Optimized coupling simulation method for fire evacuation path of dormitory building

Abstract

The invention relates to a dormitory building fire evacuation path optimization coupling simulation method, and belongs to the field of safety science and engineering. Constructing a fire cause model by fusing double theories to lock risk nodes; collecting data of personnel in a fire situation and performing simulation parameterization processing; building a BIM (Building Information Modeling) by utilizing Revit; according to the method, a fire disaster simulation result is coupled with evacuation simulation software, an evacuation simulation model is established, parameters are combined to simulate a multi-floor fire disaster scene through Pyrosim to identify three core threats of temperature, toxic gas concentration and visibility, Pyrosim and Pathfinder software are coupled to establish the evacuation simulation model, the association relationship among personnel evacuation timeliness, escape path smoothness and a fire source position is analyzed, and the fire disaster evacuation timeliness is analyzed. And an evacuation path optimization scheme is provided based on a simulation result. According to the invention, the evacuation key risk node can be accurately locked, the accuracy of evacuation simulation is improved, the coupling association between the fire environment and personnel evacuation can be established, and the safety of the evacuation path is improved.

Description

Technical Field

[0001] This invention relates to a coupled simulation method for optimizing evacuation routes in dormitory buildings during fires, belonging to the field of safety science and engineering. Background Technology

[0002] Building fires pose a global fire safety challenge due to their suddenness and rapid spread, causing not only enormous property damage but also directly threatening human lives. In densely populated university dormitories, the complexity of the building structure, high population density, and relatively concentrated escape routes significantly increase the difficulty of evacuation and fire rescue after a fire. If evacuation routes are poorly planned or rescue tactics lack scientific basis, secondary disasters such as stampedes and toxic smoke poisoning can easily occur, exacerbating the risk of injury and death.

[0003] With increasingly stringent fire safety management requirements in universities, the importance of fire emergency evacuation plans and rescue tactics design is becoming increasingly prominent. However, some university dormitory fire prevention and rescue plans still have significant shortcomings. First, the evacuation route planning lacks dynamic adaptability, failing to fully consider the fire smoke diffusion patterns, temperature field distribution, and toxic gas spread characteristics. Relying solely on fixed route designs results in insufficient timeliness of tactical escape routes, making it difficult to address complex evacuation needs under different fire source locations. Second, the selection of rescue routes is formulaic, failing to dynamically assess the fire evolution process within the building, fire source location, access risks, and exit safety. Blindly selecting attack and evacuation routes not only affects rescue efficiency but may also endanger the safety of firefighters. Third, existing simulation analysis methods often focus on a single aspect, either simulating only the fire spread process or analyzing only personnel evacuation behavior. They fail to establish a coupling relationship between dynamic changes in the fire environment and personnel evacuation behavior, leading to inaccurate identification of key evacuation risk points and hindering the scientific optimization of the plan. Finally, insufficient consideration of psychological factors in personnel behavior is lacking. The typical reaction characteristics of university students in fire situations are not fully extracted, causing discrepancies between evacuation simulations and actual scenarios, limiting the feasibility and practicality of the plan. The aforementioned deficiencies have directly led to some university dormitories' fire prevention and rescue plans becoming mere formalities, unable to cope with real fire scenarios, and have become a significant contributing factor to casualties in various building fires in recent years. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention provides a method for optimizing and simulating fire evacuation routes in dormitory buildings. This invention can accurately identify key risk nodes in evacuation, improve the accuracy of evacuation simulation, and establish a coupling relationship between the fire environment and personnel evacuation, thereby enhancing the safety of evacuation routes.

[0005] To achieve the above objectives, the present invention provides a coupled simulation method for optimizing evacuation routes in dormitory buildings during fires, comprising the following steps:

[0006] S1. By integrating trajectory intersection theory and accident energy transfer theory, a fire causation model is constructed to deduce the development law of the entire stage of fire and identify key risk nodes affecting evacuation.

[0007] S2. Based on the characteristics of university dormitory buildings and personnel evacuation theory, basic background data and core behavioral and psychological data of personnel in fire scenarios are collected. The two types of data are processed by simulation parameterization to form a quantifiable set of personnel evacuation simulation input parameters. The basic background data includes statistical data on personnel's fire experience. The core behavioral and psychological data includes at least: the distribution of initial evacuation reaction behavior, the distribution of panic level, the probability of exit reselection when facing congestion, and the exit selection preference based on familiarity and distance.

[0008] S3. Construct a BIM model of the target building, combine the simulation parameters with fire simulation software to build a fire scenario and conduct simulation, and identify the core threat factors affecting effective evacuation.

[0009] S4. Couple the fire simulation results with the evacuation simulation software to establish an evacuation simulation model, analyze the correlation between the timeliness of personnel evacuation, the smoothness of escape routes, and the location of the fire source, and propose an evacuation route optimization scheme based on the simulation results; the coupling specifically refers to importing the fire simulation data generated by the fire simulation software in step S3 into the personnel evacuation simulation software, so that the environmental parameters in the evacuation simulation model change dynamically with the development of the fire.

[0010] The further improvement to the above technical solution is as follows:

[0011] Step S1 specifically includes:

[0012] S11. Based on the trajectory intersection theory, analyze the two event chains that lead to fire: unsafe human behavior and unsafe state of objects.

[0013] S12. Based on the theory of accident energy transfer, analyze the causal chain process of uncontrolled release of energy or hazardous substances;

[0014] S13. Combining the analysis results of steps S11 and S12, construct a fire causation model to clarify the patterns and key nodes of the entire fire development process, including the initiation, development, and decay stages.

[0015] In step S2, the initial evacuation response behavior is set as immediately escaping, calling the police and notifying others, confirming the authenticity, waiting for rescue, and collecting important items; the level of panic is set as being at a loss, being afraid, and being calm; the probability of reselecting an exit when facing congestion is set as patiently waiting, cutting in line directly, and looking for other exits; the exit selection preference is set as taking the elevator, following the signs, choosing the nearest passage, choosing a familiar passage, and following the crowd.

