Combustion Field Research Articles

Flame propagation characteristics and inherent instability in premixed carbon-free hydrogen/ammonia/oxygen combustibles

Hydrogen-ammonia fuel is an ideal clean energy, and its use in the combustion field is important for achieving energy transformation. This study carried out a series of flame propagation experiments of hydrogen/ammonia/oxygen mixtures and analyzed the instability phenomenon and the competition mechanism of inherent instability during flame propagation at different fuel ratios (0.5–2.0), equivalence ratios (0.5–1.5), and initial pressures (0.1 atm − 1.0 atm). The results show that the hydrogen/ammonia combustion in oxygen is extremely susceptible to instability. Only when the P0 = 0.1 atm, does the flame surface remain smooth in the observable region. Crack initiation and development are observed on the flame surface at P0 = 0.5 atm, and the flame propagated to a late stage with a sudden increase in velocity, which is due to the destabilization of the flame. The increase in fuel ratio gradually shifts the Lewis value away from 1, but does not change the state of thermal-diffusional factor on flame stability. The flame thickness reaches the minimum at φ = 0.8. As the initial pressure increases from 0.1 atm to 1.0 atm, the Lewis number remains almost constant, while the flame thickness, Markstein length, and critical instability radius all decrease rapidly. At the theoretical equivalence ratio and fuel ratio of 1.0, the flame thickness decreased rapidly from 0.143 mm to 0.015 mm, with a decrease of 89.5 %. The flame thickness for hydrogen/ammonia combustion in air is approximately five times that in oxygen for the same operating conditions, implying that the oxygen atmosphere greatly increases the hydrodynamic instability of the hydrogen/ammonia premixed flame. The critical Peclet number (Pecr) of the hydrogen/ammonia/oxygen flames rises exponentially with increasing Markstein number (Ma), and the empirical correlation can be expressed as Pecr = 166.11 × exp (Ma/1.04) + 47.86 (P0 = 0.5 atm) and Pecr = 538.29 × exp (Ma/0.53) + 142.78 (P0 = 1.0 atm).

Research on characteristic chemical time scales for MILD combustion based on partition PCA method

MILD combustion represents a more moderated reaction process in contrast to traditional combustion. The employment of hydrogen-containing syngas as fuel introduces a spectrum of intricate and diverse chemical reactions. This complexity renders traditional reliance on characteristic chemical timescales less appropriate for the analysis and modeling of Moderate or Intense Low-oxygen Dilution (MILD) combustion phenomena. In response to this inadequacy, a new technique named the partition Principal Component Analysis (PCA) method was developed. This method begins by segmenting combustion regions based on the reaction intensity, then extracts characteristic chemical time scales for each of these individual regions through the application of the PCA method. The innovative method was subsequently applied to investigate the chemical traits of the CH4/H2 mixture in both MILD and standard combustion processes with respect to the time scale. The insights obtained from this novel approach demonstrate that the characteristic chemical time scales (τc) deduced through this methodology exhibit a more accurate representation of the intrinsic characteristics of MILD combustion within the reaction region. This approach enhances the comprehension of the interplay between turbulence and chemistry in MILD combustion contexts. Furthermore, it establishes a solid groundwork for forthcoming research endeavors in the field of MILD combustion. Furthermore, the τc calculations presented in this paper have been derived mathematically, confirming their widespread applicability.

Large-eddy simulation investigation on the effects of inlet swirl/Reynolds number and fuel heating value on a turbulent kerosene spray flame

To progress toward the high efficiency and low emission aviation engine technology, it is mandatory to understand the complex phenomena including kerosene spray atomization, evaporation, turbulent mixing and combustion in the engine combustor. Nowadays with the rapid growth of computational capacity over the world, large-eddy simulation (LES) performs a more and more important role in understanding the complex multi-phase combustion process of kerosene fuels in aviation engine configuration. In the present work, LES is employed to investigate a swirling kerosene spray jet flame in a model combustor, which has been studied experimentally. The turbulent combustion is modeled by the flamelet generated manifold (FGM) approach with a 3-component surrogate kerosene skeletal mechanism. The averaged gas velocity and temperature predicted by LES generally agrees well with the experimental measurements. Local extinction is observed at the inner shear layer of the swirling spray flame, due to droplet-turbulence-flame interactions. A parametric study with 11 cases is then performed to investigate the effects of inlet swirl/Reynolds number and fuel heating value on the kerosene flame. As the swirl number increases, the enhanced turbulent mixing between kerosene fuel and air facilitates the combustion process, but combustion instabilities may occur if the swirl number is too high. A higher Reynolds number can promote the turbulent combustion via a stronger recirculation zone, but it also has strong dilution effects on the combustion field. Variations in the fuel heating value directly impact the temperature field, but the flow field, major species concentrations and droplet phase statistics are only slightly affected. These LES results give clues to achieve optimal combustion performance in realistic aviation engine configurations via better arrangements of fuel and air injections.

