Aircraft operations at high-altitude airports and during cruise phases are subjected to low-pressure environments, which significantly alter the physical and optical properties of smoke aerosols generated via cargo combustion, presenting severe challenges for conventional detection systems. Existing research has predominantly focused on macroscopic combustion parameters, while the evolutionary patterns of key microscopic parameters, such as the Sauter mean diameter (SMD) and particle number concentration, remain underexplored. This study aims to systematically elucidate the mechanisms underlying the evolution of multiwavelength smoke aerosol characteristics under low-pressure conditions, thereby providing theoretical support for enhancing the reliability of aviation-smoke detection.

This study established an integrated experimental platform based on a full-scale, dynamic-pressure and temperature-controlled chamber, capable of simulating a pressure range of 10–101 kPa. A triple-wavelength laser detection system—comprising three independent laser–detector pairs at 0.450 μm (blue), 0.532 μm (green), and 1.064 μm (infrared)—was designed and implemented. The optical path for each wavelength was independently calibrated, and the actual optical path lengths were precisely determined. The SMD and particle number concentration were retrieved using the multiwavelength extinction method and the Beer–Lambert law, combined with Mie scattering theory. Four representative fuels—beech wood, corrugated paper, n-heptane, and polyurethane—were selected to simulate typical cargo-compartment fire scenarios under smoldering and flaming conditions. Experiments were systematically conducted at three pressure levels: 90, 70, and 50 kPa. For each pressure condition, optical data were recorded at 1 s intervals across 1 500 repetitions and each set of experiments was repeated three times to ensure reproducibility. With a broad spectral span from blue to infrared wavelengths, the system provided enhanced sensitivity to particle size variations within the typical smoke aerosol range of 0.100–1.000 μm. The introduction of the third wavelength (green) served as an independent constraint, effectively reducing the common inversion multiplicity problem encountered in single-or dual-wavelength systems.

This study systematically revealed, to the best of our knowledge, for the first time, the differential responses of smoke parameters to pressure variations across combustion modes. 1) Under smoldering conditions, the SMD slightly increased with decreasing pressure (beech: 0.359→0.376 μm; paper: 0.292→0.318 μm), with variations being only < 0.020 μm, showing remarkable size stability. In contrast, flaming aerosols showed significant SMD reduction; n-heptane aerosols exhibited substantial variations exceeding 0.200 μm, while polyurethane aerosols varied < 0.050 μm, indicating higher pressure sensitivity for pure hydrocarbon fuels. 2) Regarding the particle concentration: smoldering smoke displayed a nonmonotonic trend (initial increase followed by decrease), inversely correlated with the optical power; the n-heptane flaming concentration continuously increased with decreasing pressure, whereas the polyurethane concentration decreased due to oxygen-limitation–induced pyrolysis suppression. 3) Method validation confirmed that dispersion values were < 10% for the triple-wavelength system, considerably enhancing the reliability of the particle size and concentration measurements under low-pressure conditions.

The evolution of smoke aerosol size and concentration in low-pressure environments is strongly governed by the combustion mode and fuel characteristics. Smoldering smoke exhibits notable size stability, whereas flaming smoke demonstrates significant pressure sensitivity, with fuel volatility and chemical structure being key influencing factors. The triple-wavelength extinction method, through multiwavelength collaborative constraints, effectively addresses the technical challenges of aerosol characterization at low pressures, providing crucial methodological support and a theoretical foundation for optimizing next-generation aviation smoke detection systems.

As the aircraft cargo compartment is a closed and complex environment, challenges occur in fire detection due to diverse cargo and limitations in sensor placement. Traditional fire detection methods are unable to accurately address fire recognition in such complex environments, particularly during the early stages of a fire. To address the challenge of accurately identifying the fire source localization in aircraft cargo compartments, this paper proposed a method based on a Bayesian-optimized bi-directional long short-term memory network (BO-BiLSTM).

This paper involved establishing an experimental platform of a real aircraft cargo compartment to simulate various fire scenarios. Fusion sensors were installed at multiple locations on the cargo compartment ceiling to collect multidimensional feature parameters in real time, including smoke volume fraction, CO volume fraction, and temperature. These data were used to build a fire feature database to capture the complex and dynamic changes in fire scenes. To improve the accuracy of detecting the fire source location, a bidirectional long short-term memory (BiLSTM) network was utilized, using its bidirectional information transmission mechanism to capture the forward and backward dependencies in time-series data. Meanwhile, the BiLSTM network structure was optimized using a Bayesian optimization algorithm to find the optimal combination of hyperparameters, enhancing the model's generalizability and robustness.

