Fire Technology最新文献

Ceramifiable polyolefin composites have a great application prospect in high temperature-resistant wires and cables. Inorganic fillers play an important role in ceramifiable property during the ceramization process. In this paper, we successfully reported the synthesis of ceramifiable composites by using silicon aluminum glass powder as an inorganic filler. The effects of silicon aluminum glass powder on the microstructure, thermal conductivity, bulk density, densification and mechanical properties were compared with wollastonite fibers and silica powder at different temperatures. The result showed that the mechanical behavior and densification of the composites were the highest when the silicon aluminum glass powder was used as the filler. Especially, the PZAH sample composed of 40% polyethylene, 20% zinc borate, 25% ammonium polyphosphate, 15% silicon aluminum glass powder shows exhibited the highest mechanical properties at 1000 °C. At these conditions, the bulk density of composite was 2.15 g/cm³, and flexural strength was over 19 MPa. With the increase of temperature, the reaction of ammonium polyphosphate and zinc borate led to the formation of orthophosphate glass melt. The melt binds the fillers together and ultimately forms ceramics with excellent mechanical properties. The findings in this work provided a feasible strategy for the preparation of good thermal insulation and high flexural strength PE composites.

During wildland fire spread both slope and wind act together to modify fire dynamics, commonly accelerating the rate of fire spread. To investigate this coupling effect, flow field measurements were conducted on stationary gaseous fires produced over a tilt table in a wind tunnel, with fireline intensities ranging from 41 to 123 kW/m. The angle of inclination (θ) and the wind speed (V) were varied from 0 to 30° and 0.30 to 1.27 m/s, respectively. The surface gas velocity of the fire at various downstream locations was measured using temperature-correlation velocimetry (TCV), which was enabled using streamwise temperature signals from an array of micro-thermocouples. The effect of the slope was converted to an equivalent surface velocity, (:{U}_{slope}), following the concept of fire-induced flow over an inclined surface. A momentum analysis was conducted to isolate the coupling effect of slope and wind based on (:{U}_{slope}), V, and the mean measured surface gas velocity within the attached flame region, (:{U}_{attach}). Finally, a relationship was proposed to predict the mean velocity of the downstream flow using the upstream wind velocity, flame geometry, and the angle of inclination. The proposed relationship enables estimation of downstream heat transfer from a flame to unburnt fuel ahead of the fire, providing a generalized method to evaluate the combined slope and wind effect on heating which drives forward fire spread.

The International Maritime Organization (IMO) regulates the provision of thermal protection, including Thermal Protective Immersion Suits (TPIS), for passenger vessels operating in Polar regions. These TPIS must be donned within 120 s. While IMO also requires evacuation modelling analysis to demonstrate timely abandonment of passenger vessels, the two regulatory requirements currently remain independent. TPIS usage is excluded from evacuation analysis partly due to insufficient data. This study's uniqueness and importance lies in its provision of a reliable and robust evidence base, quantifying donning times and correctness for a regulatory compliant, non-insulated TPIS, designed for use onboard passenger vessels. Donning time is defined as the duration taken to put on the TPIS, while donning correctness refers to the proper, watertight, and immersion-ready application of the TPIS. Experimental trials with 96 volunteers (67 males, 29 females) aged 18 to 72 revealed donning times from 55 to 186 s, with 39% donning correctly. Age affected donning performance (increase in time and errors with age), while gender and experience did not significantly impact it, which was inconsistent with prior donning studies of different types of TPIS. Surprisingly, watching a donning video did not significantly reduce time compared to written instructions, but it did significantly improve correctness. Clearly, donning times for this regulatory compliant TPIS exceeding 120 s question the appropriateness of suit testing protocols. Finally, donning time distributions for use in agent-based ship evacuation analysis is proposed and TPIS design improvements suggested.

