Flame Images Research Articles

Impact of Aluminium and Titanium Additives on Ignition and Combustion in Boron-Loaded Gelled Jet A-1 Fuel

ABSTRACT A high-energy metal has been identified as a possible supplementary energy source for liquid fuel, which can be used to fuel volume-limited propulsive devices. However, the stability of metal particles in liquid fuel is a significant disadvantage, which can be overcome by suspending the particles in the gel fuel network. Boron’s (B) high energy density makes it a potential choice as fuel or energetic fuel additive for propulsion systems. However, the combustion of boron particles is challenging due to a native oxide layer on the boron particle surface that acts as an inhibitor. The influences of aluminum and titanium addition into the gel fuel containing boron particles have been investigated in the present study to understand whether Al or Ti positively enhances the ignition and combustion of boron particles. A droplet combustion study was conducted for gel fuel samples containing boron, boron-aluminum, and boron-titanium combinations. The key analyses include the feature of droplet diameter regression, the effect of Al/Ti additive on the ignition delay of boron particles through spectroscopic analysis, combustion of boron particles through chemiluminescence measurement of intermediate species of boron combustion (BO2) and flame imaging, and analysis of combustion residues. The obtained results and corresponding analyses suggest that the addition of Al particles improves the ignition of boron. In contrast, Ti particles significantly enhance the combustion of boron particles loaded in gel fuel.

Neodymium oxide concentration state recognition model in neodymium molten salt electrolysis process based on flame image features

Accurate recognition of the concentration state of neodymium-oxide (Nd2O3) in the neodymium (Nd) molten salt electrolysis process provides crucial feedback information for online adjustment of process parameters during Nd metal batch production. This paper proposes a Nd2O3 concentration state recognition model based on flame image features. The model employs the lightweight flame segmentation algorithm (FEC-Net) to accurately segment the flame regions in the collected Nd electrolysis cell flame images. Then, using image processing techniques, geometric and color features of the flame regions are extracted. Finally, a Support Vector Machine (SVM) is used to establish a nonlinear mapping model between the flame features and Nd2O3 concentration states. This model classifies the collected flame image dataset to verify the Nd2O3 concentration state during a certain period of Nd molten salt electrolysis. Experiments demonstrate that the recognition accuracy of SVM reaches 96.09%, meeting the monitoring requirements of the Nd molten salt electrolysis process.

Nox Emissions Assessment of a Multi Jet Burner Operated with Premixed High Hydrogen Natural Gas Blends

Abstract Decarbonization of gas turbine combustion creates a pressing demand for new technical solutions for the combustion process. While switching to hydrogen fuels may solve the problem of carbon emissions and associated pollutants it can also lead to stability issues for swirl-stabilized combustors due to its increased reactivity. However, with jet flame burner systems, the required flashback safety can be achieved with high axial flow velocities even for premixed combustion of 100% hydrogen fuel. The development of such an engineering solution, however, requires significant effort to reach the maturity of today's swirl burners. This study examines the capacity of a premixed multi-tube jet burner to manage the chemical reactivity change over a range of volumetric blends from pure natural gas to pure hydrogen fuel. NOx emissions are measured and analyzed for atmospheric tests. The changes in emissions originate not only from altered combustion chemistry but also from changes in flame shape and turbulence intensity. To get a deeper understanding of the NOx formation process, a low-order model is designed and compared to the experimental data of technically and perfectly premixed combustion tests. Parameter variations of the low-order model are conducted to assess the influences on the NOx emission production of the multi jet burner. The information on the combustion process required for the model is obtained computationally and experimentally. Therefore, flame images are recorded and analyzed.

