A method for characterizing the temperature field non-uniformity in a concrete dam pouring bay

Abstract

The present invention provides a method for characterizing the non-uniformity of the temperature field in a concrete dam pouring bin, which mainly includes constructing a grid model of the pouring bin by using the grid method and the neighborhood analysis method, defining a characterization index for the non-uniformity of the pouring bin, and on the basis of the grid model of the pouring bin, characterizing the temperature field in the concrete dam bin by using the non-uniformity index of the temperature field in the pouring bin. By introducing the grid method and the neighborhood analysis method, the present invention constructs a grid model of the concrete bin, with the grid as the smallest research unit; by defining the grid temperature, the average temperature in the bin, the non-uniform temperature potential of the grid and the grid potential difference vector, the non-uniformity of the temperature field in each area of the pouring bin is characterized, and it has the characteristics of being able to intuitively represent the degree of non-uniformity of the temperature field and describe the transmission trend of the non-uniformity of the temperature field at each spatial position in the bin. The present invention provides a method for characterizing the non-uniformity of the temperature field distribution in a concrete dam pouring bin, and provides a theoretical basis for the analysis of the temperature field characteristics in a concrete dam pouring bin.

Description

Technical Field

[0001] The present invention belongs to the technical field of concrete dam construction, and particularly relates to a method for characterizing the non-uniformity of the temperature field in a concrete dam pouring bin. Background Art

[0002] Temperature control and crack prevention of concrete dams have always been the focus issues in the fields of dam construction safety and academic research. With the construction of a number of concrete dam projects in China, the research on the theory and methods of temperature control and crack prevention of concrete dams and their engineering practice applications have achieved fruitful results. Theory and practice show that accurately mastering the true temperature distribution of the dam body and its change law is the basis for formulating scientific and reasonable temperature control measures, and is also the basis for analyzing the generation process of concrete temperature cracks.

[0003] In recent years, the transverse joint spacing of high concrete dams constructed in China is generally 20 - 30m. Along the river direction, it is often poured in a whole bay, with a length of about 60 - 100m, and the bay area reaches 1600 - 2500m 2 and the single-bay concrete volume reaches 5000 - 7000m 3 . It is mostly constructed by pouring in sub-layers, sub-areas, and sub-stripes. Even if multiple equipment is simultaneously put into the bay at a high intensity, the single-bay concrete pouring duration is as long as 20 - 50h. The scale of concrete dam projects is getting larger and larger, the pouring size of the whole bay is increasing, the pouring duration is longer, and the concrete material zoning is more complex. Under the combined action of concrete hydration heat release and heat dissipation, the non-uniformity of the temperature distribution in the pouring bin also increases accordingly.

[0004] In the traditional concrete dam construction process, temperature characteristic indexes such as the maximum temperature of the dam body, the internal and external temperature difference, the upper and lower layer temperature difference, and the temperature drop rate are generally used to reflect the concrete pouring quality, and the concrete temperature process curve, the temperature distribution of the typical section, etc. are used to describe the distribution characteristics and evolution law of the temperature field of the concrete dam. However, with the increase in the number of temperature measurement points in the concrete bin, the problem of whether there is non-uniformity in the temperature field in the bin has begun to be gradually concerned. At present, the traditional concrete temperature indexes cannot accurately describe the non-uniformity of the temperature field in the bin, and there is no specific characterization method for the non-uniformity of the concrete temperature field in the pouring bin. Constructing a scientific characterization system for the distribution characteristics of the temperature field is the basis for describing the change law of the temperature field during the dam construction period. In summary, it is imperative and of far-reaching significance to study a method for characterizing the non-uniformity in a concrete dam pouring bin. Summary of the Invention

[0005] To solve the above technical problems, the present invention provides a method for characterizing the non-uniformity of the temperature field in a concrete dam pouring bin. By proposing a logically reasonable and highly operable method for characterizing the non-uniformity of the temperature field in the bin, it provides indexes for accurately describing the non-uniformity of the temperature field in the pouring bin, and further provides an important reference basis for formulating scientific and reasonable dynamic temperature control measures.