This paper presents a state-of-the-art review of bridge fire incidents, experimental research, and numerical studies conducted in last five decades highlighting a significant gap in current engineering practices and codes. The authors compiled an extensive database of 73 documented bridge fire accidents from 1974 to 2023, 14 experimental studies and 42 numerical analyses focusing on the behavior of bridge structures exposed to fire. Accidental fire data reveals wooden bridges being more susceptible to fire often leading to complete collapse. Steel bridges are more vulnerable to fire due to their low fire resistance as compared to concrete bridges. Common causes of bridge fires were vehicle collisions and arson, leading to varying degrees of structural damage. This underscores the need for improved fire safety and design considerations tailored to bridge materials and configurations. Experimental studies reveal a lack of data necessary for developing fire-resistant design guidelines. Numerical studies, essential for understanding of bridge fire dynamics, require validation against experimental/accident data to ensure accuracy and reliability. By integrating findings from documented accidents, experimental research, and numerical analyses, this work paves way for future efforts aimed at enhancing the fire resilience of bridges, thereby ensuring their safety and integrity in case of fire incidents.

Post-earthquake fires and aftershocks cause significant damage and are the most common and significant secondary disasters. This study conducted a simulation to assess the effects of fires and aftershocks and established a 5-piece T-shaped short-limb shear wall model using the finite element software ABAQUS and the ISO-834 standard fire curve. The evolution of the temperature field and seismic performance of the short-limb shear wall exposed to high temperatures of a three-sided fire was evaluated. The results showed that the temperature field exhibited a left–right symmetric ripple distribution, and the wall temperature decreased from the fire-affected side to the other side. The seismic performance of the specimens decreased with increasing temperature, and the damage pattern was a through-crack extending from the base of the wall limb and the middle and lower part of the web to the centre of the web. Specimen SWT650-3 exhibited optimal seismic performance when exposed to high temperatures, with a limb thickness ratio of 6.5, an axial compression ratio of 0.2, and a stirrup ratio of 1.82%. Models were used to investigate the impact of the axial pressure ratio, heating time, limb thickness ratio, and protective layer thickness on the seismic performance of the T-shaped short-limb shear walls. The higher the axial pressure ratio within a specific range, the more significant the damage to the specimen. The heating time substantially influenced the specimen’s load-bearing capacity at high temperatures. An appropriate increase in the length of the wall limb improved the load-bearing capacity of the short-limb shear wall, and an increase in the thickness of the protective layer improved the fire resistance.

This paper investigates the influence of gap size (openings/spaces between two structural members) on mass timber beam-column concealed hanger connections in fire. The presence of an intumescent sealant, and how it can influence the temperature development in a concealed connection with a small gap, is also analysed. The experimental results of sixteen concealed connections manufactured with a proprietary aluminium concealed connector are presented. The connections were exposed with a 0 mm, 3 mm, 6 mm and 10 mm gap to the ISO 834 standard fire for 60 min. Half of the samples in each sample group were protected with an intumescent fire protection sealant. The temperatures in the timber were measured at various locations around the gap and directly at the location of the aluminium bracket. These results show that unprotected 6 mm and 10 mm gaps should be avoided. The intumescent sealant performed well and limited the temperature development in the aluminium bracket significantly. When compared to the bracket temperatures of the 0 mm samples, the increase in temperatures in the unprotected samples (on average at 60-min) ranged between 62 and 258% depending on gap size, while in the protected samples temperatures were limited to between − 4% to 21%. The application of an intumescent fire sealant also improved the predictability of the thermal development in the connections and in the 3 mm gap protected samples the lowest temperatures were recorded.

