Combustion Process Research Articles

Functionalized Zr‐MOFs for Enhancing Flame Retardancy and Smoke Suppression Performance on TPU

AbstractMetal‐Organic Framework (MOF) materials can be used as flame‐retardants and smoke suppressants for polymers. As a MOF material, UIO‐66(Zr) possesses higher thermal stability and controllable pore size. In this paper, UIO‐66(Zr), NH2‐UIO‐66(Zr), and SiO2, MWCNT, and GO functionalized NH2‐UIO‐66(Zr) was fabricated to improve the flame retardant and smoke suppression performance of thermoplastic polyurethanes (TPU). The experimental results show that compared with neat TPU (LOI value 19.1 %), 2 % usage of UIO‐66(Zr) and NH2‐UIO‐66(Zr) can endow TPU with 26 % LOI value and vertical combustion rating of V‐1. Moreover, SiO2, MWCNT, and GO functionalized NH2‐UIO‐66(Zr) provide TPU with above 27.8 % LOI value and vertical combustion rating of V‐0. Meanwhile, the CCT results showed that the peak heat release rate and total heat release of TPU‐U5 (added 2 % GO‐modified NH2‐UIO‐66(Zr)) are 299.52 Kw ⋅ m−2 and 68.21 MJ m−2, much lower than that of neat TPU, which are 1180.05 Kw ⋅ m−2and 104.87 MJ m−2, respectively. The TGA, char residue EDS and XRD analysis show that SiO2, MWCNT, and GO functionalized NH2‐UIO‐66(Zr) could generate zirconium metal oxide during the combustion process, which acts as a physical barrier to prevent the TPU matrix by promoting the generation of carbonization layer. The zirconium metal oxide and carbonization layer also can confine flammable gases, which can delay the ignition time and hinder the overflow of gas. In addition, NH2‐UIO‐66(Zr) could generate non‐flammable N2 diluting the concentration of flammable gases. The mechanical properties of TPU composites can maintain more than 87 % of the neat TPU after the addition of 2 % SiO2, MWCNT, and GO functionalized NH2‐UIO‐66(Zr). These results indicate that functionalized Zr‐MOFs composites could act as excellent flame‐retardants in TPU.

Comparison of combustion and interaction mechanisms of mixed working fluids R152a and R1270: A theoretical and experimental study

In the background of global efforts to reduce carbon emissions, mixed working fluids could mitigate flammability concerns of low global warming potential (GWP) working fluids for organic Rankine cycles (ORCs) and compression refrigeration cycles. In this study, the combustion and interaction mechanisms of R152a/R1216 and R1270/R1216 were investigated. Density functional theory (DFT) and ReaxFF molecular dynamics (MD) methods were employed to analyze the combustion processes of the two binary working fluids and the formation pathways of the toxic substance HF. Additionally, the influence of R1216 on the combustion of R152a and R1270 was evaluated and compared at different reaction temperatures and component ratios. The results showed that, R1216 did not inhibit but rather promoted the consumption of R152a and R1270. Corresponding experiments demonstrate that the combustion of R152a and R1270 becomes more complete and occurs more quickly after the addition of low proportions of R1216. Subsequently, the flammability limits of the two binary working fluids were determined through experiments at different ambient temperatures. The experimental results indicated that the rise in ambient temperature expands the flammability limit ranges of both binary working fluids, and the higher the proportion of R1216, the more sensitive the flammability is to changes in ambient temperature.

A Surrogate Model of Heat Transfer Mechanism in a Domestic Gas Oven: A Numerical Simulation Approach for Premixed Flames

This paper introduces an innovative analytical model to compute flame velocities and temperatures within a premix burner in a domestic gas oven. This model significantly streamlines the heat transfer simulation process by simplifying the modeling of the thermo-chemical energy release during combustion, effectively reducing complexity and computation time. Accelerated solutions are essential at the initial design stages when comparing the effect of the oven parameter variation on the overall performance. The validation of the proposed analytical model involved experimental assessments of the temperature of the false bottom plate in a natural gas oven. The resulting data were then compared against CFD simulations performed utilizing the proposed model. The results revealed a marginal discrepancy of 4% between the experimental measurements and the outcomes generated by the model. Simulations were executed under differing conditions, encompassing scenarios with and without radiation effects. This exploration identified natural convection as the predominant heat transfer mechanism, with heat radiation contributing only modestly to the heating of the false bottom plate. Among its advantages, the proposed model offers a notable reduction in the numerical complexity of the modeling of the combustion process. Furthermore, its straightforward integration into numerical simulations involving premixed flames underscores its practical utility and versatility in evaluating design performance at the early stages of the design. Highly accurate models can be left for the final oven configuration validation.

