A method and device for air leakage early warning of a tunnel key smoke exhaust duct and a medium

Abstract

The present application relates to a kind of for tunnel key exhaust flue air leakage early warning method, device and medium, belong to tunnel exhaust safety monitoring and intelligent ventilation control field.It includes the following steps: S1, obtains input data, including the wind speed value and air pressure value of the single point of tunnel exhaust monitoring section;S2, input data is input into wind speed correction coefficient model, obtain the average wind speed of monitoring section;S3, calculate one or more of net air leakage rate and resistance abnormality index, determine air leakage risk level according to the calculation result;S4, output the early warning information matched with air leakage risk level.The present application improves the air leakage monitoring and early warning means of existing tunnel exhaust, accurately inverts section average wind speed by single point wind speed sensor, and combines with the local resistance correction of exhaust valve to calculate air leakage index, realizes tunnel exhaust air leakage early warning disposal.

Description

Technical Field

[0001] This invention belongs to the field of tunnel smoke exhaust safety monitoring and intelligent ventilation control, and relates to a method, device and medium for early warning of air leakage in tunnel smoke exhaust ducts. Background Technology

[0002] In the construction of modern transportation tunnels, centralized smoke exhaust arches are a core lifeline for fire prevention and mitigation. During long-term operation, due to foundation settlement, concrete cracking, improper closure of smoke exhaust valves, or aging of seals, smoke exhaust ducts often experience varying degrees of air leakage. Air leakage in smoke exhaust ducts not only severely reduces the effective smoke extraction volume during a fire but can also lead to the spread of smoke, seriously threatening personal safety. Therefore, real-time and accurate monitoring of smoke duct air velocity and leakage status is necessary.

[0003] In this project, mainstream monitoring methods such as manual inspection, fiber optic sensor deployment, and multi-point grid weighted methods have significant shortcomings. Manual grid inspection can only perform intermittent detection, resulting in high labor costs and an inability to provide continuous online monitoring. Deploying fiber optic sensors along the entire line incurs high hardware investment and maintenance costs. Traditional single-point velocity measurement relies on fixed empirical coefficients to calculate average wind speed, which is not adapted to actual geometric conditions such as arched flue cross-sections, absolute surface roughness, and measurement point locations, leading to large wind speed inversion errors and poor versatility. Furthermore, existing air leakage calculation models do not consider the local resistance caused by the protrusion of the exhaust valve in the civil engineering, easily misjudging normal structural pressure drop as air leakage, resulting in serious false alarms in the system.

[0004] Therefore, this invention proposes a method, device, equipment, readable storage medium, and program product for early warning of air leakage in tunnel smoke exhaust ducts. It uses a single-point wind speed sensor to accurately invert the average wind speed of the cross section and combines it with the local resistance correction of the smoke exhaust valve to calculate the air leakage index, thereby realizing early warning and handling of air leakage in tunnel smoke exhaust ducts. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide a method, device, equipment, readable storage medium and program product for early warning of air leakage in tunnel smoke exhaust ducts, so as to realize real-time monitoring and accurate early warning of wind speed and air leakage status in tunnel smoke exhaust ducts.

[0006] The present invention discloses a method for early warning of air leakage in key smoke exhaust ducts of tunnels, comprising the following steps: S1. Obtain input data, which includes the wind speed and air pressure values ​​at a single point on the monitoring section of the tunnel smoke exhaust duct; S2. A multivariate nonlinear regression method was used to perform CFD numerical simulations on tunnel smoke exhaust ducts under multiple typical working conditions to obtain the wind speed correction coefficient K, where K is the wind speed at a single point on the monitoring section. Average wind speed at the monitoring section The ratio is used to obtain the wind speed correction coefficient K by fitting the wind speed correction coefficient model with CFD numerical simulation samples. The wind speed correction coefficient K is then used to calculate the wind speed value at a single point on the monitoring section of the tunnel smoke exhaust duct. The average wind speed at the current monitoring section was calculated. Typical operating conditions include multiple levels of exhaust duct height-to-width ratio. Absolute roughness of flue wall Wind speed sensor installation height These together constitute several sets of CFD numerical simulation samples, with the wind speed correction coefficient model being... With dimensionless independent variables: , , , Multivariate nonlinear equations, denoted as the equivalent diameter of the flue, and x as the longitudinal coordinate of the wind speed sensor within the flue. S3. Calculate the leakage index using the average wind speed and air pressure values ​​of the current monitoring section and the nearest monitoring section of the end fan, and determine the leakage risk level based on the calculation results. The leakage index includes one or more of net leakage rate and resistance anomaly index. The net leakage rate is used to represent the sealing performance of the tunnel exhaust duct, and the resistance anomaly index is used to represent the fluid stability within the tunnel exhaust duct. The local resistance coefficient based on the civil engineering protrusion height of the exhaust valve is introduced to calculate the net leakage rate and the resistance anomaly index. S4. Output early warning information that matches the air leakage risk level.

