Fire Technology最新文献

Previous research has identified sociodemographic inequalities in fire prevention measures. This study examined whether sociodemographic differences persist in the Swedish population concerning fire prevention measures and particularly whether there remains an inverted u-curve related to age in protection habits. Additionally, it investigated whether fire protection practices are influenced by the level of societal protection. The research utilised survey data and register data from The Swedish Civil Contingencies Agency and Statistics Sweden. A latent class analysis was conducted, dividing respondents into four latent classes, followed by two binomial regression analyses. The study revealed three key findings regarding fire protection measures. First, certain demographic groups, namely the young, women, single and childfree households, low-income and low-education individuals, immigrants, and urban residents, are disproportionately lacking optimal fire safety measures. Second, although a safety maturity curve is still observed, older adults in Sweden today are considerably more protected compared to 15–20 years ago, indicating that safety practices employed during middle age continue into old age. Third, a trend is observed where individuals living in areas with more efficient professional rescue services tend to have lower levels of personal fire protection, suggesting a rational choice based on the perceived level of societal protection.

A novel mathematical model for the critical ventilation velocity to prevent smoke backlayering in tunnels is presented, addressing limitations of prior approaches. The basis of the model is a rigorous characterisation of the physical processes by the characteristic quantities. Empirical parameters within the new model are determined, to align with results from both full-size and small-scale tunnel experiments. Data from numerical simulations (CFD, Computational Fluid Dynamics), validated by known test data, are then used to estimate the effects of tunnel slope and other parameters on the critical velocity. The model is seen to approximate the critical velocity well, following all trends identified by test data and CFD parameter studies. The empirically calibrated equation permits prediction of the critical velocity beyond the narrow range of tunnel geometries where known results already give an answer. The resulting equation has practical application for tunnel design.

Asphalt blended with flame retardant undergoes thermal aging during utilization, which significantly impacts its physical and chemical characteristics concurrently. This study focuses on analyzing the effectiveness of aluminum hydroxide/zinc borate (ATH/ZB) flame retardant on varying the combustion performance of asphalt, particularly after undergoing thermal aging (85 min and 270 min aging simulations were performed, corresponding to short-term and long-term thermal aging, respectively). The results showed that ATH/ZB flame-retardant can increase the softening point of asphalt before and after aging. ATH/ZB flame-retardant asphalt (FRA) after aging had fewer carbonyl and sulfoxide groups than base asphalt (BA), demonstrating the superior anti-oxidation capability of FRA. ATH/ZB delays the peak heat release rate (PHRR) commencement by over 200 s, decreases the PHRR intensity by more than 170 kW/m², and decreases the asphalt’s combustion activation energy, which was owing to the fact that the sacrifice of the thermolabile flame retardant protects the asphalt from being aggressively combusted. Whereas, thermal aging enhances the PHRR intensity of FRA by 83 kW/m², which is owing to the reduction of ATH/ZB content in FRA after aging. Aging deteriorates the flame retardant’s capability on anti-combustion.

Graphical Abstract

Battery specific heat capacity is essential for calculation and simulation in battery thermal runaway and thermal management studies. Currently, there exist several non-destructive techniques for measuring the specific heat capacity of a battery. Approaches incorporate thermal modeling, specific heat capacity computation via an external heat source, and harnessing internal battery-generated heat. Accurately measuring the specific heat capacity of a battery by fast, intuitive, and general experimental methods has significant application value. This paper proposes a simple but precise method (the heating-waiting method) for measuring the specific heat capacity of the battery based on a constant temperature environment. A calibration scheme was designed to obtain the specific heat capacity calculation parameters. Specific experiments were designed to maximize the external heat received by the battery. Homogeneous temperature distribution within the battery facilitates the precise determination of the battery’s specific heat capacity. Results demonstrate that utilizing accelerating rate calorimeter (ARC) as a reliable heating source can greatly enhance the precision of the test (from 2.30% to 0.29%). Optimizing the experimental apparatus is advantageous in mitigating the confounding effects of extraneous variables on the experimental outcomes, thereby enhancing the reliability and operability. Hence, it is vital to devise a trial plan based on the battery’s attributes to guarantee the scheme’s universality and practicability.

Fighting structure fires necessitates the deployment of an effective response force (ERF) capable of ensuring both effective firefighting and the safety of firefighters. The article aims to investigate the effect of company staffing on ERF response times and to compare these findings with data from the National Institute of Standards and Technology’s (NIST) Report on residential fireground field experiments. The investigation revolves around modeling the crew size of fire engines and ladder trucks through a 4-step approach. Given the challenges of obtaining publicly available apparatus response data at the national level needed for any ERF time calculations, the approach taken is based on utilizing local incident and apparatus response data from a single fire department. Three datasets are developed, corresponding to 3-person, 4-person, and 5-person crews. Comparison among these datasets hinges on calculating the 90th percentile of ERF assembly times and total response times, as well as assessing the percentage of times the target response times are met. The results show improvements in response times across all up-staffing scenarios, underscoring the direct positive effect of crew size up-staffing on ERF response times. Specifically, when transitioning from 3-person to 4-person crews, the biggest improvements occur in moderate and high-risk structure fire incidents, with moderate-risk fires seeing a reduction of over 2 full minutes in all response time segments. Elevating crew sizes from 4-person to 5-person teams yields the most significant gains in special risk structure fires, resulting in a remarkable 10-min improvement in both ERF assembly time and total response time. In conclusion, this study provides recommendations for optimizing incident data quality and considerations to take into account when making decisions for crew upstaffing.