Predict Future Transient Fire Heat Release Rates Based on Fire Imagery and Deep Learning

The fire heat release rate (HRR) is a crucial parameter for describing the combustion process and its thermal effects. In recent years, some studies have employed fire scene images and deep learning algorithms to predict real-time fire HRR, which has led to the advancement of HRR prediction in terms of both lightweightness and real-time monitoring. Nevertheless, the development of an early-stage monitoring system for fires and the ability to predict future HRR based on current moment data represents a crucial foundation for evaluating the scale of indoor fires and enhancing the capacity to prevent and control such incidents. This paper proposes a deep learning model based on continuous fire scene images (containing both flame and smoke features) and their time-series information to predict the future transient fire HRR. The model (Att-BiLSTM) comprises three bi-directional long- and short-term memory (Bi-LSTM) layers and one attention layer. The model employs a bidirectional feature extraction approach, followed by the introduction of an attention mechanism to highlight the image features that have a critical impact on the prediction results. In this paper, a large-scale dataset is constructed by collecting 27,231 fire scene images with instantaneous HRR annotations from 40 different fire trials from the NIST database. The experimental results demonstrate that Att-BiLSTM is capable of effectively utilizing fire scene image features and temporal information to accurately predict future transient HRR, including those in high-brightness fire environments and complex fire source situations. The research presented in this paper offers novel insights and methodologies for fire monitoring and emergency response.

Real-time determination of combustion degree by laser-induced breakdown spectroscopy

The combustion of fuels in industries is one of the sources of greenhouse gases, posing a considerable threat to human health and the natural environment. The monitoring of the combustion process and the measurement of combustion degrees, therefore, are crucial and significant. In this work, an experimental system was developed based on Laser-induced breakdown spectroscopy (LIBS). Charcoals with different combustion degrees were taken as samples, and in the LIBS spectra of charcoals, the characteristic line of C and the molecular bands of CN and CaO were observed. Moreover, a univariate calibration model based on the exponential fitting curve was constructed to predict the combustion degree according to the variation of the C I line intensity under different combustion degrees. Furthermore, different algorithms including support vector machine (SVM), partial least squares (PLS), random forest (RF), and convolutional neural network (CNN) were applied to establish the multivariate calibration model, and the prediction performance was compared according to the 10-fold cross-validation. Particle swarm optimization (PSO) was used to optimize hyperparameters of the selected SVM-based multivariate calibration model. Principal component analysis (PCA) and linear discriminant analysis (LDA) were employed to extract spectral features and reduce dimensions for the improvement of the model's performance, with the final root mean square error of prediction (RMSEP) and mean relative error (MRE) of the LDA-PSO-SVM calibration model of 0.035 and 3.877%, respectively. Finally, c. The results prove feasible to realize the real-time determination of combustion degree to control carbon emissions and monitor carbon concentration through the LIBS-based method with machine learning, which also provides new ideas for the application of LIBS in the atmospheric field.

Effects of Dimethyl Ether and Propane Blends on Knocking Behavior in a Boosted SI Engine

