Subway tunnel fire spreading prediction method

Abstract

The invention discloses a subway tunnel fire behavior spreading prediction method, which comprises the steps of 1, fire source type digital modeling, 2, fire scene initialization, 3, dynamic fire source loading and ignition triggering, 4, fire behavior spreading multi-physics field coupling calculation, 5, ventilation system dynamic response, 6, visual risk thermodynamic diagram generation, and 7, fire behavior spreading prediction. Step 7, carrying out real-time early warning on the safety margin; the method has the beneficial effects that a total factor modeling capability is realized, multi-dimensional parameter input from fire source characteristics to a tunnel environment is supported, a scene range covering a typical subway fire disaster is expanded, solving is realized through a multi-physics field simultaneous equation, a fire spreading error is reduced, smoke diffusion simulation precision is improved, and a dynamic ventilation adjustment and safety early warning mechanism is realized. And more buffer time is provided for emergency response.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of fire spread prediction, in particular to a subway tunnel fire spread prediction method. BACKGROUND

[0002] With the acceleration of urbanization, as a large-capacity and efficient public transportation mode, subway has become a core component of modern urban transportation. However, due to the characteristics of closed space, dense personnel and complex equipment, subway tunnels have significant risks such as rapid fire spread, uncontrollable smoke diffusion and difficult personnel evacuation. According to statistics, more than 70% of casualties in global subway fire accidents are caused by uncontrolled fire spread and toxic smoke diffusion. Traditional fire prediction methods based on empirical formulas or static models cannot effectively respond to complex and variable tunnel fire scenarios.

[0003] The current industry technology faces some core challenges. First, existing fire source models rely on preset scenarios and cannot quickly respond to changes in the combustion characteristics of new materials. Second, the control strategy of the ventilation system is mostly in a fixed mode, lacking dynamic response capability to the coupling of multiple factors such as temperature and toxicity. Third, the visualization early warning means only presents the distribution of a single physical quantity, which cannot meet the demand of comprehensive risk assessment for emergency decision-making. Under this background, developing fire prediction technology with full-process dynamic simulation and intelligent control capability has become a key breakthrough to improve the level of subway fire safety. SUMMARY

[0004] The purpose of the present application is to solve the above-mentioned problems, and therefore a subway tunnel fire spread prediction method is proposed.

[0005] The purpose of the present application can be achieved by the following technical solution: a subway tunnel fire spread prediction method, comprising:

[0006] Step 1: Digital modeling of fire source type, establishing a fire source characteristic database, presetting typical fire source types, associating dynamic combustion parameters for each fire source type, having a self-defined fire source expansion mechanism, and automatically generating the combustion characteristic curve of undefined fire through interpolation algorithm;

[0007] Step 2: Fire scene initialization, spatial parameter configuration, inputting tunnel geometric topology, ventilation system layout and escape passage location, injecting environmental parameters, setting initial temperature, humidity and air flow velocity field, and labeling flammable material distribution hot spot area;

[0008] Step 3: Dynamic fire source loading and ignition triggering, positioning the fire source point in the three-dimensional tunnel model, associating the fire source type in step 1, managing multiple fire sources cooperatively, and automatically identifying the interaction logic of fire sources;

[0009] Step four: fire spread multi-physical field coupling calculation, including heat release rate dynamic calculation, temperature field and smoke diffusion coupling equation solving;

[0010] Step five: dynamic response of ventilation system;

[0011] Step six: visualization of risk thermodynamic map generation;

[0012] Step seven: real-time warning of safety margin.

[0013] Further, the typical fire source types include wood, plastic, oil, cable, and chemicals, and the wood, plastic, oil, cable, and chemicals are associated with dynamic combustion parameters, including combustion temperature peak, mass burning rate, smoke main component and proportion, and toxic gas generation rate.

[0014] Further, the automatic identification of fire source interaction logic specifically includes:

[0015] S1: mapping multiple fire source coordinates to tunnel model grid nodes;

[0016] S2: real-time calculation of straight-line distance between fire sources based on Euclidean distance formula;

[0017] S3: dynamically setting influence radius according to fire source type;

[0018] S4: marking the coupling area;

[0019] S5: physical field coupling rule engine establishes a multi-fire source interaction rule library.

[0020] Further, the dynamic response of the ventilation system is based on a double-factor coupling adjustment model and an adjustment algorithm to automatically adjust the fan speed.