[0016] Step S3 specifically includes:

[0017] S31. Use architectural modeling software to construct a detailed 3D model of the dormitory building, including the building structure, room layout, and door and window locations;

[0018] S32. Import the simulation parameters and BIM model into the simulation software;

[0019] S33. Divide the computational grid within the simulation domain and set up detection slices for temperature, CO concentration, and visibility in key areas;

[0020] S34. Based on the building type and the characteristics of combustibles, set the heat release rate growth model and the maximum heat release rate of the fire source;

[0021] S35. Based on the principle of fire scene selection, at least three different fire source locations on different floors are set in the three-dimensional model;

[0022] S36. Run fire dynamics simulation to analyze the changes in temperature, CO concentration and visibility at breathing height in corridors and stairwells over time under various fire scenarios, and identify safe evacuation time windows and danger zones.

[0023] The fire source locations include at least the dormitories near the top floor staircase, the dormitories near the middle floor staircase, and the middle dormitory on the first floor; the heat release rate growth model adopts the t² rapid fire model, with the maximum heat release rate set to 6MW; the simulation time is set to 600s.

[0024] Step S4 specifically includes:

[0025] S41. Import the building information model constructed in step S31 into the personnel evacuation simulation software, and set the number and distribution of personnel in each room according to the actual use of the building.

[0026] S42. Use the data of dangerous areas and critical time of danger obtained in step S35 as dynamic environmental constraints to input the personnel evacuation simulation model.

[0027] S43. Operation personnel evacuation simulation model to obtain the personnel flow time curve of each exit, the congestion points of the evacuation route, and the overall and sub-regional evacuation completion time.

[0028] S44. Compare and analyze the differences in the smoothness of evacuation routes and the total evacuation time under different fire source locations to reveal the influence mechanism of fire source location on evacuation efficiency.

[0029] Step S4 also includes formulating optimization measures based on the simulation analysis results, wherein the optimization measures include at least one of the following:

[0030] (1) Add signs and lighting to the congested road sections shown in the simulation, and clear debris from the passageways;

[0031] (2) Develop evacuation plans for different areas based on the evacuation time obtained from the simulation;

[0032] (3) Based on the analysis results of the fire spread path, alarm and sprinkler devices are added at specific locations;

[0033] (4) Based on the risk points revealed by the simulation, conduct targeted safety knowledge dissemination and escape skills training.

[0034] As can be seen from the above technical solution, the present invention provides a coupled simulation method for optimizing evacuation routes in dormitory buildings during fires. This method involves constructing a fire causation model by integrating two theories to identify risk nodes; collecting personnel data under fire scenarios and performing simulation parameterization; building a BIM model using Revit; coupling the fire simulation results with evacuation simulation software to establish an evacuation simulation model; combining parameters to simulate multi-story fire scenarios using Pyrosim to identify the three core threats: temperature, toxic gas concentration, and visibility; coupling Pyrosim and Pathfinder software to establish an evacuation simulation model; analyzing the correlation between the timeliness of personnel evacuation, the smoothness of escape routes, and the location of the fire source; and proposing an evacuation route optimization scheme based on the simulation results. The present invention has the following advantages over existing technologies:

[0035] (1) The technical solution adopted in this invention is based on dual theories to build a full-chain analysis system, which breaks through the limitations of traditional single-link research, systematically analyzes the root causes of fire risk and evacuation laws, and is more scientific.

[0036] (2) The technical solution adopted in this invention extracts the psychological and behavioral characteristics of students in a fire through targeted surveys and transforms them into simulateable parameters. Then, it deeply links these parameters with Pyrosim fire simulation and Pathfinder evacuation simulation, making the simulation model more in line with the actual scenario and improving the accuracy and reliability of the evacuation simulation results. This provides clear and efficient route guidance for emergency evacuation in dormitory fires, effectively reducing evacuation time and lowering the risk of casualties in fires.

[0037] (3) The technical solution adopted in this invention realizes the dynamic linkage between fire development and personnel evacuation through multi-software coupling, which can accurately reveal the impact of different fire source locations on evacuation and provide a direct basis for path optimization.

[0038] (4) The optimization measures proposed by the technical solution adopted in this invention are highly targeted, which not only reduces the cost of fire protection facility optimization, but can also be extended to various densely populated buildings, thus combining economic applicability and social safety benefits. Attached Figure Description

[0039] Figure 1 A flowchart of a coupled simulation method for optimizing evacuation routes in a dormitory building fire;

[0040] Figure 2 A schematic diagram of the BIM model of the target building;

[0041] Figure 3 Scene A: Detector position display diagram;

[0042] Figure 4 Scene B detector location display diagram;

[0043] Figure 5 Scene C detector location display diagram;

[0044] Figure 6 Slice location display diagram;

[0045] Figure 7 Temperature display of a slice over 50 seconds in scenario A;

[0046] Figure 8 Temperature display graph of a slice taken in Scenario A over 300 seconds;

[0047] Figure 9 Temperature display graph of a slice taken over 600 seconds in scenario A;

[0048] Figure 10 CO concentration display image of a 50-second slice in Scene A;

[0049] Figure 11 CO concentration display image for a 300s slice in Scene A;

[0050] Figure 12 CO concentration display image for a 600s slice in Scene A;

[0051] Figure 13 Visibility display of a 50-second slice in Scene A;

[0052] Figure 14 Visibility display of a 300-second slice in Scene A;

[0053] Figure 15 Visibility display of a 600s slice in Scene A;

[0054] Figure 16 Schematic diagram of dormitory evacuation model;

[0055] Figure 17 Scenario A: Evacuation Time Curve;

[0056] Figure 18 Scenario A model exit evacuation rate. Detailed Implementation

[0057] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments, but the scope of protection of the present invention is not limited to the following embodiments.

[0058] The flowchart of a coupled simulation method for optimizing evacuation routes in dormitory buildings during fires, provided in the technical solution of this invention, is as follows: Figure 1 As shown, it includes the following steps:

[0059] S1. Integrating trajectory intersection theory and accident energy transfer theory, a fire causation model is constructed to deduce the development pattern of the entire fire stage and identify key risk nodes affecting evacuation; the specific steps are as follows:

[0060] S11. Based on the trajectory intersection theory, analyze the two event chains that lead to fire: unsafe human behavior and unsafe state of objects.

[0061] S12. Based on the theory of accident energy transfer, analyze the causal chain process of uncontrolled release of energy or hazardous substances;

[0062] S13. Combining the analysis results of steps S11 and S12, construct a fire causation model to clarify the patterns and key nodes of the entire fire development process, including the initiation, development, and decay stages.

