Search Results (1,504)

A computational fluid dynamics (CFD) numerical simulation methodology was implemented to model transient heating processes in steel industry reheating furnaces, targeting combustion efficiency optimization and carbon emission reduction. The effects of oxygen concentration (O₂%) and different fuel types on the flow and heat transfer characteristics were investigated under both oxygen-enriched combustion and MILD oxy-fuel combustion. The results indicate that MILD oxy-fuel combustion promotes flue gas entrainment via high-velocity oxygen jets, leading to a substantial improvement in the uniformity of the furnace temperature field. The effect is most obvious at O₂% = 31%. MILD oxy-fuel combustion significantly reduces NOx emissions, achieving levels that are one to two orders of magnitude lower than those under oxygen-enriched combustion. Under MILD conditions, the oxygen mass fraction in flue gas remains below 0.001 when O₂% ≤ 81%, indicating effective dilution. In contrast, oxygen-enriched combustion leads to a sharp rise in flame temperature with an increasing oxygen concentration, resulting in a significant increase in NOx emissions. Elevating the oxygen concentration enhances both thermal efficiency and the energy-saving rate for both combustion modes; however, the rate of improvement diminishes when O₂% exceeds 51%. Based on these findings, MILD oxy-fuel combustion using mixed gas or natural gas is recommended for reheating furnaces operating at O₂% = 51–71%, while coke oven gas is not. Full article

A brick kiln was experimentally studied to measure the transient temperature of hot gases and the compressive strength of the bricks, using pine wood as fuel, in order to evaluate the thermal performance of the actual system. In addition, a transient combustion model based on computational fluid dynamics (CFD) was used to simulate the combustion of natural gas in the brick kiln as a hypothetical case, with the aim of investigating the potential benefits of fuel switching. The theoretical stoichiometric combustion of both pine wood and natural gas was employed to compare the mole fractions and the adiabatic flame temperature. Also, the transient hot gas temperature obtained from the experimental wood-fired kiln were compared with those from the simulated natural gas-fired kiln. Furthermore, numerical simulations were carried out to obtain the transient hot gas temperature and NOx emissions under stoichiometric, fuel-rich, and excess air conditions. The results of CO₂ mole fractions from stoichiometric combustion demonstrate that natural gas may represent a cleaner alternative for use in brick kilns, due to a 44.08% reduction in emissions. Contour plots of transient hot gases temperature, velocity, and CO₂ emission inside the kiln are presented. Moreover, the time-dependent emissions of CO₂, H₂O, and CO at the kiln outlet are shown. It can be concluded that the presence of CO mole fractions at the kiln outlet suggests that the transient combustion process could be further improved. The low firing efficiency of bricks and the thermal efficiency obtained are attributed to uneven temperatures distributions inside the kiln. Moreover, hot gas temperature and NOx emissions were found to be higher under stoichiometric conditions than under fuel-rich or excess of air conditions. Therefore, this work could be useful for improving the thermal–hydraulic and emissions performance of brick kilns, as well as for future kiln design improvements. Full article

This study presents a novel approach to address these challenges by introducing automobile platinum honeycomb catalysts into biomass combustion systems. The study employed a dual methodology, combining experimental investigations and a Life Cycle Assessment (LCA) case study, to comprehensively evaluate the catalyst’s performance and environmental impacts. The catalyst’s ability to facilitate combustion without open flame formation and its operational efficiency throughout combustion phases position it as a promising avenue for reducing gaseous and particulate matter emissions. The LCA considers multiple impact categories, employing the ReCiPe 2008 Hierarchist midpoint and endpoint perspective to assess environmental effects. The experimental results show that the catalyst effectively reduced CO, SO₂, and particulate emissions. Temperatures below 400 °C diminished the catalyst’s performance. The catalyst achieved a 100% CO conversion rate at specific temperatures of 427.4–490.3 °C. The findings highlight the potential for a 34% reduction in environmental impacts when replacing conventional rice husk combustion with the catalyst-integrated system. Notably, the study emphasizes the significance of sustainable catalyst manufacturing processes and cleaner electricity sources in maximizing environmental benefits. In conclusion, the integration of platinum honeycomb catalysts into biomass combustion systems, exemplified by rice husk combustion, emerges as a promising strategy for achieving more sustainable and environmentally friendly bioenergy production. Full article