[0021] Further, the double factor includes:

[0022] Temperature factor: The formula is used to quantify the risk impact of temperature on the spread of subway tunnel fire, where T r isk represents the temperature risk factor, whose value ranges from 0 to 1, and the larger the value represents the higher the fire risk caused by temperature, T is the real-time temperature monitored in the subway tunnel, T max 0 is the normal environmental temperature in the tunnel, and T

[0023] Toxic factor: Where C risk represents the comprehensive toxicity risk value, which is used to quantify the overall toxicity hazard degree of multiple toxic gases mixed in the subway tunnel fire, w iis the weight coefficient of the ith toxic gas, reflecting the relative importance of the gas in the comprehensive toxicity evaluation, C i represents the actual concentration of the ith toxic gas in the fire environment, C i,LEL is the lower explosive limit concentration of the ith toxic gas, which is used as a reference value for standardizing concentration data, and the formula integrates the concentration-to-lower explosive limit concentration ratio of different toxic gases by weighted summation to obtain the overall risk level of toxic gases in the fire environment.

[0024] Further, the adjustment algorithm is as follows:

[0025] u = u min +(u max -u min )·(T r isk+C r isk-T r iskC r isk);

[0026] wherein u is the final adjusted wind speed, u min and u max represent the minimum and maximum values of the wind speed allowed to be adjusted, and the formula dynamically adjusts the wind speed in a nonlinear manner by combining temperature risk and concentration risk to ensure that, in the event of a fire, the spread of the fire is suppressed and smoke extraction is assisted by reasonably controlling the wind speed of the ventilation system.

[0027] Further, the visual risk thermodynamic map is generated based on risk index calculation for color scale mapping.

[0028] Further, the risk index calculation formula is as follows:

[0029] R = ω T T risk + ω C C risk + ω Q Q risk , wherein R represents the comprehensive risk index of the subway tunnel fire, ω T , ω C , and ω Q are weight coefficients of temperature risk, smoke concentration risk, and ventilation flow risk, respectively, T risk , C risk , and Q risk represent temperature risk value, smoke concentration risk value, and ventilation flow risk value, respectively.

[0030] Further, the color scale mapping uses the HSV color space, with the risk value R corresponding to the hue and the transparency corresponding to the smoke concentration.

[0031] Compared with the prior art, the present application has the beneficial effects that:

[0032] Full-factor modeling capability, supporting multi-dimensional parameter input from fire source characteristics to tunnel environment, improving the scene range covering typical subway fire;

[0033] Solving by multi-physical field simultaneous equations, reducing fire spread error and improving smoke diffusion simulation accuracy;

[0034] Dynamic ventilation regulation and safety warning mechanism provide more buffer time for emergency response. BRIEF DESCRIPTION OF DRAWINGS

[0035] In order to facilitate the understanding of those skilled in the art, the present application will be further described below in conjunction with the drawings.

[0036] Figure 1 The flowchart of the subway tunnel fire spread prediction method of the present application. DETAILED DESCRIPTION

[0037] The technical solutions of the present application will be described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0038] Please refer to Figure 1 The subway tunnel fire spread prediction method shown in the figure comprises:

[0039] Step 1: Digital modeling of fire source type, establishing a fire source characteristic database, presetting typical fire source types, associating dynamic burning parameters for each fire source type, having a self-defined fire source expansion mechanism, and automatically generating the burning characteristic curve of undefined fire source by interpolation algorithm;

[0040] Typical fire source types include wood, plastic, oil, cable and chemicals, and each fire source type is associated with dynamic burning parameters, and the expression is:

[0041] Fire source parameter set

[0042] In the formula, is the highest burning temperature of fire source i, is the mass burning rate of fire source i, is the main component and proportion of fire source i smoke, is the toxic gas generation rate of fire source i.

[0043] The way of generating the burning characteristic curve of undefined fire source by interpolation algorithm is:

[0044]

[0045] where y(t) is the target combustion parameter (HRR / temperature / smoke concentration), y k is the user input discrete parameter value, φ k (t) is the basis function (determines the interpolation form);

[0046] Typical curve analogy, automatically match the closest preset fire curve as the base;

[0047] Complete calculation taking heat release rate as an example:

[0048]

[0049] where Akima is the spline interpolation algorithm, is the input discrete data point, τ is the pulsation period, t is the time domain, t decay is the decay start time, σ is the decay speed parameter;

[0050] Step 1 establishes the physical basis of fire simulation, providing input for subsequent calculations.