[0007] Optionally, when 0% ≤ net air leakage rate < 5% and 0 ≤ resistance anomaly index ≤ 1.1, the air leakage risk level is determined to be healthy; When 5% ≤ net air leakage rate < 12% or 1.1 < resistance anomaly index ≤ 1.3, the air leakage risk level is determined to be minor air leakage; When 12% ≤ net air leakage rate < 25% or 1.3 < resistance anomaly index ≤ 1.6, the air leakage risk level is determined to be significant air leakage; When the net air leakage rate is greater than 25% or the resistance anomaly index is greater than 1.6, the air leakage risk level is determined to be severe damage.

[0008] Optionally, when the risk level is severe damage, a smoke extraction plan is executed, and the terminal is linked to lock and locate the target. When the risk level is significant air leakage, a maintenance work order is pushed, the frequency of the input data collection is increased, and a frequency conversion power compensation command is sent to the end fan.

[0009] Optionally, one or more of the following conditions must be met: (1) The horizontal distance between the monitoring section and the opened exhaust valve is ≥20 times the equivalent diameter of the exhaust duct; (2) The horizontal distance between the monitoring section and the opened smoke exhaust valve is ≥30m; (3) The horizontal distance between the monitoring section and the fan at the end of the flue is ≥ 10 times the equivalent diameter of the flue; (4) The wind speed sensor that collects the input data is installed at the longitudinal center line at the top of the monitoring section, with a vertical downward installation depth of 5cm to 15cm.

[0010] Optionally, the wind speed correction factor is a dimensionless correction factor. In the formula, , Independently selected from dimensionless variables: , , , , , , , The regression coefficients are to be solved.

[0011] Optionally, the following steps are also included: S5. Construct a wind speed distribution model for the monitoring section, and infer the wind speed distribution of the monitoring section based on the input data; S6. Construct a three-dimensional monitoring model of the tunnel exhaust duct, and map the wind speed distribution and air leakage status of the monitoring section to the three-dimensional model of the tunnel exhaust duct.

[0012] A leakage early warning device for a key smoke exhaust duct in a tunnel includes modules for implementing the leakage early warning method for the key smoke exhaust duct in a tunnel: The input data acquisition module is used to acquire input data; The wind speed correction module uses multivariate nonlinear regression to perform CFD numerical simulations on tunnel smoke exhaust ducts under multiple typical working conditions to obtain the wind speed correction coefficient K, where K is the wind speed at a single point on the monitoring section. Average wind speed at the monitoring section The ratio is used to obtain the wind speed correction coefficient K by fitting the wind speed correction coefficient model with CFD numerical simulation samples. The wind speed correction coefficient K is then used to calculate the wind speed value at a single point on the monitoring section of the tunnel smoke exhaust duct. The average wind speed at the current monitoring section was calculated. ; The early warning level determination module is used to calculate the results of the air leakage index and determine the air leakage risk level. The early warning module is used to issue an early warning for the tunnel exhaust duct section where the monitoring section is located, based on the air leakage risk level.

[0013] The computer device of the present invention includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method.

[0014] The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described method.

[0015] The present invention provides a computer program product comprising a computer program that, when executed by a processor, implements the steps of the above-described method.