Disodium terephthalate as a multifunctional additive for simultaneous enhancing the energy release performance and weakening the water reactivity of aluminum

In this study, disodium terephthalate (Na2TP) has been successfully introduced as a novel multifunctional additive for simultaneous enhancing the energy release performance and weakening the water reactivity of micro-aluminum (μ-Al). Seven μ-Al/Na2TP composites with different contents of Na2TP (R-100 to R-700) and four comparison samples have been prepared to investigate the thermochemical behaviors, combustion performances and water reactivity. Importantly, structure characterizations as well as kinetic parameters (e.g., Ea, ΔS‡) have been performed to reveal the impacts of Na2TP on μ-Al oxidation. Moreover, incorporating Na2TP into μ-Al shows the enhancement of energy release, with R-400 exhibiting the highest heat release (4970 J/g) in the main oxidation stage, compared to μ-Al (1696 J/g). Interestingly, μ-Al shows an increased Ea while R-400 exhibits a decreased Ea as the oxidation proceeds. The decomposition of Na2TP generates both heat and CO2 gases, which are beneficial for destroying alumina shell and increasing oxidation kinetics, in consistent with higher positive ΔS‡ value, rougher surface, and higher surface O content for oxidation of R-400 compared to μ-Al. As a result, the enhanced combustion performances of higher peak pressure, pressurization rate and characteristic shock velocity as well as brighter/larger flame images have been achieved by μ-Al/Na2TP composites. On the other hand, water reactivity of μ-Al has been significantly suppressed by introducing Na2TP in terms of the 85.4 % decrease in heat release, elevated Ea, and alteration of reaction mechanism. Consequently, taking organic Na2TP as a multifunctional additive, two birds of enhancing the energy release and weakening the water reactivity is harvested, showing great potentials in improving the performances of metal-based energy systems.

Experimental and numerical investigations of soot formation in propane laminar coflow flames in O2/N2 and O2/CO2 atmospheres

Oxygen-rich combustion is a new type of clean combustion technology with important application prospects. At present, there are few detailed analyses on the mechanism changes of soot formation under oxygen enrichment of propane. In this paper, the effects of oxygen-rich (O2/N2, O2/CO2) combustion on soot formation in the propane laminar flow coaxial jet diffusion flame were investigated by using the experiment and numerical simulation. The flame image was taken by a color (CCD) camera in the visible band, and the two-dimensional distribution of temperature and soot volume fraction in the flame was reconstructed. In numerical simulation, the soot production model considers a detailed description of nucleation via collisions among heavy polycyclic aromatic hydrocarbons, particle aggregation, polycyclic aromatic hydrocarbons condensation, surface growth and oxidation through the hydrogen abstraction acetylene addition mechanism. The results show that the experimental results were compared with the numerical simulation results, and the numerical simulation results predict the temperature and soot change of oxygen-rich flame well. With the increase in oxygen concentration, the peaks of soot and temperature in the two oxygen-rich atmospheres gradually increased. With the increase in oxygen concentration, the peaks of soot and temperature in the two oxygen-rich atmospheres gradually increased. Comparing the combustion mechanism changes of the two oxygen-rich combustion systems, it was found that the hydrogen abstraction acetylene addition mechanism was the main cause of soot generation. The OH oxidation is dominated near the fuel nozzle side, and O2 oxidation is dominant downstream. With the substitution of CO2 for N2, the chemical effect of CO2 reduces the flame temperature and inhibits the formation mechanism and oxidation mechanism of soot to some extent. This in turn leaded to a reduction in the amount of soot produced.

The influence of air distribution ratio on the fluctuation characteristics and stability of horizontal jet diesel non-premixed flame under extreme sub-atmospheric pressures

The burning problem in the plateau environment attracts more and more people’s attention, and the plateau adaptability and flame stability of burner are the focus of recent research. The main purpose of this paper is to reveal the horizontal jet spray flame fluctuation characteristics and stability change rule under extremely low pressure (0.05 MPa) at a high altitude (about 5500 m) inhabited by human beings. Flame morphology and temperature properties were investigated in a low-pressure chamber with different secondary air ratios (23–53 %). It is found that the normalization of the flame trajectory equation is not affected by the ratio of secondary air. The temperature in the flame recirculation zone can be increased by 110 K as the proportion of secondary air increases to 45 %, but the maximum temperature is still lower than 1500 K. A continuous flame image processing method was implemented to define and analyze the flame fluctuation region. The results show that the dimensionless fluctuation parameter γ can predict the flame stability in advance than β. When the secondary air ratio is 40 %, the flame has the largest upward fluctuation; and when it exceeds 45 %, the flame stability becomes worse. This work is the first to obtain the results of the study on flame morphology and stability of horizontal jet spray flame distribution ratio under extremely low atmospheric pressure, which provides a theoretical scientific basis for the design of plateau burner, flame control, and flame stability improvement.