[0051] Step 2: Fire scene initialization, spatial parameter configuration, input tunnel geometric topology (length / slope / cross section), ventilation system layout (fan position / power), escape passage position, environmental parameter injection, set initial temperature, humidity, airflow velocity field, mark flammable material distribution hot spot area, build a physical environment framework through step 2;

[0052] Step 3: Dynamic fire source loading and ignition triggering, locate the fire source point in the three-dimensional tunnel model, associate the fire source type in step 1; multi-fire source cooperative management, automatic identification of fire source interaction logic;

[0053] The automatic identification of fire source interaction logic specifically includes:

[0054] S1: Map multiple fire source coordinates to tunnel model grid nodes;

[0055] S2: Based on the Euclidean distance formula, real-time calculation of straight-line distance between fires, formula as follows:

[0056]

[0057] where d ij represents the spatial straight-line distance between the i-th fire and the j-th fire, (x i ,y i ,z i ) is the coordinate of the i-th fire in three-dimensional space, (x j ,y j ,z j) is the coordinate of the jth fire source in three-dimensional space. Through this formula, the proximity of different fire sources in space can be quantified, providing basic data support for subsequent multi-fire source collaborative loading and interaction analysis.

[0058] S3: Dynamically set the influence radius according to the type of fire source;

[0059] For example, the influence domain radius of oil fire is 5 minutes of burning rate, and that of plastic is 3 minutes of burning rate;

[0060] S4: Label the coupling region;

[0061] When d ij ≤(R i +R j ), it is marked as a strong coupling region, triggering interaction calculation, R i is the influence radius of fire source i, and R j is the influence radius of fire source j;

[0062] S5: The multi-fire source interaction rule library is established by the physical field coupling rule engine, as follows:

[0063]

[0064] In the formula, Q rad_total represents the total heat radiation flux received by the target surface, n is the total number of fire sources, Q rad_i is the heat radiation power of the ith fire source, α abs is the absorption rate of the target surface to heat radiation, with a value range of 0-1, and 1-α abs represents the reflectivity of the target surface to heat radiation, A i is the radiation area of the ith fire source, Based on the spherical diffusion principle of point source radiation, it is used to describe the law of heat radiation attenuation with distance. This formula considers the radiation power, reflectivity, radiation area and distance attenuation to quantify the superposition effect of multi-fire source heat radiation on the target surface by summing the heat radiation flux contributed by each fire source;

[0065] The interaction mechanism between multiple fire sources is systematically integrated and clarified, providing theoretical basis and computational model support for accurately predicting the combustion behavior, heat radiation propagation law and fire spread trend in multi-fire source scenarios.

[0066] Step four: Multi-physical field coupling calculation of fire spread, including dynamic calculation of heat release rate, temperature field and smoke diffusion coupling solution equation;

[0067] The dynamic calculation formula of heat release rate is:

[0068]

[0069] where χ is the combustion efficiency factor, ΔH c is the fuel heat value, A f is the fire area, is the mass burning rate of fire source type i, t is the time unit;

[0070] The temperature field and smoke diffusion coupling equation is expressed as:

[0071]

[0072] where ρ is the fluid density, representing the mass of fluid per unit volume, C p represents the heat required to raise the temperature of a unit mass of fluid under constant pressure, T is the temperature, t is the time, u is the fluid velocity vector, is the gradient operator, used to describe the rate of change of physical quantities in space, k is the thermal conductivity, is the heat source term, Y i is the mass fraction of the i-th component, reflecting the proportion of each component in the smoke, D i is the diffusion coefficient of the i-th component, S i is the source term of the i-th component, representing the generation or consumption rate of the i-th component per unit volume.

[0073] Step five: The dynamic response of the ventilation system automatically adjusts the fan speed based on the double-factor coupling adjustment model and adjustment algorithm, and the double-factor includes:

[0074] Temperature factor: The formula is used to quantify the risk of temperature on the spread of subway tunnel fire, where T r isk represents the temperature risk factor, whose value ranges from 0 to 1, and the larger the value, the higher the risk of fire caused by temperature, T is the real-time temperature monitored in the subway tunnel, T0 is the normal environmental temperature in the tunnel, T max is the critical temperature threshold set to trigger a sharp rise in fire risk;

[0075] Toxicity factor: where C risk represents the comprehensive toxicity risk value, which is used to quantify the overall toxicity hazard degree of multiple toxic gases mixed in the subway tunnel fire, w i is the weight coefficient of the i-th toxic gas, reflecting the relative importance of the gas in the comprehensive toxicity evaluation, C i represents the actual concentration of the i-th toxic gas in the fire environment, C i,LELis the lower explosive limit concentration of the ith toxic gas, which is used as a reference value for standardizing concentration data, and the formula integrates the concentration ratio of different toxic gases to the lower explosive limit concentration by weighted summation to obtain the overall risk level of toxic gases in the fire environment;

[0076] The adjustment algorithm is as follows:

[0077] u = u min +(u max -u min )·(T r isk+C r isk-T r iskC r isk);

[0078] Wherein, u is the final adjusted wind speed, u min and u max represent the minimum and maximum values of the wind speed allowed to adjust, and the formula dynamically adjusts the wind speed in a nonlinear manner by combining temperature risk and concentration risk to ensure that when a fire occurs, the spread of the fire is suppressed and smoke is assisted by reasonably controlling the wind speed of the ventilation system.

