A method and system for reconstructing time-varying temperature fields in highway tunnel fires

Abstract

This invention relates to a method for reconstructing the time-varying temperature field of a highway tunnel fire, comprising the following steps: S1: determining the ignition range of vehicles with different fire source powers in a tunnel fire; S2: determining the ignition time of vehicles near the fire source during the development of a tunnel fire; S3: calibrating the heat release rate of multiple fire sources in a scaled-down tunnel fire test; S4: setting the heat release rate curve for numerical simulation of tunnel fire spread; S5: establishing a tunnel fire temperature field database; S6: designing the sensor array layout for reconstructing the temperature field of a tunnel fire; S7: proposing an interpolation method for reconstructing the temperature field of a tunnel fire; S8: generating a real-time temperature cloud map of the tunnel fire temperature field; S9: drawing reconstructed temperature field cloud maps of different sensor array layouts and interpolation method combinations; S10: evaluating the interpolation accuracy of each layout for reconstructing the temperature field of a tunnel fire, predicting the fire spread distance and time based on the temperature field reconstruction method, improving the intelligence level of fire rescue, and maximizing the protection of people's lives and property.

Description

Technical Field

[0001] This invention belongs to the field of tunnel fire safety technology, and relates to a method and system for reconstructing the time-varying temperature field of a highway tunnel fire. Background Technology

[0002] Fire, a typical hazard during tunnel operation, is characterized by rapid spread, high temperatures, and difficulty in rescue. After a fire breaks out, the heat radiation and convection of flames and smoke can easily cause the fire to spread and ignite stranded vehicles, resulting in mass casualties among people inside the tunnel.

[0003] Because the dense smoke generated during a tunnel fire turns the tunnel into a "black box," the effectiveness of traditional fire monitoring methods is limited. The direction and distance of fire spread, the number of fire sources, and the temperature field are difficult to ascertain, hindering firefighting and rescue efforts. However, current research on tunnel fire spread largely relies on the assumption of a single, fixed fire source, failing to consider the potential spread caused by the fire source during its development. While numerous experimental studies have investigated dual fire sources, most involve quantitative and rate-controlled fuel supply, neglecting the mutual thermal feedback effects between fire sources in actual spreading fires. Furthermore, they overlook the time lag in the emergence of multiple fire sources in spreading fires, arguing that they do not appear simultaneously and reach peak HRR. In terms of temperature field reconstruction, existing research often simplifies the tunnel into a one-dimensional model, ignoring the lateral spread of the fire. Moreover, current temperature sensor layouts struggle to capture temperature field changes caused by lateral fire spread, resulting in insufficient data to meet the needs of visually reconstructing the temperature field.

[0004] Therefore, there is an urgent need for a high-precision time-varying temperature field reconstruction method for tunnel fires to study the evolution characteristics of HRR under mutual thermal feedback of fire sources, optimize the layout of temperature sensors, and predict the fire spread distance and time based on the temperature field reconstruction method; to provide effective theoretical and technical support for fire control and extinguishing, personnel escape and evacuation, and fire emergency rescue during the fire development stage, while also improving the intelligence level of fire rescue and ensuring the safety of people's lives and property. Summary of the Invention

[0005] In view of this, in order to solve the problems of neglecting the time delay of the occurrence of multiple fire sources and neglecting the lateral spread of fire in the above-mentioned existing research on the spread of fire in tunnels, which makes it difficult to accurately study the temperature field changes caused by the spread of fire and has poor adaptability, the present invention provides a method and system for reconstructing the time-varying temperature field of a highway tunnel fire.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A method for reconstructing the time-varying temperature field of a highway tunnel fire includes the following steps:

[0008] S1: Determine the ignition range of vehicles with different fire source powers in tunnel fires: Based on the critical ignition heat flux of each component of the vehicle (tire, bumper, wheel arch, fuel tank, mudguard and hubcap), combined with the formulas for fire source heat radiation range, smoke heat radiation range and smoke temperature decay model, the ignition range of vehicles under different fire source powers is obtained.