[0006] In order to achieve the above technical features, the object of the present invention is achieved as follows: A method for characterizing the temperature field non-uniformity in a concrete dam pouring bin, which includes the following steps:

[0007] Step 1: Define a concrete grid model using the grid method neighborhood analysis method, select the actual pouring bin and perform grid division on it, and count the grid attributes;

[0008] Step 2: Define two basic temperature indicators, namely the grid temperature of the grid and the average temperature of the pouring bin;

[0009] Step 3: Define the characterization indicators of the grid non-uniform temperature field, such as the non-uniform temperature potential of the grid and the potential difference vector;

[0010] Step 4: Characterize the non-uniformity of the concrete temperature field at different times according to the calculated non-uniform temperature potential and potential difference vector.

[0011] The specific steps of Step 1 are as follows:

[0012] Step 1-1, determine the coordinate system:

[0013] Select the river-flow direction as the x-axis, the cross-river direction as the y-axis, and the vertical direction as the z-axis;

[0014] Step 1-2, determine the overall grid model size:

[0015] Based on the actual size of the pouring bin in the actual project and considering leaving spare space for the concrete river-flow direction and cross-river direction boundaries, jointly determine the size of the overall grid model;

[0016] Step 1-3, divide the overall grid model into individual grids, use an individual grid as the smallest research unit, and determine the size of the individual grid:

[0017] Principle for selecting the individual grid size: The individual concrete grid model is a regular hexahedron, and all individual grids should be able to form a complete overall grid model without redundant individual grids; while ensuring sufficient individual grid density, minimize the number of individual grids to ensure calculation convenience;

[0018] Step 1-4, use the neighborhood analysis method to define, define the space between the grid and its adjacent grids as the "neighborhood" of the grid, and count the attributes of all grids;

[0019] Step 1-5, obtain the centroid space coordinates (x i 、y i 、z i ) of each individual grid, and the space coordinates (x j 、y j 、z j ) of each concrete temperature measurement point.

[0020] The specific division process of the attributes of the grids in the above steps 1-4 is as follows: Simplify the entity grids. It is stipulated that the grids containing concrete within the regular hexahedron region of the grid are defined as entity grids; among the entity grids, the grids with cooling water pipes inside the grid space are defined as entity grids with cooling water pipes, the grids without cooling water pipes are defined as grids without cooling water pipes, and the grids on the boundary surface are defined as boundary grids; the grids without concrete are defined as empty grids; number the grids. It is stipulated that the upstream surface along the river is the front surface, and all grids are numbered in the order from bottom to top and from left to right.

[0021] The specific steps of step 2 are as follows:

[0022] Step 2-1, for the entity grids, select the spatial coordinates (x i , y i , z i ) of each single grid space, and the spatial coordinates (x j , y j , z j ) of each temperature measurement point of the concrete. Calculate the distance d from the centroid of the single grid to the nearest temperature measurement point. The distance d can be shown by formula (1):

[0023]

[0024] Step 2-2, use the temperature at the centroid point of the grid to represent the grid temperature T i at this time; for the entity grids, count the shortest distance d between all entity grid grids and the temperature measurement points. It is stipulated that when more than 50% of the distances d from the centroid to the measurement points are less than the integer δ, select δ as the threshold; when the distance d from the centroid of the grid to the nearest measurement point is less than δ, the grid temperature T i at this moment is the measured temperature T 测 of the nearest temperature measurement point; if the distance d from the centroid of the grid to the nearest optical fiber measurement point is greater than δ, select the distances from the three nearest measurement points as d1, d2, and d3, and the measured temperatures of the three optical fiber measurement points are T 测1 , T 测2 , T 测3 . The centroid point temperature T i of the grid is determined by the inverse distance method, that is, as shown in formula (2), so as to determine the temperatures of all entity grids at each time point;

[0025]

[0026] If the grid is an empty grid, then select the grid temperature T i at this point to be equal to the ambient temperature T 环 ;

[0027] Step 2-3: Select the average temperature of 30 measured points along the river direction from upstream to downstream in the middle of the bin, and the average temperature in the bin at this moment It can be expressed by formula (3):

[0028]

[0029] The specific steps of step 3 are as follows:

[0030] Step 3-1: Take the average temperature in the bin As the evaluation benchmark for the non-uniformity of the temperature field in the bin, the non-uniform temperature potential T of this grid i ′ Is the difference between the measured temperature of this measurement grid and the average temperature in the bin, representing the degree of non-uniformity of the concrete temperature field at this grid position, that is, as shown in formula (4):

[0031]