[0063] S2. Based on the characteristics of university dormitory buildings and personnel evacuation theory, basic background data and core behavioral and psychological data of personnel in fire scenarios are collected. The two types of data are processed by simulation parameterization to form a quantifiable set of personnel evacuation simulation input parameters. The basic background data includes statistical data on personnel's fire experience. The core behavioral and psychological data includes at least: the distribution of initial evacuation reaction behavior, the distribution of panic level, the probability of exit reselection when facing congestion, and the exit selection preference based on familiarity and distance.

[0064] The initial evacuation response behaviors are set as follows: immediately escape, call the police and notify others, confirm the authenticity, wait for rescue, and collect important items; the level of panic is set as follows: bewildered, afraid, and calm; the probability of exit reselection when facing congestion is set as follows: patiently wait, cut in line directly, and look for other exits; the exit selection preference is set as follows: take the elevator, follow the signs, choose the nearest passage, choose a familiar passage, and follow the crowd.

[0065] S3. Construct a BIM model of the target building, and use fire simulation software to build and simulate a fire scenario based on the simulation parameters, identifying the core threat factors affecting effective evacuation; specifically including:

[0066] S31. Use architectural modeling software to construct a detailed 3D model of the dormitory building, including the building structure, room layout, and door and window locations;

[0067] S32. Import the simulation parameters and BIM model into the simulation software;

[0068] S33. Divide the computational grid within the simulation domain and set up detection slices for temperature, CO concentration, and visibility in key areas;

[0069] S34. Based on the building type and the characteristics of combustibles, set the heat release rate growth model and the maximum heat release rate of the fire source;

[0070] S35. Based on the principle of fire scene selection, at least three different fire source locations on different floors are set in the three-dimensional model;

[0071] S36. Run fire dynamics simulation to analyze the changes in temperature, CO concentration and visibility at breathing height in corridors and stairwells over time under various fire scenarios, and identify safe evacuation time windows and danger zones.

[0072] The fire source locations include at least the dormitories near the top floor staircase, the dormitories near the middle floor staircase, and the middle dormitory on the first floor; the heat release rate growth model adopts the t² rapid fire model, with the maximum heat release rate set to 6MW; the simulation time is set to 600s.

[0073] S4. Couple the fire simulation results with the evacuation simulation software to establish an evacuation simulation model. Analyze the correlation between the timeliness of personnel evacuation, the smoothness of escape routes, and the location of the fire source, and propose an optimized evacuation route plan based on the simulation results. Specifically, the coupling refers to importing the fire simulation data generated by the fire simulation software in step S3 into the personnel evacuation simulation software, so that the environmental parameters in the evacuation simulation model dynamically change with the development of the fire. This includes:

[0074] S41. Import the building information model constructed in step S31 into the personnel evacuation simulation software, and set the number and distribution of personnel in each room according to the actual use of the building.

[0075] S42. Use the data of dangerous areas and critical time of danger obtained in step S35 as dynamic environmental constraints to input the personnel evacuation simulation model.

[0076] S43. Operation personnel evacuation simulation model to obtain the personnel flow time curve of each exit, the congestion points of the evacuation route, and the overall and sub-regional evacuation completion time.

[0077] S44. Compare and analyze the differences in the smoothness of evacuation routes and the total evacuation time under different fire source locations to reveal the influence mechanism of fire source location on evacuation efficiency.

[0078] Step S4 also includes formulating optimization measures based on the simulation analysis results, wherein the optimization measures include at least one of the following:

[0079] (1) Add signs and lighting to the congested road sections shown in the simulation, and clear debris from the passageways;

[0080] (2) Develop evacuation plans for different areas based on the evacuation time obtained from the simulation;

[0081] (3) Based on the analysis results of the fire spread path, alarm and sprinkler devices are added at specific locations;

[0082] (4) Based on the risk points revealed by the simulation, conduct targeted safety knowledge dissemination and escape skills training.

[0083] Using a university dormitory building as a simulation, the simulated dormitory building has a total area of ​​6190 square meters. 2 The building has six floors, each 3.6 meters high, with 22 rooms per floor, including a management office and a supervisor on each floor. The other rooms house six students each, for a total of 762 people. Due to the significant danger of using elevators in the event of a fire, they are not used and are omitted from the model. The simulated building has three exits and two staircases for evacuation. The simulation sets the horizontal speed of people in an emergency at 1.2 m / s and the speed on the stairs at 0.6 m / s. Based on the analysis of the hazards of various fire-related materials, and to optimize evacuation and avoid obstructing the escape route's vision and protecting them from the dangers of high-temperature smoke, the smoke generated by the fire must not reach the heads of those escaping. Therefore, temperature and visibility data at a height of 2 meters are selected as the critical hazard standards, with 60°C as the critical temperature point and 10 meters as the critical visibility point. In this embodiment, basic background data and core behavioral and psychological data of personnel under fire scenarios are collected using a questionnaire method combined with evacuation drills. Based on the above method, a coupled simulation of dormitory building fire evacuation routes is performed. The specific steps are as follows:

[0084] S1. Integrating trajectory intersection theory and accident energy transfer theory, a fire causation model is constructed to deduce the development pattern of the entire fire stage and identify key risk nodes affecting evacuation; the specific steps are as follows:

[0085] S11. Based on the trajectory intersection theory, analyze the two event chains that lead to fire: unsafe human behavior and unsafe state of objects.

[0086] S12. Based on the theory of accident energy transfer, analyze the causal chain process of uncontrolled release of energy or hazardous substances;

[0087] Based on the trajectory intersection theory and the accident energy transfer theory, it can be determined that the human factors that cause fires are mainly manifested in the use of faulty electrical appliances, fire sources, and unauthorized personnel; while the physical factors are mainly manifested in aging wiring and lack of fire extinguishing equipment.

[0088] S13. Combining the analysis results of steps S11 and S12, construct a fire causation model to clarify the patterns and key nodes of the entire fire development process, including the initiation, development, and decay stages.

[0089] In the initial stage of a fire, the ignition point causes localized combustion, the flames continue to spread, the burning area gradually expands, and eventually the fire becomes fully burning. At this point, the fire has reached its peak, the temperature inside the space reaches its highest point, the items are gradually burned to ashes, and the fire begins to decline.

