties assigned in Step 2 include density, specific heat, thermal conductivity, and elastic modulus.

[0015] Furthermore, the material properties of the mortar part are assigned according to the size of the porosity and the variation of each material property with the porosity.

[0016] Furthermore, the method for assigning the material properties of the mortar part is as follows:

[0017]

[0018]

[0019]

[0020]

[0021]

[0022] In the formula, T is the temperature; and k p are the thermal conductivities of the matrix and the pores respectively; and C p，p are the specific heats of the matrix and the pores respectively; and ρ p are the densities of the matrix and the pores respectively; and E p are the elastic moduli of the matrix and the pores respectively, V p is the porosity, V P (T) is the high-temperature porosity, k m 、C p，m 、ρ m and E m are the thermal conductivity, specific heat, density and elastic modulus of the mortar respectively.

[0023] Furthermore, in step 3, the probability density distribution of each material property is obtained by regression analysis using the Weibull distribution as follows:

[0024]

[0025] Then, the relationships between the scale parameter a, the range parameter b and the contents of each component as well as the unit size are obtained through regression, specifically:

[0026]

[0027]

[0028]

[0029]

[0030]

[0031]

[0032]

[0033]

[0034] In the formula, a k , a ρ and are the proportional parameters of the thermal conductivity field, specific heat field and density field amplitude based on Weibull statistics, b k , b ρ and is the corresponding range parameter, l E is the unit size, V p is the porosity, V sf is the volume fraction of steel fiber, V ca is the volume fraction of coarse aggregate.

[0035] Furthermore, in step 3, the autocorrelation function adopts a Gaussian elliptic autocorrelation function, which is expressed as:

[0036]

[0037] Autocorrelation length and cell size l E The relationship is:

[0038] l x = l y =0.45l E ;

[0039] In the formula, l x , l y is the correlation length, l E is the unit size, x and y are the horizontal and vertical coordinates, r is the correlation factor, which is 0. is the autocorrelation function

[0040] Furthermore, the conversion relationship between the property values ​​of different materials at room temperature obtained in step 4 is:

[0041]

[0042]

[0043]

[0044] In the formula, k 0 , C P,0 , ρ 0 and E C,0 They are thermal conductivity, specific heat, density and elastic modulus at room temperature respectively.

[0045] Further, the evolution relationships of different material properties with temperature in Step 4 are as follows:

[0046]

[0047]

[0048]

[0049]

[0050] In the formula, k 0 , C P,0 , ρ 0 and E C,0 are the thermal conductivity, specific heat, density, and elastic modulus at room temperature, respectively; k T , C P,T , ρ T and E C,T are the thermal conductivity, specific heat, density, and elastic modulus at high temperature, respectively.

[0051] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention takes into account the non-uniform random distribution of the cement matrix and the spatial random distribution of the inclusion phase, so it can more reasonably reflect the non-uniform characteristics of the core UHPC in the UHPCST component; The macro-meso scale high-temperature numerical model established for the UHPCFST component can reflect the influence of the non-uniformity and randomness of the core UHPC in the component on the discreteness of the cross-section temperature field and the mechanical response of the component, can better simulate the failure mode and thermodynamic response of the UHPCST component, has a low computational cost, and is applicable to the simulation of the thermodynamic performance at the component level. Description of the Drawings

[0052] Figure 1 is the flow chart of the modeling method for simulating the high-temperature performance of the UHPCFST component in the embodiment of the present invention;

[0053] Figure 2 is the discretization of the equivalent high-temperature material based on the double random meso model in the embodiment of the present invention;

[0054] Figure 3 is the equivalent probability mathematical model of the UHPC high-temperature material properties in the embodiment of the present invention;

[0055] Figure 4 is the discretization of the random field in the embodiment of the present invention;

[0056] Figure 5 is the macro-meso scale high-temperature numerical model of the UHPCFST component in the embodiment of the present invention;

[0057] Figure 6For the calculation of the macro-meso scale high temperature numerical model of the UHPCFST component in the embodiment of the present invention;

[0058] Figure 7 For the application of the macro-meso scale high temperature numerical model of the UHPCFST component in the embodiment of the present invention, wherein, (a) is the simulation of the random temperature field, (b) is the verification of the temperature field, (c) is the simulation of the mechanical response, and (d) is the verification of the mechanical response. Specific embodiments

[0059] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0060] It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments may be combined with each other.