[0032] Step 3-2: According to the regulations in step 1-1, the front and back of the grid are the positive x-axis direction, the left and right sides of the grid are the positive y-axis direction, and the upper and lower sides of the grid are the positive z-axis direction; it is assumed that the difference in non-uniform temperature potential from the adjacent grid in the x-axis direction is It is assumed that the difference in non-uniform temperature potential from the adjacent grid in the y-axis direction is It is assumed that the difference in non-uniform temperature potential from the adjacent grid in the z-axis direction is

[0033] Define the vector sum of the differences in non-uniform temperature potential between the grid and the six adjacent grids in the neighborhood as the potential difference vector The numerical value of this vector is calculated as shown in formula (5), and the direction angle α of the vector is as shown in formula (6); through the positive and negative signs of the temperature potential differences in the x, y, and z-axis directions, determine the spatial quadrant where the combined potential difference vector is located. The spatial vector direction area is defined with reference to the mathematical space coordinate system, with a total of eight spatial positions. A positive potential difference vector value indicates that the temperature is transferred out of the grid, and a negative value indicates that the temperature is transferred into the grid;

[0034]

[0035]

[0036] The specific steps of step 4 are as follows:

[0037] Step 4-1: Use the SQL database to calculate the non-uniform temperature potential and potential difference vector of all grids at different times according to the defined requirements, and display the summary table of the non-uniform temperature potential and potential difference vector

[0038] The present invention has the following beneficial effects:

[0039] 1. The characterization method for the non-uniformity of the temperature field in the concrete dam pouring bin provided by the present invention can solve the problem that there are no characteristic indicators to describe the temperature field in the bin at the present stage. By constructing a grid model of the pouring bin, evaluation indicators such as the average temperature in the bin, the non-uniformity temperature potential, and the potential difference vector are defined, providing a characterization for accurately describing the uniformity of the temperature field in the pouring bin, and further providing an important reference basis for formulating scientific and reasonable dynamic temperature control measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The present invention will be further described below with reference to the drawings and embodiments.

[0041] Figure 1 Solid model of the pouring bin for the example of the present invention;

[0042] Figure 2 Grid model of the pouring bin for the example of the present invention;

[0043] Figure 3 Centroid coordinates of the grid for the example of the present invention;

[0044] Figure 4 Statistical table of the optical fiber temperature measurement point coordinates for the example of the present invention;

[0045] Figure 5 Table of the distances between the grid and the optical fiber measurement points for the example of the present invention;

[0046] Figure 6 Statistical chart of the shortest distances between the grid and the measurement points for the example of the present invention;

[0047] Figure 7 Grid temperature table for the example of the present invention;

[0048] Figure 8 Average temperature table in the bin for the example of the present invention;

[0049] Figure 9 Statistical table of the non-uniform temperature potential of the grid for the example of the present invention;

[0050] Figure 10 Vector synthesis diagram of the potential difference vector for the example of the present invention;

[0051] Figure 11 Spatial quadrant illustration diagram of the present invention;

[0052] Figure 12 Statistical table of the grid potential difference vector for the example of the present invention;

[0053] Figure 13 Summary table of the non-uniform temperature potential and potential difference vector of the grid for the example of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0054] The embodiments of the present invention will be further described below with reference to the drawings.

[0055] Example 1:

[0056] Refer to Figures 1-13 , a method for characterizing the temperature field non-uniformity in a concrete dam pouring bin, the method comprising the following steps:

[0057] Step 1: Apply the grid method and the domain analysis method to establish a concrete grid model, perform grid segmentation on the solid concrete, count the grid attributes, and construct a concrete grid model:

[0058] The specific steps are as follows:

[0059] Step 1-1: Determine the coordinate system: Select the river direction as the x-axis, the transverse river direction as the y-axis, and the vertical direction as the z-axis;

[0060] Step 1-2: Determine the overall grid model size: Obtain the actual size of the pouring bin. On this basis, while ensuring that the total volume of the grid can enclose the pouring bin, there is still remaining space on the riverward and transverse riverward boundaries, leaving at least a 1m or more area for each exposed surface of the concrete, and the total grid model still remains a cube model. Determine the size of the overall grid model accordingly. The size of this pouring bin is 24m * 23m * 3m, where the riverward length is 23m. The solid model of the pouring bin is as Figure 1 shown. To ensure that there is at least a 1m or more exposed area on the riverward and transverse riverward boundaries of the pouring bin, the overall grid model size is therefore selected as 28m * 27m * 3m, with a riverward length of 27m. The grid model is as Figure 2 shown.