[0090] S2. Based on the characteristics of university dormitory buildings and personnel evacuation theory, basic background data and core behavioral and psychological data of personnel in fire scenarios are collected. The two types of data are processed by simulation parameterization to form a quantifiable set of personnel evacuation simulation input parameters. The basic background data includes statistical data on personnel's fire experience. The core behavioral and psychological data includes at least: the distribution of initial evacuation reaction behavior, the distribution of panic level, the probability of exit reselection when facing congestion, and the exit selection preference based on familiarity and distance.

[0091] The initial evacuation response behaviors are set as follows: immediately escape, call the police and notify others, confirm the authenticity, wait for rescue, and collect important items; the level of panic is set as follows: bewildered, afraid, and calm; the probability of exit reselection when facing congestion is set as follows: patiently wait, cut in line directly, and look for other exits; the exit selection preference is set as follows: take the elevator, follow the signs, choose the nearest passage, choose a familiar passage, and follow the crowd.

[0092] This study combines evacuation drills with questionnaires to collect basic background data and core behavioral and psychological data of personnel in fire scenarios. The main purpose of the questionnaire method is to collect participants' responses through standardized questions, thereby accumulating data suitable for statistical analysis. The formula for determining the sample size for the questionnaire is:

[0093] ;

[0094] In the formula, n is the sample size, i.e., the number of valid questionnaires to be collected in the actual survey; Δ represents the error range, i.e., the allowable deviation between the survey results and the actual situation in the population; N is the population size, i.e., the total number of students in the university dormitory building; p is the population proportion, i.e., the estimated proportion of people with a certain characteristic in the population, such as the estimated proportion of students who have experienced a fire; q=1-p, i.e., the estimated proportion of people without a certain characteristic in the population; t is the probability percentage, a statistic corresponding to the confidence level, reflecting the credibility of the survey results, expressed as %.

[0095] In this embodiment, 300 valid questionnaires were collected, exceeding the sample size and thus meeting the sampling principle. Statistical results showed:

[0096] (1) Fire experience

[0097] The survey found that 11.76% of students had experienced fires or other emergencies, while the remaining 88.24% had not.

[0098] (2) First reaction to a fire

[0099] People's first reaction to a fire primarily impacts evacuation speed, with prior preparation time playing a significant role. Table 1 shows that the highest percentages (64.3%) chose to immediately escape and the highest percentage (23.7%) chose to immediately call the fire department and notify others. 9.7% of respondents confirmed the fire's existence before making a decision. 2.0% chose to wait for rescue in a safe location. Only 0.3% of respondents wanted to gather important belongings before escaping. These reactions indirectly demonstrate people's self-preservation instincts during a fire, reflecting real-life situations.

[0100] Table 1 First Response to Fire

[0101]

[0102] (3) Selection of evacuation method

[0103] Table 2 shows that 49.0% of people chose to evacuate by following signs and 25.6% chose the nearest exit. 11.7% chose to evacuate via a familiar exit or by following the crowd, both behaviors indicating a degree of uncertainty. However, a very small minority (2.0%) chose to use the elevator for evacuation.

[0104] Table 2 Selection of Fire Evacuation Methods

[0105]

[0106] (4) Response to congestion at emergency exits

[0107] According to Table 2, 33.3% of people will queue and wait patiently for evacuation, 2.0% will lose patience and cut in line, and 64.7% will use other exits for evacuation.

[0108] Table 3 Responses to Congestion

[0109]

[0110] (5) Psychological state during a fire

[0111] According to Table 4, 45.4% of people felt scared and panicked, 27.3% felt nervous and at a loss, and 27.3% remained very calm.

[0112] Table 4. Psychological state during a fire

[0113]

[0114] S3. Construct a BIM model of the target building, and use fire simulation software to build and simulate a fire scenario based on the simulation parameters, identifying the core threat factors affecting effective evacuation; specifically including:

[0115] S31. Use architectural modeling software to construct a detailed 3D model of the dormitory building, including the building structure, room layout, and door and window locations;

[0116] In this embodiment, the building modeling software is Revit. First, the BIM model is built using Revit and an IFC file is exported, which can effectively realize the collaboration of each software and ensure the effective interaction of various data such as materials, dimensions and other information.

[0117] S32. Import the simulation parameters and BIM model into the simulation software;

[0118] In this embodiment, the simulation software is Pyrosim. Simulation parameters and the BIM model are imported into Pyrosim for fire simulation, and finally, personnel evacuation simulation is completed in Pathfinder software. Pyrosim is used for fire simulation based on computational fluid dynamics and can accurately predict fire smoke flow, fire temperature, and the distribution of toxic and harmful gas concentrations. Pyrosim includes not only modeling, boundary condition setting, fire source setting, and combustion material adjustment functions, but also the use of FDS, Smoke View, and post-processing of calculation results. The constructed model can be run directly in Pyrosim, such as... Figure 2 As shown.

[0119] S33. Divide the computational grid within the simulation domain and set up detection slices for temperature, CO concentration, and visibility in key areas;

[0120] The formula for mesh generation is as follows:

[0121] ;

[0122] In the formula, D* is the characteristic diameter of the fire, in meters; Q is the total heat release rate, in kilowatts (kW); ρ is the air density, in kilograms per cubic meter (kg / m³); c pρ is the specific heat of air, in kJ / (kg·K); T is the temperature of the ambient air, in K; g is the acceleration due to gravity, in m / s². 2 .

[0123] S34. Based on the building type and the characteristics of combustibles, set the heat release rate growth model and the maximum heat release rate of the fire source;

[0124] (1) The development of a fire is generally divided into four stages: the initial stage, the bursting stage, the fully developed stage, and the decay stage. In the initial stage, the combustion range is relatively small, the flame is not very high, the combustion intensity is weak, the temperature and radiant heat at the fire scene are very low, and the rate of fire development and spread is slow. In the bursting stage, the combustibles in a certain part ignite, and the surface of all combustibles in the system quickly transforms into the fire nature of simultaneous combustion. In the fully developed stage, the fire development rate is very fast, the combustion intensity gradually increases, the temperature continues to rise, the surrounding combustibles are heated, the convection gas becomes stronger, and the combustion range expands rapidly. With the increase of time, the fire temperature rises rapidly, the combustion rate continues to increase, the combustion range spreads rapidly, and the fire will engulf the entire structure or building, and the fire reaches the intense stage. In the decay stage, because the combustion time is very long, the combustibles become less, or because the combustion space is closed, the limited oxygen in the space is constantly consumed, thus causing the combustion rate to slow down until it gradually extinguishes. The heat release rate according to different building types is shown in Table 5 below.