[0079] Step six: the visualization risk thermodynamic map is generated based on the risk index calculation for color scale mapping.

[0080] The risk index calculation formula is as follows:

[0081] R = ω T T risk + ω C C risk + ω Q Q risk , wherein R represents the comprehensive risk index of the subway tunnel fire, ω T , ω C , ω Q are weight coefficients of temperature risk, smoke concentration risk and ventilation flow risk, T risk , C risk , Q risk represent temperature risk value, smoke concentration risk value and ventilation flow risk value respectively.

[0082] The color scale mapping adopts HSV color space, and the risk value R corresponds to the hue, and the transparency corresponds to the smoke concentration.

[0083] Step seven: real-time warning of safety margin, the warning content includes:

[0084] Remaining safety time calculation:

[0085] The formula is used for calculating the remaining safe evacuation time of personnel in the subway tunnel fire scene, wherein T safe represents the highest environmental temperature limit that the human body can tolerate, and the human body will face serious injury or even life-threatening if the temperature exceeds this limit, T is the current environmental real-time temperature, which is obtained by real-time monitoring through a sensor, dT / dt represents the rate of change of temperature with time, i.e. the temperature rising speed, which reflects the intensity of fire spread, and the remaining safe time (RST) is obtained by the difference between the current temperature and the safe temperature, divided by the temperature change rate, which directly reflects the remaining time available for safe evacuation of personnel under the current fire development situation.

[0086] Escape path conflict index calculation:

[0087] Wherein CI represents the escape path conflict index, which is used to quantify the degree of congestion or conflict of personnel in the escape passage; C i represents the real-time personnel density of the i-th monitoring point in the escape passage, reflecting the current number of personnel gathered at the point; C i,LEL is the maximum allowable personnel density of the i-th monitoring point, i.e. the upper limit of personnel that the point can accommodate under safe and smooth traffic conditions, when the calculated conflict index CI is greater than or equal to 0.5, the system will trigger a path warning, prompting the management personnel to take timely measures to guide the flow of people and ensure the smoothness of the escape passage.

[0088] Example 1:

[0089] In a certain subway tunnel fire scenario, the system digitally models the fire source type, builds a fire source characteristic database, and through preset typical fire source data such as wood burning temperature 300-800℃, rate 0.05-0.15m / min, and associated characteristic smoke composition, if the detected fire source is of a non-typical type, the fire source name and initial burning parameters can be input through a self-defined extension interface, and an interpolation algorithm is used to generate a burning characteristic curve, completing the digital modeling of the fire source type;

[0090] The tunnel three-dimensional coordinate data is analyzed, the initial temperature field is set, the fire source point is determined through coordinate picking, the nearest grid unit is automatically matched, and the fire source type is associated to load the corresponding burning parameters to generate the initialization burning module. If there are multiple fire sources, the system calculates the proximity between the fire sources, automatically identifies the thermal radiation superposition, and realizes the collaborative management of multiple fire sources;

[0091] The heat release rate dynamic calculation formula is combined with the temperature field and smoke diffusion coupling equation, the control equation is discretized by the finite volume method (FVM), and the SIMPLE algorithm is used to process to accurately simulate the fire spread;

[0092] Real-time calculation of temperature factor and toxicity factor, dynamic adjustment of wind speed through wind speed adjustment algorithm;

[0093] The comprehensive risk index is calculated, the risk value R is mapped to the HSV color space to generate a visual risk heat map, tunnel longitudinal sections, cross sections, three-dimensional views are provided, supercritical risk areas are marked in real time and wind speed vector arrows are superimposed, and fire risk distribution is intuitively displayed;

[0094] During the whole fire simulation and response process, parameters are continuously monitored, once it is found that the key indicators are close to or break through the safety threshold, the safety margin real-time warning is triggered immediately, and timely and accurate information support is provided for emergency decision-making.

[0095] The preferred embodiments disclosed above are only used to help explain the present application. The preferred embodiments do not describe all the details and limit the present application to the specific embodiments described. Obviously, many modifications and changes can be made according to the content of the present application. The present application is selected and described in detail to better explain the principles and practical applications of the present application, so that those skilled in the art can well understand and utilize the present application. The present application is limited by the claims and their full scope and equivalents.