[0061] Step 1-3: Perform segmentation of the overall grid model into individual grids, and determine and determine the size of an individual grid with the individual grid as the smallest research unit. Principle for selecting the size of an individual grid: The individual concrete grid model is a regular hexahedron, and all individual grids should be able to form a complete overall grid model without any redundant individual grids; while ensuring sufficient density of individual grids, reduce the number of grids as much as possible to ensure computational convenience.

[0062] Perform segmentation of the overall grid model into individual grids, and determine and determine the size of an individual grid with the individual grid as the smallest research unit; considering the comprehensive factors of computational volume and computational complexity, the total number of individual grids should be less than 3000, and the individual grid should be a regular hexahedron. Based on this, the size of the regular hexahedron grid is selected as 1m * 1m * 1m, and the total number of grids is 2268.

[0063] Steps 1-4: Define the "neighborhood" of each grid by using the domain analysis method, which is the space of the grid and its adjacent grids; count the attributes of all grids: simplify the physical grids, and define the grids containing concrete within the regular hexahedron area of the grid as physical grids; among the physical grids, the grids with cooling water pipes inside the grid space are defined as physical grids with cooling water pipes, the grids without cooling water pipes are defined as grids without cooling water pipes, and the grids on the boundary surface are defined as boundary grids; the grids without concrete are defined as empty grids. Number the grids, with the upstream surface along the river as the front, and number all grids in the order from bottom to top and from left to right. Count the grid attributes. The number of physical grids is 1723, among which the number of boundary grids is 770, the number of grids with cooling water pipes is 672, and the number of empty grids is 545.

[0064] Step 1-5: Obtain the centroid coordinates (x i , y i , z i ) of each individual grid, and the coordinates (x j , y j , z j ) of each temperature measurement point of the concrete. Use CAD to obtain the centroid coordinates of each individual grid and the coordinates of all optical fiber temperature measurement points, and store the statistical information in the SQL database, as shown in Figure 3 , Figure 4 .

[0065] The detailed steps of Step 2 are as follows:

[0066] Step 2-1: Assume to obtain the spatial coordinates x i , y i , z i of each individual grid, and the spatial coordinates x j , y j , z j of each temperature measurement point of the concrete. Define the distance between the centroid of the grid and the nearest temperature measurement point as d, and the distance d can be shown by formula (1).

[0067]

[0068] Use SQL to calculate the distance between each grid and each optical fiber temperature measurement point, select the shortest distance from each grid to the optical fiber temperature measurement point, and summarize and record it in the grid and optical fiber measurement point distance table, as shown in Figure 5 .

[0069] Step 2-2: Use the temperature at the centroid point of the grid to represent the grid temperature T i at this time; for the physical grids, count the shortest distance d between all physical grid centroids and temperature measurement points. It is stipulated that when more than 50% of the centroid-to-measurement point distances d are less than the integer δ, then select δ as the threshold.

[0070] Statistical analysis is performed on the shortest distances of all grids, and the statistical results are as Figure 6 shown. According to the definition, δ = 3m.

[0071] If the distance d from the centroid of the grid to the nearest measurement point is less than 3m, the grid temperature T at this moment i is the measured temperature T of the nearest temperature measurement point 测 ; if the distance d from the centroid of the grid to the nearest optical fiber measurement point is greater than 3m, the distances to the three nearest measurement points are selected as d1, d2, and d3, and the measured temperatures of the three optical fiber measurement points are T 测1 , T 测2 , T 测3 , and the temperature T of the centroid point of this grid i is determined using the inverse distance method, as shown in formula (2). In this way, the temperatures of all entity grids at each time point are determined.

[0072]

[0073] If this grid is an empty grid, then the selected grid temperature at this point is equal to the ambient temperature T 环 .

[0074] The grid temperatures of each grid at different times are calculated using SQL, and the calculation results are stored in the grid temperature table, as Figure 7 shown.

[0075] Step 2-3: Select the average temperature of 30 measured points along the river direction from upstream to downstream in the middle of the silo. The average temperature in the silo at this moment can be expressed as formula (3).

[0076]

[0077] Select 10 measuring points in the middle of the silo in each river direction of the second, third, and fourth layers of the pouring silo as the temperature measurement characteristic points of the whole silo, and a total of 30 measuring points are used as the calculation points for the average temperature in the silo. The average temperatures at different times are calculated using SQL, and the calculation results are stored in the average temperature table, as Figure 8 shown.