[0009] S2: Determine the vehicle ignition time near the fire source during the development of a tunnel fire: Obtain the vehicle ignition time TTI within a specific range by fitting the critical ignition heat flux and ignition time from step S1 using a fitting function, and use the time t at each time point as an example. i The TTI is calculated to determine the expected ignition time ETI, and the earliest expected ignition time ETOI is found to satisfy ignition.

[0010] S3: Calibrate the heat release rate of multiple ignition sources in the scaled-down tunnel fire test: Based on the weight loss method and the ignition range determined in step S1 and the ignition time determined in step S2, calibrate the heat release rate (HRR) of multiple ignition sources in the scaled-down tunnel fire test.

[0011] S4: Set the heat release rate curve for numerical simulation of fire spread in tunnel: Based on the calibration results in step S3, construct the fire spread situation in the fire scenario, and calculate and analyze the characteristics of the heat release rate curve under different spacing and longitudinal wind speed.

[0012] S5: Establish a temperature field database for tunnel fires: After clarifying the development curves of heat release rate for single-source combustion and multi-source fires, obtain more detailed temperature field data and real-time heat release rate data through numerical simulation (setting 49 working conditions including single and dual fire sources).

[0013] S6: Design of the layout of the temperature field sensor array for tunnel fire reconstruction: Temperature sensors are installed on the left and right lanes of the tunnel respectively, and temperature sensors are arranged in a "chessboard" and "triangular mesh" pattern on the top of the tunnel.

[0014] S7: Propose an interpolation method for reconstructing the temperature field of a tunnel fire: By writing Python code, the interpolation region is gridded, and three two-dimensional interpolation methods, namely Kriging interpolation, inverse distance weighted interpolation, and spline interpolation, are used to interpolate the grid points to realize the real-time drawing of the temperature cloud map.

[0015] S8: Generate a real-time temperature field cloud map of the tunnel fire temperature field: Integrate the physical model of temperature decay of the tunnel ceiling with the interpolation algorithm of step S7. When the fire source is not directly below the temperature sensor, a virtual highest temperature point is set directly above the fire source. The temperature decay model is used to calculate the temperature of the virtual point and assign weights. The remaining parts are calculated using only the interpolation algorithm.

[0016] S9: Draw the reconstructed temperature field cloud map with different sensor array layouts and interpolation method combinations: Calculate the temperature value of the known coordinate point at the top of the tunnel using the cubic spline interpolation function in step S7, and map it to the preset color scale using the coolwarm function in the matplotlib library. Finally, make the color of each grid point represent the temperature at its location to form a two-dimensional temperature cloud map that intuitively shows the temperature gradient change, and compare it with the actual temperature cloud map.

[0017] S10: Evaluate the interpolation accuracy of various layouts for tunnel fire temperature field reconstruction: Take the ceiling temperature data of a certain working condition, use the three interpolation methods in step S7, and cross-validate the results; evaluate the accuracy by the mean relative error (MRE), the lower the MRE value, the higher the accuracy of the interpolation method; evaluate the robustness by the mean absolute error (MAE) and root mean square error (RMSE), the lower the MAE and RMSE values, the smaller the error of the interpolation result, the higher the accuracy, and the better the robustness; thus, derive an interpolation method suitable for generating temperature cloud maps of tunnel fires.

[0018] Furthermore, the formula for the range of heat radiation from the fire source in S1 is:

[0019] (1)

[0020] In the formula: Q rf Radiated heat received by adjacent vehicles, kW / m 2 ; ρ is the transmittance of air to thermal radiation, typically taken as 1; r is the distance between the target object and the virtual point source via the connecting line, in meters; θ is the angle between the connecting line and the normal direction of the target object; Q r Q represents the radiated power of the virtual point source, expressed in MW. r =χ r Q, χ r The flame radiation fraction is 0.2 to 0.32. In this study, the ignition source fuel is anhydrous ethanol, which is 0.25. Q is the ignition source heat release rate.