The experimental results indicate the following. First, the model was validated using a sliding window approach. When the number of windows was set to 8, the accuracy of the BO-BiLSTM model reached 97.2%. Compared with traditional models, such as recurrent neural networks (RNN), gated recurrent units (GRU), long short-term memory (LSTM) networks, and unoptimized BiLSTM models, the accuracy increased by 22%, 21%, and 2.6%, respectively. Second, in robustness tests with missing features, the BO-BiLSTM model maintained good stability. When only temperature and CO volume fraction were used as inputs, the model achieved an accuracy of 80.5%, which increased to 82.2% when using temperature and smoke volume fraction. Meanwhile, With when temperature and CO volume fraction were considered as inputs, the accuracy was 75.6%. The combination of temperature and smoke volume fraction performed the best, showing a strong correlation between these two features, with smoke volume fraction being more accurate for fire source localization. Finally, in the analysis of the model under sensor failure conditions, even with the number of functioning sensors reduced to four, the BO-BiLSTM model maintained an accuracy of 59.4%, significantly outperforming other models and demonstrating its advantages in complex and dynamic fire environments. The accuracy of the BiLSTM and LSTM models was lower than that of BO-BiLSTM, but their accuracy declined more gradually as the number of sensors increased, indicating some degree of resistance to interference. The GRU model performed better than the RNN model; however, when the number of damaged sensors was three or four, the accuracy of the GRU model was significantly lower than that of BO-BiLSTM, LSTM, and BiLSTM. The RNN model performed the worst in all scenarios, with its accuracy rapidly declining as the number of damaged sensors increased, dropping to approximately 45.2%.

By significantly enhancing the accuracy and efficiency of fire source localization, this study provides essential technical support for the early detection, rapid response, and effective management of aircraft cargo compartment fires, which can help reduce fire risks and ensure safe and reliable air transportation.

The frequent occurrence of fire accidents poses a serious threat to the safety of people's lives and properties. Thus it is vital to improve the accuracy and efficiency of fire warning systems. Currently, fire alarm sensors or detector systems can be used to provide an early warning of potential fire hazards. Existing conventional fire alarm detectors include infrared (IR) and smoke detectors, that trigger alarms by detecting heat radiation or smoke particles. However, these systems are susceptible to interference from environmental factors, resulting in false alarms or delayed warnings (>100 s).

Unlike traditional smoke alarms, new fire warning sensors can provide timely responses in the early stages of a fire, providing a stronger guarantee for fire safety. Therefore, there has been increasing interest in smart fire warning materials and sensors that combine traditional passive fire retardant strategies with active fire alarm response. Carbon-based two-dimensional (2D) nanomaterial graphene oxide (GO), a typical representative of smart fire warning materials, is characterized by its positive feedback between electrical conductivity and temperature. This paper reviews local and international research progress in the field of GO-based fire early warning sensors. The working principle of GO-based sensors is first summarized, followed by detailed descriptions of current research on GO body materials and GO coating materials. Furthermore, we analyze in depth the practical applications of such sensors in a variety of application scenarios and requirements, demonstrating their wide range of application prospects. We also categorize GO-based fire warning sensor warning signals into traditional and remote and IoT-based alarm signals and then elaborate on these. Finally, we provide a comprehensive summary of the research on GO-based fire early warning sensors, which shows that GO-based fire alarm materials can provide sensitive fire alarm signals within < 10s, making them more sensitive than conventional fire alarm systems. Based on such updated information, we summarize the future research directions in this field.

Future research should focus on several aspects. First, the fire warning and fire protection performance of the GO coating can be further optimized by developing new coating materials and improving the structural design, while ensuring that it can quickly respond to fire and effectively stop it from spreading. Second, in optimizing the design of the response of organics to GO, researchers should consider the thermal response sensitivity of the functional groups and the properties of the organics themselves. In particular, quantifying the number of functional groups and the effect of pyrolysis of organics on fire warning can help establish a synergistic quantitative relationship between them. Such a relationship helps to precisely regulate the properties of the materials, thus achieving accurate and efficient fire warning functions. Third, a more reasonable preparation method must be proposed to realize the precise control of the number and type of functional groups on the GO surface. This can be achieved by precisely controlling the conditions of the chemical reaction, including temperature, time, and pH levels. Finally, the GO fire warning and fireproof coating technology must be integrated with the Internet of Things to realize real-time data monitoring, as well as remote control and automated response systems to improve their level of intelligence.