[0061] The present invention will be further described below in conjunction with specific embodiments, but it is not limited to the present invention.

[0062] The embodiment of the present invention provides a modeling method for simulating the high temperature performance of a UHPCFST component combining macro and meso scales. Figure 1 For the specific flow chart of the present invention, in order to further explain the technical solutions of the present invention, the present invention will be elaborated in detail below with specific examples. The specific implementation cases include the following steps:

[0063] Step 1: Establish a double random geometric model of UHPC according to the material properties of ultra-high performance concrete (UHPC) and divide it into multiple equal-sized units;

[0064] When constructing the model, UHPC is regarded as a heterogeneous material composed of mortar and inclusion phases. The mortar contains a matrix and pores, and the inclusion phases contain coarse aggregates and steel fibers. In a conventional finite element software, a square UHPC meso-scale geometric model with a side length greater than 100 mm and capable of considering both the non-homogeneity of the mortar and the spatial random distribution of the inclusion components is established. Among them, the non-homogeneity of the mortar part is characterized by the porosity, and the inclusion phases such as coarse aggregates and fibers are randomly generated in geometric shapes and randomly placed according to the set gradation and content. Finally, a double random geometric model considering the non-homogeneous random distribution of the cement matrix and the spatial random distribution of the inclusion phases is obtained. The model effect is shown in Figure 1 ;

[0065] Such as Figure 2As shown, the double-random geometric model is regularly divided into N×N (N≥10) small square units. Among them, each small square unit will contain mortar matrices and inclusion phases with different shapes and areas according to its spatial position.

[0066] Step 2: Assign corresponding material properties to the components contained in each unit. Calculate the macroscopic equivalent thermal conductivity, specific heat, density, and equivalent elastic modulus corresponding to each unit according to the assigned material properties, and then assign the calculation results to the coordinates of the corresponding unit center point in sequence to form the discrete field distribution result of the material properties;

[0067] In this step, the material properties assigned to the matrix and inclusion components in each small square unit include density, specific heat, thermal conductivity, and elastic modulus at different high temperatures, etc. Among them, the material properties of the inclusion components are assigned according to the method in Table 1. See Table 1 for details.

[0068] Table 1 Assignment method of high-temperature thermodynamic parameters of inclusion components

[0069]

[0070]

[0071] Note: The relevant parameter values in the table are reference values, and the relevant values can be determined according to specific test results.

[0072] The material properties of the mortar part, such as density ρ m , specific heat C p,m , thermal conductivity k m and elastic modulus E m are assigned according to the size of the porosity and the mathematical expressions of each material property changing with the porosity. Among them, the equation of the porosity changing with high temperature is shown in formula (1), and the relationship between the matrix and the porosity is shown in formulas (2) to (5).

[0073]

[0074]

[0075]

[0076]

[0077]

[0078] In the formula, T is the temperature; kp correspond to the thermal conductivities of the matrix and the pores respectively; C p,p correspond to the specific heats of the matrix and the pores respectively; ρ pThey correspond to the density of the matrix and pores respectively; E p They correspond to the elastic modulus of the matrix and pores respectively, and V p is the porosity, and V P (T) is the high-temperature porosity, k m 、C p,m 、ρ m and E m are the thermal conductivity, specific heat, density, and elastic modulus of the mortar respectively.

[0079] After assigning material properties to the matrix and inclusion components according to the above formulas respectively, the material properties of the matrix and inclusion components inside each unit are obtained.

[0080] According to the material properties of the matrix and inclusion components inside each unit, the macroscopic equivalent thermal conductivity, specific heat, and density of each small unit are calculated by the steady-state solution method in the "heat transfer module" in Comsol software. The macroscopic equivalent elastic modulus of each small square unit is calculated in the "solid mechanics" module, and then the calculated equivalent material property values of each unit, such as thermal conductivity, specific heat, density, and elastic modulus, are assigned to the coordinates of the corresponding unit center point in turn to form the discrete field distribution result of the material properties.