[0016] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention only requires the deployment of single-point wind speed and air pressure sensors. The average wind speed of the cross section is inverted through the wind speed correction coefficient model. The local resistance coefficient calibrated by the civil engineering protrusion height of the smoke exhaust valve is introduced. The leakage risk level is divided by the net air leakage rate and the resistance anomaly index. This eliminates false air leakage misjudgment caused by the valve civil engineering structure, effectively reducing the amount of sensing equipment and reducing manual operation and maintenance costs. 2. This invention matches the fan control commands to achieve leakage monitoring and compensated control of smoke extraction capacity; 3. This invention uses multiple sets of CFD numerical simulation samples to fit dimensionless wind speed correction coefficients, covering typical working conditions such as the height-to-width ratio of the flue, the absolute roughness of the wall surface, and the installation height of the wind speed sensor; by analyzing the sensitivity of the K value to the installation position error, the optimal installation depth range is determined, and combined with the cross-sectional location constraints of the fully developed fluid section, the interference of near-field turbulence is avoided, the impact of construction and installation errors is reduced, and the accuracy of single-point wind speed inversion and engineering versatility are taken into account. 4. This invention constructs a three-dimensional monitoring model of cross-sectional wind speed distribution and flue gas exhaust. Based on an offline CFD flow field database, the full cross-sectional wind speed distribution is restored through spatial interpolation. The leakage classification status is mapped to the three-dimensional model, which intuitively presents the spatial distribution of leakage, facilitates rapid fault location, and improves the efficiency of operation and maintenance visualization management. Attached Figure Description

[0017] Figure 1 This is a flowchart of an embodiment of the present invention; Figure 2 This is a schematic diagram of the velocity distribution in a cross-section CFD numerical simulation according to an embodiment of the present invention; Figure 3 This is a spatial discrete distribution diagram of the wind speed correction coefficient K for different parameters according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the fitting effect of the wind speed correction coefficient K according to an embodiment of the present invention; Figure 5This is a schematic diagram of the error verification of the wind speed correction coefficient K according to an embodiment of the present invention; Figure 6 This is a three-dimensional monitoring model of a tunnel smoke exhaust duct according to an embodiment of the present invention. Detailed Implementation

[0018] This invention provides a method for early warning of air leakage in key smoke exhaust ducts of tunnels, comprising the following steps: S1. Obtain input data, which includes the wind speed and air pressure values ​​at a single point of the tunnel exhaust duct monitoring section. The air pressure value is the static pressure value of the monitoring section, which is used to calculate the air pressure difference between adjacent monitoring sections. S2. A multivariate nonlinear regression method was used to perform CFD numerical simulations on tunnel smoke exhaust ducts under multiple typical working conditions to obtain the wind speed correction coefficient K, where K is the wind speed at a single point on the monitoring section. Average wind speed at the monitoring section The ratio is used to obtain the wind speed correction coefficient K by fitting the wind speed correction coefficient model with CFD numerical simulation samples. The wind speed correction coefficient K is then used to calculate the wind speed value at a single point on the monitoring section of the tunnel smoke exhaust duct. The average wind speed at the current monitoring section was calculated. Typical operating conditions include multiple levels of exhaust duct height-to-width ratio. Absolute roughness of flue wall Wind speed sensor installation height These together constitute several sets of CFD numerical simulation samples, and the wind speed correction coefficient model is as follows:

[0019] In the formula, , Independently selected from dimensionless variables: , , , , , , , Let be the regression coefficients to be solved; denoted as the equivalent diameter of the flue, and x as the longitudinal coordinate of the wind speed sensor within the flue. S3. Calculate the leakage index using the average wind speed and air pressure values ​​of the current monitoring section and the nearest monitoring section of the end fan, and determine the leakage risk level based on the calculation results. The leakage index includes one or more of net leakage rate and resistance anomaly index. Net leakage rate is used to represent the sealing performance of the tunnel exhaust duct, and resistance anomaly index is used to represent the fluid stability in the tunnel exhaust duct. The local resistance coefficient based on the civil engineering protrusion height of the exhaust valve is introduced to calculate the net leakage rate and resistance anomaly index. S4. Output early warning information that matches the air leakage risk level.

[0020] Optionally, net air leakage rate The calculation formula is:

[0021] in, The flow coefficient is 0.60~0.65 for sharp-edge orifice exhaust ports and 0.50~0.70 for conventional exhaust ports. λ is the friction factor, which can be calculated iteratively using the Colebrook formula or obtained from the Moody diagram, with a maximum value of 0.002. d is the distance between adjacent exhaust ports. The equivalent diameter of the flue gas duct. , , Let be the static pressure value of the i-th monitoring section. Let i be the volumetric flow rate at the i-th monitoring section. The volumetric flow rate is the value at the monitoring section closest to the end fan. Let be the average wind speed at the i-th monitoring section. The average wind speed at the monitoring section closest to the end fan. Where A is the air density and A is the monitoring cross-sectional area. The local resistance coefficient is determined based on the civil engineering protrusion height of the smoke exhaust valve. , Based on the frontal drag coefficient, The windward area of ​​the smoke exhaust outlet.