<div>Dimethyl ether (DME) is an alternative fuel that, blended with propane, could be an excellent alternative for exploring the use of fuels from renewable sources. DME–propane blends are feasible for their comparable physicochemical properties; these fuels may be pressured as liquids using moderate pressure at ambient temperature. Adding a proportion of DME with a low octane number to a less reactive fuel like propane can improve the combustion process. However, the increased reactivity of the mixture induced by the DME could lead to the early appearance of knocking, and this tendency may even be pronounced in boosted SI engines. Hence, this study experimentally analyzes the effect of E10 gasoline (baseline) and DME–propane blends, with varying proportions of DME in propane ranging from 0% to 30% by weight, in increments of 5% on knocking tendency, combustion characteristics, gaseous emissions, and particle number concentration, under different intake pressure conditions (0.8, 0.9, 1.0, and 1.1 bar) in an SI engine. The results show that as the proportion of DME in the propane blend rises, the knocking tendency becomes more pronounced. That behavior intensifies with increasing intake pressure, but with 20% DME in the propane blend, reaching the maximum brake torque (MBT) without knocking in the four boosted conditions is feasible. The presence of knock limited the advance of combustion phasing and decreased the gross indicated thermal efficiency (ITEg) with E10 gasoline and 25% and 30% DME in propane blends under 1.0 and 1.1 bar boosted conditions. In these knock-limited circumstances, the NOx emissions decreased due to the retarded phasing, and THC and PN emissions increased due to the lower combustion stability, considerably raising the concentration of accumulation mode particles in the particle size distribution (PSD) compared to the other fuel blends tested.</div>

Transient spray combustion characteristics in a gas–liquid pintle rocket engine under acoustic excitation

In order to investigate the acoustic oscillation characteristics of gas–liquid pintle rocket engines and elucidate the path by which spray combustion process provides energy to the combustor pressure oscillation, a LOX/GCH4 pintle engine with rectangular combustor was designed. By adding transverse velocity disturbance for the first time, the acoustic response of spray combustion process was simulated, and the effect of excitation amplitude on acoustic response was researched. Numerical results show that the adopted transverse velocity disturbance can excite the first-order transverse acoustic oscillation with same excitation frequency in the engine combustor. The acoustic response maintenance mechanism under extrinsic excitation is summarized for pintle engines. Besides, the temperature distribution inside the engine combustor tends to be uniform, and the low-frequency oscillation caused by the flame transverse swing gradually disappears. The amplitude of combustor pressure oscillation is dominated by excitement amplitude and phase difference between the pressure and heat release in combustion reaction region. In addition, the time-averaged combustor pressure can be amplified mainly by transverse velocity disturbance. The research work can provide a reference for related fire tests on the acoustic response of a subscale gas–liquid pintle engine.

Investigation of Combustion and NO/SO2 Emission Characteristics during the Co-Combustion Process of Torrefied Biomass and Lignite.

This paper investigates the combustion characteristics and pollutant emission patterns of the mixed combustion of lignite (L) and torrefied pine wood (TPW) under different blending ratios. Isothermal combustion experiments were conducted in a fixed bed reaction system at 800 °C, and pollutant emission concentrations were measured using a flue gas analyzer. Using scanning electron microscopy (SEM) and BET (nitrogen adsorption) experiments, it was found that torrefied pine wood (TPW) has a larger specific surface area and a more developed pore structure, which can facilitate more complete combustion of the sample. The results of the non-isothermal thermogravimetric analysis show that with the TPW blending ratio increase, the entire combustion process advances, and the ignition temperature, maximum peak temperature, and burnout temperature all show a decreasing trend. The kinetic equations of the combustion reaction process of mixed gas were calculated by Flynn-Wall-Ozawa (FWO) and Kissinger-Akahira-Sunose (KAS) kinetic equations. The results show that the blending of TPW reduces the activation energy of the combustion reaction of the mixed fuel. When the TPW blending ratio is 80%, the activation energy values of the mixed fuel are the lowest at 111.32 kJ/mol and 104.87 kJ/mol. The abundant alkali metal ions and porous structure in TPW reduce the conversion rates of N and S elements in the fuel to NO and SO2, thus reducing the pollutant emissions from the mixed fuel.

