Reactor Combustion Model Research Articles

Assessing damage degree of solid rocket motor under various cook-off conditions

This research is focused to investigate the cook-off response of a solid rocket motor (SRM) utilizing ammonium perchlorate/hydroxyl‑terminated polybutadiene (AP/HTPB) propellants under various heating environments, also assessing the damage degree of the motor casing and exploring the formation mechanism of ignition site. We have developed a three-dimensional (3-D) numerical model that encapsulates a range of response behaviors, including cook-off and mechanical destruction of the SRM. This model integrates multi-step thermal decomposition and combustion reaction models for the propellant, as well as a damage response model for the SRM. The response mechanisms of the SRM under varying thermal conditions have been simulated using a validated model. The study reveals that under both fast cook-off condition (FCC) and slow cook-off condition (SCC), the motors experienced deformation and rupture. Under FCC, ignition initiated at the motor head, reaching a maximum pressure of approximately 800 MPa near the ignition point. This led to the separation of the motor head from the main body, producing numerous casing fragments with the fragment cloud's radius showing a linear correlation to time (R = 0.73289t + 28.24286). The ignition delay was 224.5 s at an ignition temperature of 551.83 K. In contrast, under SCC, ignition occurred at the propellant's midsection inner surface, with a significantly lower maximum pressure of about 700 MPa. The motor expanded at this site, with the expansion radius exhibiting a nonlinear relationship to time (R = 0.19102t2 + 0.0096t + 32.97464). The casing then ruptured, causing extensive damage to both ends and generating a debris cloud with a radius that correlated linearly to time (R = 0.82147t + 26.70826). More severe longitudinal tearing, exceeding the FCC scenario, appeared in the motor body. The ignition delay time was 18.82 h. with an ignition temperature of 557.49 K.

Study on the impact of heat loss on premixed hydrogen/air deflagration in closed pipelines

This study developed a one-step chemical reaction model for hydrogen combustion and investigated the effect of heat loss on premixed hydrogen/air deflagration in closed pipelines using numerical simulations with the CFD code GASFLOW-MPI. The simulation results were compared and verified with experimental data. The numerical simulations accurately predicted the flame front structure, pressure, and flame tip position during the deflagration of premixed hydrogen/air. Furthermore, a comparison of adiabatic simulation and heat transfer simulation results revealed that large-scale vortices within the turbulent flow were closely linked to heat loss during the tulip flame stage. Simultaneously, heat loss reduced pressure and flame tip position by diminishing the flame wrinkling factor caused by thermodiffusive instability. Finally, convective heat transfer identified as the most critical factor contributing to heat loss during the deflagration of premixed hydrogen/air in closed pipelines. Specifically, during the tulip flame stage, heat loss was mainly due to convective heat transfer (73.8%∼76.3%), with radiation playing a minor role (23.7%∼26.2%). Therefore, accurate prediction of characteristic parameters during the deflagration of premixed hydrogen/air requires numerical simulations that account for both convective and radiative heat transfer.

Systematic construction of combustion reaction model for large hydrocarbon fuels based on a two-step reaction scheme

Understanding the combustion chemistry of large hydrocarbons in practical gasoline, diesel and jet fuels is critical for the reduction of pollutant emissions and increase of fuel and engine efficiencies. The primary objective of the present study is to illustrate the methodology and application of a simplified modeling approach based on a two-step reaction scheme (TSRS) for describing the combustion chemistry of large hydrocarbon fuels under engine-relevant high-temperature conditions. Under such conditions, the combustion process of large hydrocarbons consists of two distinct subsequent reaction steps: the first step is the prompt fuel decomposition, converting the parent fuel to critical intermediate products; the second step is the oxidation reactions of the intermediates. Particularly, the second step is slower, thus rate-limiting, controlling the heat release and formation of final combustion products of the entire combustion reaction process. A systematic analysis by mathematical methods was performed to obtain a unified treatment for hydrocarbons with different molecular sizes. Based on the analysis of characteristic time scale of elementary reaction and steady-state assumption, TSRS used a fuel decomposition submodel to describe the fuel molecule decompose into critical intermediate species, including ethene, propene, butenes and methane for n-alkanes, and used a detailed foundational fuel chemistry mechanism to describe the oxidation reaction of the intermediates. The entire TSRS reaction model for large hydrocarbons is compact, consisting ∼110 species and ∼800 reactions, which can be further reduced. It was shown in the present study that, by revealing the relationship between the functional group composition of linear alkanes and the distribution of intermediates, and in combination with the rate criterion, the fuel decomposition submodel can be systematically generated to facilitate the automatic construction of compact kinetic models. One unified TSRS combustion reaction model was created and demonstrated for several large hydrocarbons ranging from C8 to C16, which was validated by comprehensive datasets, including speciation, ignition delay times and laminar flame speeds.

