Transverse non-uniform temperature field partitioning and real-time control method for circular tunnel at high temperature

Abstract

The invention discloses a transverse non-uniform temperature field zoning and real-time control method for a circular tunnel at high temperature, which comprises two steps of non-uniform temperature field zoning and non-uniform temperature field active control, and is characterized in that the non-uniform temperature field zoning comprises a top combustor zoning and a side combustor zoning and aims to divide a high-temperature area which can be controlled by each combustor; the non-uniform temperature field active control comprises the steps of determining a temperature monitoring time interval, monitoring an actual temperature, calculating a temperature difference, predicting the power of the combustors and adjusting the power of the combustors; according to the method, control areas of different combustors can be defined quantitatively, convenience is provided for generation and control of the temperature field, power changes of the combustors are effectively predicted through the BP neural network, generation of the temperature field is controlled efficiently, and great convenience is provided for subsequent fire resistance research.

Description

Technical Field

[0001] The invention relates to the field of tunnel fire resistance in structural engineering, and in particular to a method for zoning and real-time controlling a transverse non-uniform temperature field of a circular tunnel under high temperature. Background Art

[0002] According to relevant statistics, since the 21st century, the average annual growth rate of China's highway tunnels has reached 20%. By the end of 2021, a total of 118 large-diameter shield tunnel projects had been started in the country, and 61 ultra-large diameter shield tunnel projects with a diameter of over 14m had been built.

[0003] As the number and scale of tunnels continue to expand and traffic flow increases, the probability of fires in tunnels is also increasing. When a fire occurs, due to the shortcomings of the narrow and long tunnel structure and relatively poor ventilation conditions, a large amount of heat in the fire will be transferred to the tunnel lining structure and the surrounding strata, resulting in uneven temperature distribution. In order to reduce the harm caused by tunnel fires to structures, personnel, vehicles, etc., researchers have carried out many studies on tunnel fire resistance. However, the cost of physical tunnel fire tests is high, the test conditions are difficult to control, and the test scale is limited. Conventional fire test furnaces can only produce uniform temperature fields and cannot restore the non-uniform temperature fields of tunnel fires. If the lining structure is placed in the existing combustion furnace for fire resistance research, it cannot fully reflect the performance of the lining structure in actual fires, and the research results are greatly limited.

[0004] At present, it is more convenient and cost-effective to study the temperature field of tunnel fires through numerical simulation methods than to conduct full-scale tests. At the same time, compared with scaled tests, there is no need to consider the influence of size effects, and it can efficiently study the problems related to the non-uniform temperature field of tunnel fires. Research on the lateral temperature distribution law of tunnel fires has mostly focused on ventilation speed, fire source size, fire source location, tunnel slope, etc. It can be found that the factors affecting the cross-sectional temperature distribution of tunnels are complex. At present, the existing research on the cross-sectional temperature distribution of tunnels mainly focuses on the temperature field distribution law, and there is little research on the regional division of the cross-sectional temperature field.

[0005] In addition, there is limited research on furnace temperature control technology, and conventional fire resistance test furnaces mostly use computer monitoring + PLC field control equipment. PID (proportional, integral, differential) controllers have the characteristics of simple structure and strong robustness, and are widely used in the industrial field, covering more than 90% of industrial processes. However, the temperature field generated by the building component fire resistance test furnace is relatively uniform, and the temperature of one area in its control system is controlled by one burner, while the non-uniform temperature field may involve one burner controlling the temperature of multiple areas at the same time, which cannot be achieved by the PID algorithm. Due to the complexity of furnace temperature changes, many phenomena cannot be directly described based on data expressions. In summary, the control of tunnel non-uniform temperature field has the characteristics of nonlinearity, high timeliness requirements, and high precision requirements. Traditional furnace temperature control methods often find it difficult to achieve ideal control effects. Summary of the invention

[0006] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to provide a method for zoning and real-time control of the transverse non-uniform temperature field of a circular tunnel under high temperature, which can solve the problem of difficulty in real-time generation and control of the non-uniform temperature field in the current research in the field of tunnel fire resistance, and provide support for research in the fields of tunnel fire resistance and fire safety.