Extraction of K2CO3 from empty palm fruit bunch ash and properties analysis

Empty oil palm fruit bunches (EPFB) are the largest solid waste generated from palm oil mills. Accounting for 21-23 % of the total fresh fruit bunches. EFB ash contains K (Potassium) which can be recovered as K2CO3 through extraction. This research aims to recover K2CO3 from empty oil palm fruit bunches (EPFB) as a catalyst raw material because it is alkaline using the K2CO3 extraction method from the ash of empty oil palm fruit bunches (EPFB), which begins with a combustion process at a temperature of 700 oC to obtain ash. Ash extraction was carried out in 2 stages using water as a solvent. The K2CO3 yield from empty palm fruit bunch ash reached 57.28 %. The alkalinity test showed that the K2CO3 content in the solid extract reached 85.91 %. The properties of the ash and K2CO3 produced were analyzed using XRF, XRD and FTIR. The results of XRF analysis show that the potassium content in the ash contains the element K (potassium) which is quite large, namely 71.24 % and after extraction the purity of potassium oxide reaches 97.08 %. XRD analysis of the results of the synthesis of potassium carbonate from empty oil palm fruit bunches showed that the intensity of the material beam was obtained in a 2ϴ pattern which was lower than the intensity pattern of pure potassium carbonate. The results of FTIR analysis show that the spectrum of carbonate ions from the EPFB C-O bend is visible at 1359,879 and 700.75 cm-1 and these results are similaire to the pure carbonate ion vibration mode.

Exposure of benzo[a]pyrene induces HCC exosome-circular RNA to activate lung fibroblasts and trigger organotropic metastasis.

Benzo[a]pyrene (B[a]P), a carcinogen pollutant produced by combustion processes, is present in the western diet with grilled meats. Chronic exposure of B[a]P in hepatocellular carcinoma (HCC) cells promotes metastasis rather than primary proliferation, implying an unknown mechanism of B[a]P-induced malignancy. Given that exosomes carry bioactive molecules to distant sites, we investigated whether and how exosomes mediate cancer-stroma communications for a toxicologically associated microenvironment. Exosomes were isolated from B[a]P stimulated BEL7404 HCC cells (7404-100Bap Exo) at an environmental relevant dose (100 nmol/L). Lung pre-education animal model was prepared via injection of exosomes and cytokines. The inflammatory genes of educated lungs were evaluated using quantitative reverse transcription PCR array. HCC LM3 cells transfected with firefly luciferase were next injected to monitor tumor burdens and organotropic metastasis. Profile of B[a]P-exposed exosomes were determined by ceRNA microarray. Interactions between circular RNA (circRNA) and microRNAs (miRNAs) were detected using RNA pull-down in target lung fibroblasts. Fluorescence in situ hybridization and RNA immunoprecipitation assay was used to evaluate the "on-off" interaction of circRNA-miRNA pairs. We further developed an adeno-associated virus inhalation model to examine mRNA expression specific in lung, thereby exploring the mRNA targets of B[a]P induced circRNA-miRNA cascade. Lung fibroblasts exert activation phenotypes, including focal adhesion and motility were altered by 7404-100Bap Exo. In the exosome-educated in vivo model, fibrosis factors and pro-inflammatory molecules of are up-regulated when injected with exosomes. Compared to non-exposed 7404 cells, circ_0011496 was up-regulated following B[a]P treatment and was mainly packaged into 7404-100Bap Exo. Exosomal circ_0011496 were delivered and competitively bound to miR-486-5p in recipient fibroblasts. The down-regulation of miR-486-5p converted fibroblast to cancer-associated fibroblast via regulating the downstream of Twinfilin-1 (TWF1) and matrix metalloproteinase-9 (MMP9) cascade. Additionally, increased TWF1, specifically in exosomal circ_0011496 educated lungs, could promote cancer-stroma crosstalk via activating vascular endothelial growth factor (VEGF). These modulated fibroblasts promoted endothelial cells angiogenesis and recruited primary HCC cells invasion, as a consequence of a pre-metastatic niche formation. We demonstrated that B[a]P-induced tumor exosomes can deliver circ_0011496 to activate miR-486-5p/TWF1/MMP9 cascade in the lung fibroblasts, generating a feedback loop that promoted HCC metastasis.