This paper explores the impact of applying a powder additive in the form of halloysite and mullite on the thermal protection properties of a composite. The authors used CES R70 epoxy resin with CES H72 hardener, modified by varying the amount of powder additive. The composite samples were exposed to a mixture of combustible gases at a temperature of approximately 1000 °C. The primary parameters analyzed during this study were the temperature on the rear surface of the sample and the ablative mass loss of the tested material. The temperature increase on the rear surface of the sample, which was exposed to the hot stream of flammable gases, was measured for 120 s. Another key parameter considered in the data analysis was the ablative mass loss. The charred layer of the sample played a crucial role in this process, as it helped block oxygen diffusion from the boundary layer of the original material. This charred layer absorbed thermal energy until it reached a temperature at which it either oxidized or was mechanically removed due to the erosive effects of the heating factor. The incorporation of mullite reduced the rear surface temperature from 58.9 °C to 49.2 °C, and for halloysite, it was reduced the rear surface temperature to 49.8 °C. The ablative weight loss dropped from 57% to 18.9% for mullite and to 39.9% for halloysite. The speed of mass ablation was reduced from 77.9 mg/s to 25.2 mg/s (mullite) and 52.4 mg/s (halloysite), while the layer thickness loss decreased from 7.4 mm to 2.8 mm (mullite) and 4.4 mm (halloysite). This research is innovative in its use of halloysite and mullite as functional additives to enhance the ablative resistance of polymer composites under extreme thermal conditions. This novel approach not only contributes to a deeper understanding of composite behavior at high temperatures but also opens up new avenues for the development of advanced thermal protection systems. Potential applications of these materials include aerospace structures, fire-resistant components, and protective coatings in environments exposed to intense heat and flame. Full article

In response to the limited research on fire detection in cotton pickers and the issue of low detection accuracy in visual inspection, this paper proposes a computer vision-based detection method. The method is optimized according to the structural characteristics of cotton pickers, and a lightweight improved YOLOv11 algorithm is designed for cotton fire detection in cotton pickers. The backbone of the model is replaced with the MobileNetV2 network to achieve effective model lightweighting. In addition, the convolutional layers in the original C3k2 block are optimized using partial convolutions to reduce computational redundancy and improve inference efficiency. Furthermore, a visual attention mechanism named CBAM-ECA (Convolutional Block Attention Module-Efficient Channel Attention) is designed to suit the complex working conditions of cotton pickers. This mechanism aims to enhance the model’s feature extraction capability under challenging environmental conditions, thereby improving overall detection accuracy. To further improve localization performance and accelerate convergence, the loss function is also modified. These improvements enable the model to achieve higher precision in fire detection while ensuring fast and accurate localization. Experimental results demonstrate that the improved model reduces the number of parameters by 38%, increases the frame processing speed (FPS) by 13.2%, and decreases the computational complexity (GFLOPs) by 42.8%, compared to the original model. The detection accuracy for flaming combustion, smoldering combustion, and overall detection is improved by 1.4%, 3%, and 1.9%, respectively, with an increase of 2.4% in mAP (mean average precision). Compared to other models—YOLOv3-tiny, YOLOv5, YOLOv8, and YOLOv10—the proposed method achieves higher detection accuracy by 5.9%, 7%, 5.9%, and 5.3%, respectively, and shows improvements in mAP by 5.4%, 5%, 4.8%, and 6.3%. The improved detection algorithm maintains high accuracy while achieving faster inference speed and fewer model parameters. These improvements lay a solid foundation for fire prevention and suppression in cotton collection boxes on cotton pickers. Full article