[0021] Furthermore, the formula for the range of flue gas thermal radiation in step S1 is:

[0022] (2)

[0023] In the formula: Q rs This represents the amount of heat radiated by smoke received by adjacent vehicles, in W / m³. 2 ;ε r The value represents the flue gas emissivity, taken as 0.8; T represents the flue gas temperature, in K.

[0024] Furthermore, the fitted function relationship in step S2 is as follows:

[0025] (3)

[0026] In the formula: t is the ignition time, in seconds; Radiative heat flux, kW / m 2 Goodness of fit (R) 2 The value is 0.99, and the two show a power-law relationship, indicating that the ignition time decreases according to a power law as the radiative heat flux increases.

[0027] Expected ignition time is:

[0028] (4)

[0029] The actual ignition time is:

[0030] (5)

[0031] In the formula: This refers to the time set of the fire's development, growth, and stabilization phases.

[0032] Furthermore, the numerical simulation in S5 uses FDS, and the FDS acquisition frequency is set to 3 times / s; each working condition can obtain 1050 rows of data including temperature, wind speed, and heat release rate (HRR) to expand the dataset.

[0033] Furthermore, in step S6, the temperature sensors at both ends of the tunnel top are 0.5m away from the left and right sidewalls, and the distance between adjacent temperature sensors is 5~10m.

[0034] Furthermore, S7 uses the pandas library to read CSV table data containing temperature data and sensor coordinate data, and matplotlib.pyplot is used for plotting.

[0035] Furthermore, the expression for Kriging interpolation in two-dimensional interpolation methods is:

[0036] (6)

[0037] In the formula: z(x) represents the unknown position. The estimated value; n is the number of known observation points; Z i Given point x i The actual observed value at the location; λi is the interpolation weight, which is calculated by the Kriging criterion based on the positional relationship between each known point in space and the unknown target point, as well as a predefined covariance function.

[0038] The expression for inverse distance weighted interpolation is:

[0039] (7)

[0040] In the formula: The number of known observation points; It is the first The attribute values ​​of each observation point; For the first The weights of each observation point to P, ; Indicates the point to be interpolated With the Observation points The Euclidean distance between them; This is the weighting exponent, usually greater than zero, used to control the rate at which the weight decays with distance;

[0041] The expression for spline interpolation is:

[0042] Each cubic polynomial Represented as:

[0043] (8)

[0044] In the formula: a i b i c i and d i These are the coefficients of the polynomial, which need to be determined through interpolation conditions and other constraints. To ensure... In x i Passing through point x i y i .have This means The smoothness conditions for cubic spline interpolation include continuity, first derivative continuity, and second derivative continuity:

[0045] (9)

[0046] These conditions can be transformed into a series of linear equations, forming a system of linear equations. By solving this system of equations, we can obtain... , and The value of is obtained, thus yielding the complete cubic spline interpolation function.

[0047] Furthermore, the physical model for temperature attenuation in the tunnel ceiling of S8 is as follows:

[0048] (10)

[0049] In the formula: Temperature at the reference location; Distance from reference position Temperature at a distance of meters; The initial ambient temperature is denoted by K, which is the attenuation coefficient. The unit of temperature is K.

[0050] Furthermore, the average relative error in S10 is specifically as follows:

[0051] (11)

[0052] The mean absolute error is as follows:

[0053] (12)

[0054] The root mean square error is specifically:

[0055] (13)

[0056] In the formula: This represents the temperature values ​​at each temperature measurement point; n is the number of samples. This is a temperature estimate.

[0057] A system based on a time-varying temperature field reconstruction method for highway tunnel fires, characterized in that it includes:

[0058] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the processor to implement the aforementioned method for reconstructing the time-varying temperature field of a highway tunnel fire.

[0059] The beneficial effects of this invention are as follows:

[0060] The present invention discloses a time-varying temperature field reconstruction method for highway tunnel fires. Compared with traditional methods, this method has good reliability and feasibility. It can be used to optimize the layout of temperature sensors and predict the fire spread distance and time based on the temperature field reconstruction method. It provides effective theoretical and technical support for fire control and extinguishing, personnel escape and evacuation, and fire emergency rescue during the fire development stage. At the same time, it improves the intelligence level of fire rescue and maximizes the protection of people's lives and property.