[0022] Optionally, resistance anomaly index The calculation formula is:

[0023] In the formula, The pressure difference between two adjacent monitoring sections. The pressure drop due to frictional resistance along the frictional path is calculated based on the Darcy-Wiesbach formula. The local resistance coefficient is determined based on the civil engineering protrusion height of the smoke exhaust valve. This represents the average wind speed between two adjacent monitoring sections. d is the distance between two adjacent exhaust valves.

[0024] Understandably, the resistance fluctuation index γ is the ratio of the actual pressure difference between two adjacent monitoring sections to the theoretically expected pressure drop. A value close to 1 indicates that the actual flow state matches the theoretical expectation and the flow is stable; a significant deviation of γ from 1 indicates abnormal resistance changes and the presence of risks such as air leakage or local blockage.

[0025] Optionally, when 0% ≤ net air leakage rate < 5% and 0 ≤ resistance anomaly index ≤ 1.1, the air leakage risk level is determined to be healthy.

[0026] Optionally, when 5% ≤ net air leakage rate < 12% or 1.1 < resistance anomaly index ≤ 1.3, the air leakage risk level is determined to be minor air leakage.

[0027] Optionally, when 12% ≤ net air leakage rate < 25% or 1.3 < resistance anomaly index ≤ 1.6, the air leakage risk level is determined to be significant air leakage.

[0028] Optionally, when the net air leakage rate is >25% or the resistance anomaly index is >1.6, the air leakage risk level is determined to be severe damage.

[0029] Optionally, when the risk level is severe damage, the smoke extraction plan will be implemented and the terminal will be linked to lock and locate the location.

[0030] Optionally, when the risk level is significant air leakage, a maintenance work order is pushed, the frequency of input data collection is increased, and a frequency conversion power compensation command is sent to the end fan.

[0031] Optionally, when the risk level is healthy or minor air leakage, no proactive, external alarm or notification mechanism is triggered; Optionally, the monitoring section is located inside the arched flue, away from the flue valve and the end fan.

[0032] Optionally, the horizontal distance between the monitoring section and the opened exhaust valve is ≥20 times the equivalent diameter of the exhaust duct.

[0033] Optionally, the horizontal distance between the monitoring section and the opened smoke exhaust valve is ≥30m.

[0034] Optionally, the horizontal distance between the monitoring section and the end fan of the flue is ≥10 times the equivalent diameter of the flue.

[0035] Optionally, the wind speed sensor for collecting input data is located at the longitudinal centerline at the top of the monitoring section, with a vertical downward installation depth of 5cm to 15cm.

[0036] Optionally, the wind speed correction factor is a dimensionless correction factor. In the formula, , Independently selected from dimensionless variables: , , , , , , , Let be the regression coefficients to be solved; denoted as the equivalent diameter of the flue, and x as the longitudinal coordinate of the wind speed sensor within the flue.

[0037] Optionally, typical operating conditions include 5 levels of flue height-to-width ratio, 3 levels of flue wall absolute roughness, and 5 levels of sensor installation height, totaling 75 sets of CFD numerical simulation samples.

[0038] Optionally, the aspect ratio ranges from 0.158 to 0.203, the absolute roughness of the wall ranges from 0.003 to 0.005, and the installation height of the wind speed sensor is 5cm, 8cm, 10cm, 12cm, or 15cm.

[0039] Optionally, the following steps may be included after step S4: S5. Construct a wind speed distribution model for the monitoring section, and infer the wind speed distribution of the monitoring section based on the input data; S6. Construct a three-dimensional monitoring model of the tunnel exhaust duct, and map the wind speed distribution and air leakage status of the monitoring section to the three-dimensional model of the tunnel exhaust duct.

[0040] Optionally, in step S5, the cross-sectional wind speed distribution model is generated by spatial interpolation based on the offline CFD flow field database according to the wind speed at a single point.