Photoelectric fire smoke detectors are sensitive to false alarms caused by nuisance aerosols, which causes massive losses. To address this, techniques such as multiple optical channels and wavelengths have been developed to capture more particle scattering information. This paper presents a numerical simulation-based approach using broadband light (400–800 nm) to capture multi-dimensional scattering information for particle discrimination. Using Mie scattering theory, we generated scattering spectrum for five types of fire smoke and five types of nuisance aerosols across various angles, which were then fed into five machine learning models for classification. By introducing random measurement noise, we tested model robustness. These results indicate that several forward scattering angles (45°–55°, 65°-75°) combined with nonlinear machine learning models like Random Forest and XGBoost achieved 100% precision and recall in discriminating fire smoke from nuisance aerosols. Additionally, the method accurately classified various fire smoke particle types with nearly 100% accuracy. This study highlights the potential of broadband visible light sources in fire detection, offering a robust solution to reduce false alarms and improve detection accuracy.

Early fire detection in industrial environments is critical to preventing equipment damage, personal injury, and operational disruptions. Traditional smoke detectors, while effective, often experience delays due to the time required for smoke to reach sensors, allowing fires to spread. Manual fire watch operations and human surveillance of camera feeds are resource-intensive and prone to human error. To address these challenges, this paper explores the application of convolutional neural networks for automated fire detection, specifically in industrial settings. By leveraging 11 different pre-trained machine vision models from TensorFlow and enhancing them with transfer learning on a custom-built industrial fire dataset, we optimized fire detection performance. We analyzed each machine vision model architecture in terms of its depth, width, and input image resolution, considering both resource requirements and detection accuracy. We further explored the option of combining multiple models into an ensemble classifier to evaluate whether the performance improvements could justify the much greater computational complexity and other practical impacts. A cost-benefit analysis is presented to evaluate the trade-offs between performance and computational expense. Our findings identify that EfficientNetV2L, specifically tailored for industrial applications, provides the optimal balance between costs involved in training and using the model versus the overall fire detection performance. Additionally, we present a qualitative analysis of model performance using the technique of gradient-based class activation mapping to provide explainability by visualizing model decisions.

The heat release rate is a crucial indicator of fire behavior in the confined environments of tunnels, making it crucial to quickly assess and predict fire conditions. This study investigates the prediction of heat release rates of tunnel fire using flame images through deep learning techniques. Initially, a series of tunnel fire experiments were conducted under various ignition points with forced ventilation to capture flame images across different scenarios. Next, according to the temporal changes in the flame images, the Deep Residual Network (ResNet18) was used to extract flame features. Subsequently, the Long Short-term Memory (LSTM) model was employed to track the temporal evolution of these flame features and relate them to the heat release rates at corresponding time intervals. The results indicate that the ResNet18-LSTM model effectively captures the dynamics during the growth and decay phases of the fire, although its predictive capability is limited around the peak heat release rate. Additionally, comparisons with three other models (CNN-GRU, CNN-LSTM, ResNet18-GRU) demonstrate that the ResNet18-LSTM model maintains a level of predictive accuracy. Specifically, it achieves an average R² increase of 15.49%, 28.12%, and 26.15% over CNN-GRU, CNN-LSTM, and ResNet18-GRU, respectively. Moreover, the average MAE is reduced by 14.88%, 27.56%, and 29.13%, while the average RMSE decreases by 19.17%, 30.21%, and 28.93%.

A crucial requirement of current hood-based calorimetry techniques is the complete encapsulation, collection, and measurement of combustion products. This approach is limited by the need to build supporting infrastructure to facilitate the anticipated fire sizes, such as adequate burn space, a hood structure, and an exhaust system. A proposed approach towards calorimetry is examined in this work, where a sampling plane is established with sampling points placed along the radial direction of the smoke plume. Previous studies were large-scale field trials in outdoor scenarios, with uncertainties contributed by factors such as wind, unknown sampling locations, and limited repeat experiments. This study reduces the uncertainty of this approach by measuring the heat release rate (HRR) of medium-scale kerosene pool fires ((D = 0.8{text{ m}})) floating on calm and wavy water in a controlled laboratory environment with known sampling locations. Results show good agreement with fuel regression analysis and the applicability of the point-based approach in laboratory environments without complete capture of combustion products.