Experimental study on hydrogen–oxygen spontaneous ignition limits in supercritical water using a wall-cooled reactor

Hydrogen is a promising fuel for many industrial applications because of its carbon-free properties. The characteristics of hydrogen–oxygen reaction in supercritical water were thoroughly investigated using a newly constructed experimental facility. Three typical temperature profiles were obtained, representing different reaction states: mild oxidation, transition state, and intense combustion. The spontaneous ignition limit was mapped based on 42 experimental data points. The minimum ignition temperature decreases with increasing fuel concentration, reaching as low as 517 °C at a hydrogen concentration of 23.2 mol%. The reliability of the spontaneous ignition limit was confirmed by the first hydrogen hydrothermal flame images obtained. The initial reactor temperature and fuel concentration increase allows the hydrogen–oxygen reaction to transition from mild oxidation to intense combustion. The oxygen excess ratio does not affect the reaction states, but it affects the temperature profile and hydrogen conversion efficiency. The introduction of cooling water changes the concentration and temperature of the reactant mixture in the reactor, thus affecting the stability of the hydrothermal flame.

Modified reconstruction of boiler numerical temperature field based on flame image feature extraction

ABSTRACT Computational fluid dynamics (CFD) method is a common method to obtain the temperature field distribution within the furnace. However, in order to reduce computational complexity, the numerical simulation involve simplifications in boundary conditions and model parameters. As a result, the temperature field distribution deviates from the actual operating conditions. This paper proposes a numerical temperature field modification method based on flame images. Flame images corresponding to the operational conditions are collected using the Industrial Flame Monitoring System (IFMS). The flame images are preprocessed, and the contour of the flame’s core region is extracted using the Mask Region-based Convolutional Neural Network (Mask R-CNN) method. The geometric features of the flame are extracted, and matrix calculations are applied to modify the numerical temperature field. The modified temperature field exhibits a deviation in the combustion center, aligning with the actual operation of the boiler. A comparison between the modified temperature field and the temperature measurements from flue gas shows consistent temperature trends. The absolute error (AE) under different operating conditions is under 8.8 K, and the relative error (RE) remains below 1.3%. The analysis results demonstrate that it can enhance the accuracy of temperature field calculations by the modification from flame image features.

Supersonic combustion field evolution prediction in scramjet engine using a deblurring multi-scale attention network

Traditional deep learning technologies often overlook crucial spatiotemporal information when reconstructing supersonic combustion flow fields. These models can only reconstruct the current flow field, resulting in poor visual quality due to the atomization effect. This research introduces experimental studies on scramjet combustion flows with variable injection pressures at the inlet of a scramjet Mach 2.5, creating image datasets of flame evolution processes over different predicted time spans (5, 10, and 15 ms). It develops a multi-scale attention algorithm with deblurring (DB-MSAN) capabilities to predict the flame image after a certain period using the wall pressure signal from the previous moment. The algorithm employs two multi-head attention channels at different scales to extract timing information, encouraging the model to focus on the anisotropy of the input data and to identify more significant features. The DB module utilizes the adapted atmospheric scattering model to jointly estimate the transmission map and atmospheric light, achieving an exceptional defogging effect with a minimal computational expense. The model’s overall performance is assessed regarding efficiency and accuracy, comparing DB-MSAN’s performance differences with those of Chen’s and the ResNet16 models across multi-span, large-span, various test sets, and sparse wall measurement points. Experimental results indicated that, compared to other methods, DB-MSAN achieves a maximum peak signal-to-noise index improvement of 76.10 % and an SSIM index improvement of 155.26 % when predicting average-quality images. The prediction accuracy remains stable, even with sparse pressure input. In addition, DB-MSAN strikes an optimal balance between the number of parameters and model accuracy; it represents only 16.97 % of the volume compared to Chen’s model, which is 585 MB.