Quantitative acetone/toluene planar laser‐induced fluorescence measurements of equivalent ratio

AbstractIn this work, we establish a quantitative relationship between the fluorescence signal intensity of acetone and toluene and their concentration, and acetone/toluene planar laser‐induced fluorescence (PLIF) is developed to measure the concentrations of two components simultaneously, providing an equivalent ratio distribution of the flow field. Due to a thorough consideration of the tracer effect on each other's fluorescence signals, the uncertainty in the PLIF concentration measurement is reduced to 10.4%. Experimental results of OH‐PLIF and acetone/toluene PLIF indicate that the equivalence ratio distribution obtained by acetone/toluene PLIF in cold‐state flow fields can be employed to predict and analyze the trend of equivalence ratio distribution in the combustion field.

Multi-View Synthesis of Sparse Projection of Absorption Spectra Based on Joint GRU and U-Net

Tunable diode laser absorption spectroscopy (TDLAS) technology, combined with chromatographic imaging algorithms, is commonly used for two-dimensional temperature and concentration measurements in combustion fields. However, obtaining critical temperature information from limited detection data is a challenging task in practical engineering applications due to the difficulty of deploying sufficient detection equipment and the lack of sufficient data to invert temperature and other distributions in the combustion field. Therefore, we propose a sparse projection multi-view synthesis model based on U-Net that incorporates the sequence learning properties of gated recurrent unit (GRU) and the generalization ability of residual networks, called GMResUNet. The datasets used for training all contain projection data with different degrees of sparsity. This study shows that the synthesized full projection data had an average relative error of 0.35%, a PSNR of 40.726, and a SSIM of 0.997 at a projection angle of 4. At projection angles of 2, 8, and 16, the average relative errors of the synthesized full projection data were 0.96%, 0.19%, and 0.18%, respectively. The temperature field reconstruction was performed separately for sparse and synthetic projections, showing that the application of the model can significantly improve the reconstruction accuracy of the temperature field of high-energy combustion.

Deep learning-driven analysis for cellular structure characteristics of spherical premixed hydrogen-air flames

The extraction of cellular structure feature on the spherical premixed flame surface faces accuracy challenges. The Schlieren technique was employed to obtain the hydrogen-air premixed spherical flames images in a constant volume vessel at room temperature and atmospheric pressure under an equivalent ratio of 0.8 in this work. A bio-inspired Cellpose 2.0, driven by deep learning, is innovatively introduced to train the cell segmentation model in the combustion field. After labeling and training cells of different shapes and sizes, an efficient and accurate model suitable for cell feature extraction was finally obtained to identify and quantify various cells characteristics, such as number, size, and distribution. Results show that the average precision (AP) during the model online pre-training process reaches 0.625. Meanwhile, the critical flame radius of transition acceleration obtained is 36 mm and the crack length tends to grow linearly after the flame radius exceeds this critical point. Additionally, the average cell area gradually converges to a stable value after the flame radius exceeds the uniform cellularity critical radius. The cell segmentation model obtained in this work can be further used to train different spherical flames under various conditions, helping to develop hydrogen combustion and explosion modelling.

Predicting detonation cell size of biogas–oxygen mixtures using machine learning models

Detonation cell size is a very important parameter describing the detonation process, used both for explosion safety analysis and for the design of detonation combustion chambers. Typically it has been studied either experimentally or by CFD simulations; both options are costly in terms of money and time. However, progress in the machine learning (ML) methods opened a third way of obtaining cell size. When trained properly, such models are capable of giving rapid, accurate predictions. Utilization of machine learning in the combustion field is gaining more attention from the research community. In this study, the process of training, testing, and evaluation of three different machine learning models for predicting biogas–oxygen mixture detonation cell size is presented. The models include: linear regression (LR), support vector regression (SVR), and neural network (NN). The dataset used for training and testing comes from the experimental studies conducted previously by the authors. It was shown that all the models give very good results with support vector regression proving to be the best.