[0007] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0008] The present invention provides a method for zoning and real-time controlling a horizontal non-uniform temperature field in a circular tunnel under high temperature, including two steps of zoning the non-uniform temperature field and actively controlling the non-uniform temperature field;

[0009] In the non-uniform temperature field partitioning step, the burners are divided into top burners and side burners;

[0010] The top burner partitioning method is as follows: the burner is established in the Fluent simulation, and the control area is determined by the angle between the center lines of the burner, which includes the left and right sides of the burner; the principle for determining the angle is that the area formed by the angle must completely surround the high-temperature temperature field generated by the burner;

[0011] The side burner partitioning method is as follows: Based on Fluent simulation, the side burners need to be simulated based on two factors: the angle between the burner and the horizontal line and the number of burners, to find the best burner partition, and use the best angle as the quantitative indicator of the final partition result;

[0012] The non-uniform temperature field active control step is carried out after the non-uniform temperature field is partitioned. The process of using a small number of burners to control the temperature of multiple areas is as follows:

[0013] (1) Determine the temperature monitoring time interval; starting from 0 seconds, monitor the temperature change every t seconds, that is, Δ t =Δt-1 =t seconds;

[0014] (2) monitoring the actual temperature; monitoring the actual temperature values ​​of the five areas within a time period of t seconds, and then transmitting the temperature signal to the control system, so that the temperature of the five areas in each time period can be observed at the control end;

[0015] (3) Calculate the temperature difference; based on the given target temperature curve and the actual temperature monitored, calculate the temperature value Δ that needs to be adjusted for each area within the time period T , Δ T The calculation method of is as shown in formula 2;

[0016] (4) Predicting burner power: Based on the temperature values ​​that need to be adjusted in each area during the time period, the power adjustment value Δ of each burner is predicted through the neural network. W ;

[0017] (5) Adjusting the burner power; converting the burner power adjustment value into a signal and outputting it to the control system, which makes adjustments based on the burner power at the previous moment, and finally achieving the target temperature curve of each area by adjusting the power of each burner;

[0018] Δ t =t 2 -t 1 =Δ t-1 =t 1 -t 0 =10s (1)

[0019] Δ T =T 2 -T 1 (2)

[0020] Δ w =W 2 -W 1 (3)

[0021] In the formula, Δ T is Δ t The temperature value that each area needs to rise or fall within a certain period of time; T 1 is Δ t Time t 1 The actual temperature value at the moment; T 2 is Δ t Time end is t 2 The target temperature at the moment; W 1 is Δ t Time t 1 Burner power that does not change at any time; W 2 is Δ t Time t 1 Burner power after time change; ΔW To change the burner power.

[0022] Preferably, when the side burners are zoned, the principle for selecting the best angle is that the angle between adjacent control lines of adjacent areas is less than 10 degrees, so as to prevent the two areas from being close to each other, causing the range of the common control area of ​​the two burners to become narrower, making it easier to achieve uniform temperature;

[0023] At the same time, the angle between adjacent control lines in adjacent areas is greater than 40 degrees, which prevents the top and side burners from jointly controlling a wide range of areas, increasing the complexity of operation, resulting in increased control difficulty in actual operation, which is not conducive to precise temperature control.

[0024] Preferably, in the step of determining the temperature monitoring time interval, since the shortest time for the current temperature sensor to transmit test data is 10 seconds, the temperature change is monitored every 10 seconds starting from 0 seconds.

[0025] Preferably, in the non-uniform temperature field zoning step, the burner nozzles that are at 90° to the horizontal line are set as top burners, and the remaining burners are set as side burners.

[0026] Preferably, the burner power is predicted by a BP neural network; specifically, by inputting the temperature difference of each area in the tunnel, i.e., the difference between the actual temperature measured by the thermocouple and the target curve temperature, the initial power and initial temperature of each burner, and outputting the power change value required for the burner to achieve the target temperature, the burner power is cyclically adjusted to realize the burner power prediction demand based on the BP neural network.