Fire Smoke Target Detection Incorporating PBCA

Background: Fire incidents occur in complex scenarios, where the dynamic positions and varying scales of flames and smoke pose challenges for fire detection. To improve the stability, localization accuracy, and detection precision of small targets in fire detection, a fused PBCA method for fire and smoke object detection has been proposed in this paper, called FS-YOLOv8. Background: Fire incidents occur in complex scenarios, where the dynamic positions and varying scales of flames and smoke pose challenges for fire detection. To improve the stability, localization accuracy, and detection precision of small targets in fire detection, a fused PBCA method for fire and smoke object detection has been proposed in this paper, called FS-YOLOv8. Objective: The objective of this approach was to improve the detection accuracy of flames and smoke, enhance the robustness of the system, and strengthen the feature representation capability. It aimed to optimize the contribution of feature maps at different scales, allowing the network to capture inter-channel correlations while preserving precise localization information of the targets. Furthermore, it aimed to enhance the learning ability of small-scale flame and smoke objects. Method: Firstly, DCN (Deformable Convolutional Network) was integrated into the CSPDarknet backbone network to extract features from flame and smoke images. Subsequently, a module called PBCA was designed by combining BiFPN (Bidirectional Feature Pyramid Network) and coordinate attention. Finally, a small object detection layer, YOLO HEAD-4, was constructed. Results: The experimental results of our proposed FS-YOLOv8 method on a self-made dataset demonstrated improved detection accuracy compared to other conventional methods. Therefore, the FS-YOLOv8 method effectively enhanced the performance of object detection in fire and smoke scenarios. Conclusion: The FS-YOLOv8 method has been found to effectively improve the performance of object detection in fire and smoke scenarios, enhance the robustness of the system, strengthen the feature representation capability, and amplify the learning ability of small-scale flame and smoke objects. other: NO

Effect of Temperatures on Flame Propagation of Diethyl Ether Spray Explosion

ABSTRACT The study aims to identify the flame propagation in the diethyl ether (DEE) spray explosion through mathematical analysis by processing the gray-level histogram of the flame images. Different ambient and material temperatures’ effect on flame propagation and structure was investigated using a high-speed camera in a 20-L explosion vessel. The results showed that the overall duration could be divided into combustion and deflagration processes. With the ambient temperature increased, the combustion duration showed a trend of first decreasing, then increasing, and then decreasing again. The combustion zone first increased and then decreased. The explosion duration showed an overall decreasing trend. The explosion duration was longer than the combustion duration, ranging from 152.75 to 307 ms. The overall combustion and explosion duration trend was consistent with the explosion duration. When the ambient temperature was 308.15 K, the combustion duration reached its minimum value of 13.25 ms, and the equivalent radius of the combustion zone reached its maximum value of 52.89 mm. With the increase in material temperature, both the combustion and explosion durations showed a trend of first decreasing and then increasing, with combustion durations ranging from 12.42 to 16.25 ms and explosion durations ranging from 106 to 117.5 ms, respectively. When the material temperature was 308.15 K, the combustion zone expansion rate reached its maximum value of 10.20 m/s, and the explosion zone expansion rate reached its maximum value of 13.36 m/s. The effect of ambient temperature on the development of combustion zone is greater than that of material temperature, which is mainly reflected in the obvious change of equivalent radius.

Effects of ignition methods and control parameters on the lean combustion performance and flame geometry of jet ignition based on constant-volume combustion chamber