[0081] Step 3: Conduct probability statistics on the amplitude sizes of the discrete fields of different material properties obtained in Step 2, and obtain the probability density distribution of each material property through regression analysis using the Weibull distribution, and obtain the correlation length and autocorrelation distribution of each material property according to the autocorrelation function;

[0082] Conduct probability statistics on the amplitude sizes of the discrete fields of different material properties obtained, and obtain the mean value, coefficient of variation, and corresponding probability density distribution curve of the amplitudes of different material properties. In this embodiment, the probability density distribution of each material property is obtained by conducting probability statistics using the Weibull distribution as follows:

[0083]

[0084] Then, the relationship between the scale parameter a and the range parameter b and the content of each component and the unit size is obtained through regression, specifically:

[0085]

[0086]

[0087]

[0088]

[0089]

[0090]

[0091]

[0092] wherein, a k 、 a ρ and are the proportional parameters of the thermal conductivity field, specific heat field and density field amplitudes based on Weibull statistics respectively, b k 、 b ρ and are the corresponding range parameters, l E is the element size, V p is the porosity, V sf is the volume fraction of steel fibers, V ca is the volume fraction of coarse aggregates;

[0093] According to the Gaussian-type elliptical autocorrelation function (Formula (15)), combined with the discrete fields of different material properties obtained in Step 2 (including density, specific heat, thermal conductivity, elastic modulus, etc. at different high temperatures), two-dimensional data regression is performed through Formula (15) to calculate the correlation lengths (l x 、l y ) of each discrete field, so as to obtain the autocorrelation distribution of the corresponding material property value discrete field. In this embodiment, the adopted autocorrelation function is the Gaussian-type elliptical autocorrelation function, specifically:

[0094]

[0095] And the relationship between the correlation length and the element size (l E ) obtained by regression is:

[0096] l x =l y =0.45l E (16)

[0097] wherein, l x 、l y are the correlation lengths, l E is the element size, x and y are the horizontal and vertical coordinates, r is the correlation factor, taking 0, is the autocorrelation function.

[0098] Step 4: Perform regression on each material property value to obtain the conversion relationship between the property values of different materials at room temperature and the evolution law of different material properties with temperature, and then combine the probability density distribution and correlation length in Step 3 to construct an equivalent probability mathematical model for each high-temperature material property;

[0099] Based on the property values of each material obtained in the above steps, the conversion relationships between the property values of different materials at room temperature are obtained by regression, specifically as follows:

[0100]

[0101]

[0102]

[0103] And the evolution relationships of the properties of different materials with temperature are obtained as follows:

[0104]

[0105]

[0106]

[0107]

[0108] In the formula, k 0 , C P,0 , ρ 0 and E C,0 are the thermal conductivity, specific heat, density, and elastic modulus at room temperature, respectively; k T , C P,T , ρ T and E C,T are the thermal conductivity, specific heat, density, and elastic modulus at high temperature, respectively.

[0109] Then, combined with the probability density distribution and correlation length in step 3, an equivalent probability mathematical model of the properties of each high-temperature material is constructed. This model describes the discrete field of material properties at room temperature in the form of a mathematical expression, and takes into account the correlation between different properties and the autocorrelation of the field. The specific form is shown in Figure 3 .

[0110] Step 5: Generate a two-dimensional random field of high-temperature physical parameters based on the equivalent probability mathematical model in step 4, and then expand the two-dimensional random field to a three-dimensional random field;

[0111] Such as Figure 4As shown in the figure, based on the equivalent probability mathematical model in Step 4, a random field generation algorithm is used to generate the random field of high-temperature physical parameters. During the process, first, the relative order of the amplitude magnitudes of the thermal conductivity field is determined through the autocorrelation function (Equation (15)). At the same time, the amplitudes of the physical field are generated according to the probability density distribution function (Equations (1)-(14)). The amplitudes are sorted and matched according to the relative order. Then, by combining the cross-correlations between different material property values (Equations (17)-(19)) and the evolution law with temperature (Equations (20)-(23)), a two-dimensional random field of all high-temperature physical parameters is finally formed. At the same time, the two-dimensional random field is extended to a three-dimensional random field. During the process, it is assumed that the amplitude probability density distribution of the three-dimensional random field is the same as that of the two-dimensional one in terms of the correlation length that determines the spatial correlation. Therefore, the probability density distribution of the three-dimensional random field can still be expressed by Equations (1)-(14), while the correlation function and correlation length of the three-dimensional random field can be expressed by Equations (24) and (25) respectively;

[0112]

[0113] l x =l y =l z =0.45l E (25)

[0114] In the formula, l x , l y and l z are the correlation lengths in the three directions of the three-dimensional random field respectively.