Combustion Field Prediction and Diagnosis via Spatiotemporal Discrete U-ConvLSTM Model

Combustion Field Prediction and Diagnosis via Spatiotemporal Discrete U-ConvLSTM Model

Исследование новой микрофакельной горелки с помощью программы Ansys Fluent

A study of a new micro-flame burner device (MFBD) for the combustion chamber of a gas turbine unit (GTU) using the Ansys Fluent program is presented. Research objective: determination of the optimal operating modes of the investigated burner, initial air parameters for maximum combustion efficiency, examination of the uniformity of the temperature field, and the concentration of nitrogen oxides in the combustion chamber of the gas turbine unit with the MFBD. The features of the design and operation of the micro-flame burner device are described, as well as the stages of modeling combustion processes in the Ansys Fluent software. The calculations conducted using various simulation parameters in this program allowed the evaluation of the efficiency of the new MFBD, determination of the temperature field distribution, and the nitrogen oxide content in the combustion products. The nitrogen oxide emissions in the combustion products during different modes of operation did not exceed 9 ppm. This data will be useful for further optimization and development of GTUs taking into account the requirements of efficiency and environmental safety. The presented study demonstrates the application of Ansys Fluent in the development of new technologies in the field of combustion and energy

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.

Hydrogen fuelling a passenger car spark ignition engine for a cleaner environment

The tightening of the restrictions on polluting emissions in the last decade and the possibility of the disappearance of the internal combustion engine in the field of transport must motivate the researchers in the field of internal combustion engines to find new solutions to fuel the spark ignition engine, aiming to reduce the negative impact of polluting emissions on the environment. Unconventional fuels represent a very good choice to fuel the spark ignition engines, based on their excellent combustion properties and hydrogen stands out among them. This paper presents experimental results of fuelling a car spark ignition engine with mixtures of gasoline and hydrogen. The experimental investigations have shown an improvement in the combustion process of the engine, a reduction in the energetic fuel consumption, an increase in the brake thermal efficiency and a decrease in the pollutant emissions level.

A non-axisymmetric temperature field reconstruction method based on the compressive sensing background oriented Schlieren method

Measurement of the combustion temperature field is an extremely important issue in industrial production. Temperature is one of the key parameters in combustion studies. With the temperature field distribution of the combustion field obtained, heat transfer, heat convection, and heat radiation can be calculated directly and efficiently. Traditional background oriented Schlieren (BOS) is an effective method for non-axisymmetric temperature field measurements, but it requires simultaneous Schlieren imaging at multiple angles for tomographic reconstruction, which will greatly limit its application. In this paper, the compressive sensing algorithm is introduced into the temperature field reconstruction, which establishes the system of equations between the deflection angle and the refractive index gradient. Then, the reconstruction of the non-axisymmetric temperature field is realized by solving the underdetermined system of equations by the method of solving the sparse solution through the compressive sensing. First, light offsets across the non-axisymmetric temperature field are measured by the under-angled BOS system and image processing method. Second, the spatial refractive index field is reconstructed by the compressive sensing BOS method proposed in this paper. Finally, the spatial temperature field is obtained. The experimental results show that by comparing the iRadon reconstruction algorithm and the compressive sensing reconstruction algorithm, the temperature field reconstructed by the compressive sensing under the condition of the under-angled sampling of projection data had a higher accuracy than that reconstructed by the tomographic reconstruction algorithm under the same condition. The average error of the temperature field was reduced from 34.6 to 29.7 K under the same measurement conditions; however, the accuracy is better maintained by using the compressive sensing algorithm under the condition of undersampling projection.

Flame state recognition method of a scramjet based on PLIF image fusion features and an artificial neural network

In this study, a pattern recognition model is proposed to differentiate the flame state of a scramjet using an artificial neural network. The flame images are obtained from a scramjet ground test utilizing planar laser-induced fluorescence (PLIF). By extracting basic features, Hu moments and Zernike moments, the preprocessed flame images are compressed to mine valuable information. In order to reduce redundant input features and improve the efficiency of model operation, the partial least squares (PLS) method is introduced for feature screening and fusion. Then, a back propagation neural network (BPNN) model for multi-flame classification is established and analyzed. Finally, the flame states are determined by comparing the probabilities of different states. In order to optimize the recognition performance, the fusion features are studied and discussed. Experimental results show that when the filtered 11-dimensional features are used as input, the average recognition rate for the four states can reach 97.4%. These results demonstrate the significant potential of integrating PLIF and advanced data analysis methods, thereby broadening their application to intricate combustion fields.