[0027] The beneficial effects of the present invention are:

[0028] 1. The present invention proposes a non-uniform temperature field zoning method for a circular tunnel, which can effectively and quantitatively define the control areas of different burners and facilitate the generation and control of the temperature field.

[0029] 2. The present invention also proposes a real-time active control method for non-uniform temperature field. Through the BP neural network, the power change of the burner can be effectively predicted, and the temperature field generation can be efficiently controlled, which provides great convenience for subsequent fire resistance research.

[0030] 3. The real-time active control method of the non-uniform temperature field proposed by the present invention can optimize the energy use of the burner and avoid unnecessary energy consumption, thereby achieving energy conservation and emission reduction, and meeting the needs of sustainable development. At the same time, the implementation of the present invention also helps researchers to have a deeper understanding of the development mechanism of tunnel fires and provide a theoretical basis and experimental support for fire prevention and control technology under non-uniform temperature fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0032] Figure 1 A partition case diagram provided for an embodiment of the present invention;

[0033] Figure 2 A schematic diagram of a neural network prediction concept provided by an embodiment of the present invention;

[0034] Figure 3 The dimensions of the small tower combustion vehicle provided in the embodiment of the present invention.

[0035] Figure 4 A schematic diagram of a tower combustion vehicle and a tunnel structure provided by an embodiment of the present invention;

[0036] Figure 5 A schematic diagram of two-dimensional dimensions of a model provided in an embodiment of the present invention;

[0037] Figure 6 A schematic diagram of the final mesh division of the tunnel model provided by an embodiment of the present invention;

[0038] Figure 7 A schematic diagram of mesh division of a tunnel model provided by an embodiment of the present invention;

[0039] Figure 8 A schematic diagram of the temperature monitoring line provided in an embodiment of the present invention;

[0040] Fig. 9 A schematic diagram of the temperatures of different monitoring lines of a top burner provided in an embodiment of the present invention;

[0041] Fig.10 A schematic diagram of temperature distribution under top burner control provided by an embodiment of the present invention;

[0042] Fig.11 A schematic diagram of the main control area when the nozzle angle of the side burner provided in the embodiment of the present invention is 30°;

[0043] Fig.12 A schematic diagram of the main control area when the nozzle angle of the side burner provided in the embodiment of the present invention is 45°;

[0044] Fig.13 A schematic diagram of the main control area when the nozzle angle of the side burner provided in the embodiment of the present invention is 60°;

[0045] Fig.14A schematic diagram of the temperature field regional distribution provided by an embodiment of the present invention;

[0046] Fig.15 The temperature distribution diagram when all burners are turned on provided by the embodiment of the present invention;

[0047] Fig.16 A schematic diagram of a data-driven model control performance simulation verification provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] like Figures 1 to 3 As shown, this embodiment provides a method for zoning and real-time control of the horizontal non-uniform temperature field in a circular tunnel under high temperature, specifically:

[0050] Step 1: Determine the tunnel and burning car model

[0051] A combustion vehicle model and a tunnel model are set up in advance. This embodiment shows a small tower combustion vehicle, and the three-dimensional diagram of the vehicle body is as follows: Figure 3 As shown. The top width of the small tower burner vehicle is 525mm, the bottom width is 1150mm, the height is 840mm, and the longitudinal length is 1100mm. This size ensures that the flame of the burner can fully cover the tunnel cross-section, and enables the burner vehicle to flexibly adapt to tunnel sections of different lengths to achieve effective temperature control. The angle between the plane direction of the burners on both sides and the plane direction of the top burner is set to 45°, and the direction of each burner nozzle is perpendicular to the plane. However, the direction of the burner flame is not fixed, and the flame direction can be adjusted by connecting an external refractory conduit so that it is not limited to the vertical plane direction.

[0052] According to the specification of GB / T 51438-2021, the design standard of shield tunnel engineering, the thickness of the concrete segment is 300mm. Combined with the above size information, the integrated model diagram of the tower burner and the tunnel is constructed, as shown in the figure below: Figure 4 shown.