A Novel Synthesis of ZnNb2O6 Nanoparticles via Combustion Method: Supercapacitor and Photocatalytic Properties

AbstractIn light of the current study's demonstration of the synthesis of Zn doped niobium metal oxide a low‐cost approach is provided for the synthesis of ZnNb2O6 nanoparticles. Using the urea and oxalyl dihydrazide (ODH) as fuel and complexing agent, ZnNb2O6 NPs are synthesized via the solution combustion process. The X‐ray diffraction (XRD) patterns reveal that ZnNb2O6 has a columbite‐orthorhombic structure. The field emisssion‐scanning electron microscopy (FE‐SEM) micrographs revealed the presence of uniform and spherically shaped morphology for ZnNb2O6. The role of ZnNb2O6 in the photocatalytic degradation of fuchsin acid dye is investigated under UV‐rays. The remarkable photocatalytic degradation activity of the material is demonstrated with 90% degradation rate. The fabricated electrode is used for the electrochemical analysis of the resultant material. The capacitance of 223 and 198 Fg−1 at a scan rate of 5 Ag−1, respectively, are measured in a 3‐electrode using the chemically generated ZnNb2O6, as per the Galvanostatic charge discharge (GCD) experiment. Furthermore, it exhibited excellent cycling stability, maintaining over 85% of its initial capacitance even after 2000 cycles. The goal of this research is to develop a solution combustion method for synthesizing ZnNb2O6 NPs from urea and ODH so that it may be more effectively tested for electrochemical, sensing, and photocatalytic uses.

EFFECT OF DIESEL ENGINE DECARBONISATION ON SELECTED EXHAUST VALUES

In this article, the authors refer to the measurement of acoustic emission, where they found that this methodology can determine the combustion characteristics in the combustion chamber of the engine and thus it is possible to confirm a better combustion process and overall reduction of engine noise after decarbonization. The formation of diesel exhaust soot is also supported by the authors' paper, in which they describe how such soot is formed and what effect the addition of special mixtures to the combustion process has on the formation of such carbon deposits. The article uses BG technologies for deep engine decarbonization, including intake and exhaust parts. This technology is one of many on the market, but it was only with this technology that the authors managed to achieve the relevant results that this article will introduce to. For the test, we selected vehicles that had a problem to pass at the emission control.

Performance optimization of cement calciner based on CFD simulation and machine learning algorithm

In order to improve the combustion efficiency and decomposition rate of the cement calciner and reduce pollutant emission, a performance optimization method based on Computational Fluid Dynamics (CFD) numerical simulation integrated with machine learning is proposed. The Multiphase Particle-in-cell (MP-PIC) method and the chemical reaction models are employed to simulate the coal combustion and CaCO3 decomposition process, whose calculation results are combined with the industrial practical data of the cement plant, so as to establish a more comprehensive training database. On this basis, a novel Topology Particle Swarm Optimization algorithm integrating with Convolutional Neural Network and Long Short-Term Memory (RITPSO–CNN–LSTM) algorithm model is established to predict combustion efficiency, decomposition rate, and NOx emission, respectively. Results show that compared with two other relative basic algorithm models, the prediction error of the proposed algorithm model for the three targets is minimal with the average relative error of 0.045 %, 0.038 %, and 0.021 %, respectively. The addition of CFD simulation data makes the prediction model more applicable with higher stability and accuracy. Based on the prediction results, Grey Wolf Optimizer (GWO) algorithm is employed to optimize operating parameters, and finally the average optimization amount of combustion efficiency, decomposition rate, and NOx emission are 2.17 %, 2.24 %, and 6.15 ppm, respectively, which meet the optimization requirements.

Spray combustion characteristics and soot formation potential of Nano-Al2O3 diesel at low ambient temperatures

