Search Results (7)

Accurate fire source location in an aircraft cargo compartment cannot be determined by common design practices. This study proposes an advanced fire location inversion framework based on a Convolutional Long-Short-Term Memory (ConvLSTM) network. A self-designed interpolation preprocessing module is introduced to realize the integration of spatial and temporal sensor data. The model was trained and validated using a comprehensive database generated from large-scale fire dynamics simulations. Hyperparameter optimization, including a learning rate of 0.001 and a 5 × 5 convolution kernel size, can effectively avoid the systematic errors introduced by interpolation preprocessing, further enhancing model robustness. Validation in simplified scenarios demonstrated a mean squared error of 0.0042 m and a mean positional deviation of 0.095 m for the fire source location. Moreover, the present study assessed the model’s timeliness and reliability in full-scale cabin complex scenarios. The model maintained high performance across varying heights within cargo compartments, achieving a correlation coefficient of 0.99 and a mean absolute relative error of 1.9%. Noteworthily, reasonable location accuracy can be achieved with a minimum of three detectors, even in obstructed environments. These findings offer a robust tool for enhancing fire safety systems in aviation and other similar complex scenarios. Full article

Controlling the simulated smoke flow field is important in the airworthiness verification experiment for the smoke detection system in aircraft cargo compartments to accurately replicate actual fire smoke. In existing studies, the unsteady adjustment performance of the actuator to the simulated smoke flow field has not been comprehensively evaluated, and the model-based closed-loop flow control method encounters the unmodeled dynamics of the complex turbulent flow field. To solve the aforementioned problems, this study first uses the system identification method to obtain transfer function models for different actuation modes. Moreover, the transient adjustment capabilities of different actuation modes for the simulated smoke flow field are thoroughly evaluated. Then, an adaptive flow control law based on a radial basis function neural network is designed based on the selected actuating mode. Furthermore, closed-loop control experiments based on the adaptive control law are performed. The root locus of the transfer functions for two different actuation modes are compared, which reveals that adjusting the flow rate of simulated smoke exhibits a better stability margin than the actuation mode that regulates the upward momentum of simulated smoke. The experimental results in a full-scale mock-up of an aircraft cargo compartment demonstrate that the designed control law realizes dynamic tracking control with the unsteady concentration of actual fire smoke as the control target. Compared with that of PID control, the root mean square error of the control system is reduced by more than 40%. The simulated smoke under the closed-loop control obtains a light-transmission response equivalent to that of the actual fire smoke within a 5% error margin. The proposed closed-loop adaptive flow control method for simulated smoke approximates the unsteady process of actual fire smoke. It provides technical support for the replacement of actual fire smoke in the airworthiness verification experiment of smoke detection in aircraft cargo compartments. Full article

During flight, aircraft cargo compartments are in a confined state. If a fire occurs, it will seriously affect flight safety. Therefore, fire detection systems must issue alarms within seconds of a fire breaking out, necessitating high real-time performance for aviation fire detection systems. In addressing the issue of fire target detection, the YOLO series models demonstrate superior performance in striking a balance between computational efficiency and recognition accuracy when compared with alternative models. Consequently, this paper opts to optimize the YOLO model. An enhanced version of the FDY-YOLO object detection algorithm is introduced in this paper for the purpose of instantaneous fire detection. Firstly, the FaB-C3 module, modified based on the FasterNet backbone network, replaces the C3 component in the YOLOv5 framework, significantly decreasing the computational burden of the algorithm. Secondly, the DySample module is used to replace the upsampling module and optimize the model’s ability to extract the features of small-scale flames or smoke in the early stages of a fire. We introduce RFID technology to manage the cameras that are capturing images. Finally, the model’s loss function is changed to the MPDIoU loss function, improving the model’s localization accuracy. Based on our self-constructed dataset, compared with the YOLOv5 model, FDY-YOLO achieves a 0.8% increase in mean average precision (mAP) while reducing the computational load by 40%. Full article