Due to the high flammability of nonflame-retardant pure acrylonitrile-butadiene-styrene (ABS), a material often used for passenger luggage, it is easily ignited by open flames, posing risks to aviation operations. Therefore, in-depth research on the pyrolytic combustion characteristics of ABS at high temperatures and high radiation intensities is crucial for the safe operation of aircraft.

This study evaluated the thermal stability and combustion characteristics of ABS under different heating rates and radiation intensity conditions using thermogravimetric analysis and cone calorimeter systems. This study also analyzed the variations in the characteristic parameters of ABS.

The results show that the pyrolysis process of ABS can be divided into an initial volatilization stage, a rapid decomposition stage, a residual combustion stage, and a pyrolysis termination stage. In the rapid decomposition stage, when ABS reaches temperatures of approximately 310 ℃ to 343 ℃, the main polymer chains of ABS undergo cleavage, breaking down into different components, such as acrylonitrile and polyethylene monomers, leading to the decomposition of polymer molecules. When heated, the main chain of ABS ruptures. The molecular structure of ABS contains different components, such as styrene and butadiene, which are prone to decomposition and cross-linking reactions upon heating, resulting in the occurrence of the pyrolysis process. An increase in heating rate significantly shortens the pyrolysis time and enhances the maximum thermal decomposition rate. As the radiation intensity increases, the combustion process of ABS accelerates, with the heat release rate increasing and the peak heat release rate increasing by 53%. The combustion and ignition times decrease by 32% and 78%, respectively, because of the increase in material temperature and the exacerbation of heat conduction and convection phenomena leading to an increase in heat release rate. Under low radiation intensities, ABS cannot rapidly absorb energy to reach combustion conditions. However, as the radiation intensity increases, ABS can rapidly absorb sufficient energy for faster decomposition, thus shortening the combustion time. The generation time of carbon monoxide (CO) and carbon dioxide (CO₂) is enhanced, and the maximum generation amounts of CO₂ and CO increase by 49% and 74%, respectively. The oxygen consumption increases and the oxygen consumption rate accelerates due to the intensified molecular motion caused by thermal radiation, leading to a faster reaction with oxygen in the air. The mass loss time is enhanced, the remaining sample mass decreases, and the maximum mass loss rate increases by 53.8%. Based on the thermal penetration model, 2 mm thick ABS material is classified as a thermally thin material, and verification is conducted. Based on the ignition time model, a critical radiative heat flux formula is established, and the critical radiative heat flux is calculated to be 16.255 kW/m². Finally, according to the fire performance indicators, as the radiation intensity increases, the material combustion rate increases, releasing higher amounts of heat, leading to faster fire growth and development, thereby increasing fire risk. The fire risk of ABS is positively correlated with the radiation intensity.

This study concludes that ABS exhibits a high fire risk. This research provides crucial data and practical references on the fire risks associated with ABS material for safe aviation operations.

With the rapid advancement of the aviation industry, ensuring aircraft safety, particularly in sensitive areas like cargo holds, is of paramount importance. Fires in aircraft cargo can be triggered by various factors, such as electrical malfunctions, hazardous materials, or environmental conditions, and pose significant threats to passengers and crew. Given the growing complexity of fire detection in these confined spaces, more reliable and accurate fire detection methods are urgently needed. Traditional fire detection systems, which primarily depend on single-sensor technologies, like smoke or heat detectors, have long been criticized for their high false alarm rates and limited accuracy. These deficiencies often result in delayed responses or unnecessary interventions, which ultimately compromise operational safety and efficiency. Therefore, this study aims to develop an innovative fire detection system that can overcome the limitations of conventional methods while meeting the advanced safety standards of modern aviation.

