A fire-fighting and smoke exhaust optimization control method based on CFD simulation

Abstract

This invention discloses a fire protection and smoke exhaust optimization control method based on CFD simulation, belonging to the fields of instrumentation / building environment and equipment engineering / fire protection and ventilation control technology. The method includes: establishing a 3D building model and a CFD simulation model; setting fire scenarios and boundary conditions; performing smoke flow simulation calculations; and improving the system's response speed, control accuracy, and reliability through intelligent algorithms and optimized control strategies, thereby achieving intelligent management of the fire protection system.

Description

Technical Field

[0001] This invention relates to an optimized control method for fire prevention and smoke exhaust based on CFD simulation, belonging to the fields of instrumentation / building environment and equipment engineering / fire ventilation control technology. Background Technology

[0002] Smoke is one of the leading causes of death and injury in building fires. Statistics show that over 80% of fire-related deaths are due to smoke inhalation and asphyxiation. As a crucial component of building fire protection systems, the design rationality of smoke control and exhaust systems directly impacts the effectiveness of evacuation and fire rescue during a fire. Currently, traditional smoke control and exhaust system designs rely heavily on designers' experience, employing fixed smoke extraction and air supply volumes, and lacking scientific simulation and verification methods. Existing technologies typically design according to the minimum requirements in regulations, failing to fully consider the differences in building interior space layout, fire load distribution, and evacuation routes.

[0003] Existing smoke control methods suffer from the following main shortcomings: First, they employ fixed threshold control, failing to dynamically adjust smoke extraction strategies according to the fire's development stage; second, they lack in-depth analysis of smoke flow patterns, leading to unreasonable smoke exhaust outlet placement and dead zones; third, the linkage between smoke control systems and fire alarm systems is simplistic, failing to achieve refined control; and fourth, they lack real-time evaluation and feedback optimization mechanisms for smoke extraction effectiveness. Therefore, there is an urgent need for a fire-fighting smoke control optimization method based on CFD simulation, capable of scientifically designing smoke control schemes according to building characteristics and fire scenarios, and achieving intelligent and refined control of smoke control systems. Summary of the Invention

[0004] The purpose of this invention is to avoid the problems of existing smoke control and exhaust systems relying on experience, lacking scientific verification, and having extensive control, and to provide a fire smoke control and exhaust optimization control method based on CFD simulation. This method uses computational fluid dynamics simulation technology to predict the flow law of smoke, optimize the design of smoke control and exhaust systems, and achieve intelligent smoke control and exhaust.

[0005] To achieve the above objectives, the present invention adopts the following technical solution, comprising the following steps: Step 1: Create a 3D building model and a CFD simulation model Establish an accurate 3D geometric model based on architectural CAD drawings, including room layout, door and window positions, ventilation openings, etc.; select appropriate turbulence models (such as k-ε models) and combustion models to establish CFD simulation calculation models; divide the calculation mesh and refine the mesh in key areas.

[0006] Step 2: Set the fire scenario and boundary conditions Based on the building's function and fire load distribution, typical fire scenarios are set; parameters such as the location of the fire source, heat release rate curve, and type of combustible are determined; and boundary conditions such as ambient temperature, pressure, and ventilation conditions are set.

[0007] Step 3: Perform flue gas flow simulation calculations Transient simulation calculations were performed using CFD software to simulate the flow, diffusion, and settling of fire smoke inside a building; parameters such as smoke concentration distribution, temperature distribution, and visibility distribution at different times were analyzed; and changes in smoke layer height and smoke propagation paths were calculated.

[0008] Step 4: Evaluate the effectiveness of existing smoke control and exhaust solutions. Based on the simulation results, evaluate the effectiveness of existing smoke control and exhaust design schemes; identify smoke accumulation areas, smoke exhaust dead zones, and smoke impacts on evacuation paths; and calculate the available safe evacuation time (ASET) and required safe evacuation time (RSET).

[0009] Step 5: Optimize the design of the smoke control and exhaust system Based on the simulation analysis results, the location, number, and size of the smoke exhaust outlets were optimized; the air supply and smoke exhaust volumes were adjusted to ensure the positive pressure requirements of the stairwell and vestibule; and the start-up sequence and control logic of the smoke control system were optimized.