[0115] Step 6: Establish a core UHPC solid model, divide the core UHPC solid model into multiple equal-sized grid cells, and then assign the three-dimensional random field in Step 5 to the grid cells of the core UHPC solid model according to the spatial position, so as to form a finite element equivalent mesoscopic model of heterogeneous UHPC. Then, establish a macroscopic steel pipe solid model, and finally combine the macroscopic steel pipe solid model and the finite element equivalent mesoscopic model of heterogeneous UHPC to obtain a macro-mesoscopic high-temperature numerical model of UHPCFST columns;

[0116] In this step, as Figure 5 shown in the figure, in the ABAQUS numerical software, first establish a core UHPC solid model, divide the core UHPC solid model into grid cells with a size of 10 mm, and then assign the three-dimensional random field in Step 5 to the grid cells of the core UHPC solid model according to the spatial position, so as to form a finite element equivalent mesoscopic model of heterogeneous UHPC; then establish a macroscopic steel pipe solid model, and perform conventional operations such as assigning corresponding steel pipe properties and mesh division. Finally, combine the macroscopic steel pipe solid model and the finite element equivalent mesoscopic model of heterogeneous UHPC to obtain a macro-mesoscopic high-temperature numerical model of UHPCFST columns;

[0117] Step 7: Apply the initial conditions and boundary conditions related to temperature load and mechanical load to the macro-meso scale high temperature numerical model of the UHPCFST column, so as to establish the macro-meso scale high temperature numerical model of the UHPCFST component. Then, perform numerical solution on the macro-meso scale high temperature numerical model of the UHPCFST component through a general finite element solver to obtain the simulation results of the temperature field and thermo-mechanical coupling of the UHPCFST component;

[0118] In the finite element software, apply the initial conditions and boundary conditions related to temperature load and mechanical load to the macro-meso scale high temperature numerical model of the UHPCFST column, so as to establish the macro-meso scale high temperature numerical model of the UHPCFST component ( Figure 6 ). In this embodiment, the initial temperature is set to 20 °C, the heating regime adopts the ISO-834 standard heating curve, both ends of the UHPCFST column are set as hinged, the upper end adopts the control method of displacement loading, and finally, perform numerical solution on the model through a general finite element solver to obtain the simulation results of the temperature field and thermo-mechanical coupling of the UHPCFST component. The simulation results are as shown in Figure 7 .

[0119] The above are only the preferred embodiments of the present invention, and do not limit the implementation manners and protection scope of the present invention. For those skilled in the art, it should be realized that all the equivalent replacements and obvious changes made by using the content of the specification of the present invention should be included in the protection scope of the present invention.