[0053] In addition, when designing a tower combustion vehicle, several holes need to be reserved on one side of the vehicle body for the connection of the burner air intake duct and air intake duct inside the vehicle body with the external air intake duct and air duct. Similarly, a hole needs to be opened at one end of the tunnel plug to allow the gas pipe and air duct to pass through. After the pipeline connection is completed, the hole is tightly closed to prevent the internal temperature and heat loss to ensure safety during the experiment. However, due to the complexity of the pipeline connection, it is simplified during modeling and the details are not clearly drawn in the model. At the same time, in the design, the wheel height at the bottom of the vehicle body is about 135mm, and the track height and its bottom support are about 160mm. Therefore, a height of 300mm is left from the bottom of the combustion vehicle body to the tunnel ground for the use of wheels and tracks. However, when building the model, the model is simplified, the wheels and tracks are not drawn, the gap at the bottom of the vehicle body is left vacant, and it is regarded as a fluid domain. The two-dimensional size diagram of the model is as follows Figure 5 shown.

[0054] The simulation software used is Fluent, and the SIMPLE solution algorithm is used. The initial temperature is set to room temperature, that is, T = 300K, and the relative atmospheric pressure is 0Pa. These parameters indicate the starting state of the simulation. In order to ensure the effectiveness of the simulation, a grid sensitivity analysis is performed to compare the longitudinal temperature of the tunnel under five different grid numbers: 60,000, 135,000, 200,000, 260,000, and 400,000. The longitudinal temperature of the tunnel is as follows Figure 6 As shown in the figure, it can be found that except for the 60,000 grids, the calculation accuracy of the remaining grids is similar. Therefore, the overall model grid size is selected as 80mm, and the grid is encrypted at the burner nozzle and the exhaust port, and the grid size is 40mm. The final grid division of the tunnel model is shown in the figure. Figure 7 shown.

[0055] Step 2: Non-uniform temperature field partitioning method

[0056] In order to accurately perform quantitative zoning, it is necessary to design the layout of temperature monitoring points. The model established in this paper takes the center line as the axis and is symmetrical on both sides. Therefore, temperature measurement points are arranged only on one side of the center line to improve the convenience and efficiency of analysis. On the section where the burner nozzle is located, starting from the center line, a straight line connected to the center of the burner car is established every 5° on the left side of the center line, such as Figure 8 As shown, there is no temperature monitoring line on the right. The temperature monitoring line is named "line". For example, the temperature monitoring line with an angle of 10° based on the center line is represented by "line10".

[0057] A temperature monitoring point is set on each temperature monitoring line, and three temperature monitoring points are set on each straight line. The outermost temperature monitoring point is 200mm away from the tunnel wall, the innermost temperature monitoring point is 500mm away from the combustion vehicle wall, and the middle temperature monitoring point is placed at the midpoint of the line connecting the outermost temperature monitoring point and the innermost temperature monitoring point. The average temperature of the monitoring line is the average value of all the measuring points on the line.

[0058] Top burner control area division

[0059] For the top burner, select the 0°~30° monitoring line to compare the temperature change (the burner injection rate is 17m / s), such as Fig. 9 As shown in the figure, it can be found that the temperatures of the three monitoring lines at 0°, 5°, and 10° are significantly higher than the temperatures of the monitoring lines at other angles. The temperature distribution cloud diagram under the control of the top burner is shown in the figure. Fig.10 As shown in the figure, the angle between the black line and the center line is 10°. According to the aforementioned angle selection principle, the temperature of the black line includes the high-temperature temperature field area. Therefore, the range of 0 to 10° to the left and right of the center line is defined as the control area of ​​the top burner.

[0060] Side burner control area division

[0061] For the side burners, it is necessary to comprehensively consider the nozzle angle and the number of openings of the side burners, among which the angles considered are 30°, 45° and 60°, and the number of openings considered are single-sided opening of the burner and simultaneous opening of both sides. Fig.11 , 12 , 13 show the maximum control area when the two burner opening numbers are at 30°, 45° and 60° respectively.