Claims

1. A method of predicting a fire spread in a subway tunnel, characterized by, The method comprises the following steps: Step 1: digital modeling of fire source type, establishment of fire source characteristic database, presetting typical fire source type, association of dynamic combustion parameters for each fire source type, expansion mechanism for self-defined fire source, automatic generation of combustion characteristic curve of undefined fire source through interpolation algorithm; Step 2: fire scene initialization, spatial parameter configuration, input of tunnel geometric topology, ventilation system layout and escape passage position, injection of environmental parameters, setting of initial temperature, humidity and air flow velocity field, and labeling of flammable material distribution hot spot area; Step 3: dynamic fire source loading and ignition triggering, positioning of fire source point in the three-dimensional tunnel model, association with the fire source type in step 1, cooperative management of multiple fire sources, and automatic identification of fire source interaction logic; Step 4: multi-physical field coupling calculation of fire spread, including dynamic calculation of heat release rate, temperature field and smoke diffusion coupling equation; Step 5: dynamic response of ventilation system; Step 6: generation of visual risk thermodynamic map; Step 7: real-time warning of safety margin.

2. The method of claim 1, wherein, The typical fire source types include wood, plastic, oil, cable and chemical, and the wood, plastic, oil, cable and chemical are associated with dynamic combustion parameters, and the dynamic combustion parameters include combustion temperature peak, mass burning rate, main components and proportion of smoke, and toxic gas generation rate.

3. The method of claim 1, wherein the method further comprises: The automatic identification of fire source interaction logic specifically comprises: S1: mapping multiple fire source coordinates to tunnel model grid nodes; S2: real-time calculation of straight-line distance between fire sources based on Euclidean distance formula; S3: dynamic setting of influence radius according to fire source type; S4: labeling of coupled areas; S5: establishment of multi-fire source interaction rule library by physical field coupling rule engine.

4. The method of claim 1, wherein the method further comprises: The dynamic response of the ventilation system is based on a double-factor coupling adjustment model and an adjustment algorithm to automatically adjust the fan air speed.

5. The method of claim 4, wherein the method further comprises: The double factors include: Temperature factor: The formula is used to quantify the impact of temperature on the risk of fire spread in a subway tunnel, where T r isk represents the temperature risk factor, whose value ranges from 0 to 1, and the larger the value, the higher the risk of fire caused by temperature, T is the real-time temperature monitored in the subway tunnel, T0 is the normal environmental temperature in the tunnel, and T max is the critical temperature threshold set to trigger a sharp rise in fire risk. Toxic factors: wherein, C risk represents the comprehensive toxicity risk value, which is used to quantify the overall toxicity hazard degree of the mixed toxic gases in the subway tunnel fire, w i is the weight coefficient of the i-th toxic gas, reflecting the relative importance of the gas in the comprehensive toxicity evaluation, C i represents the actual concentration of the i-th toxic gas in the fire environment, C i,LEL is the lower explosive limit concentration of the i-th toxic gas, which is used as a reference value for standardizing the concentration data. This formula integrates the concentration-to-lower explosive limit concentration ratio of different toxic gases through weighted summation, obtaining the overall risk level of toxic gases in the fire environment.

6. The method of claim 5, wherein the method further comprises: The adjustment algorithm is specifically as follows: u = u min + (u max - u min ) · (T r isk + C r isk - T r iskC r isk) ; Wherein, u is the final adjusted wind speed, u min and u max respectively represent the minimum and maximum values of the allowable adjusted wind speed. This formula dynamically adjusts the wind speed in a non-linear manner by combining the temperature risk and concentration risk, ensuring that when a fire occurs, the fire spread is suppressed and smoke is assisted by reasonably controlling the wind speed of the ventilation system.

7. The method of claim 1, wherein the method further comprises: The generation of the visual risk thermodynamic map is based on risk index calculation for color scale mapping.

8. The method of claim 7, wherein the method further comprises: The risk index calculation formula is as follows: R = ω T T risk + ω C C risk + ω Q Q risk wherein R represents the comprehensive risk index of subway tunnel fire, ω T , ω C , ω Q are weight coefficients of temperature risk, smoke concentration risk and ventilation flow rate risk respectively, T risk , C risk , Q risk represent temperature risk value, smoke concentration risk value and ventilation flow rate risk value respectively.

9. The method of claim 8, wherein the method further comprises: The color scale mapping adopts HSV color space, and the risk value R corresponds to the hue, and the transparency corresponds to the smoke concentration.

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