[0061] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0062] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:

[0063] Figure 1 This is a flowchart illustrating the process of establishing a tunnel fire temperature field database in a method for reconstructing a time-varying temperature field of a highway tunnel fire according to the present invention.

[0064] Figure 2 (a) is a checkerboard-shaped layout in the method for reconstructing the time-varying temperature field of a highway tunnel fire according to the present invention;

[0065] Figure 2 (b) is the second checkerboard layout in the method for reconstructing the time-varying temperature field of a highway tunnel fire according to the present invention;

[0066] Figure 2 (c) is a graph evaluating the accuracy of checkerboard-shaped interpolation points in a method for reconstructing a time-varying temperature field in a highway tunnel fire according to the present invention.

[0067] Figure 3 (a) is a triangular mesh layout in the method for reconstructing the time-varying temperature field of a highway tunnel fire according to the present invention;

[0068] Figure 3 (b) is the second triangular mesh layout in the method for reconstructing the time-varying temperature field of a highway tunnel fire according to the present invention;

[0069] Figure 3 (c) is a graph evaluating the accuracy of checkerboard-shaped interpolation points in a method for reconstructing a time-varying temperature field in a highway tunnel fire according to the present invention.

[0070] Figure 4 This is a flowchart of the target plane mesh creation process in S7 of the method for reconstructing the time-varying temperature field of a highway tunnel fire according to the present invention.

[0071] Figure 5 This is a flowchart of the real-time generation of temperature cloud map in S7 of the method for reconstructing the time-varying temperature field of a highway tunnel fire in this invention.

[0072] Figure 6 In the present invention, a method for reconstructing the time-varying temperature field of a highway tunnel fire (S9) employs Kriging interpolation. Sensors are arranged in a checkerboard pattern, with one sensor every 5 meters, to reconstruct a two-dimensional temperature cloud. Figure 1 ;

[0073] Figure 7 In the present invention, a method for reconstructing the time-varying temperature field of a highway tunnel fire (S9) employs an inverse distance-weighted average interpolation method, with a checkerboard layout of sensors, one sensor every 5m, to reconstruct a two-dimensional temperature cloud. Figure 2 ;

[0074] Figure 8In the present invention, a method for reconstructing the time-varying temperature field of a highway tunnel fire (S9) employs cubic spline interpolation and a checkerboard layout of sensors, with one sensor placed every 5 meters, to reconstruct a two-dimensional temperature cloud. Figure 3 ;

[0075] Figure 9 This is the actual temperature cloud map in S9 of the time-varying temperature field reconstruction method for highway tunnel fires in this invention. Detailed Implementation

[0076] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention.

[0077] like Figures 1-9 The method for reconstructing the time-varying temperature field of a highway tunnel fire, as shown, includes the following steps:

[0078] S1: Based on the critical ignition heat flux of each component of the vehicle (tire, bumper, wheel arch, fuel tank, mudguard and hubcap), combined with the formulas for the heat radiation range of the fire source, the heat radiation range of the flue gas, and the flue gas temperature decay model, the ignition range of the vehicle under different fire source powers is obtained.

[0079] The formula for the range of heat radiation from the fire source is:

[0080] (1)

[0081] In the formula: Q rf Radiated heat received by adjacent vehicles, kW / m 2 ; ρ is the transmittance of air to thermal radiation, typically taken as 1; r is the distance between the target object and the virtual point source via the connecting line, in meters; θ is the angle between the connecting line and the normal direction of the target object; Q r Q represents the external radiated power (MW) of the virtual point source. r =χ r Q, χ r The flame radiation fraction is 0.2 to 0.32. In this study, the ignition source fuel is anhydrous ethanol, which is 0.25. Q is the ignition source heat release rate.

[0082] The formula for the range of flue gas thermal radiation is:

[0083] (2)

[0084] In the formula: Q rs This represents the amount of heat radiated by smoke received by adjacent vehicles, in W / m³.2 ;ε r The value represents the flue gas emissivity, taken as 0.8; T represents the flue gas temperature, in K.

