Fire Technology最新文献

Z-shaped passages are commonly used for connecting underground spaces such as subway entrances and mining laneways to the ground surface, consisting of alternating horizontal and inclined sections. As Z-shaped passages are typically used for evacuation, the unidirectional flow of gases within the passage makes determining the location of the neutral plane (NP) crucial for positioning evacuation exits. This study investigates both longitudinal and vertical temperature distributions and reveals the unique inclination of the neutral plane in Z-shaped passages. Through theoretical analysis and numerical simulations, the study examines ceiling smoke temperature rise and downstream temperature decay under varying heat release rates (HRR) and passage slopes. A new correlation for maximum ceiling temperature rise and a model for downstream temperature decay are established. Additionally, the vertical temperature distribution of smoke in the passage is derived. Finally, by combining the spatial temperature distribution of smoke in the passage, a theoretical model for predicting the neutral plane is developed and validated through comparisons with simulation results. The proposed model demonstrates high accuracy in predicting the inclined neutral plane in Z-shaped passages. These insights provide critical guidance for designing efficient smoke control and evacuation systems in complex underground environments.

The ability of carbon dioxide gaseous species profile measurements to track the time of visual fire extinction was examined using a small-scale backdraft compartment. Carbon dioxide (CO₂) concentrations were measured using a tunable diode laser absorption spectroscopy (TDLAS) system and a multi-gas analyzer (MGA). The compartment was modified to pass laser light through a lower region of the compartment, near the burner surface, and in the over-fire region where the flame was well-mixed. A thermocouple probe and gas sampling lines for extracted gas samples to the MGA system were installed close to the two laser paths. A borescope was used to visually confirm flame extinction times for nominal 37.5 kW methane fires, analyzed with ignition to fuel off times of approximately 210 s, 270 s, and 300 s. The MGA system validated carbon monoxide (CO) and CO₂ profile abilities to accurately track visual fire extinction in lower and upper positions, with roughly 2.7% and 8% error relative to visual fire extinction, respectively. The CO₂ TDLAS system demonstrated 0.7% and 1.4% error relative to visual extinction times in the lower and upper positions respectively. The ability of the TDLAS system to track visual fire extinction at a physical position away from the burner surface more accurately than the MGA system at the lower position indicates the potential of this measurement technique to monitor fire extinction in a variety of optically-inaccessible environments.

Evaluating the flexural capacity of post-fire corroded RC beams is crucial for post-disaster repair and structural reinforcement. In this study, a machine learning (ML)-based prediction model is developed to estimate the flexural capacity of RC beams affected by nonlinear degradation mechanisms such as fire damage and corrosion. In order to construct the dataset required for ML model training, test data considering fire and corrosion scenarios are collected to form the initial dataset. Regarding the issue of small-sample, improvements are made to the Gaussian Mixture Model Variable Sampling Generation (GMM-VSG) to expand the dataset. The GMM-VSG model has been added with physical constraints and dynamic weight adjustments to generate data more suitable for this work. The improved GMM-VSG is considered to effectively expand the sample space of the dataset and improved its performance in ML model. Based on the expanded dataset, four single ML models and two ensemble learning models are employed to develop the predictive model. Among single ML models, the XGBoost model demonstrates the highest predictive accuracy, with a coefficient of determination (R²) of 97.44%. After XGBoost is combined with MLP to form an ensemble learning model, R² increases by 1.67% to 99.11%. The SHapley Additive exPlanations (SHAP) method is applied to interpret the XGB-MLP model's parameter impacts and to facilitate parameter analysis. Additionally, to achieve the target reliability index, capacity reduction coefficients are proposed for corroded RC beams subjected to natural cooling and water cooling conditions. Finally, an easy-to-use and interactive user interface was developed to support the practical application of the proposed model.

Lithium-ion battery (LiB) powered devices are in use every day and function as designed, however there have been reports of fatal fires involving LiB powered micro-mobility vehicles from around the globe. When a LiB fails and starts a fire, it is typically the result of the battery suffering thermal runaway. Failure of LIBs in micro-mobility vehicles has been shown to create a rapidly growing ignition source that is capable of igniting nearby combustible materials within the room of origin and forcing the room to flashover in less than a minute. The impact of such rapid-fire growth has been reported in the media around the world. This type of rapid-fire growth is almost unprecedented in residential buildings and calls into question: can residential sprinklers control such a rapidly developing fire? This study investigates the impact of such rapid-fire growth in a residential building with both full-scale bedroom and living room fires started from the thermal runaway of a LiB in a sit-on e-scooter. Results from this study quantify the impact of residential sprinklers and clearly show the effectiveness of residential fire sprinklers on fires resulting from thermal runaway of sit-on e-scooters.