Aircraft cargo compartment fires are one of the main threats to the safety of civil aircraft. In this study, a series of numerical simulations on the fire suppression performance of water mist in cargo compartments was carried out to examine the effects of the spray pattern, droplet size, and nozzle layout. The fire dynamics simulator (FDS) code was used to construct a fire suppression scenario in a full-scale aircraft cargo compartment. The results show that the extinguishment time of a corner fire was longer compared with center and sidewall fires due to the relatively larger distance from the nozzle and, therefore, a lower effective number of droplets reaching the flame area. Solid and hollow spray patterns showed significant differences in the spray coverage area. For a fixed flow rate, the hollow spray showed better fire suppression performance than solid spray. When the droplet size varied from 50 to 400 µm, the fire extinguishment time first increased and then decreased, corresponding to the dominant mechanism of the smothering effect of small droplets and the cooling effect of large droplets. In addition, the nozzle layout affected the water coverage on the ground of the cargo compartment. With an increase in nozzle number, the water mist flux was more evenly distributed and the fire extinguishment effect also increased. Full article

The flashover mechanism in an aircraft cargo compartment under low pressure was investigated in this study. A series of fire experiments were conducted in a scale model of a one-quarter volume FAA standard aircraft cargo compartment at 96 kPa and 60 kPa. The ignition of single-walled corrugated cardboard was chosen as the criterion of the flashover. The influence of different fire sizes and fuel types on the flashover was studied by comparing the average temperature of the smoke layer, the radiation heat flux at the floor level, and the heat release rate of the fire source. The critical condition and behavior of the flashover were analyzed. The results show that under low pressure, the flashover occurs at a higher temperature and radiation heat flux. Increasing the fire source size brings the flashover forward. At 60 kPa and 96 kPa, the cardboard ignites under a flashover when the average temperature of the smoke layer reaches 551 °C and 450 °C, and the average radiant heat flux at the floor level reaches 19.6 kW/m² and 14 kW/m², respectively. In addition, the minimum fire size for a flashover is directly proportional to the heat of evaporation and inversely proportional to the heat of combustion. Full article

A fire in the cargo compartment has a major impact on civil aviation flight safety, and according to the airworthiness clause of the CCAR-25, the detector must sound an alarm within 1 min of a fire in the cargo compartment. As for the cargo compartment of large transport aircrafts, the internal space is high and open, and the smoke movement speed becomes slower with significant cooling in the process of diffusion. Hysteresis can occur in smoke detectors because of their internal labyrinth structure, which causes the detector’s internal and external response signals to be out of sync. This research employs a numerical simulation to examine the detector response parameters under an ambient wind speed of 0.1–0.2 m/s and fits a Cleary two-stage hysteresis model, where τ₁= 0.09u^−1.43 and τ₂= 0.67u^−1.59. Finally, multiple full-scale cargo cabin experiments were conducted to validate the prediction model. The results show that the model’s predicted alarm range is 43.1 s to 49.0 s, and the actual alarm time obtained by the experiment falls within this interval, confirming the model’s accuracy and providing theoretical support for the structural design and layout of the aircraft cargo cabin smoke detector. Full article

The increasing incidence of aircraft cargo compartment fires in recent years has caused heavy losses and drawn attention to understanding the combustion process, especially under low pressure. The present paper aims at exploring the conditions of flashover at different pressures. Experiments were conducted in Guanghan (96 kPa) and Kangding (60 kPa), which are located at different altitudes. Five different sizes of pool fires were tested and smoke temperature and heat flux were measured for flashover criteria analysis. Corresponding simulations were performed using a commercial software FDS to examine the pressure effect on the flashover phenomenon and to validate the simulation results. The critical conditions for the onset of flashover in a 1/4 MPS compartment were found to be (1) an average upper layer temperature of about 450 °C at 96 kPa or about 550 °C at 60 kPa, and (2) radiant heat flux at floor level of about 10 to 15 kW/m² at 96 kPa or 20 to 25 kW/m² at 60 kPa. By using the average upper layer temperature as a criterion in modeling, a relatively accurate prediction of flashover onset time could be obtained at 96 kPa and a delay of about 25 s was found at 60 kPa. Full article