[0010] Step 6: Establish a real-time monitoring and feedback optimization mechanism Temperature sensors, smoke sensors, CO sensors and other monitoring equipment are deployed inside the building; environmental parameters are collected in real time and compared with CFD simulation results for verification; the operating parameters of the smoke control and exhaust system are dynamically adjusted based on actual monitoring data.

[0011] Step 7: Construct an intelligent control decision-making system Establish an intelligent control model based on fuzzy logic or neural networks; automatically select the optimal smoke prevention and exhaust strategy according to the fire development stage and smoke diffusion trend; and realize intelligent linkage between the smoke prevention and exhaust system, fire alarm system, and emergency lighting system.

[0012] The CFD simulation calculation uses the finite volume method to solve the Navier-Stokes equations, taking into account physical processes such as buoyancy effects, turbulent transport, and flue gas radiation.

[0013] The optimization algorithm employs either a genetic algorithm or a particle swarm optimization algorithm, with the optimization objectives of minimizing the range of smoke impact and maximizing the safety of evacuation routes.

[0014] The comparison and verification of real-time monitoring data and simulation data adopts data assimilation technology to continuously correct the simulation model parameters and improve prediction accuracy. Beneficial effects

[0015] Compared with the prior art, the present invention has the following advantages: 1. Improved the scientific nature of smoke control and exhaust system design. CFD simulation technology can accurately predict the flow pattern of fire smoke inside a building, providing a scientific basis for the design of smoke control and exhaust systems and avoiding the blindness of traditional experience-based design methods.

[0016] 2. Enhanced the intelligence level of smoke prevention and exhaust control. By employing intelligent control algorithms, the system can automatically adjust smoke prevention and exhaust strategies based on the fire's development stage and smoke diffusion pattern, achieving refined control and improving the system's response speed and handling effectiveness.

[0017] 3. Improved safety during personnel evacuation. By accurately predicting the impact range of flue gas and the safety of evacuation routes, we can optimize evacuation directions, extend the available safe evacuation time, and improve the success rate of personnel evacuation.

[0018] 4. Feedback optimization of system operation was implemented. By comparing and verifying real-time monitoring data with simulation data, the simulation model and control strategy are continuously corrected, enabling the system to have self-learning and self-optimization capabilities.

[0019] 5. Reduced system operating energy consumption By dynamically adjusting the smoke exhaust volume and air supply volume according to actual needs, energy waste in fixed operation mode is avoided, and energy-saving operation is achieved.

[0020] 6. Improved system reliability and maintainability Real-time monitoring and fault warnings can promptly detect system anomalies, allow for preventative maintenance, reduce system failure rates, and extend equipment lifespan. Attached Figure Description

[0021] Figure 1 This is a system architecture diagram of the present invention, showing the connection relationship between the CFD simulation module, data acquisition module, intelligent control module and smoke exhaust execution module.

[0022] Figure 2 This is a flowchart of the method of the present invention, which shows the complete process from building modeling, scene setting, simulation calculation to system optimization.

[0023] Figure 3 This is a data processing flowchart of the present invention, which shows the detailed process of sensor data acquisition, preprocessing, feature extraction and pattern recognition.

[0024] Figure 4 This is the control logic diagram of the present invention, which illustrates the working logic of the intelligent decision engine and the switching conditions of each control strategy.

[0025] Figure 5 This is a network topology diagram of the present invention, illustrating the communication network structure between monitoring devices, controllers, and execution devices.

[0026] Figure 6 This is a state transition diagram of the present invention, which shows the working state of the smoke control and exhaust system at different stages of a fire and their transition relationships. Example

[0027] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to specific embodiments.

[0028] Example 1: Optimization of Smoke Control and Exhaust Systems in Commercial Complexes A commercial complex has a building area of ​​100,000 square meters, with 5 floors above ground and 2 floors underground. The method of this invention is used for the optimized design of the smoke control and exhaust system.

[0029] First, a 3D geometric model was created based on the architectural CAD drawings, including the layout of shops on each floor, the location of the atrium, and evacuation stairwells. The model was divided into an unstructured mesh with a total of approximately 5 million grid cells, with the mesh being fined in key areas such as stairwells and the atrium.

[0030] A typical fire scenario is set up: a fire occurs in a supermarket on the basement level, with a fire source power of 5MW, located in the center of the supermarket. CFD simulation calculations are performed using FDS software, with a simulation time of 1800 seconds.