Claims

1. A modeling method for simulating the high - temperature performance of UHPCFST members, characterized in that, it includes the following steps: Step 1: Establish a UHPC double - random geometric model according to the material properties of ultra - high - performance concrete (UHPC), and divide it into multiple equal - sized units; Step 2: Assign corresponding material properties to each component contained in each unit. Calculate the macroscopic equivalent thermal conductivity, specific heat, density, and equivalent elastic modulus of each unit according to the assigned material properties, and then assign the calculation results to the coordinates of the corresponding unit center point in sequence to form the discrete - field distribution result of material properties; Step 3: Conduct probability statistics on the amplitude sizes of the discrete fields of different material properties obtained in Step 2, and use the Weibull distribution to obtain the probability density distribution of each material property through regression analysis, and obtain the correlation length and autocorrelation distribution of each material property according to the autocorrelation function; Step 4: Regress the values of each material property to obtain the conversion relationship between the property values of different materials at normal temperature and the evolution law of different material properties with temperature. Then, combined with the probability density distribution and correlation length in Step 3, construct an equivalent probability mathematical model for each high - temperature material property; Step 5: Generate a two - dimensional random field of high - temperature physical parameters through a random - field generation algorithm based on the equivalent probability mathematical model in Step 4, and then expand the two - dimensional random field to a three - dimensional random field; Step 6: Establish a core UHPC solid model, divide the core UHPC solid model into multiple equal - sized grid units, and assign the three - dimensional random field in Step 5 to the grid units of the core UHPC solid model according to the spatial position, so as to form a finite - element equivalent mesoscopic model of heterogeneous UHPC. Then, establish a macroscopic steel - pipe solid model, and combine the macroscopic steel - pipe solid model and the finite - element equivalent mesoscopic model of heterogeneous UHPC to obtain a macro - mesoscopic high - temperature numerical model of UHPCFST columns; Step 7: Apply the initial conditions and boundary conditions related to temperature load and mechanical load to the macro - mesoscopic high - temperature numerical model of UHPCFST columns, so as to establish a macro - mesoscopic high - temperature numerical model of UHPCFST members. Then, numerically solve the macro - mesoscopic high - temperature numerical model of UHPCFST members through a general finite - element solver to obtain the simulation results of the temperature field and thermo - mechanical coupling of UHPCFST members.

2. The modeling method for simulating the high - temperature performance of UHPCFST members according to claim 1, characterized in that, in Step 1, when constructing the UHPC double - random geometric model, UHPC is regarded as a heterogeneous material including mortar and inclusion phases. The mortar part is characterized by porosity to represent its heterogeneity, and the inclusion phases randomly generate geometric shapes and are randomly placed according to the set gradation and content.

3. The modeling method for simulating the high - temperature performance of UHPCFST members according to claim 2, characterized in that, the material properties assigned in Step 2 include density, specific heat, thermal conductivity, and elastic modulus.

4. The modeling method for simulating the high - temperature performance of UHPCFST members according to claim 3, characterized in that, The material properties of the mortar part are assigned according to the size of the porosity and the variation of each material property with the porosity.

5. The modeling method for simulating the high-temperature performance of UHPCFST members according to claim 4, characterized in that, the method for assigning the material properties of the mortar part is: Wherein, T is the temperature; and are the thermal conductivities of the matrix and the pores respectively; and are the specific heats of the matrix and the pores respectively; and are the densities of the matrix and the pores respectively; and are the elastic moduli of the matrix and the pores respectively, is the porosity, V P (T) is the high-temperature porosity, k m 、 C p,m 、 ρ m and E m are the thermal conductivity, specific heat, density and elastic modulus of the mortar respectively.

6. The modeling method for simulating the high-temperature performance of UHPCFST members according to claim 1, characterized in that, in step 3, the probability density distribution of each material property obtained by regression analysis using the Weibull distribution is:

7. The modeling method for simulating the high-temperature performance of UHPCFST members according to claim 1, characterized in that, in step 3, the autocorrelation function adopts a Gaussian-type elliptical autocorrelation function, expressed as: Autocorrelation length and unit size l E The relationship is as follows: In the formula, , are the correlation lengths, is the cell size, x and y are the horizontal and vertical coordinates, r is the correlation factor, (x, y) is the autocorrelation function.

8. The modeling method for simulating the high-temperature performance of UHPCFST members according to claim 1, characterized in that, the conversion relationship between the property values of different materials at normal temperature obtained in step 4 is: In the formula, k 0 , C P,0 , ρ 0 and E C,0 are the thermal conductivity, specific heat, density, and elastic modulus at room temperature, respectively.

9. The modeling method for simulating the high-temperature performance of UHPCFST members according to claim 1, characterized in that, the evolution relationship of different material properties with temperature T in step 4 is: Wherein, k 0 , C P,0 , ρ 0 and E C,0 are the thermal conductivity, specific heat, density and elastic modulus at room temperature, respectively; k T , C P,T , ρ T and E C,T are the thermal conductivity, specific heat, density and elastic modulus at high temperature, respectively.

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