[0125] Table 5 Heat release rates of different building types

[0126]

[0127] Given that the building under study is a university dormitory without sprinklers, the internal structure of the dormitory is relatively simple, and the people are very familiar with the indoor environment, the maximum heat release rate of the fire source in the building is set to 6MW according to Table 5.

[0128] Fires are classified into two types: steady-state and unsteady-state. In the steady-state development process, the power of the fire source is independent of time. This embodiment simulates the unsteady-state process, and the unsteady-state model is further defined by t. 2 Fire is used as a representative example. To ensure the accuracy of the simulation, the critical time for a fire to reach its full development stage needs to be determined. The specific formula is as follows:

[0129] ;

[0130] In the formula, Q is the heat release rate in KW; α is the fire growth coefficient in KW / s2; and t is the fire growth time in s.

[0131] Based on the data in Table 6 of the international standard "Fire Safety Engineering - Part 4: Setting up fire scenarios and selecting fire scenarios" (ISO / TS16733), the fire growth type of the university student dormitory building is determined to be a rapid fire, so the simulation time is set to 600s.

[0132] Table 6 Four Standard t 2 fire

[0133]

[0134] (2) In this embodiment, based on actual investigation of university dormitory buildings, the flammable items inside the dormitory mainly include bedding, clothing, shoes, books, desks and wooden wardrobes. Based on the proportion of cotton products, books and wooden products in the dormitory, the fire source material is set as wood and polyurethane.

[0135] S35. Based on the principle of fire scene selection, at least three different fire source locations on different floors are set in the three-dimensional model;

[0136] In this embodiment, based on the principle of fire scenario selection, fire scenarios are set up at three locations: the low floor, the middle floor, and the high floor. Three fire source locations are set up within this simulated building, numbered as follows:

[0137] (1)Scenario A: The fire source is placed in the first dormitory room next to the west staircase on the 6th floor of the dormitory building. It is set as a fire caused by improper use of electricity. The focus is on observing the impact of the fire on the evacuation of people on the floor.

[0138] (2)Scenario B: Select the first dormitory room on the right side of the east staircase on the 4th floor as the location of the fire.

[0139] (3)Scenario C: Choose the middle dormitory room on the south side of the first floor as the fire location.

[0140] In three fire scenarios in a university dormitory building, all windows on both sides of the corridor are set to open, while other windows on the floor are set to close. All three exits on the first floor are set to open.

[0141] S36. Run fire dynamics simulation to analyze the changes in temperature, CO concentration and visibility at breathing height in corridors and stairwells over time under various fire scenarios, and identify safe evacuation time windows and danger zones.

[0142] Based on the different detector settings in scenarios A, B, and C, the fire scenarios A, B, and C are analyzed. The specific detector locations for scenarios A, B, and C are as follows: Figure 3 , Figure 4 and Figure 5As shown in the diagram, each detector is positioned at the entrance to the dormitory room where the fire occurred and at the east and west stairwells. These detectors include temperature sensors, CO sensors, and visibility sensors. Their geographical locations are indicated by numbers W, E, and F, respectively. For example, the temperature detector at the west stairwell on the 6th floor is numbered T-W6, the CO concentration detector at the east stairwell on the 4th floor is numbered CO-E4, and the detector at the entrance to the dormitory room where the fire started on the 1st floor is numbered V-F1, and so on. By increasing the number of measuring points and distributing them over a wider area, the accuracy and reliability of the fire simulation results can be improved. In fire simulation experiments, a reasonable number of measuring points can be set according to specific circumstances and distributed reasonably within the area to be measured to obtain more accurate results. In this embodiment, 9 detectors are set on each floor, for a total of 27 detectors.

[0143] In this embodiment, to better observe the changes in floor temperature, CO content, and visibility over time, some slices are set up, such as... Figure 6 As shown, these slices provide data visualizations at different heights and locations, allowing for a deeper understanding of the fire's development. Analysis of the slice data yielded important conclusions regarding the fire's burning area and the rate of fire spread.

[0144] S361. Simulation Result Analysis under Scenario A

[0145] According to the 3Dsmokeview dynamic diffusion image of smoke over 600 seconds output by Pyrosim, when the fire source was in the first bedroom next to the west staircase on the 6th floor, the smoke diffused from the room into the corridor and quickly spread along the ceiling to both sides of the corridor, surrounding bedrooms, and the staircase. The analysis is as follows, considering temperature, CO concentration, and visibility:

[0146] (1) Temperature distribution on each floor during a fire

[0147] The conclusion drawn from setting a temperature slice with Y=1.0m in the middle of the corridor yields floor temperature slices at t=50s, t=300s, and t=600s after the fire, as shown below. Figure 7 , Figure 8 and Figure 9 As shown.

[0148] By observing the images, it can be seen that within 50 seconds of the fire starting, the temperature inside the burning room had already reached a dangerous 75°C. As time progressed, at 300 seconds, the temperature in areas such as the corridor gradually increased, indicating that the fire was spreading. At 600 seconds, the temperature in the burning room and most areas on both sides of the corridor reached a dangerous level of 75°C, which is within the ignition temperature range of flammable materials. This indicates that the fire had reached a very serious stage, and if effective measures were not taken immediately, it could lead to even more serious consequences.

[0149] (2) CO content status of the floor during the fire

[0150] A gas slice is set up at Y=1.0m in the middle of the corridor to observe the CO concentration distribution. The CO concentration at t=200s, t=400s, and t=600s can be obtained, as shown below. Figure 10 , Figure 11 and Figure 12 As shown.

[0151] The images show that at 50 seconds, the CO concentration in the middle of the corridor was less than 0.0015 mol / mol. At 300 seconds, the CO concentration in the corridor on the 6th floor began to rise rapidly at the top, and the concentration in most places reached 0.0015 mol / mol. At 600 seconds, the overall CO concentration on the 6th floor was 0.0015 mol / mol. Because there was no upward evacuation route when the fire occurred on the top floor, smoke and toxic gases could not be quickly discharged from the building, resulting in a large accumulation of CO on the 6th floor.

[0152] (3) Visibility analysis of floors during fire

[0153] Based on the visibility slice set at Y=1.0m in the middle of the corridor, the visibility changes over time after the fire at t=50s, t=300s, and t=600s. Figure 13 , Figure 14 and Figure 15 As shown.