Effect of ammonia-air combustion melting on the color and physical properties of soda lime silicate

Ammonia is a carbon-free fuel; hence, it does not generate CO2 during combustion. However, ammonia has a different combustion reaction and flue gas composition from conventional city gas; therefore, its impact on the glass-melting process and glass quality is unknown. In this study, batches of soda lime silicate glass and cullet raw materials were melted using ammonia or city gas as the fuel. Furthermore, the glass melted in the combustion and exhaust gas fields was analyzed to evaluate the effects on composition and color tone. The type of fuel and combustion atmosphere were found to affect the color and glass transition temperature. The batches and cullet materials that melted in the combustion field were colored because of combustion-atmosphere-induced changes in the valence of the Fe in the raw materials, and the coloration of this glass was more affected by the cullet than by the batches. Meanwhile, the glass melted in the exhaust gas field of ammonia combustion exhibited a lower glass transition temperature owing to the higher presence of H2O in the exhaust gas. The temperature was measured by four thermocouples (TC1–4) installed inside the furnace. The glass transition temperature of the glass at the TC4 position in the exhaust gas field using ammonia as fuel was 24 and 9 °C lower than those of the reference batch and cullet, respectively. Ammonia can be used as a decarbonized fuel for glass-melting furnaces by designing raw materials for soda lime silicate glass. However, the raw materials containing carbonates emitted CO2 in the exhaust gases during ammonia combustion. Therefore, to achieve a carbon-neutral glass manufacturing process, CO2 derived from both raw materials and fuel should be reduced.

Methanol-based fuel boiler: Design, process, emission, energy consumption, and techno-economic analysis

Methanol-based alternative energy sources are critical to maintaining energy security, reducing the need for oil imports, and safeguarding the environment in China. However, the use of methanol-based fuels in the field of thermal combustion has not been thoroughly investigated. Therefore, an evaluation of the costs, emissions, and usage of methanol boilers is necessary. The study finds that methanol heaters and air source heat pumps have comparable annual cost values. Methanol heating furnaces emit the least amount of NOx and produce no SO2. After the refurbishment, the boiler's emissions and energy consumption have significantly decreased upon conversion of coal-fired steam boilers to methanol-based fuel hot water boilers. Nevertheless, the economy has also somewhat declined. Additionally, 5499 tce (standard coal) less energy will be used during the heating season, and 1.8 million CNY less will be spent on dust removal and desulfurization. Unfortunately, the economic benefits of methanol are only apparent when the cost of methanol fuel is less than 1900 CNY/t. The research presented in this paper provides an essential technological foundation for the development of methanol-fueled fuel burners and for the renovation of small and medium-sized coal-fired boilers.

Effect of Semi-Transverse Ventilation Velocity on Combustion Characteristics of Pool Fire Sources in a Scaled Tunnel

Compared to longitudinal ventilation, there are few studies on fire source development under semi-transverse ventilation. This work studied the influence of semi-transverse ventilation on the combustion characteristics of fire sources in a scaled tunnel. The burning rate and heat transfer feedback during pool fire combustion were revealed under different longitudinal and transverse ventilation velocities. The results showed that transverse ventilation had little influence on combustion characteristics, and the burning rate was more obviously affected by longitudinal ventilation. The heat convection feedback increased monotonically with the increase of the longitudinal ventilation, which led to the increase of the total heat feedback on the fuel. The heat radiation feedback changed little, and the heat conduction feedback decreased monotonically with the increase of the longitudinal ventilation velocity. By aid of a Fire Dynamics Simulator, it was found that the flame tilted downstream and was in the flow line of the lower cold air flow coming from upstream and the upper hot smoke flow outgoing in the downstream direction. The transverse ventilation of 2 m/s or lower hardly affected the combustion field of the fire source. Therefore, semi-transverse ventilation is preferable to longitudinal ventilation from the point of view of limiting fire expansion.

Quantum chemistry and kinetics of hydrogen sulphide oxidation.

A fundamental understanding of the acid gas (H2S and CO2) chemistry is key to efficiently implement the desulphurisation process and even the production of clean fuels such as hydrogen or syngas. In this work, we developed a new kinetic model for the pyrolysis and oxidation of hydrogen sulphide by merging two previously reported models with the goal of covering a wider range of conditions and including the effect of carbon dioxide. The resulting model, which consists of 75 species and 514 reactions, was used to conduct rate of production and sensitivity analysis in plug flow reactor simulations, and the results were used to determine the most prominent reactions in which hydrogen sulphide, molecular hydrogen, and sulphur monoxide are involved. The resulting list of important reactions was screened and the kinetics of three of them, i.e., SO2 + S2 → S2O + SO, S2O + S2 → S3 + SO, and SO + SH → S2 + OH, was found to warrant further investigation. With the goal of improving the accurancy of our new kinetic model, we carried out a robust quantum chemistry and Rice-Ramsperger-Kassel-Marcus master equation study to obtain, for the first time, the forward and reverse rate constants for those three reactions at temperatures and pressures of interest for combustion and atmospheric chemistry. This work is the first step of a kinetic study that is aimed at improving the understanding of the chemistry of the pyrolysis and oxidation of H2S, highlighting the importance of sulphur-sulphur interactions and providing a fundamental basis for future kinetic models of H2S not only in the field of combustion, but also in atmospheric chemistry.