[0085] S2: Obtain the vehicle ignition time (TTI) within a specific range by fitting the critical ignition heat flux and ignition time using a fitting function, and set the time to each time t. i The calculated TTI (Expected Time of Ignition) is used to determine the Expected Time of Ignition (ETI), and the Earliest Expected Time of Ignition (ETOI) is identified. The reliability of this method is verified by comparing it with the traditional ignition time prediction method FTP (flux-time product).

[0086] The fitted function relationship is:

[0087] (3)

[0088] In the formula: t is the ignition time, in seconds; Radiant heat flux kW / m 2 Goodness of fit (R) 2 The value is 0.99, and the two show a power-law relationship, indicating that the ignition time decreases according to a power law as the radiative heat flux increases.

[0089] Expected ignition time is:

[0090] (4)

[0091] The actual ignition time is:

[0092] (5)

[0093] In the formula: This refers to the time set of the fire's development, growth, and stabilization phases.

[0094] S3: Calibration of heat release rate from multiple fire sources in scaled-down tunnel fire tests:

[0095] Based on the weightlessness method and the calculated ignition range and ignition time, the heat release rate of multiple fire sources in tunnel fires during scaled-down tests (18 working conditions) was calibrated. The initial fire source with the larger peak HRR was designated as fire source A, while the newly added fire source with the smaller peak HRR that developed over time was designated as fire source B.

[0096] S4: Set the heat release rate curve for numerical simulation of tunnel fire spread:

[0097] Based on the calibration results of multiple fire sources, the fire spread situation in the fire scenario is constructed, the fire spread condition under the mutual influence of multiple fire sources is explored, and the heat release rate curves under different spacing and longitudinal wind speed are calculated and their characteristics are analyzed.

[0098] S5: Reference Figure 1 Establish a database of temperature fields for tunnel fires:

[0099] After clarifying the heat release rate development curves for single-source combustion and multi-source combustion, more detailed temperature field data and real-time HRR data were obtained through numerical simulation (setting 49 operating conditions including single and dual fire sources). The FDS acquisition frequency was set to 3 times / s, and 1050 rows of data including temperature, wind speed, and HRR were obtained for each operating condition to expand the dataset.

[0100] S6: Reference Figures 2-3 Layout of temperature field sensor array for tunnel fire reconstruction:

[0101] To improve monitoring accuracy and coverage, an additional row of temperature sensors was added to both the left and right lanes of the tunnel, forming a wider temperature sensor array as shown in the diagram. Simultaneously, to capture the spread of fire across the tunnel's cross-section, an additional row of temperature sensors was installed at the tunnel ceiling, 0.5m from each of the left and right sidewalls, arranged in a checkerboard and triangular mesh pattern.

[0102] S7: Interpolation method for reconstructing the temperature field of a tunnel fire:

[0103] The interpolation region is modified by writing Python code. Figure 4 The gridding operation shown interpolates grid points to achieve real-time plotting of temperature contour maps. Specifically, the pandas library is used to read CSV table data storing temperature data and sensor coordinates, and matplotlib.pyplot is used for plotting.

[0104] The temperature distribution of the tunnel ceiling can be considered as a continuous surface, which is a typical two-dimensional interpolation problem. Common two-dimensional interpolation methods include Kriging interpolation, inverse distance weighted interpolation, and spline interpolation.

[0105] The core expression for Kriging Interpolation (KI) is:

[0106] (6)

[0107] In the formula: z(x) represents the unknown position. The estimated value; n is the number of known observation points; Z i Given point x iThe actual observed value at the location; λi is the interpolation weight, which is calculated by the Kriging criterion based on the positional relationship between each known point in space and the unknown target point, as well as a predefined covariance function.

[0108] The formula for inverse distance weighted interpolation (IDW) is as follows:

[0109] (7)

[0110] In the formula: The number of known observation points; It is the first The attribute values ​​of each observation point; For the first The weights of each observation point to P are generally defined as: ; Indicates the point to be interpolated With the Observation points The Euclidean distance between them; This is the weighting exponent, usually greater than zero, used to control the rate at which the weight decays with distance.