[0154] The images show that at 50 seconds, the visibility of the corridor on the 6th floor was less than 3 meters. At 300 seconds, the visibility of the entire corridor except for the 6th floor dropped to below 3 meters. At 600 seconds, the visibility at both ends of the corridors on the 5th and 4th floors was also below 3 meters.

[0155] S362. Simulation Result Analysis in Scenario B

[0156] The fire occurred in the first bedroom on the right side of the east staircase on the fourth floor. Smoke spread from the room into the corridor and quickly spread along the ceiling to both sides of the corridor, surrounding bedrooms, and the staircase. The impact of the fire was analyzed from three aspects: temperature, CO content, and visibility.

[0157] (1) Temperature distribution on each floor during a fire

[0158] A temperature slice with a Y=1.0m depth was set in the middle of the corridor. Simulation results showed temperature slices at t=50s, t=300s, and t=600s. The simulation results indicate that at 50s, after the hot smoke was released in the burning room, it spread east and west along the corridor, causing the hot smoke on the 4th floor to rapidly spread east and west from the burning dormitory room at the top of the corridor, forming a high-temperature zone with a maximum temperature reaching 80℃. Simultaneously, the increased temperature on the ceiling area on the east side of the 5th-floor corridor indicates that the fire smoke had already spread to the 5th floor at this time. At 300s, increased temperatures were also observed on both sides of the 6th-floor corridor, indicating that the smoke from the 4th floor had spread to the 6th floor through the stairwell. At 600s, the temperatures on both the 5th and 6th floors showed an upward trend, with each floor showing a decreasing trend towards the middle of the corridor. Furthermore, the temperature gradually decreased with increasing floor height, suggesting that people on higher floors were less threatened by temperature during evacuation.

[0159] (2) CO content status of the floor during the fire

[0160] A CO concentration slice was set at Y=1.0m in the middle of the dormitory corridor, and CO concentration slices at t=50s, t=300s, and t=600s were selected to observe the CO content. It can be concluded that when the fire occurred in the first dormitory room on the right side of the east staircase on the 4th floor, the CO concentration was highest at the door of the burning dormitory room, and the CO concentration decreased with increasing distance from the burning dormitory room. Due to the lower density of CO compared to air, the CO content in the upper part of the corridor was higher than that in the lower part. Since smoke spread to the 5th and 6th floors mainly through the windows of the east and west corridors and the stairwells, it was clearly observed at 600s that the CO content on the 4th floor was significantly higher than that on other floors, and the CO content in the corridors of the 5th and 6th floors decreased layer by layer. Furthermore, it can be observed that the CO concentration in the east stairwell was generally higher than that in the west stairwell, and both concentrations gradually spread from the 4th floor to other floors. Based on the distribution of CO levels in university student dormitories, it can be seen that when a fire occurs in the first dormitory room on the right side of the east staircase on the fourth floor, the east staircase is more threatened by CO than the west staircase. Therefore, the west staircase is more conducive to evacuation and escape.

[0161] (3) Visibility analysis of floors during fire

[0162] The visibility simulation results for the corridor at different time points in Scenario B show that at t=50s, the visibility in most locations in the 4th-floor corridor is less than 3m, as are the visibility at the east-facing windows on the 5th and 6th floors. Visibility in the corridors on floors 1-3, and in other locations, is generally around 30m. At t=300s, the visibility in the 3rd-floor corridor also begins to decrease, indicating that smoke has spread to the 3rd floor through the stairwell and windows. At t=600s, the visibility in the corridors above the 3rd floor is essentially 0m.

[0163] Analysis of Simulation Results in S363 and Scenario C

[0164] When the fire occurred in the middle dormitory room on the south side of the first floor, smoke spread from the room into the corridor and quickly spread along the ceiling to both sides of the corridor, surrounding dormitories, and the staircase, and then spread from the stairwell to the 2nd to 6th floors. The impact of the fire was analyzed from three aspects: temperature, CO content, and visibility.

[0165] (1) Temperature distribution on each floor during a fire

[0166] A temperature slice with a Y=1.0m depth was set in the middle of the corridor. Based on the simulation results, temperature slice diagrams of the floor at t=50s, t=300s, and t=600s were observed. The simulation results show that at 50s, hot smoke spread from the burning dormitory into the corridor, then diffused to the east and west sides of the corridor, causing the temperature to rise rapidly from the burning dormitory at the top of the first-floor corridor, forming a zone of rising temperature. At 300s, the entire first-floor corridor was a high-temperature zone, indicating that the smoke had penetrated the entire floor. At 600s, a high-temperature zone also appeared at the east-facing corridor window on the second floor. The higher the floor, the lower the temperature and the less affected by the fire. Therefore, it can be concluded that people on higher floors are less threatened by temperature during evacuation.

[0167] (2) CO content status of the floor during the fire

[0168] A CO concentration slice was set at Y=1.0m in the middle of the dormitory corridor, and CO concentration slices at t=50s, t=300s, and t=600s were selected to observe the CO content. It can be seen that when the fire started in the middle of the south-facing room on the first floor, the CO concentration at the entrance of the room where the fire occurred was the highest, and it gradually decreased towards both ends of the corridor. Due to the lower density of CO compared to air, the CO content in the upper part of the corridor was higher than in the lower part. Since the smoke spreading to floors 2-6 mainly diffused upwards through the windows at both ends of the east and west corridors, it was clearly observed at 600s that the CO content on the first floor was significantly higher than on other floors, while the CO content in the corridors of other floors was slightly lower.

[0169] (3) Visibility analysis of floors during fire

[0170] The simulation results of corridor visibility at different time points in Scenario C show that at t=50s, the visibility in most areas of the first-floor corridor is far below the critical value of 10m for safe escape. Smoke spreads upwards through the windows at both ends of the corridor, causing the visibility at one end of the second-floor corridor to also be below 10m. The third floor is also slightly affected, while the visibility in the corridors of the fourth, fifth, and sixth floors is almost unaffected. At 300s, the visibility in a large area of ​​the first-floor corridor is below 5m. By 600s, the visibility of the entire building has been affected, and at this point, the first-floor corridor is no longer suitable for evacuation.