[0062] It can be found that when the burner nozzle angle is 30°, the area controlled by the top burner and the side burner is relatively wide, which is not conducive to precise temperature control. When the burner nozzle angle is 60°, the area directly controlled by the non-top burner and the side burner is smaller, which makes it easier to form a uniform temperature field, which is not conducive to achieving the specific needs of the non-uniform temperature field in the tunnel. These two angles do not meet the selection principle of the optimal angle of the side burner. Therefore, the burner distribution with a nozzle angle of 45° can better balance the control range of the top burner and the side burner.

[0063] In summary, the final division of the non-uniform temperature field is as follows: Fig.14 shown.

[0064] Turn on all burners to verify the effectiveness of the proposed temperature field division method. Fig.15 It can be seen that the temperature within the direct control area of ​​the three groups of burners is significantly higher than that in the other two areas, and the darker colors are concentrated within the edge lines of the direct control area of ​​the burners, proving that the division of the areas is reasonable.

[0065] Step 3: Real-time active control method for non-uniform temperature field

[0066] The idea of ​​using a small number of burners to control the temperature of multiple zones is as follows:

[0067] (1) Determine the temperature monitoring time interval. Since the shortest time for the temperature sensor currently used in the laboratory to transmit test data is 10 seconds, the temperature change is monitored every 10 seconds, that is, Δ t =Δ t-1 =10s;

[0068] (2) Monitor the actual temperature. Monitor the actual temperature values ​​of the five areas within a 10s period, and then transmit the temperature signal to the control system. The temperature of the five areas in each time period can be observed at the control end;

[0069] (3) Calculate the temperature difference. Based on the given target temperature curve and the actual temperature monitored, calculate the temperature value Δ that needs to be adjusted for each area during the time period. T , Δ T The calculation method is as shown in formula 4-11.

[0070] (4) Predict burner power. Based on the temperature values ​​that need to be adjusted in each area during this time period, the power adjustment value Δ of each burner is predicted through the neural network. W .

[0071] (5) Adjust the burner power. The burner power adjustment value is converted into a signal and output to the control system, which makes adjustments based on the burner power at the previous moment. Ultimately, the target temperature curve of each area is achieved by adjusting the power of each burner.

[0072] Δ t =t 2 -t 1 =Δ t1 =t 1 -t 0 =10s (4)

[0073] Δ T =T 2 -T 1 (5)

[0074] Δ w =W 2 -W 1 (6)

[0075] In the formula, Δ T is Δ t The temperature value that each area needs to rise or fall within a certain period of time; T 1 is Δ t At the beginning of time (ie t 1 The actual temperature value at the time T 2 is Δ t At the end of time (t 2 The target temperature at the time 1 is Δ t At the beginning of time (ie t1 Burner power unchanged at time ; W 2 is Δ t At the beginning of time (t 1 Burner power after change (at time); Δ W To change the burner power.

[0076] In the above step 4, it is necessary to predict the burner power. The modeling process of the BP neural network used in the prediction will be introduced in detail below.

[0077] First, determine the input and output of the neural network. According to the active control idea, the network input and output are as follows: the input layer is the initial temperature, the initial power of burner 1, the initial power of burner 2, the initial power of burner 3, the changing temperature of area 1, the changing temperature of area 2, the changing temperature of area 3, the changing temperature of area 4 and the changing temperature of area 5; the output layer parameters are determined to be the changing power of burner 1, the changing power of burner 2 and the changing power of burner 3.

[0078] The training samples of the neural network are obtained through simulation. The simulation samples cover the entire possible range of input and output. The specific settings are as follows: the initial temperature range is set to 300-1500K, 300K is the normal temperature state, and 1500K is the highest temperature setting. The initial temperature is the average temperature of the five areas, and 200K is used as a temperature range to try to make the temperatures of the five areas and their average temperatures in the same temperature range. In the neural network training, the temperature range is input in the form of numbers, for example, the number of the 300-500K temperature range is 1, and so on, a total of six temperature ranges. The initial power values ​​of the burner are set to 0kW, 84.98kW, 158.21kW, 231.44kW, 304.68kW, 377.91kW, 451.14kW, 524.36kW, and 597.59kW, with a total of 9 horizontal values ​​covering the two states of the burner on and off. In addition, the horizontal value of the burner power after the change is the same as the initial power of the burner.