Nanoparticle additives have the potential to enhance combustion efficiency and reduce emissions in liquid combustion processes. This study thoroughly investigates the spray evaporation, ignition, and combustion characteristics of nanoparticle/fuel blends, especially under challenging environmental conditions, to improve combustion efficiency and reduce emissions. Employing diffused background illumination, shadowgraph, Mie scattering, high-speed natural luminosity, and two-color method, the spray combustion dynamics of thermally stable nano-Al2O3/diesel (NAD) blends were examined within a constant volume combustion chamber. Diesel, a prevalent primary or auxiliary fuel, represents other highly volatile renewable fuels, while Al2O3, a significant component in combustion fly ash, is noted for its specific catalytic properties and ability to re-burn, eliminating organic toxic elements. The results indicated that NAD blends exhibit a faster evolution rate for both liquid and vapor sprays compared to pure diesel, attributed to the increased kinetic energy of breakup droplets. Under most experimental conditions, incorporation of nano-Al2O3 slightly reduced the ignition delay time due to enhanced heat transfer. Notably, nano-Al2O3 altered the ignition mechanism of the diesel spray, transitioning it from partially premixed to diffusion combustion and relocating the ignition point nearer to the injector. The combustion flame of NAD blends demonstrated increased luminosity, a shorter flame lift-off length, and variations in start-of-injection and total-injection-nozzle-lift-off. However, due to a high equivalence ratio mixture between the liquid phase spray and combustion region, the soot concentration in NAD blends was higher than that in diesel. Additionally, the elevated flame temperature in NAD blends significantly increased the soot concentration. Overall, nano-Al2O3 markedly enhances diesel combustion, especially in low ambient temperatures, suggesting its impressive potential for the high-efficiency and low-emission utilization of renewable fuels or the combustion of recycled waste solvents.

Combined exhaust gas and optical investigation of methanol DI-engine with focus on the fuel spray-wall interaction

Liquid e-fuels such as methanol represent a possible solution to emission-neutral drivetrains. Reduced emissions from the combustion process increase the influence of cylinder wall interaction of the fuel spray and the influence of the fuel ingress into the lubricating oil. Combining emission analysis methods and optical measurements allows a deeper understanding of the processes around the piston assembly group and cylinder wall. This paper aims to increase the understanding of the processes resulting from fuel spray and cylinder wall interaction. Emission measurements from a single-cylinder SI research engine were gathered across multiple operating parameters. Optical measurements were taken at similar operating points using an optically accessible engine. A laser-induced fluorescence (LIF) setup with dyed fuel was used for the optical measurements. This paper focuses on thermodynamic steady state measurements at 2000 rpm, varying loads between 3 and 11 bar IMEP, and corresponding optical measurements at 11 bar IMEP. The measurements of both engines were correlated, and a more profound understanding of the processes involved and their influence on emission behavior was derived. Measurements showed a lower particle emission behavior with a tendency of a higher PN10 to PN23 ratio and higher formic acid emissions using methanol fuel compared to gasoline. A higher wall film interaction with methanol could be visualized, and possible effects were correlated to the exhaust emission measurements. Tests with two start of injection timings (SOI) of 430° crank angle (CA) after fired top dead center, as used for gasoline operation, and 550°CA as an optimized SOI for methanol operation were compared. A correlation between the results from the thermodynamic engine and the optically accessible engine was demonstrated. The optical measurements showed lower penetration depths for the optimized SOI and lower fuel spray-to-piston interaction. The thermodynamic measurements have shown higher efficiencies and fewer emissions for the optimized SOI.

Thermo-chemical nonequilibrium effects on combustion characteristics of a transverse jet in the scramjet

As the Mach number increases, the local static temperature gradually approaches the characteristic gas vibration/dissociation temperature, and significant thermo-chemical relaxation processes occur. The thermo-chemical nonequilibrium phenomenona could affect the mixing and combustion process and become a factor that must be considered for scramjet design. In this paper, a large eddy simulation solver based on the two-temperature model is developed. The transfer terms between vibrational and trans-rotational energy are modeled by the Landau–Teller formulation and the vibrational-dissociation coupling is considered by adopting the Park model. Three sets of simulations based on the Hyshot Ⅱ engine configuration with different models were performed simultaneously to validate numerical methods and analyze results. The results reveal that the shock, cross-injection of fuel, and Prandtl-Meyer flow induce significant nonequilibrium regions. The vibration relaxation process in the jet mixing region is frozen, the vibration temperature is much higher than the trans-rotational temperature, and the extra energy is converted from the vibration mode to the trans-rotational mode, resulting in the increase of the trans-rotational temperature and the advance of auto-ignition in the nonequilibrium condition. Considering the vibration-dissociation coupling, the higher vibration temperature promotes dissociation. It inhibits the generation of active radicals in the ignition process, weakening the enhancement effect of nonequilibrium on auto-ignition. In the expanded nozzle, the concentrations of molecules and trans-rotational temperature in nonequilibrium cases are slightly lower than those in the equilibrium case due to the enhancement of dissociation.