[0171] Based on the building's structural characteristics, three fire scenarios were comprehensively simulated and analyzed. The changes in corridor temperature, CO concentration, and visibility over 600 seconds were analyzed to compare the hazards under each scenario. In fire scenario A, the fire smoke spread rapidly throughout the entire sixth floor and continued to spread to the floors below. In scenario B, the area above the fire floor was more dangerous, while areas below the fire floor were also affected to some extent. In scenario C, the temperature, CO concentration, and visibility on both sides of the corridor generally showed the same distribution trend. Choosing safer escape routes during evacuation increases the probability of a safe escape for the crowd.

[0172] S4. Couple the fire simulation results with the evacuation simulation software to establish an evacuation simulation model. Analyze the correlation between the timeliness of personnel evacuation, the smoothness of escape routes, and the location of the fire source, and propose an optimized evacuation route plan based on the simulation results. Specifically, the coupling refers to importing the fire simulation data generated by the fire simulation software in step S3 into the personnel evacuation simulation software, so that the environmental parameters in the evacuation simulation model dynamically change with the development of the fire. This includes:

[0173] S41. Import the building information model constructed in step S31 into the personnel evacuation simulation software, and set the number and distribution of personnel in each room according to the actual use of the building.

[0174] S42. Use the data of dangerous areas and critical time of danger obtained in step S35 as dynamic environmental constraints to input the personnel evacuation simulation model.

[0175] In this embodiment, the personnel evacuation simulation software is Pathfinder, and the simulated university dormitory building has a total area of ​​6190m². 2The building has six floors, each 3.6 meters high, with 22 rooms on each floor. Each floor also has a management office and a manager. The other rooms house six students each, for a total of 762 people. Due to the high risk of using elevators in the event of a fire, elevators are not used and will be omitted from the model. The simulated building has three exits and two staircases for evacuation. In the simulation, the horizontal speed of personnel in an emergency was set at 1.2 m / s, and the speed on the stairs was set at 0.6 m / s. Based on the analysis of the hazards of various substances produced by the fire, and to optimize evacuation and escape without obstructing the escapee's vision or exposing them to the dangers of high-temperature smoke, the smoke generated by the fire on the floor must not reach the heads of the escapees. Therefore, temperature and visibility data at a height of 2m were selected as the critical hazard standards, with 60℃ as the critical temperature point and 10m as the critical visibility point. The critical times for escape routes in the three fire scenarios outlined in step S35 were used to confirm the critical hazards, and all data were imported into the evacuation simulation software. The schematic diagram of the evacuation model is shown below. Figure 16 As shown.

[0176] The simulation model assumptions and personnel parameter settings are as follows:

[0177] (1) Before simulating the escape and evacuation of the university dormitory building, in order to reduce the influence of unnecessary factors on the results, the following assumptions were made about the actual situation: the escape model is to simulate the evacuation of the university student dormitory building, and all evacuees have the basic ability to complete the evacuation; during the evacuation process, people are independent of each other, and the behavior of companions and assistance is not considered. The main purpose is to escape successfully; the reaction and response time of personnel are not considered during the simulation process, and the fire and the evacuation of personnel start from 0s; and there are no disabled people in the simulated situation.

[0178] (2) The composition of personnel in university dormitories is relatively simple, generally consisting of only students and dormitory management personnel. However, each person has their own unique characteristics, which can have a certain impact on the evacuation process under real evacuation conditions. It is very difficult to set the real-time movement speed of personnel in simulation software. The model selected is a female dormitory building in a university, so the average movement speed of personnel is set to 1.3 m / s, the average shoulder width is 0.42 m, and the average height is 1.65 m.

[0179] S43. Operation personnel evacuation simulation model to obtain the personnel flow time curve of each exit, the congestion points of the evacuation route, and the overall and sub-regional evacuation completion time.

[0180] The evacuation simulation results are analyzed as follows:

[0181] During the personnel evacuation simulation, personnel response time was not considered because the software could not be set. Table 7 shows the successful evacuation data and completion times for scenarios A, B, and C. Taking scenario A as an example, the personnel evacuation time curve is shown in [the table]. Figure 17 .

[0182] Table 7 Number of people successfully evacuated from Scenario A to Scenario C and evacuation time

[0183]

[0184] Based on the fire scenario simulation, the safe evacuation time for the dormitory building at this university was 600 seconds. The charts show that when the fire source was located in the dormitory next to the east staircase, due to the tendency of people to seek immediate escape, they would quickly switch to another route if congestion occurred. Therefore, when the fire occurred in the first dormitory room on the right side of the east staircase on the fourth floor, the escape speed was faster and the evacuation time was shortest. Analysis indicates that during the simulated evacuation process, the number of people decreased significantly in the initial stage, and the evacuation speed was relatively fast. However, the evacuation speed gradually slowed down later, leading to a "congestion" phenomenon.

[0185] S44. Compare and analyze the differences in the smoothness of evacuation routes and the total evacuation time under different fire source locations to reveal the influence mechanism of fire source location on evacuation efficiency.

[0186] An analysis of the 3D effects of the Pathfinder software revealed that the university dormitory building has three emergency exits: the east staircase exit, the west staircase exit, and the west corridor exit. According to... Figure 18 As shown, in fire scenario A, no one was passing through the west exit of the corridor at 65 seconds, and the evacuation of people from the east staircase exit was completed at 198 seconds. At 320 seconds, there was widespread congestion on the west staircase on the sixth floor, so some people chose to evacuate via the east staircase. Similarly, in fire scenario B, no one was passing through the west exit of the corridor at 63 seconds, and at 200 seconds, due to large-scale congestion on the west staircase, some people chose to evacuate via the east staircase. Similarly, in fire scenario C, no one was passing through the west exit of the corridor at 63 seconds, and at 201 seconds, there was large-scale congestion on the west staircase, causing some people to choose to evacuate via the east staircase.

[0187] Step S4 also includes formulating optimization measures based on the simulation analysis results, wherein the optimization measures include at least one of the following:

[0188] (1) Add signs and lighting to simulated congested road sections, clear debris from passageways, and supplement escape equipment in high-rise dormitories to ensure that passageways are unobstructed and usable.

[0189] (2) Develop evacuation plans for different areas based on the simulated time, clarify the responsibilities of the guides, conduct real-life drills and revise the plans each semester.

[0190] (3) Install alarm and sprinkler devices according to the fire spread path, regularly inspect fire-fighting equipment, and post a schematic diagram of the facility location.