[0041] Optionally, in step S6, the leakage risk level is divided into independent units based on the interval between adjacent smoke exhaust valves of the monitoring section (the interval length is the sum of the length of the upstream smoke exhaust valve itself and the distance between the smoke exhaust valves). The four leakage risk levels are represented by green, yellow, orange and red, respectively, which are healthy, slightly leaking, significantly leaking and severely damaged, so as to realize the visualization rendering display.

[0042] Example 1

[0043] Please see Figures 1-6 A method for early warning of air leakage in key smoke exhaust ducts of tunnels includes the following steps S0~S4. S0. Taking an arched smoke exhaust duct in a shield tunnel as an example, measure the geometric parameters and wall parameters: The cross-sectional height H = 2.102 m, the cross-sectional width W = 11.254 m, and the cross-sectional area S = 16.775 m². 2 The cross-sectional perimeter C = 24.45m, and the equivalent diameter D h =4S / C≈2.744m, aerodynamic viscosity =1.825×10 -5 Pa·s, exhaust duct end fan pressure P=1100Pa, wall absolute roughness =0.003m (corresponding to the conventional value of concrete pouring surface), wall relative roughness e= / Dh =0.00109, air density =1.225kg / m 3 (Standard atmospheric pressure at 20℃) S1. Several monitoring sections are set up along the tunnel exhaust duct. The horizontal distance between the monitoring section and the opened exhaust valve is ≥20 times the equivalent diameter of the exhaust duct or ≥30m. The horizontal distance between the monitoring section and the exhaust fan at the end of the exhaust duct is ≥10 times the equivalent diameter of the exhaust duct. The single-point wind speed sensor and air pressure sensor are set at the top longitudinal centerline of the monitoring section and installed vertically downward to a depth of 10cm.

[0044] S2. A multivariate nonlinear regression method was used to perform CFD numerical simulations on tunnel smoke exhaust ducts under multiple typical working conditions to obtain the wind speed correction coefficient K, where K is the wind speed at a single point on the monitoring section. Average wind speed at the monitoring section The ratio is used to obtain the wind speed correction coefficient K by fitting the wind speed correction coefficient model with CFD numerical simulation samples. The wind speed correction coefficient K is then used to calculate the wind speed value at a single point on the monitoring section of the tunnel smoke exhaust duct. The average wind speed at the current monitoring section was calculated. Typical operating conditions include multiple levels of exhaust duct height-to-width ratio. Absolute roughness of flue wall Wind speed sensor installation height These together constitute several sets of CFD numerical simulation samples; CFD numerical simulations were conducted on 75 typical operating conditions on the server. The aspect ratio (H / W) of the flue gas duct was set to five levels (0.158, 0.174, 0.186, 0.195, 0.203), and the absolute surface roughness was... Three horizontal values ​​(0.003, 0.004, 0.005) were selected, and five horizontal values ​​(5cm, 8cm, 10cm, 12cm, 15cm) were selected for the installation height r. Each working condition was run transiently for more than 2000 steps, and a total of 6600 high-reliability feature point data points of the flow field space were extracted. Figure 2 This is a CFD velocity distribution cloud map of a monitoring section of an arched flue under a typical working condition. The wind speed distribution within the section is non-uniform, and wind speed correction is required to obtain the average wind speed of the section.

[0045] Figure 3 To account for the spatial discrete distribution of each dimensionless parameter on the wind speed correction coefficient K, the nonlinear influence of surface multiple parameters, and the coupling effect, multiple regression modeling is adopted; relative installation depth r / The height-to-width ratio (H / W) of the exhaust duct and its longitudinal relative position (x / (x is the longitudinal coordinate of the measuring point within the flue), relative roughness of the wall surface / Significant nonlinear effects and coupling effects exist. Therefore, these four dimensionless geometric parameters are selected to construct a logarithmic-quadratic polynomial model:

[0046] In the formula, , Let represent 4 dimensionless independent variables, and b be the regression coefficient to be solved. The 4 independent variables correspond to 4 linear terms, 4 quadratic terms, and 6 pairwise interaction terms.

[0047] The fitting results for the wind speed correction factor K are shown below:

[0048] like Figures 4-5 As shown, the on-site measurement and comparison with the 9-point grid anemometer showed that the error between the K model and the calculated K value was mostly within 10%, which meets the engineering requirements.

[0049] K represents the speed measurement at a single point on the cross-section. With average wind speed The ratio; the single-point wind speed collected in real time using a single-point wind speed sensor. The average wind speed at the current monitoring section is calculated by calling the wind speed correction factor K. .