Activation of the Process of Layer Combustion of Coal with Iron Nitrate and Waste of Metal Rolling Production

Activation of the Process of Layer Combustion of Coal with Iron Nitrate and Waste of Metal Rolling Production

Shape/penetration analysis and comparisons of isolated spray plumes in a multi-hole Diesel spray

Fuel injection systems significantly impact the combustion process and play a key role in reducing harmful exhaust emissions in internal combustion engines. For dual-fuel (DF) engines operating in gas mode, ignition of the main fuel is typically controlled by directly injected liquid pilot fuel. Liquid pilot fuel’s initial penetration and total mass considerably impact exhaust emissions and combustion stability. We investigated the spray morphology of a multi-hole diesel fuel injector within a constant-volume spray chamber using high-speed shadowgraphy and Mie-scattering measurements. Two methodologies were employed. The first one utilized a nozzle equipped with a thimble structure to isolate a single plume. The second methodology known as plume-blocking, involved sealing the orifices of the multi-hole nozzle to generate a single-spray plume. Our findings revealed that the plume-blocking approach demonstrated greater penetration than the thimble-equipped nozzle. The rapid penetration of this method may restrict its applicability to single-spray studies. Sprays generated from this partially sealed nozzle exhibited noticeable disparities compared to an unblocked nozzle, whereas a nozzle equipped with a thimble produced similar outcomes to the standard nozzle. The orifices when sealed, modify the flow distribution within the sac volume, which consequently affects the spray characteristics. In summary, this research provides insights into the impacts of various plume isolation methods on spray morphology, thereby enhancing the understanding of spray behaviour in transient conditions by comparing plume variations and disturbances under various fuel pressure and ambient conditions.

Experimental research on dynamic response characteristics of lean premixed flame excited by acoustic standing wave

The compact combustor holds great potential in the aerospace field. However, it faces challenges in terms of interaction between acoustic waves and flames, which affects combustion efficiency and intensity. In this study, the dynamic response of lean premixed flames under the excitation of a transverse standing wave acoustic field with different frequencies and amplitudes. The results indicate that the flame's response frequency is consistent with the excitation frequency. Decreasing the sound wave frequency and increasing the sound amplitude both lead to an increase in flame area oscillation, thereby enhancing the heat release rate. However, excessively high sound pressure values can enhance fuel diffusion, resulting in a reduction in flame area or even local flame extinction, which significantly decreases flame intensity. This research provides valuable insights into the behavior of lean premixed flames under acoustic excitation, contributing to the optimization of combustion processes and improvement of efficiency and performance in compact combustors.

Prediction of oxygen content in flue gas of power plant with stacked target-enhanced autoencoder and attention-based LSTM

Accurate and robust measurement of the oxygen content in the flue gas of power plant is significant to optimal the coal combustion process. Conventional hard measurement algorithms have low accuracy, short lifetime and high cost. Therefore, it is urgent to establish a precise and reliable soft measurement model. However, how to extract effective feature representations from complex process data is still the difficult and hot spot in the soft measurement field. The paper develops a novel soft measurement model, which integrates stacked target-enhanced autoencoder (STAE) with attention mechanism-based long short-term memory (ALSTM) in view of the above issues. The data cleaning strategy is firstly designed to filter the redundant data from the original data. Then STAE is developed to extract the useful features, which introduces target output into each autoencoder to ensure the feature representation is output-related and stacks several autoencoder units to learn high-level information. The extracted hierarchical features can not only represent the input data, but also have high correlation with the target output. A three-layer-based attention network is designed to allocate different weights on the different levels’ features. Finally, due to the dynamic characteristic of the problem, a LSTM-based prediction network is dexterously designed to learn the potential relation from extracted features to target output, i.e., oxygen content. Numerous real-word data is collected from a 300 MW power plant and extensive experiments are conducted. The maximum prediction errors of A and B are 0.42 and 0.41. The 95.1 % prediction errors of A side are within [−0.2, 0.2], and the 96.2 % prediction errors of B side are within [−0.2, 0.2].