As more buildings around the world are becoming accessible, the question of egressibility, i.e., accessible evacuation, is important to assess. To achieve egressibility for people who are unable to use the stairs, areas of refuge or occupant evacuation elevators are the two dominant evacuation strategies identified in previous research. A key challenge with these strategies is that they require certain behaviours from the evacuees to be successful. Thus, the aim of this scoping review is to summarise and analyse the research performed within the area, with a focus on the behavioural aspects, in order to identify gaps in the knowledge and provide a foundation for further research. Over 5,000 papers were screened, and a total of 34 papers were selected for in-depth analysis. The review concludes that behavioural aspects related to areas of refuge have not been given much research attention, and the few studies published are based on evaluation of hypothetical scenarios. Evacuation elevators have received more research attention, but the majority of the studies published involve hypothetical scenarios. Hypothetical scenario experiments, sometimes called behavioural intent experiments, can be argued to have low validity. Thus, this review highlights the need for further research on behavioural aspects and design of systems that support the use of both evacuation elevators and areas of refuge. It also highlights that where the preferred methodologies of field or case studies are not readily available to fill the knowledge gap, the use of laboratory and VR methodologies, particularly where they gather perspectives from intended end users, have significant potential to progress understanding on how the use of areas of refuge and occupant evacuation elevators can contribute to egressibility.

As an important infrastructure, urban power substation contributes to human life, economy and society. However, once a fire occurs, it may bring catastrophic casualties, economic losses and adverse social impacts. Due to the low frequency of fire and data scarcity with great uncertainty, weak attention is given to fire risk assessment of urban power substations. For this problem, combining Bayesian network and fuzzy set theory, a novel fuzzy Bayesian network (FBN) based on an improved similarity aggregation method and fuzzy analytic hierarchy process is proposed in this work for fire risk assessment of urban power substations considering data uncertainty. This work systematically identifies the potential accident causes of urban power substations and related firefighting behavior for mitigating fire consequences. Based on the identified causes, the probability of the urban power substation accident is estimated through FBN, considering data uncertainty caused by insufficient historical data and knowledge. This work also studies the consequences of urban power substation accidents, taking into account the effectiveness of firefighting behavior and the around ambient characteristics including distribution characteristics of people, buildings, and important property. The performance of the developed methodology has been demonstrated through case studies. The proposed method has the ability to evaluate the probability of the urban power substation accident, obtain the most likely types of accidents, predict the likelihood of varying degrees of fire consequences, and identify critical events and firefighting failure behavior that lead to fire accidents in urban power substations.

This study aims to facilitate the study of traffic dynamics in wildfire evacuation scenarios. To do so, it defines a set of traffic performance indicators to investigate traffic dynamics before and during wildfire evacuation events. These indicators include the following: system efficiency, travel time ratio, level of service, and jam time. To highlight the benefits and effectiveness of using these indicators, we demonstrated their application through the context of the 2020 Silverado fire in California, USA. In total, 66,924 traffic data points from 18 locations were obtained through the publicly available dataset of the California Department of Transportation. Results indicate a 7.5 km/h speed reduction during evacuation compared to routine conditions. In addition, traffic performance indicators confirmed that evacuation conditions may increase the times needed to reach destinations. This paper also demonstrates the need for using dedicated relationships for wildfire evacuation for developing, calibrating, and validating traffic modeling tools.

Understanding thermal hazards and improving safety measures are two key aspects in virtual fire drill and evacuation simulation. Accurate simulation of radiative heat flux on the surface of human body in fire is critical for the two aspects. However, in these scenarios, the movement trajectories of firefighters and evacuees are uncertain due to fire conditions. Previous studies applied the backward Monte Carlo ray tracing method to calculate the radiant heat flux on moving targets with uncertain trajectories. However, this method is computationally expensive and lack of accuracy. To address these limitations, this study proposes an Adaptive Resampling Backward Ray Tracing Method (ARBRTM) for efficiently calculating surface radiative heat flux in fire. The ARBRTM integrates a uniform solid angle segmentation technique and an adaptive resampling method based on flame and hot gas edge detection, enabling high accuracy and computational efficiency. Validation against experimental data from pool fire tests demonstrates that ARBRTM achieves lower error rates compared to traditional backward Monte Carlo methods, even with fewer rays. The results highlight the method’s ability to capture significant variations in thermal radiation properties, improving simulation accuracy across varying distances and positions. This novel approach provides a robust and efficient tool for simulating radiative heat flux in complex fire scenarios, with potential applications in fire safety engineering and firefighter protection.

Urban underground tunnels, particularly bifurcated roads, are essential to modern transportation systems but face significant fire safety challenges. This study investigates the fire resilience of urban bifurcated tunnels under natural ventilation, focusing on how the ramp slope and curvature influence the fire thermal environment and the associated safety implications–a topic with limited quantified research. By combining model-scale fire experiments with numerical simulations, the research quantifies the effects of tunnel geometry–specifically, slope and curvature–on smoke temperature distribution, flame morphology, and longitudinal temperature attenuation. Key findings show that increasing the tunnel slope accelerates smoke flow downstream, resulting in significant temperature increases, while curvature has a comparatively subtler influence on the transverse distribution of high-temperature zones. The maximum observed temperature difference across curvature variations was limited to 20 K (less than 5% variability), whereas slope variations induced temperature changes of up to 70 K (approximately 17% variability). A semi-empirical model for maximum temperature rise and longitudinal temperature attenuation was developed based on these findings under specific boundary conditions, offering essential insights for fire-safe underground infrastructure design. This work advances fire safety standards and informs emergency response strategies in complex urban tunnel systems.

Graphical Abstract