[0078] The detailed steps of Step 3 are as follows:

[0079] Step 3-1: Taking the average temperature in the silo as the evaluation benchmark for the non-uniformity of the temperature field in the silo, the non-uniform temperature potential T i ′ of this grid is the difference between the measured temperature of this measured grid and the average temperature in the silo, representing the degree of non-uniformity of the concrete temperature field at this grid position, that is, as shown in formula (4)

[0080]

[0081] Use SQL to calculate the uneven temperature potential of each grid at each time point, and store the calculation results in the uneven temperature potential table. The results are as Figure 9 shown.

[0082] Step 3-2: Define the sum vector of the uneven temperature potential differences between the grid and the six adjacent-direction grids as the potential difference vector According to the regulations in Step 1-1, the front and back of the grid are in the positive x-axis direction, the left and right sides of the grid are in the positive y-axis direction, and the upper and lower sides of the grid are in the positive z-axis direction, as Figure 10 shown; suppose the difference in uneven temperature potential from the adjacent grid in the x-axis direction is Suppose the difference in uneven temperature potential from the adjacent grid in the y-axis direction is Suppose the difference in uneven temperature potential from the adjacent grid in the z-axis direction is

[0083] Define the sum vector of the uneven temperature potential differences between this point and the six adjacent-direction measurement points as the potential difference vector The numerical magnitude of this vector is calculated as shown in formula (5), the direction angle α of the vector is as shown in formula (6), and the vector synthesis diagram of the potential difference vector is as Figure 10 shown; determine the spatial quadrant of this synthesized potential difference vector through the positive and negative signs of the temperature potential differences in the x, y, and z-axis directions. The spatial vector direction region is defined with reference to the mathematical space coordinate system, with a total of eight spatial positions. A positive value of the potential difference vector indicates that the temperature is transferred out of the grid, and a negative value indicates that the temperature is transferred into the grid. When x = 1, y = 1, z = 1, it is in the first spatial quadrant; when x = 1, y = -1, z = 1, it is in the second spatial quadrant; when x = -1, y = -1, z = 1, it is in the third spatial quadrant; when x = -1, y = 1, z = 1, it is in the fourth spatial quadrant; when x = 1, y = 1, z = -1, it is in the fifth spatial quadrant; when x = 1, y = -1, z = -1, it is in the sixth spatial quadrant; when x = -1, y = -1, z = -1, it is in the seventh spatial quadrant; when x = -1, y = 1, z = -1, it is in the eighth spatial quadrant; the spatial quadrant relationship is as Figure 11 shown

[0084]

[0085]

[0086] Use SQL to calculate the potential difference vectors of each grid according to the definition of the potential difference vector, and store the calculation results in the potential difference vector table, as Figure 12 shown.

[0087] The detailed steps of Step 4 are as follows:

[0088] Match the raster attribute table, uneven temperature potential, and potential difference vector table in SQL according to the raster number, and complete the characterization results of temperature inhomogeneity at different positions of the raster, as Figure 13 shown.