[0050] S3. Introduce the local resistance coefficient based on the civil engineering protrusion height of the smoke exhaust valve, and calculate two leakage evaluation indicators for the monitoring section: net air leakage rate and resistance anomaly index.

[0051] Net air leakage rate The calculation formula is:

[0052] In the formula, For the flow coefficient, the value is 0.60~0.65 for the sharp-edge orifice exhaust port and 0.50~0.70 for the conventional exhaust port. It needs to be calibrated by experiment or by referring to the experience of similar projects. The friction factor along the pipeline can be calculated iteratively using the Colebrook formula or obtained from the Moody diagram. After acceptance testing of the tunnel smoke exhaust duct, the maximum value is the standard value of 0.002, where d is the distance between adjacent smoke exhaust outlets. The equivalent diameter of the flue gas duct. , , Let be the static pressure value at the i-th monitoring section; Let i be the volumetric flow rate at the i-th monitoring section. The volumetric flow rate at the monitoring section closest to the end fan. The average wind speed at the i-th monitoring section The average wind speed at the monitoring section closest to the end fan. Where A is the air density and A is the monitoring cross-sectional area. The local resistance coefficient is determined based on the civil engineering protrusion height of the smoke exhaust valve. , The frontal drag coefficient is set to 1.0~1.2. The windward area of ​​the smoke exhaust outlet; Resistance Anomaly Index The calculation formula is:

[0053] In the formula, The pressure difference between two adjacent monitoring sections. The pressure drop due to frictional resistance along the frictional path is calculated based on the Darcy-Wiesbach formula. The local resistance coefficient is determined based on the civil engineering protrusion height of the smoke exhaust valve. The average wind speed between two adjacent monitoring sections; d is the distance between two adjacent exhaust valves.

[0054] when <5% and When the value is ≤1.1, it is considered healthy, and the normal collection instructions are maintained; when 5% ≤ <12% or 1.1% When the value is ≤1.3, it is considered a slight air leakage, and the normal data collection command is maintained; when 12% ≤ <25% or 1.3% When the value is ≤1.6, it is determined to be a significant air leakage, a maintenance work order is pushed, the sampling frequency of the wind speed and air pressure sensors is increased, and a frequency converter power compensation command is sent to the end fan; when >25% or When the value is greater than 1.6, it is considered a serious breach. The smoke extraction plan is then executed, and the terminal is locked and located to ensure that the effective smoke extraction capacity at the end of the flue is always maintained above the safety threshold.

[0055] like Figure 6As shown, S4 uses the interval between adjacent smoke exhaust valves at the monitoring section (the interval length is the sum of the length of the upstream smoke exhaust valve itself and the distance between the smoke exhaust valves) as an independent unit to bind the air leakage health status, with green, yellow, orange, and red representing four air leakage states: healthy, slight air leakage, significant air leakage, and severe damage, respectively. A detailed 3D BIM solid model of the tunnel is rendered on the central control room's large screen using the WebGL engine, mapping the air leakage status of the monitoring section to the 3D model of the smoke exhaust duct. When a segment is at the orange level, maintenance personnel click on that orange grid unit. In addition to displaying the real-time single-point wind speed curve on the 3D large screen, a wind speed distribution model of the monitoring section is constructed using spatial quadratic interpolation. Based on the offline CFD flow field database, a panoramic dynamic flow velocity distribution cloud map of the section is quickly rendered according to the single-point wind speed.

[0056] The descriptions of the orientation and relative positional relationships of the structures in this invention, such as front, back, left, right, up, and down, do not constitute a limitation of this invention, but are merely for the convenience of description.