This paper presents an experimental and numerical investigation on the thermal response of composite slabs under fire condition, considering various slab geometries. Fire tests were conducted on six composite slabs to obtain the temperature distributions exposed to the ISO 834 standard fire curve for a duration of 210 min. The results indicated that the depth of the concrete significantly affects the temperature of the unexposed surface, while the height of the steel deck has minimal impact. During heating, water vapor and condensation occurred on all tested slabs, causing a delay in the early temperature development of the concrete. The temperature distribution across slab cross-sections was subsequently calculated using numerical simulations. The numerical models were then validated using experimental data. The challenge of precisely simulating the interface between steel deck and concrete was resolved in this numerical model.

This work presents a study on the formation of laboratory- scale fire whirls using forest fuels to replicate real-world fire whirls. A total of 48 experiments are conducted using three distinct types of forest fuels, namely Pinus Roxburghii, Shorea Robusta, and Grevillea Robusta, with three fuel pans of diameter 0.2 m, 0.3 m, and 0.4 m, respectively. Different characteristics such as Mass Burning Rate (MBR), Heat Release Rate (HRR), flame height, flame temperature, radiative heat flux and fire effluents are measured for a range of HRRs 40-346 kW and correlations are developed in terms of circulation and diameter. In comparison to free burning, the HRR was found to be increased by 300% with imposed rotation which was further increased up to 65% with fuel rotation. The flame heights are also increased by 30% in the case of imposed rotation and 40% with both imposed and fuel rotation. A detailed measurement of fire effluents revealed that their concentrations for fire whirls were reduced in the range of 50% to 400% compared to free burning fires. Furthermore, the developed correlations were applied to fire whirls of HRRs (up to ∼ 1900 kW) and their validity in predicting the characteristics was ensured.

Smoggy interference caused by indoor fires makes machine vision technology challenging to apply in the fire rescue field. Smoke and condensed water vapor aerosol from suppression activities limit visibility, making image matching difficult. To overcome this problem, an image restoration method for indoor smoke scenes is proposed. First, the dark channel prior algorithm for indoor smoke scenes is improved, and the atmospheric light estimation method is optimized by combining the density peak clustering algorithm and position constraint. A model update approach is also advanced to achieve real-time dehazing of image sequences. Afterward, the effect of photometric changes caused by the image restoration on matching is analyzed. The feature matching is performed using the pyramid Lucas–Kanade (LK) optical flow method, while the random sampling consistency algorithm is used to eliminate outliers. Finally, an indoor smoke dataset is created to evaluate the algorithm, and a comprehensive analysis of the algorithm's limitations is conducted to provide a thorough understanding of the algorithm's potential shortcomings. The evaluations confirm that the proposed method can effectively improve the robustness and accuracy of indoor smoke scene image matching. The percentage increase in robustness is close to 100%, and the accuracy has increased by 10%. Overall, this approach holds practical value for the fire rescue field, and it may encounter limitations in handling scenarios with dense smoke, dark smog, and dynamic flames. Further improvements and optimizations are required to address these challenges.

Evaluating adiabatic surface temperature (AST) as the thermal response of fire-exposed bridge elements is a complex and time-consuming task. Correspondingly, this study streamlined fire dynamic simulator (FDS) and machine learning (ML) in a surrogate model to predict the AST of suspension bridge tower. For this, various FDS simulations were conducted for suspension bridge tower exposed to different vehicular fire conditions incorporating factors such as vehicle type, exposure duration, and wind conditions to generate a diverse bridge fire dataset for training of ML algorithms. Eight ML models were evaluated using performance metrics, whereby the random forest model demonstrated exceptional consistency and reliability in a fivefold cross-validation, maintaining a high R² value of 0.99 across all tests and showing stable MAE and MSE metrics, confirming its superior performance and robustness in predictive accuracy. The proposed surrogate model offers a robust and efficient tool for enhancing the resilience of bridge fire evaluations by providing a time-efficient solution that adapts quickly to a range of fire conditions.

To promptly and efficaciously extinguish fires involving lithium-ion batteries and address the issues of prolonged firefighting duration and substantial water usage within the domain of fire safety, this study explores the suppressive impact of hydrogel on the thermal runaway in high-capacity lithium-ion batteries utilized in electric vehicles. Firstly, the 135 Ah lithium-ion battery used in electric vehicles was used as the test object, which was subjected to thermal runaway through electric heating. On this basis, water and hydrogel fire extinguishing experiments were carried out. Secondly, the microstructure of the hydrogel after heat treatment was observed under environmental scanning electron microscope. The results show that hydrogel has better cooling and thermal runaway control effects than water. The cooling effect of 10 kg hydrogel can be twice that of 20 kg water. At the same time, the interval time of prolonged thermal runaway propagation of hydrogel is more than three times that of water with the same dose, which can bring longer safety time for rescue and escape. Furthermore, the superior cooling mechanism of hydrogel is attributed to its ability to adhere to heated surfaces, thereby enhancing the utilization of its internal water content for sustained cooling.