To tackle these challenges, this research introduces an improved multiparameter fire detection method leveraging an advanced self-attention mechanism within the Transformer model architecture. The approach integrates data from multiple sensors, including carbon monoxide, smoke, humidity, and temperature sensors, to capture a wide range of environmental parameters in aircraft cargo holds. Data are gathered by simulating realistic fire scenarios within a laboratory setting, ensuring that the system is trained on diverse datasets that reflect the unpredictable nature of fire development in cargo spaces. The core of the proposed method is a Transformer-based model that incorporates two key innovations: local attention mechanism and multiscale feature extraction. The local attention mechanism addresses the computational complexity of processing long sequences of input data by dividing the data into smaller, manageable windows. This allows the model to focus on localized features without the burden of analyzing the entire sequence at once, making it more efficient and suitable for real-time applications. Furthermore, the multiscale feature extraction module processes data in parallel across different time windows, capturing short-term fluctuations and long-term trends, which is crucial for detecting gradual fires, such as slow-burning or smoldering fires, that traditional systems may miss.

The proposed method was rigorously evaluated through a series of experiments on a fire detection dataset designed to mimic real-world conditions in aircraft cargo holds. A range of hyperparameters, including sequence lengths, activation functions, dropout rates, and optimizers, was tested to fine-tune model classification performance. Results revealed that the optimized model significantly outperformed traditional approaches, such as convolutional neural networks, recurrent neural networks, and long short-term memory networks, in terms of classification accuracy, particularly under challenging conditions involving noisy or incomplete sensor data. The model excelled at distinguishing between fire and non-fire events, showcasing its superior ability to handle real-world fire scenarios. Moreover, the Transformer's intrinsic parallel computing capability reduced training times, making it a practical solution for time-sensitive fire detection applications in aviation.

This study presents a novel multiparameter fire detection system that integrates an improved self-attention mechanism with local attention and multiscale feature extraction, offering several advantages over traditional models. The proposed method achieves higher accuracy, lower computational complexity, and faster training times, making it highly suitable for deployment in aircraft cargo hold fire detection systems. The promising results from the laboratory-based experiments suggest that this method can be readily adapted to real-world operational settings. Future research will focus on further validating the model's performance in live environments, aiming to extend its applicability to other safety-critical domains beyond aviation, such as industrial safety and transportation systems.

With the rapid and continuous advancement of urbanization at an astonishing pace, fire accidents are happening with increasing frequency globally. A sudden fire outbreak holds a significantly high probability of causing extensive and severe harm to society. Research conducted on image-based fire detection algorithms is highly beneficial and valuable in terms of extracting the detailed morphological features of fires or smoke, aiding in effectively improving the efficiency of fire warnings.

This study presents and introduces an improved version of the YOLOv8 algorithm. Initially, the neck network of the algorithm is strengthened by integrating the SlimNeck lightweight module. Then, the inference framework of the YOLOv8 algorithm is substituted with slicing-aided hyper inference (SAHI) to further enhance the capability of the algorithm to detect small targets. Moreover, fire and smoke are two crucial target categories in fire scenarios. Given the inherent complexity of fire image backgrounds, which frequently contain numerous interferences from nonfire categories, fire dataset targets are classified as fire, smoke, and default.

Experimental results clearly indicate that the SlimNeck-YOLOv8 algorithm showcases superior fire detection performance compared with other related advanced algorithms. In contrast to the YOLOv8 algorithm, the recall rate of this algorithm is elevated by 2.7%, mean average precision (mAP) is increased by 0.2%, and detection speed is accelerated by 35 frames/s. Simultaneously, with the developed algorithm, the computational burden is effectively reduced.

By integrating SlimNeck and SAHI, respectively, to optimize the network structure and inference framework of the YOLOv8 algorithm, the improved YOLOv8 algorithm is utilized for detecting fire and smoke, which has, to a certain extent, remedied the shortcomings of the YOLOv8 algorithm for this purpose. To effectively verify the performance and effectiveness of the proposed algorithm, the model is not merely trained on the fire dataset but is trained on coco128 dataset under precisely the same training epochs and parameters. This is done with the specific aim of conducting comprehensive tests to accurately evaluate model performance. The improved algorithm proposed in this study has successfully achieved the expected goals of significantly enhancing the mAP, recall, and speed of the YOLOv8 algorithm for detecting fire and smoke and concurrently reducing the rates of missed and false detections. This advancement holds great promise for enhancing the reliability and effectiveness of fire detection systems, providing prior and more accurate warnings to minimize potential losses and damages caused by fires. The combination of innovative techniques and targeted optimizations presented in this research offers valuable insights and practical solutions in the fire safety field and related applications.