Claims

1. A method for early warning of air leakage in key smoke exhaust ducts of tunnels, characterized in that, Includes the following steps: S1. Obtain input data, which includes the wind speed and air pressure values ​​at a single point on the monitoring section of the tunnel smoke exhaust duct; S2. A multivariate nonlinear regression method was used to perform CFD numerical simulations on tunnel smoke exhaust ducts under multiple typical working conditions to obtain the wind speed correction coefficient K, where K is the wind speed at a single point on the monitoring section. Average wind speed at the monitoring section The ratio is used to obtain the wind speed correction coefficient K by fitting the wind speed correction coefficient model with CFD numerical simulation samples. The wind speed correction coefficient K is then used to calculate the wind speed value at a single point on the monitoring section of the tunnel smoke exhaust duct. The average wind speed at the current monitoring section was calculated. Typical operating conditions include multiple levels of exhaust duct height-to-width ratio. Absolute roughness of flue wall Wind speed sensor installation height These together constitute several sets of CFD numerical simulation samples, with the wind speed correction coefficient model being... With dimensionless independent variables: , , , Multivariate nonlinear equations, denoted as the equivalent diameter of the flue, and x as the longitudinal coordinate of the wind speed sensor within the flue. S3. Calculate the leakage index using the average wind speed and air pressure values ​​of the current monitoring section and the nearest monitoring section of the end fan, and determine the leakage risk level based on the calculation results. The air leakage index includes one or more of net air leakage rate and resistance anomaly index. The net air leakage rate is used to represent the sealing performance of the tunnel exhaust duct, and the resistance anomaly index is used to represent the fluid stability within the tunnel exhaust duct. The net air leakage rate and the resistance anomaly index are calculated by introducing a local resistance coefficient based on the civil engineering protrusion height of the exhaust valve. S4. Output early warning information that matches the air leakage risk level.

2. The air leakage early warning method according to claim 1, characterized in that, When 0% ≤ net air leakage rate < 5% and 0 ≤ resistance anomaly index ≤ 1.1, the air leakage risk level is determined to be healthy; When 5% ≤ net air leakage rate < 12% or 1.1 < resistance anomaly index ≤ 1.3, the air leakage risk level is determined to be minor air leakage; When 12% ≤ net air leakage rate < 25% or 1.3 < resistance anomaly index ≤ 1.6, the air leakage risk level is determined to be significant air leakage; When the net air leakage rate is greater than 25% or the resistance anomaly index is greater than 1.6, the air leakage risk level is determined to be severe damage.

3. The air leakage early warning method according to claim 2, characterized in that, When the risk level is severe damage, the smoke extraction plan will be executed, and the terminal will be linked to lock and locate the target. When the risk level is significant air leakage, a maintenance work order is pushed, the frequency of the input data collection is increased, and a frequency conversion power compensation command is sent to the end fan.

4. The air leakage early warning method according to claim 3, characterized in that, One or more of the following conditions must be met: (1) The horizontal distance between the monitoring section and the opened exhaust valve is ≥20 times the equivalent diameter of the exhaust duct; (2) The horizontal distance between the monitoring section and the opened smoke exhaust valve is ≥30m; (3) The horizontal distance between the monitoring section and the fan at the end of the flue is ≥ 10 times the equivalent diameter of the flue; (4) The wind speed sensor that collects the input data is installed at the longitudinal center line at the top of the monitoring section, with a vertical downward installation depth of 5cm to 15cm.

5. The air leakage early warning method according to claim 3, characterized in that, The wind speed correction factor is a dimensionless correction factor. In the formula, , Independently selected from dimensionless variables: , , , , , , , The regression coefficients are to be solved.

6. The air leakage early warning method according to claim 3, characterized in that, It also includes the following steps: S5. Construct a wind speed distribution model for the monitoring section, and infer the wind speed distribution of the monitoring section based on the input data; S6. Construct a three-dimensional monitoring model of the tunnel exhaust duct, and map the wind speed distribution and air leakage status of the monitoring section to the three-dimensional model of the tunnel exhaust duct.

7. A leakage early warning device for key smoke exhaust ducts in tunnels, characterized in that, Includes modules for implementing the method according to any one of claims 1 to 6: The input data acquisition module is used to acquire input data; The wind speed correction module uses multivariate nonlinear regression to perform CFD numerical simulations on tunnel smoke exhaust ducts under multiple typical working conditions to obtain the wind speed correction coefficient K, where K is the wind speed at a single point on the monitoring section. Average wind speed at the monitoring section The ratio is used to obtain the wind speed correction coefficient K by fitting the wind speed correction coefficient model with CFD numerical simulation samples. The wind speed correction coefficient K is then used to calculate the wind speed value at a single point on the monitoring section of the tunnel smoke exhaust duct. The average wind speed at the current monitoring section was calculated. ; The early warning level determination module is used to calculate the results of the air leakage index and determine the air leakage risk level. The early warning module is used to issue an early warning for the tunnel exhaust duct section where the monitoring section is located, based on the air leakage risk level.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 6.

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