Pre-chamber jet ignition provides one of the most reliable thermal efficiency improvement technologies for future gasoline engines, especially hybrid gasoline engines. The effects of jet ignition control parameters on the jet flame morphology and combustion characteristics are still unclear, for example, the fuel injection mass in the pre-chamber and the time interval between low-flow injection and ignition. The paper explored the effects of jet ignition stoichiometric and lean combustion performance on the flame propagation process of high-energy spark ignition, passive and active pre-chamber, using direct flame imaging in a constant-volume combustion chamber. The jet ignition performance under lean combustion conditions is further enhanced by optimizing the jet ignition control parameters. Based on the luminance characterization of the image, the active pre-chamber with additional low-flow injection can provide 281% of the ignition intensity equivalent to high-energy spark plug ignition. Increasing the fuel injection mass in the pre-chamber can improve heat release and enhance ignition performance. However, the jet ignition performance will drop sharply due to the too-rich pre-chamber gas mixture. Extending the mixing time interval after ignition will enrich the lean ambient mixture outside the pre-chamber and increase the ignition performance. Over-mixing will occur if the mixing time interval is too long, especially under a few injected fuel mass situations. The radial stretch coefficient of the jet flame is defined to characterize the flame geometry during the jet flame propagation process. The jet ignition performance reaches its best when the jet flame propagates at high speed in both radial and circumferential directions, namely a wide jet flame.

An image-processing method based on regional separation-parameter coupling for the stability analysis of biodiesel flame

Flame stability is vital to obtain good combustion performance. Therefore, the development of flame stabilization technologies and stability evaluation methods has always received considerable research attention. However, the existing evaluation methods have low precision, which makes it difficult to accurately assess the flame stability. To address this issue, we proposed an effective flame state evaluation method in this study. We conducted relevant combustion experiments, and applied the proposed method to evaluate the flame stability of biodiesel. Furthermore, we established a numerical evaluator of the flame state based on a regional separation-parameter coupling image-processing method. This numerical evaluator combines the statistical characteristics of six flame parameters, which we derived from the flame images. The experimental results suggested that the proposed method can be used to determine some characteristic parameters that cannot be obtained by traditional methods, such as oscillation frequency and propagation velocity. Notably, these characteristic parameters are important for the evaluation of flame stability. At an oxygen concentration of 24 % and fuel pressure of 0.5 MPa, we subdivided the combustion state of biodiesel into stable, relatively stable, unstable, and extinguished states. We observed that by decreasing the airflow rate from 0 to 12.50 m3/h, the flame color changed from orange to green, then blue, and finally to yellow-green. We also observed, contraction and spreading in the flame highlight region. The color space analysis revealed that the larger the blue-to-red ratio, the more complete the combustion, which led to a blue flame. Overall, the proposed evaluation method provides a useful guide for regulating the combustion of biodiesel. Furthermore, these result can be generalized to assess the combustion states of other fuels.

A deep learning-based dynamic deformable adaptive framework for locating the root region of the dynamic flames.

Traditional optical flame detectors (OFDs) in flame detection are susceptible to environmental interference, which will inevitably cause detection errors and miscalculations when confronted with a complex environment. The conventional deep learning-based models can mitigate the interference of complex environments by flame image feature extraction, which significantly improves the precision of flame recognition. However, these models focus on identifying the general profile of the static flame, but neglect to effectively locate the source of the dynamic flame. Therefore, this paper proposes a novel dynamic flame detection method named Dynamic Deformable Adaptive Framework (DDAF) for locating the flame root region dynamically. Specifically, to address limitations in flame feature extraction of existing detection models, the Deformable Convolution Network v2 (DCNv2) is introduced for more flexible adaptation to the deformations and scale variations of target objects. The Context Augmentation Module (CAM) is used to convey flame features into Dynamic Head (DH) to feature extraction from different aspects. Subsequently, the Layer-Adaptive Magnitude-based Pruning (LAMP) where the connection with the smallest LAMP score is pruned sequentially is employed to further enhance the speed of model detection. More importantly, both the coarse- and fine-grained location techniques are designed in the Inductive Modeling (IM) to accurately delineate the flame root region for effective fire control. Additionally, the Temporal Consistency-based Detection (TCD) contributes to improving the robustness of model detection by leveraging the temporal information presented in consecutive frames of a video sequence. Compared with the classical deep learning method, the experimental results on the custom flame dataset demonstrate that the AP0.5 value is improved by 4.4%, while parameters and FLOPs are reduced by 25.3% and 25.9%, respectively. The framework of this research extends applicability to a variety of flame detection scenarios, including industrial safety and combustion process control.