Swirl spray combustion has attracted significant attention due to its common usage in gas turbines. However, the high pressure in many practical applications remains a major obstacle to the deep understanding of flame stability and pollutant formation. To address this concern, this study investigated a swirl spray flame fueled with n-decane at elevated pressure. Planar laser-induced fluorescence (PLIF) of OH and polycyclic aromatic hydrocarbons (PAHs) were used simultaneously, enabling the distinction of the locations of OH, PAHs, and mixtures of them, providing detailed information on flame structure and evolution of PAHs. The effects of swirl number and ambient pressure on reaction zone characteristics and PAHs’ formation were studied, with the swirl number ranging from 0.30 to 1.18 and the pressure ranging from 1 to 3 bar. The data suggest that the swirl number changes the flame structure from V-shaped to crown-shaped, as observed at both atmospheric and elevated pressures. Additionally, varying swirl numbers lead to the initiation of flame divergence at distinct pressure levels. Moreover, PAHs of different molecular sizes exhibit significant overlap, with larger PAHs able to further extend downstream. The relative concentration of PAH increased with pressure, and the promoting effect of pressure on producing larger PAHs was significant. Full article

This study investigates the effects of diffusion modeling and swirl intensity on flow fields and NO emissions in CH₄/NH₃ non-premixed swirling flames using large eddy simulations (LESs). Simulations are performed for a 50/50 ammonia–methane blend at three global equivalence ratios of 0.77, 0.54, and 0.46 and two swirl numbers of 8 and 12, comparing the unity Lewis number (ULN) and mixture-averaged diffusion (MAD) models against the experimental data includes OH-PLIF and ON-PLIF reported in a prior study by the KAUST group. Both models produce similar flow fields, but the MAD model alters the flame structure and species distributions due to differential diffusion (DD) and limitations in its Flamelet library. Notably, the MAD library lacks unstable flame branch solutions, leading to extensive interpolation between extinction and stable branches. This results in overpredicted progress variable source terms and reactive scalars, both within and beyond the flame zone. The ULN model better reproduces experimental OH profiles and localizes NO formation near the flame front, whereas the MAD model predicts broader NO distributions due to nitrogen species diffusion. Higher swirl intensities shorten the flame and shift NO production upstream. While a low equivalence ratio provides enough air for good mixing, lower ammonia and higher NO contents in exhaust gases, respectively. Full article

Sustainable aviation fuels (SAFs) are vitally important for aviation decarbonization. The laminar burning velocity (LBV), a key parameter reflecting the combustion behavior of fuel/oxidizer mixtures, serves as a fundamental metric for evaluating SAF performance. This paper systematically reviews and evaluates the LBV experiment method and the performance of traditional aviation fuel, SAFs produced via different pathways, and individual components (n-alkanes, iso-alkanes, cycloalkanes, and aromatic hydrocarbons, as well as the impacts of isomers and homologues) in aviation fuels. It is found that LBV values of different SAFs exhibit significant fluctuations, approaching or slightly deviating from those of conventional aviation fuels. Carbon number, branching degree, substituent types, and testing methods in the components all affect LBV performance. Specifically, increased branching in iso-alkanes reduces LBV, cyclohexane and benzene show higher LBV than their methylated counterparts (methylcyclohexane and toluene), and n-alkylcyclohexanes/benzenes with short (C1–C3) side chains demonstrate minimal LBV variation. Spherical flame methods yield more consistent (and generally lower) LBV values than stagnation flame techniques. These findings provide insights for optimizing SAF–conventional fuel blends and enhancing drop-in compatibility while ensuring operational safety and usability. Full article