[0031] Simulation results show that, without mechanical smoke extraction, smoke spreads to the entire basement level within 300 seconds and to the ground floor via the stairwell within 600 seconds. Smoke intrusion into the stairwell occurs at 450 seconds, affecting personnel evacuation.

[0032] Based on the simulation results, the design of the smoke control and exhaust system was optimized: the number of smoke exhaust outlets was increased on the first basement floor, and the smoke exhaust volume was increased from the original 10 m³ / s to 15 m³ / s; a positive pressure air supply system was installed in the stairwell, with an air supply volume of 8 m³ / s; and the location of the smoke exhaust outlets was optimized to avoid the main path of smoke flow.

[0033] The optimized simulation results show that the smoke is effectively controlled in a local area on the first basement level, the stairwell remains smoke-free, and the available safe evacuation time for personnel is extended from the original 450 seconds to more than 1200 seconds.

[0034] In actual operation, 200 monitoring points, including temperature sensors, smoke sensors, and CO sensors, are deployed to collect environmental parameters in real time. An intelligent control system is established to dynamically adjust the exhaust fan speed and air supply pressure based on the monitoring data. When the temperature in a certain area exceeds a set threshold, the exhaust volume in the vicinity of that area is automatically increased.

[0035] After a year of operation, the system successfully warned and dealt with three initial fires, preventing the fires from spreading. The system's energy consumption is reduced by approximately 25% compared to traditional fixed modes, and maintenance costs are reduced by approximately 30%.

[0036] Example 2: Intelligent Smoke Control System for High-Rise Office Buildings A high-rise office building has 30 floors above ground and a building height of 120 meters. An intelligent smoke control system is established using the method of this invention.

[0037] A CFD model of the building was established, taking into account the thermal pressure effect and wind pressure effect caused by the building height. Simulation analyses were conducted on various fire scenarios for different floors and seasons.

[0038] Simulation analysis revealed that in winter, due to the thermal pressure effect, the upward spread of flue gas is about 30% faster than in summer; in high-altitude areas, wind pressure has a significant impact on flue gas diffusion, with the diffusion of flue gas on the windward side being suppressed and the diffusion of flue gas on the leeward side being accelerated.

[0039] Based on the simulation results, a hierarchical and zoned control strategy is established: the low zone (floors 1-10) adopts conventional control parameters; the middle zone (floors 11-20) increases the air supply pressure by 10%; and the high zone (floors 21-30) adjusts the control parameters in real time according to wind speed and direction.

[0040] The system deploys an intelligent control system that integrates fire alarm systems, meteorological monitoring systems, and building automation systems. When a fire occurs, the system automatically selects the optimal smoke control and exhaust strategy based on the fire location, floor height, and outdoor weather conditions.

[0041] In a live fire drill, a fire was simulated on the 15th floor of an office building. Within 10 seconds of fire confirmation, the system activated the corresponding smoke control and exhaust equipment, dynamically adjusting control parameters based on real-time monitoring data. Smoke was effectively contained on the 15th floor and in the stairwells, while other floors remained smoke-free. Evacuation time was reduced by approximately 20% compared to expectations.

[0042] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A fire smoke control optimization method based on CFD simulation, characterized in that, Includes the following steps: 1) Establish a 3D building model and a CFD simulation model; 2) Set the fire scenario and boundary conditions; 3) Perform flue gas flow simulation calculations; 4) Evaluate the effectiveness of existing smoke control and exhaust systems.

2. The method according to claim 1, characterized in that, The data acquisition includes real-time acquisition of temperature, smoke concentration, pressure, and flow parameters through a sensor network.

3. The method according to claim 1, characterized in that, The data processing includes data preprocessing, feature extraction, anomaly detection, and pattern recognition.

4. The method according to claim 1, characterized in that, The control decision-making employs intelligent algorithms, including fuzzy control, neural networks, or expert systems.

5. The method according to claim 1, characterized in that, The method also includes linkage control with fire alarm systems, emergency lighting systems, and evacuation guidance systems.

6. The method according to claim 1, characterized in that, The control parameters are dynamically adjusted using an optimization algorithm, which may include a genetic algorithm, a particle swarm optimization algorithm, or an ant colony optimization algorithm.

7. The method according to claim 1, characterized in that, It also includes establishing a feedback optimization mechanism to adjust the control strategy in real time based on the system's performance.

8. The method according to claim 1, characterized in that, It also includes fault detection and early warning functions, which automatically alarm and switch to standby mode when a system abnormality is detected.

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