Claims

1. A method for characterizing the non-uniformity of the temperature field in a concrete dam pouring bay, characterized in that: It includes the following steps: Step 1: Use the grid neighborhood analysis method to define the concrete grid model, select the actual pouring bin and divide it into grids, and then calculate the grid attributes; Step 2: Define two basic temperature indicators: grid temperature and average temperature of the casting bin; Step 3: Define the grid's non-uniform temperature potential and potential difference vector; Step 4: Characterize the non-uniformity of the concrete temperature field at different times based on the calculated non-uniform temperature potential and potential difference vector; The specific steps of step 3 are: Step 3-1, taking the average temperature in the silo as the evaluation benchmark for the non-uniformity of the temperature field in the silo, the non-uniform temperature potential of this grid is the difference between the measured temperature of this grid and the average temperature in the silo, representing the degree of non-uniformity of the concrete temperature field at this grid position, that is, as shown in formula (4): Step 3-2, according to the provisions of Step 1-1, the front and back of the grid are in the positive x-axis direction, the left and right sides of the grid are in the positive y-axis direction, and the upper and lower sides of the grid are in the positive z-axis direction; let the difference in the non-uniform temperature potential from the adjacent grid in the x-axis direction be , let the difference in the non-uniform temperature potential from the adjacent grid in the y-axis direction be , let the difference in the non-uniform temperature potential from the adjacent grid in the z-axis direction be ; Define the synthetic vector of the uneven temperature potential difference between the grid and the six adjacent grids in the neighborhood as the potential difference vector , the numerical magnitude of this vector is calculated as shown in formula (5), and the direction angle of the vector is as shown in formula (6); by the positive and negative signs of the temperature potential differences in the x, y, and z axis directions, determine the spatial quadrant where the synthetic potential difference vector is located. There are a total of eight spatial positions for the spatial vector direction region, which are defined with reference to the mathematical space coordinate system. A positive value of the potential difference vector indicates that the temperature is transferred out of the grid, and a negative value indicates that the temperature is transferred into the grid; 。 2. The characterization method for the temperature field non-uniformity in the concrete dam pouring bin according to claim 1, characterized in that, The specific steps of step 1 are: Step 1-1, determine the coordinate system: Select the direction along the river as the x-axis, the direction across the river as the y-axis, and the vertical direction as the z-axis; Step 1-2, determine the overall grid model size: The overall grid model size is determined based on the actual size of the actual project pouring bin and taking into account the space left for the concrete along and across the river boundary surfaces. Steps 1-3: Divide the overall grid model into individual grids, using a single grid as the minimum research unit and determining the size of the individual grid: Principles for selecting single grid size: A single concrete grid model is a regular hexahedron. All single grids should be able to form a complete overall grid model, and there should be no redundant single grids. While ensuring sufficient single grid density, the number of single grids should be reduced as much as possible to ensure calculation convenience. Steps 1-4 use the neighborhood analysis method to define the grid and its adjacent grid spaces as the "neighborhood" of the grid, and then count the attributes of all grids; Steps 1-5, obtain the spatial coordinates of the centroid of each individual grid ( , , ), and the spatial coordinates of each temperature measurement point of the concrete ( , , ).

3. The characterization method for the temperature field non-uniformity in a concrete dam pouring bin according to claim 2, characterized in that, The specific process of dividing the attributes of the grid in steps 1-4 is as follows: simplifying the solid grid, defining the grid containing concrete within the grid regular hexahedron area as a solid grid; in the solid grid, the grid containing cooling water pipes within the grid space is defined as a solid grid containing cooling water pipes, the grid not containing cooling water pipes is defined as a grid not containing cooling water pipes, the grid on the boundary surface is defined as a boundary grid; the grid not containing concrete is defined as an empty grid; the grids are numbered, defining the upstream side along the river as the front side, and numbering all grids in order from bottom to top and from left to right.

4. The characterization method for the temperature field non-uniformity in a concrete dam pouring bay according to claim 3, wherein, The specific steps of step 2 are: Step 2-1: For the entity grid, select the spatial coordinates of each individual grid ( , , ), and the spatial coordinates of each temperature measurement point of the concrete ( , , ). Calculate the distance from the centroid of the individual grid to the nearest temperature measurement point as d , and the distance d is shown by formula (1): Step 2-2: Use the centroid temperature of the grid to represent the grid temperature at this time ; For solid grids, calculate the shortest distance between all solid grid cells and temperature measurement points d . It is stipulated that when more than 50% of the centroid distances to the measurement points d are less than the integer , select as the threshold; when the distance between the centroid of the grid and the nearest measurement point d is less than , the grid temperature at this moment is the measured temperature of the nearest temperature measurement point ; If the distance from the centroid of the grid to the nearest optical fiber measurement point d is greater than , select the distances to the three nearest measurement points as , , . The measured temperatures of the three optical fiber measurement points are , , . The temperature of the centroid point of this grid Use the inverse distance method, as shown in formula (2), to determine the temperatures of all entity grids at each time point; If the grid is an empty grid, select the grid temperature of this point equal to the ambient temperature ; Step 2-3: Select the average temperature of 30 measured points along the river direction from upstream to downstream in the middle of the silo, and the average temperature in the silo at this moment It is expressed as formula (3): 。 5. A method for characterizing the temperature field non-uniformity in a concrete dam pouring bin according to claim 4, characterized in that, The specific steps of step 4 are: Step 4-1: Use the SQL database to calculate the uneven temperature potential and potential difference vectors of all grids at different times according to the definition requirements, and display the summary table of uneven temperature potential and potential difference vectors.

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