[0079] The number of hidden layer nodes is generally determined based on an empirical formula to determine the value range, and then the neural network is trained based on different values, and the final value is determined based on the training results. According to the empirical formula, the number of hidden layer nodes is estimated to be in the range of 5 to 13.

[0080]

[0081] h=log 2 m (8)

[0082] Where α is a natural number between 1 and 10, and m, h, and n are the number of nodes in the input layer, hidden layer, and output layer.

[0083] The trial calculations were performed for different numbers of hidden layer nodes, and the average of the five trial calculation results was taken, as shown in Table 1. It can be seen from the results in the table that when the number of hidden layer nodes is 11, the average error of the model is small, so the number of hidden layer neurons in this model is 11.

[0084] Table 1 Calculation results of different hidden layer node numbers

[0085]

[0086] The sample set capacity of the neural network is estimated based on the following two empirical methods:

[0087] (1) Estimation based on the relationship between the sample set capacity and the neurons in each layer of the network

[0088] P = (5-10) × (mh + hn) (9)

[0089] (2) Estimation based on the relationship between sample set capacity and training error ε

[0090]

[0091] For a three-layer BP neural network, n w The calculation method is as follows:

[0092] n w =m(h+1)+h(n+1) (11)

[0093] After calculation, the sample capacity range was preliminarily determined to be 300 to 1720.

[0094] The sample design method adopts the orthogonal design method. According to this method, the initial power and the changed power of the three burners are orthogonally designed, and the orthogonal design table (Table 2) L81.9.6 is obtained. L81.9.6 means that 81 samples are designed based on the orthogonal design method under the 6-factor 9-level working condition, and then a comprehensive design is carried out with the initial temperature, and finally the sample set capacity is determined to be 486.

[0095] Table 2 Factors and their level values

[0096]

[0097]

[0098] The normalization method for neural network training data is the maximum value method, and its formula is as follows:

[0099]

[0100] The model sample set division ratio is 7:1.5:1.5, that is, the number of sample sets in the training set, validation set, and test set are 340, 73, and 73, respectively. In addition, the trial calculation results of different sample set division ratios (Table 3) verify the effectiveness of the division method.

[0101] Table 3 Calculation results of different sample set division ratios

[0102]

[0103] The initialization method, activation function, learning algorithm, error function, learning rate, etc. in the BP network were calculated in a similar way, and the final hyperparameters were determined as shown in Table 4.

[0104] Table 4 Model parameter configuration

[0105]

[0106] In addition, this model uses MAE as the model error evaluation indicator, and the calculation of the model accuracy is shown in Formula 13.

[0107]

[0108] According to the above model parameter configuration, the model is trained using the training set and the validation set, and the results are shown in Table 5. According to the results, the prediction accuracy among the model training set, validation set, and test set is slightly different, and there is no overfitting phenomenon. All variable powers can be accurately predicted, which verifies that the method has high accuracy and thus has good control ability.

[0109] Table 5 Prediction accuracy of training set, validation set and test set

[0110]

[0111] Finally, in Fluent, the neural network is used to predict data and control the formation of the temperature field in the tunnel, preliminarily verifying the application performance of the neural network.

[0112] Compare and analyze the temperature change curves of the five areas in the figure with the HC fire curve ( Fig.16 ), it can be found that the deviation is generally small, especially at high temperature, the temperature curves of the five regions gradually tend to be consistent with the HC curve, the degree of agreement is high, and the error does not exceed 10%, which shows the accuracy of the neural network model in control.