SootImage: An image recreation, post-processing validation procedure for sooting axisymmetric flames

Validation is a vital part of any computational fluid dynamics study. Validation is done by comparing the computed results to the experimental measurements performed in a known configuration. In sooting flames, such a comparison is non-trivial since the measurements have a wide range of uncertainty due to difficulties in directly measuring soot volume fractions. This work introduces a different way to verify the computations using a software package called SootImage. In this proposed methodology, comparisons are not made directly to the computed properties of interest (soot volume fraction and temperature). Instead, a post-processing procedure is performed to obtain an image of the flame based on the computed properties. This reconstructed image is compared to an actual image of the flame being studied. The algorithm of the image reconstruction utilized within SootImage is presented in detail. Finally, the usage of SootImage is demonstrated on a co-flowing, laminar ethylene/air diffusion flame. Program summaryProgram Title: SootImageCPC Library link to program files:https://doi.org/10.17632/nc5myw64km.1Developer's repository link:https://github.com/VictorChernov/SootImageLicensing provisions: CC BY NC 3.0Programming language: MATLABNature of problem: The software allows comparing images of a real flame with the recreated image of a simulated flame. This requires two major steps. One is the recreation of the image of an axisymmetrical flame from the computed soot volume fraction and temperature fields for a known camera and optical configuration. The second is cropping and position images to obtain a meaningful comparison.Solution method: The solution recreates the image by integrating the soot thermal radiation over a line-of-sight. Soot is assumed to emit as gray body. The integrated radiation is passed through the color filters of the camera and is translated to pixel values. After the process is done for the whole flame, the width, height and origin of both flames are found, and images that can be easily compared are created.

Modelling flame-to-fuel heat transfer by deep learning and fire images

In numerical fire simulations, the calculation of thermal feedback from the flame to the solid and liquid fuel surface plays a critical role as it connects the fundamental gas-phase flame burning and condensed-phase fuel gasification. However, it is a computationally intensive task in CFD fire modelling methods because of the requirement of a high-resolution grid for calculating the interface heat transfer. This paper proposed a real-time prediction of the flame-to-fuel heat transfer by using simulated flame images and a computer-vision deep learning method. Different methanol pool fires were selected to produce the image database for training the model. As the pool diameters increase from 20 to 40 cm, the dominant flame-to-fuel heat transfer shifts from convection to radiation. Results show that the proposed AI algorithm trained by flame images can predict both the convective and radiative heat flux distributions on the condensed fuel surface with a relative error below 20%, based on the input of real-time flame morphology that can be captured by a larger grid size. Regardless of growing or decaying fires or puffing flames induced by buoyancy, this method can further predict the non-uniform distribution of heat transfer coefficient on the interface rather than using empirical correlations. This work demonstrates the use of AI and computer vision in accelerating numerical fire simulation, which helps simulate complex fire behaviours with simpler models and smaller computational costs. Highlights A total framework between AI model and fire simulation software is designed to further enhance the reliability of AI-based fire simulations. A standard pool fire simulation database is built using numerical model recommend by the IAFSS Computation Group. A deep learning model is developed to predict both the convective and radiative heat flux distributions on the condensed fuel surface using numerical images database. The demonstration showcases the application of AI and computer vision to accelerate numerical fire simulation.

Exploring electric field forces and polarization influence on combustion in Fe-additive fuel droplets