[0191] (4) Combine simulated risk warnings to popularize safety knowledge, and hold lectures to teach escape and self-rescue skills to reduce evacuation chaos.

[0192] As demonstrated by the simulation process and results in this embodiment, the dormitory fire evacuation path optimization coupled simulation method provided by this invention can systematically analyze the causes and development patterns of fires throughout their entire lifecycle, accurately pinpoint key risk nodes in evacuation, and achieve dynamic and accurate evacuation simulation by combining the behavioral and psychological characteristics of university students. Furthermore, it can establish a coupled relationship between the fire environment and personnel evacuation, scientifically revealing the inherent laws governing the location of the fire source, the smoothness of the path, and the timeliness of evacuation, providing a practical scientific basis for evacuation path optimization, fire protection facility layout, and emergency plan formulation. From a practical perspective, this method can provide accurate simulation basis for optimizing the layout of fire protection facilities in dormitory buildings and formulating evacuation plans, helping to improve the efficiency of fire emergency evacuation. From a promotional perspective, its multi-dimensional evaluation model can be extended to densely populated places such as teaching buildings and office buildings, providing a scientific reference for fire safety management in public buildings, and possessing both economic applicability and social safety benefits.

Claims

1. A coupled simulation method for optimizing evacuation routes in dormitory buildings during fires, characterized in that, Includes the following steps: S1. By integrating trajectory intersection theory and accident energy transfer theory, a fire causation model is constructed to deduce the development law of the entire stage of fire and identify key risk nodes affecting evacuation. S2. Based on the characteristics of university dormitory buildings and personnel evacuation theory, basic background data and core behavioral and psychological data of personnel under fire scenarios are collected. The two types of data are processed by simulation parameterization to form a quantifiable set of personnel evacuation simulation input parameters. The basic background data includes statistical data on people's fire experiences, and the core behavioral and psychological data includes at least: the distribution of initial evacuation response behavior, the distribution of panic level, the probability of re-selecting an exit when facing congestion, and the exit selection preference based on familiarity and distance. S3. Construct a BIM model of the target building, combine the simulation parameters with fire simulation software to build a fire scenario and conduct simulation, and identify the core threat factors affecting effective evacuation. S4. Couple the fire simulation results with the evacuation simulation software to establish an evacuation simulation model, analyze the correlation between the timeliness of personnel evacuation, the smoothness of escape routes, and the location of the fire source, and propose an evacuation route optimization scheme based on the simulation results; the coupling specifically refers to importing the fire simulation data generated by the fire simulation software in step S3 into the personnel evacuation simulation software, so that the environmental parameters in the evacuation simulation model change dynamically with the development of the fire.

2. The coupled simulation method for optimizing evacuation routes in dormitory buildings during fires, as described in claim 1, is characterized in that... Step S1 specifically includes: S11. Based on the trajectory intersection theory, analyze the two event chains that lead to fire: unsafe human behavior and unsafe state of objects. S12. Based on the theory of accident energy transfer, analyze the causal chain process of uncontrolled release of energy or hazardous substances; S13. Combining the analysis results of steps S11 and S12, construct a fire causation model to clarify the patterns and key nodes of the entire fire development process, including the initiation, development, and decay stages.

3. The coupled simulation method for optimizing evacuation routes in dormitory buildings during fires, as described in claim 1, is characterized in that: In step S2, the initial evacuation response behavior is set to immediately escape, call the police and notify others, confirm the authenticity, wait for rescue, and collect important items; the level of panic is set to be at a loss, fear, and calmness; the probability of exit reselection when facing congestion is set to wait patiently, cut in line directly, and find other exits; the exit selection preference is set to take the elevator, follow the signs, choose the nearest passage, choose a familiar passage, and follow the crowd.

4. The coupled simulation method for optimizing evacuation routes in dormitory buildings during fires, as described in claim 1, is characterized in that... Step S3 specifically includes: S31. Use architectural modeling software to construct a detailed 3D model of the dormitory building, including the building structure, room layout, and door and window locations; S32. Import the simulation parameters and BIM model into the simulation software; S33. Divide the computational grid within the simulation domain and set up detection slices for temperature, CO concentration, and visibility in key areas; S34. Based on the building type and the characteristics of combustibles, set the heat release rate growth model and the maximum heat release rate of the fire source; S35. Based on the principle of fire scene selection, at least three different fire source locations on different floors are set in the three-dimensional model; S36. Run fire dynamics simulation to analyze the changes in temperature, CO concentration and visibility at breathing height in corridors and stairwells over time under various fire scenarios, and identify safe evacuation time windows and danger zones.

5. The coupled simulation method for optimizing evacuation routes in dormitory buildings during fires, as described in claim 4, is characterized in that: The fire source locations include at least the dormitories near the top floor staircase, the dormitories near the middle floor staircase, and the middle dormitory on the first floor; the heat release rate growth model adopts the t² rapid fire model, with the maximum heat release rate set to 6MW; the simulation time is set to 600s.

6. The coupled simulation method for optimizing evacuation routes in dormitory buildings during fires, as described in claim 1, is characterized in that... Step S4 specifically includes: S41. Import the building information model constructed in step S31 into the personnel evacuation simulation software, and set the number and distribution of personnel in each room according to the actual use of the building. S42. Use the data of dangerous areas and critical time of danger obtained in step S35 as dynamic environmental constraints to input the personnel evacuation simulation model. S43. Operation personnel evacuation simulation model to obtain the personnel flow time curve of each exit, the congestion points of the evacuation route, and the overall and sub-regional evacuation completion time. S44. Compare and analyze the differences in the smoothness of evacuation routes and the total evacuation time under different fire source locations to reveal the influence mechanism of fire source location on evacuation efficiency.

7. The coupled simulation method for optimizing evacuation routes in dormitory buildings during fires, as described in claim 6, is characterized in that: Step S4 also includes developing optimization measures based on the simulation analysis results.

8. The coupled simulation method for optimizing evacuation routes in dormitory buildings during fires, as described in claim 7, is characterized in that... The optimization measures include at least one of the following: (1) Add signs and lighting to the congested road sections shown in the simulation, and clear debris from the passageways; (2) Develop evacuation plans for different areas based on the evacuation time obtained from the simulation; (3) Based on the analysis results of the fire spread path, alarm and sprinkler devices are added at specific locations; (4) Based on the risk points revealed by the simulation, conduct targeted safety knowledge dissemination and escape skills training.

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