Flame Imaging Technology Based on 64-Pixel Area Array Sensor.

High-resolution flame temperature images are essential indicators for evaluating combustion conditions. Tunable diode laser absorption spectroscopy (TDLAS) is an effective combustion diagnostic method. In actual engineering, due to the limitation of line-of-sight (LOS) measurement, TDLAS technology has the problems of small data volume and low dimensionality in measuring combustion fields, which seriously limits the development of TDLAS in combustion diagnosis. This article demonstrates a TDLAS imaging method based on a 64-pixel area array sensor to reconstruct the two-dimensional temperature field of the flame. This paper verifies the robustness of the Algebraic Reconstruction Technique (ART) algorithm through numerical simulation and studies the effects of temperature, concentration, and pressure on the second harmonic intensity based on the HITRAN database. The two-dimensional temperature field of the flame was reconstructed, and reconstruction accuracy was verified using thermocouples. The maximum relative error was 3.71%. The TDLAS detection system based on a 64-pixel area array sensor provides a way to develop high-precision, high-complexity flame temperature measurement technology.

The importance of unsteady phenomena of ammonia/methane combustion in an experimental swirl burner: Comparison of steady-state and transient simulation results

Ammonia is a potential advanced green fuel. Combustion simulations play a crucial role in facilitating the usage of ammonia in existing combustion systems as a drop-in fuel. Choosing between the unsteady and steady simulation methods should be done carefully depending on the investigated phenomena. In this study, steady-state and transient Computational Fluid Dynamics simulations were performed using detailed chemistry of the Okafor mechanism for a wide range of ammonia/methane blend combustion under lean conditions. The results revealed that the steady-state simulations could provide close values to the unsteady mean results at the outlet in magnitude, while outlet temperature and intermediate species concentration slightly differ with ammonia concentration-dependent extents. The unsteady simulations provided valuable insights into the temporal evolution of the combustion and flow field characteristics, and the Root Mean Square and mean properties were evaluated comprehensively, indicating that the oscillations highly depend on the fuel composition. The model was validated using measurement data from the literature, including Particle Image Velocimetry, OH* chemiluminescence, and pollutant emission. The velocity and OH* distribution matched acceptably, while the CO, NO, and NO2 emissions were underpredicted, which implies necessary reaction model improvements.

Physics-based reduced-order modeling of flash-boiling sprays in the context of internal combustion engines

Flash-boiling injection is one of the most effective ways to accomplish improved atomization compared to the high-pressure injection strategy. The tiny droplets formed via flash-boiling lead to fast fuel–air mixing and can subsequently improve combustion performance in engines. While most of the previous studies related to the topic focused on modeling flash-boiling sprays using three-dimensional (3D) computational fluid dynamics (CFD) techniques such as direct numerical simulations (DNS), large-eddy simulations (LES), and Reynolds-averaged Navier–Stokes (RANS) simulations, the present work introduces a reduced-order cross-sectionally averaged spray (CAS) model with significantly reduced computational cost. The proposed CAS model incorporates several physical submodels in flash-boiling sprays such as those for air entrainment, drag, superheated droplet evaporation, flash-boiling induced breakup, and aerodynamic breakup models. The CAS model is then applied to different fuels to investigate macroscopic spray characteristics such as liquid and vapor penetration lengths under flash-boiling conditions. It is found that the newly developed CAS model captures the trends in global flash-boiling spray characteristics reasonably well for different operating conditions and fuels. Moreover, the CAS model is shown to be faster by up to four orders of magnitude compared with simulations of 3D flash-boiling sprays. The novelty of the present work lies in providing a reduced-order flash-boiling spray model that offers cost-effective computational representations of complex phenomena. The proposed model can be very valuable for applications, such as experimental design, fuel screening, and creating digital twins, thus playing a crucial role in the advancement of research and development in the field of spray combustion.