[0113] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for partitioning and real-time control of the horizontal non-uniform temperature field in a circular tunnel under high temperature, characterized in that: It includes two steps: non-uniform temperature field partitioning and non-uniform temperature field active control; In the non-uniform temperature field partitioning step, the burners are divided into top burners and side burners; The top burner partitioning method is as follows: the burner is established in the Fluent simulation, and the control area is determined by the angle between the center lines of the burner, which includes the left and right sides of the burner; the principle for determining the angle is that the area formed by the angle must completely surround the high-temperature temperature field generated by the burner; The side burner partitioning method is as follows: Based on Fluent simulation, the side burners need to be simulated based on two factors: the angle between the burner and the horizontal line and the number of burners, to find the best burner partition, and use the best angle as the quantitative indicator of the final partition result; The non-uniform temperature field active control step is carried out after the non-uniform temperature field is partitioned. The process of using a small number of burners to control the temperature of multiple areas is as follows: (1) Determine the temperature monitoring time interval; starting from 0 seconds, monitor the temperature change every t seconds, that is, Δ t =Δ t-1 =t seconds; (2) monitoring the actual temperature; monitoring the actual temperature values ​​of the five areas within a time period of t seconds, and then transmitting the temperature signal to the control system, so that the temperature of the five areas in each time period can be observed at the control end; (3) Calculate the temperature difference; based on the given target temperature curve and the actual temperature monitored, calculate the temperature value Δ that needs to be adjusted for each area within the time period T , ΔT The calculation method of is as shown in formula 2; (4) Predicting burner power: Based on the temperature values ​​that need to be adjusted in each area during the time period, the power adjustment value Δ of each burner is predicted through the neural network. W ; (5) Adjusting the burner power; converting the burner power adjustment value into a signal and outputting it to the control system, which makes adjustments based on the burner power at the previous moment, and finally achieving the target temperature curve of each area by adjusting the power of each burner; D t =t2-t1=Δ t-1 =t1-t0=10s (1) D T =T2-T1 (2) Δ w =W2-W1 (3) In the formula, Δ T is Δ t The temperature value that each area needs to rise or fall within a certain period of time; T1 is Δ t The actual temperature value at the beginning of time, i.e., at time t1; T2 is Δ t The target temperature at the end of time, i.e. at time t2; W1 is Δ t The burner power at the beginning of time, i.e., at time t1, remains unchanged; W2 is Δ t The burner power after the change at the beginning of time, i.e. at time t1; Δ W To change the burner power.

2. The method for zoning and real-time control of the horizontal non-uniform temperature field of a circular tunnel under high temperature as claimed in claim 1, characterized in that: When the side burners are zoned, the principle for selecting the best angle is that the angle between adjacent control lines of adjacent areas is less than 10 degrees to prevent the two areas from being close to each other, which will narrow the range of the area controlled by the two burners together and make it easier to achieve uniform temperature. At the same time, the angle between adjacent control lines in adjacent areas is greater than 40 degrees, which prevents the top and side burners from jointly controlling a wide range of areas, increasing the complexity of operation, resulting in increased control difficulty in actual operation, which is not conducive to precise temperature control.

3. The method for zoning and real-time control of the horizontal non-uniform temperature field of a circular tunnel under high temperature as claimed in claim 1, characterized in that: In the step of determining the temperature monitoring time interval, since the shortest time for the current temperature sensor to transmit test data is 10 seconds, the temperature change is monitored every 10 seconds starting from 0 seconds.

4. The method for zoning and real-time control of the horizontal non-uniform temperature field of a circular tunnel under high temperature as claimed in claim 1, characterized in that: In the non-uniform temperature field zoning step, the burner nozzles that are 90° to the horizontal line are set as top burners, and the remaining burners are set as side burners.

5. The method for zoning and real-time control of the horizontal non-uniform temperature field of a circular tunnel under high temperature as claimed in claim 1, characterized in that: The burner power is predicted by BP neural network; specifically, by inputting the temperature difference of each area in the tunnel, that is, the difference between the actual temperature measured by the thermocouple and the target curve temperature, the initial power and initial temperature of each burner, the power change value required for the burner to achieve the target temperature is output, and the burner power is cyclically adjusted to realize the burner power prediction demand based on BP neural network.

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