Fire refers to a disaster caused by combustion that is uncontrolled in the temporal and spatial dimensions, occurring in diverse complex scenarios where timely and effective detection is crucial. However, existing fire detection methods are often challenged by the deformation of smoke and flames, resulting in missed detections. It is difficult to accurately extract fire features in complex backgrounds, and there are also significant difficulties in detecting small targets, such as small flames. To address this, this paper proposes a YOLO-Multi-scenario Fire Detector (YOLO-MFD) for multi-scenario fire detection. Firstly, to resolve missed detection caused by deformation of smoke and flames, a Scale Adaptive Perception Module (SAPM) is proposed. Secondly, aiming at the suppression of significant fire features by complex backgrounds, a Feature Adaptive Weighting Module (FAWM) is introduced to enhance the feature representation of fire. Finally, considering the difficulty in detecting small flames, a fine-grained Small Object Feature Extraction Module (SOFEM) is developed. Additionally, given the scarcity of multi-scenario fire datasets, this paper constructs a Multi-scenario Fire Dataset (MFDB). Experimental results on MFDB demonstrate that the proposed YOLO-MFD achieves a good balance between effectiveness and efficiency, achieving good effective fire detection performance across various scenarios. Full article

The increasing complexity of urban structures has significantly elevated the risk and severity of façade fires in high-rise buildings. Unlike traditional models assuming continuous fuel beds, real-world fire scenarios often involve discrete combustible materials arranged in discrete fuel matrices. This study presents a systematic investigation into the influence of lateral spacing on vertical flame propagation behavior. Laboratory-scale experiments were conducted using vertically oriented polymethyl methacrylate (PMMA) fuel arrays under nine different spacing configurations. Results reveal that lateral spacing plays a critical role in determining flame spread paths and intensities. Specifically, with a vertical spacing fixed at 8 cm, a lateral spacing of 10 mm resulted in rapid flame growth, reaching a peak flame height of approximately 96.5 cm within 450 s after ignition. In contrast, increasing the lateral spacing to 15 mm significantly slowed flame development, achieving a peak flame height of just under 90 cm at approximately 600 s. This notable transition in flame dynamics is closely associated with the critical thermal boundary layer thickness (~11.5 mm). Additionally, at 10 mm spacing, a chimney-like effect was observed, enhancing upward air entrainment and resulting in intensified combustion. These findings reveal the coupled influence of geometric configuration and heat transfer mechanisms on façade flame propagation. The insights gained provide guidance for cladding system design, suggesting that increasing lateral separation between combustible elements may be an effective strategy to limit flame spread and enhance fire safety performance in buildings. Full article

The oxygen blast furnace (OBF) process presents a promising low-carbon pathway for the smelting of vanadium–titanium magnetite (VTM). This study develops an innovative mathematical model based on mass and heat balance principles, specifically tailored to the OBF smelting of VTM. The model systematically investigates the effects of key parameters—including pulverized coal injection ratio, recycling gas volume, hydrogen content in the recycling gas, and charge composition—on furnace productivity, hearth activity, and the tuyere raceway zone. The results show that increasing the pulverized coal injection ratio slightly reduces productivity and theoretical flame temperature: for every 25 kg/tHM increase in the coal ratio, the theoretical flame temperature decreases by 21.95 °C; moreover, indirect reduction is enhanced and the heat distribution within the furnace is significantly improved. A higher recycling gas volume markedly increases productivity and optimizes hearth thermal conditions, accompanied by enhanced blast kinetic energy and an expanded tuyere raceway zone, albeit with a notable drop in combustion temperature. Increased hydrogen content in the recycling gas promotes productivity, but may weaken blast kinetic energy and reduce the stability of the raceway zone. Furthermore, a higher titanium content in the charge increases the difficulty of iron oxide reduction, resulting in lower CO utilization and reduced productivity. Full article