The application of electrical techniques such as electric field, polarization, and ionization in the field of combustion makes many positive developments possible. The electric field effect offers significant advantages in controlling flame stability and reducing soot formation during the combustion of fuels. Also, the conditioning effect can improve particle oxidation during combustion for hydrocarbon fuels containing metal particle inclusions with high calorific value. This study focuses on investigating the combustion and atomization behavior of Fe additive diesel fuel droplets at electric field strengths of E = 5 V/m, E = 6.7 V/m, and E = 10 V/m and in positive (↑) and negative (↓) electric field directions. The experiments were carried out by igniting a fuel droplet suspended on a ceramic wire with an arc at the center of the electric field between two conductive plates in a combustion chamber with optical apertures. The combustion processes were recorded with an optical system consisting of a high-speed camera and a thermal camera. Combustion and atomization behavior were characterized by sequential flame and droplet atomization images, respectively. The droplet shape change and flame behavior results were comparatively evaluated according to the d2-law. Ignition delay and extinction times were determined by processing threshold technique and maximum flame temperatures were determined by processing thermal camera images. The results showed that increasing the electric field intensity in the negative electric field direction resulted in improved soot oxidation, and Fe particles burnt efficiently. Ignition delay times showed a decreasing trend with an electric field effect and an increasing trend with the addition of Fe particles. The highest maximum flame temperature and lowest extinction time were recorded as 785 K and ∼ 578.34 ms for Diesel/Fe/100(↓) fuel droplet, respectively and Fe particles did not exhibit microexplosion phenomena at E = 10 V/m. In this study, it has been shown that an effective oxidation can be achieved for Fe particles by increasing the mobility of ionized species in the flame zone under the effect of electric field and a homogeneous thermal field can be created in the flame zone even in fuels with particle addition at increasing electric field intensities.

Research on temperature field reconstruction technology for single-dome combustion chambers based on flame image

In order to compensate for the limitations of traditional temperature measurement methods, glass plates were installed on both sides of the single-dome combustion chamber test piece, and two CCD cameras observed the combustion flame in the furnace from different positions. The obtained flame radiation images were combined into a single image through the video capture card, and then the images were input into the reconstruction algorithm software to calculate the three-dimensional temperature field in the combustion chamber in real-time. The software could dynamically display the temperature distribution at three different positions in the chamber. This marked the first time that the temperature field in the combustion chamber was obtained using this new temperature measurement technology, which preliminarily verifies the feasibility of reconstructing the temperature field in the single-dome combustion chamber through flame image temperature measurement technology.

A high accuracy method for the sintering condition recognition of rotary kiln

According to the different material sintering conditions, the sintering conditions of alumina rotary kiln can be divided into: super-heated, super-chilled, and normal. In this paper, based on Local Binary Pattern (LBP) and the primary-color method, a novel feature extraction method is proposed to obtain information about temperature in flame images without calibrating camera parameters. Through the analysis of the problem as well as the experimental phenomena, a new classification procedure is devised: in the first step, the super-chilled condition is first separated, in the second step, the normal and super-heat condition are classified. Different feature extraction methods are used in the two steps mentioned above. One is to extract the texture features of pseudo temperature images, and short-time energy is used to describe the dynamic features. The other is to extract the texture features of grey-images by our improved LBPP,Rriu2, and characterize the dynamics with sample entropy and variance. Finally, experimental results show that the our feature extraction method can effectively reduce intra-class variation, increase inter-class variation and receive a high classification accuracy.

Flame characteristics and abnormal combustion of ammonia-diesel dual-fuel engine with considering ammonia energy fractions

Carbon neutrality is a significant challenge but also an essential opportunity for internal combustion engines (ICE), and fueled with low-carbon fuels is the key for ICE to reduce carbon emissions. Ammonia is a carbon-free fuel, whereas ammonia-diesel dual-fuel (ADDF) combustion can provide efficient and clean combustion of ammonia. In this study, the flame characteristics and abnormal combustion of ADDF engines are studied by considering ammonia energy fractions and diesel injection timings. The results show that a high ammonia energy fraction led to an extended period of combustion, while the CA05 first advances and then decreases with the decrease of ammonia energy fraction. This phenomenon indicates that an easily ignitable ammonia-air mixture is also important for ammonia-diesel dual-fuel ignition. Besides, an optimal SOI is needed to maximize the promoting effect of ignited fuel on ammonia combustion, and the optimal SOI changes with the ammonia energy fraction. Flame images confirm that the weightiness of the auto-ignition and flame propagation changes with the SOI, and the auto-ignition kernels gradually reduce and flame propagation plays a more and more important role as the SOI advances. At high loads, knocking combustion will also occur for ADDF engines, although the knock intensity is low. Besides, due to the enhancement effect of the knocking pressure wave, the flames show a strong white light at the knocking onset. The current results can help understand the combustion process and thus find the optimization method of ADDF engines.