[0111] Cubic Spline Interpolation (CSI):

[0112] Each cubic polynomial It can be represented as:

[0113] (8)

[0114] In the formula: a i b i c i and d i These are the coefficients of the polynomial, which need to be determined through interpolation conditions and other constraints. To ensure... In x i Passing through point x i y i .have This means The smoothness conditions for cubic spline interpolation include continuity, first derivative continuity, and second derivative continuity:

[0115] (9)

[0116] These conditions can be transformed into a series of linear equations, forming a system of linear equations. By solving this system of equations, we can obtain... , and The value of is obtained, thus yielding the complete cubic spline interpolation function.

[0117] S8: Real-time temperature cloud map generation for tunnel fire temperature field:

[0118] The physical model of tunnel ceiling temperature attenuation (as shown in Formula 10) is integrated with an interpolation algorithm. When the fire source is not directly below the temperature sensor, a virtual highest temperature point is created directly above the fire source. The temperature attenuation model is used to calculate the temperature of this virtual point, assigning it a significant weight. For other areas, only the interpolation algorithm is used. Given the fire source location, the temperature attenuation model can be used to calculate and obtain a relatively accurate temperature above the fire source.

[0119] The physical model for temperature decay in the tunnel ceiling is as follows:

[0120] (10)

[0121] In the formula: Temperature at the reference location; Distance from reference position Temperature at a distance of meters; The initial ambient temperature is denoted by K, which is the attenuation coefficient. The unit of temperature is K.

[0122] Reference Figure 5 The real-time generation process of the temperature cloud map shown, except for the construction of the interpolation function in the gray box, involves looping once every time the temperature sensor array data is updated to generate a real-time temperature cloud map.

[0123] S9: Reconstructed temperature field plotting with different sensor array layouts and interpolation method combinations:

[0124] Reference Figures 6-9 The temperature values ​​of known coordinate points on the tunnel roof are calculated using cubic spline interpolation functions. The coolwarm function in the matplotlib library is then used to map the values ​​onto a preset color scale. Finally, the color of each grid point represents the temperature at its location, forming a two-dimensional temperature cloud map that intuitively shows the temperature gradient. The temperature cloud map is then compared with the actual temperature cloud map. All three interpolation methods can reflect the overall temperature distribution of the tunnel roof well.

[0125] S10: Evaluation of interpolation accuracy for various layouts in tunnel fire temperature field reconstruction:

[0126] Evaluation of interpolation accuracy for different layouts in tunnel fire temperature field reconstruction: Using ceiling temperature data from a specific working condition, three interpolation methods were employed, and the results were cross-validated. Accuracy was evaluated using Mean Relative Error (MRE); a lower MRE value indicates higher accuracy. Robustness was evaluated using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE); lower MAE and RMSE values ​​indicate smaller interpolation errors, higher accuracy, and better robustness. This led to the development of an interpolation method suitable for generating temperature cloud maps in tunnel fires.

[0127] The mean relative error is as follows:

[0128] (11)

[0129] The mean absolute error is as follows:

[0130] (12)

[0131] The root mean square error is specifically:

[0132] (13)

[0133] In the formula: This represents the temperature values ​​at each temperature measurement point; n is the number of samples. This is a temperature estimate.

[0134] Figure 2 (c) and Figure 3 In (c), the red dots are temperature measurement points, and their data are known. The gray dots are feature points selected for interpolation accuracy evaluation. Their data are generated through the interpolation model, and their coordinates remain unchanged in different grid layout schemes.

[0135]

[0136] Evaluation of the average relative error of each interpolation combination shows that group Z7 has the smallest average error at 0.16%, indicating that the interpolation accuracy is highest when the sensors are arranged in layout 3, making it suitable for practical applications. Group Z10 has the largest average error at 6.66%, indicating that the interpolation accuracy is lowest when the sensors are arranged in layout 2, and this combination should be avoided in practical applications. In summary, the current spacing of temperature sensors in most tunnels is 10m. If temperature sensors are arranged at a density of 10m / sensor, according to Table 1, the Z4 interpolation combination has the lowest error rate (3.01%), meaning that a triangular mesh layout and Kriging interpolation model should be used for temperature field reconstruction.