This study investigates the application of deep learning (DL) techniques for predicting temperature fields in the SM1 swirl-stabilized turbulent non-premixed flame. Two distinct DL approaches were developed using a comprehensive CFD database generated via the steady laminar flamelet model coupled with the SST k-ω turbulence model. The first approach employs a fully connected dense neural network to directly map scalar input parameters—fuel velocity, swirl ratio, and equivalence ratio—to high-resolution temperature contour images. In addition, a comparison was made with different deep learning networks, namely Res-Net, EfficientNetB0, and Inception Net V3, to better understand the performance of the model. In the first approach, the results of the Inception V3 model and the developed Dense Model were found to be better than Res-Net and Efficient Net. At the same time, file sizes and usability were examined. The second framework employs a U-Net-based convolutional neural network enhanced by an RGB Fusion preprocessing technique, which integrates multiple scalar fields from non-reacting (cold flow) conditions into composite images, significantly improving spatial feature extraction. The training and validation processes for both models were conducted using 80% of the CFD data for training and 20% for testing, which helped assess their ability to generalize new input conditions. In the secondary approach, similar to the first approach, studies were conducted with different deep learning models, namely Res-Net, Efficient Net, and Inception Net, to evaluate model performance. The U-Net model, which is well developed, stands out with its low error and small file size. The dense network is appropriate for direct parametric analyses, while the image-based U-Net model provides a rapid and scalable option to utilize the cold flow CFD images. This framework can be further refined in future research to estimate more flow factors and tested against experimental measurements for enhanced applicability. Full article

Methods for the improvement of regional lignocellulosic resources (wood and agriculture waste) were studied and analyzed using blends with optimized compositions and a selective pretreatment of the blends using microwaves to enhance their thermochemical conversion and energy production efficiency. A batch-size pilot device was used to provide the thermochemical conversion of biomass blends of different compositions, analyzing the synergy of the effects of thermal and chemical interaction between the components on the yield and thermochemical conversion of volatiles, responsible for producing heat energy at various stages of flame formation. To control the thermal decomposition of the biomass, improving the flame characteristics and the produced heat, a selective pretreatment of blends using microwaves (2.45 GHz) was achieved by varying the temperature of microwave pretreatment. Assessing correlations between changes in the main characteristics of pretreated blends (elemental composition and heating value) on the produced heat and composition of products suggests that selective MW pretreatment of biomass blends activates synergistic effects of thermal and chemical interaction, enhancing the yield and combustion of volatiles with a correlating increase in produced heat energy, thus promoting the wider use of renewable biomass resources for sustainable energy production by limiting the use of fossil fuels for heat-energy production and the formation of GHG emissions. Full article

Combustion modeling using computational fluid dynamics (CFD) offers detailed insights into the flame structure and thermo-chemical processes. Furthermore, it has been extensively used in the past to optimize industrial furnaces. Despite the increasing computational power, the prediction of the reaction kinetics in flames is still related to high calculation times, which is a major drawback for large-scale combustion systems. To speed-up the simulation, artificial neural networks (ANNs) were applied in this study to calculate the chemical source terms in the flame instead of using a chemistry solver. Since one ANN may lack accuracy for the entire input feature space (temperature, species concentrations), the space is sub-divided into four regions/ANNs. The ANNs were tested for different fuel mixtures, degrees of turbulence, and air-fuel/oxy-fuel combustion. It was found that the shape of the flame and its position were well predicted in all cases with regard to the temperature and CO. However, at low temperature levels (<800 K), in some cases, the ANNs under-predicted the source terms. Additionally, in oxy-fuel combustion, the temperature was too high. Nevertheless, an overall high accuracy and a speed-up factor for all simulations of 12 was observed, which makes the approach suitable for large-scale furnaces. Full article

In the actual installation of cables, inclined cable laying within covered cable trays is a relatively common method. To investigate the effects of different tilt angles on the combustion behavior of cables within covered cable trays, aluminum conductor polyethylene-insulated power cables were used as the test cables. The flame morphology, temperature distribution, and fire spread rate during the cable combustion process were analyzed for experimental scenarios for which the cable laying angles and the ignition positions changed. The results indicate that the inclination angle of the covered cable tray has a significant impact on flame propagation and temperature distribution. For the ignition located at the lowest part of the cable, the fire spread rate increases significantly with the tilt angle. In contrast, for the ignition located at the highest part of the cable, the fire spread rate initially decreases slightly and then increases, with a relatively smaller overall change in magnitude. Under both ignition positions, the flame spread rate significantly increases at 15–30°. Therefore, in actual cable installation processes, cables within covered troughs should avoid large-angle inclinations. Full article