[0137] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for reconstructing the time-varying temperature field of a highway tunnel fire, characterized in that, Includes the following steps: S1: Determine the ignition range of vehicles under different fire source powers in tunnel fires: Based on the critical ignition heat flux of each component of the vehicle, combined with the formulas for the heat radiation range of the fire source, the heat radiation range of the smoke, and the smoke temperature decay model, the ignition range of the vehicle under different fire source powers is obtained. S2: Determine the vehicle ignition time near the fire source during the development of a tunnel fire: Obtain the vehicle ignition time TTI within a specific range by fitting the critical ignition heat flux and ignition time from step S1 using a fitting function, and use the time t at each time point as an example. i The TTI is calculated to determine the expected ignition time ETI, and the earliest expected ignition time ETOI is found to satisfy ignition. S3: Calibrate the heat release rate of multiple fire sources in the scaled-down tunnel fire test: Based on the weight loss method and the ignition range determined in step S1 and the ignition time determined in step S2, calibrate the heat release rate of multiple fire sources in the scaled-down tunnel fire test. S4: Set the heat release rate curve for numerical simulation of fire spread in tunnel: Based on the calibration results in step S3, construct the fire spread situation in the fire scenario, and calculate and analyze the characteristics of the heat release rate curve under different spacing and longitudinal wind speed. S5: Establish a temperature field database for tunnel fires: After clarifying the development curves of heat release rate for single-source combustion and multi-source fires, obtain more detailed temperature field data and real-time heat release rate data through numerical simulation. S6: Design of the layout of the temperature field sensor array for tunnel fire reconstruction: Temperature sensors are installed on the left and right lanes of the tunnel respectively, and temperature sensors are arranged in a "chessboard" and "triangular mesh" pattern on the top of the tunnel. S7: Propose an interpolation method for reconstructing the temperature field of a tunnel fire: The interpolation area is gridded, and three two-dimensional interpolation methods, namely Kriging interpolation, inverse distance weighted interpolation, and spline interpolation, are used to interpolate the grid points to achieve real-time rendering of the temperature cloud map. S8: Generate a real-time temperature cloud map of the tunnel fire temperature field: Integrate the physical model of temperature decay of the tunnel ceiling with the interpolation algorithm of step S7. When the fire source is not directly below the temperature sensor, a virtual highest temperature point is set directly above the fire source. The temperature decay model is used to calculate the temperature of the virtual point and assign weights. The interpolation algorithm is used to calculate the temperature of the remaining parts. S9: Draw the reconstructed temperature field cloud map with different sensor array layouts and interpolation method combinations: Calculate the temperature value of the known coordinate point at the top of the tunnel using the interpolation function in step S7, and map it to the preset color scale using the coolwarm function in the matplotlib library. Finally, make the color of each grid point represent the temperature at its location to form a two-dimensional temperature cloud map that intuitively shows the temperature gradient change, and compare it with the actual temperature cloud map. S10: Evaluate the interpolation accuracy of various layouts for tunnel fire temperature field reconstruction: Take the ceiling temperature data of a certain working condition, use the three interpolation methods in step S7, and cross-validate the results; evaluate the accuracy by mean relative error (MRE); evaluate the robustness by mean absolute error (MAE) and root mean square error (RMSE); and derive an interpolation method suitable for generating tunnel fire temperature cloud maps.

2. The method for reconstructing the time-varying temperature field of a highway tunnel fire as described in claim 1, characterized in that, The formula for the range of heat radiation from the fire source in step S1 is: （1） In the formula: Q rf Radiated heat received by adjacent vehicles, kW / m 2 ; Let be the transmittance of air to thermal radiation, taken as 1; r be the distance between the target object and the virtual point source, in meters; θ be the angle between the connecting line and the normal direction of the target object; Q r Q represents the radiated power of the virtual point source, expressed in MW. r =χ r Q, χ r The flame radiation fraction is 0.2 to 0.32, and the value is 0.25 for anhydrous ethanol as the ignition source fuel. Q is the heat release rate of the ignition source.

3. The method for reconstructing the time-varying temperature field of a highway tunnel fire as described in claim 2, characterized in that, The formula for the range of flue gas thermal radiation in step S1 is: （2） In the formula: Q rs This represents the radiant heat from the exhaust gas received by adjacent vehicles, in W / m². 2 ; ε r The value represents the flue gas emissivity, taken as 0.8; T represents the flue gas temperature, in K.

4. The method for reconstructing the time-varying temperature field of a highway tunnel fire as described in claim 1, characterized in that, The fitting function relationship in step S2 is: （3） In the formula: t is the ignition time, in seconds; Radiant heat flux kW / m 2 Goodness of fit (R) 2 The value is 0.99, and the two have a power-law relationship. The ignition time decreases according to a power law as the radiant heat flux increases. Expected ignition time is: （4） The actual ignition time is: （5） In the formula: This refers to the time set of the fire's development, growth, and stabilization phases.

5. The method for reconstructing the time-varying temperature field of a highway tunnel fire as described in claim 1, characterized in that, The numerical simulation based on step S5 uses FDS, and the FDS acquisition frequency is set to 3 times / s; each working condition can obtain 1050 rows of data including temperature, wind speed, and heat release rate (HRR) to expand the dataset.

6. The method for reconstructing the time-varying temperature field of a highway tunnel fire as described in claim 1, characterized in that, In step S6, the temperature sensors at both ends of the tunnel top are 0.5m away from the left and right sidewalls, and the distance between adjacent temperature sensors is 5~10m.

7. The method for reconstructing the time-varying temperature field of a highway tunnel fire as described in claim 1, characterized in that, The expression for the Kriging interpolation method in the two-dimensional interpolation method in step S7 is as follows: （6） In the formula: z(x) represents the unknown position. The estimated value; n is the number of known observation points; Z i Given point x i The actual observed value at the location; λi is the interpolation weight; The expression for inverse distance weighted interpolation is: （7） In the formula: The number of known observation points; It is the first The attribute values ​​of each observation point; For the first The weights of each observation point to P, ; Indicates the point to be interpolated With the Observation points The Euclidean distance between them; This is the weighting index, used to control the rate at which the weight decays with distance; The expression for spline interpolation is: Each cubic polynomial Represented as: （8） In the formula: a i b i c i and d i These are the coefficients of the polynomial; to ensure In x i Passing through point x i y i ,have ,mean The smoothness conditions for cubic spline interpolation include continuity, first derivative continuity, and second derivative continuity: （9） These conditions are transformed into a series of linear equations, thus forming a system of linear equations. By solving this system of equations, we can obtain... , and The value of is obtained, thus yielding the complete cubic spline interpolation function.

8. The method for reconstructing the time-varying temperature field of a highway tunnel fire as described in claim 1, characterized in that, The physical model for the temperature decay of the tunnel ceiling in step S8 is as follows: （10） In the formula: Temperature at the reference location; Distance from reference position Temperature at a distance of meters; The initial ambient temperature is denoted by K, which is the attenuation coefficient. The unit of temperature is K.

9. The method for reconstructing the time-varying temperature field of a highway tunnel fire as described in claim 1, characterized in that, The average relative error in step S10 is specifically as follows: （11） The mean absolute error is as follows: （12） The root mean square error is as follows: （13） In the formula: This represents the temperature values ​​at each temperature measurement point; n is the number of samples. This is a temperature estimate.

10. A system based on a time-varying temperature field reconstruction method for highway tunnel fires, characterized in that, include: At least one processor; And a memory communicatively connected to at least one of the processors; wherein the memory stores instructions executable by the processor to implement the time-varying temperature field reconstruction method for highway tunnel fires as described in any one of claims 1 to 9.

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