Investigation on coal/coal gangue mixtures co-combustion via TG-DSC tests, multicomponent reaction model, and artificial neural network

Coal gangue (CG) is the main hazardous solid waste produced during coal mining activities; it increases the combustion risk of coal/coal gangue (C/CG) mixtures. In the present work, the combustion characteristics, interactions, combustion reaction model, and kinetic parameters of C/CG co-combustion were investigated under seven different mixing ratios. Thermogravimetric data revealed that the mixture of 70 % coal and 30 % CG exhibited the most strongest synergistic effect and heat release capability, whereas that with 30 % coal and 70 % CG showed the most strongest inhibitory effect. Additionally, a multicomponent combustion reaction model for coal/CG mixture co-combustion was established. We found that the three-component combustion reaction model could appropriately describe the combustion process of C/CG mixtures. Furthermore, as the CG ratio increased to 90 %, the reaction participation of volatile and inorganic matter gradually increased, whereas that of organic matter gradually decreased. The impact of CG content on the activation energy of the reaction system depends on the conversion rate. Finally, the temperature-increasing rate, mixing ratio, and finishing temperature were considered as the import data, artificial neural network (ANN) prediction was performed based on the thermogravimetric data, results show ANN-42 (3 × 9 × 17 × 1) model was the most appropriate for predicting the co-combustion of coal/CG mixtures.

Insight into kinetics and thermodynamics of distillers’ dried grains with solubles (DDGS) combustion using an approach simultaneously determining frequency factor and reaction model

The Kissinger and master plots methods are commonly employed to estimate the frequency factor and reaction model, respectively. However, two methods cannot always provide accurate results, especially for solid-state reaction processes with varying kinetic parameters. A new approach for simultaneously determining the frequency factor and reaction model has been developed. The applicability of the new approach was verified through analyzing a theoretically simulated process. The suitability for combustion conversion of distillers’ dried grains with solubles (DDGS) was analyzed through physicochemical characterization and kinetic and thermodynamic investigation. The effective activation energies for DDGS combustion from the Friedman isoconversional method varied significantly from 129.5 to 206.3 kJ mol−1 in the conversion range 0.05–0.95. The frequency factor range and reaction model for DDGS combustion determined by the newly developed approach were 1.7 × 1011–4.2 × 1016 s−1 and f(α) = 5α1.277(1-0.582α)23.877[-ln(1-α)]−0.100, respectively. Thermodynamic analysis revealed that the changes in Gibbs free energy, enthalpy and entropy for DDGS combustion were in the range of 146.7–162.2 kJ mol−1, 125.3–200.4 kJ mol−1, and -42.5 – 57.0 J mol−1 K−1, respectively, which indicating DDGS combustion has a great potential for energy conversion.

Digital twin of biomass/coal co-firing circulating fluidized bed boiler by using computational fluid dynamics simulation

Combining biomass fuel with coal in circulating fluidized bed (CFB) boilers is attractive as the increasing of energy demand. The characteristics of biomass/coal co-firing in the industrial boiler are then investigated using computational fluid dynamics (CFD). In this study, fuels of interest are coal, wood chips, and bark. The fuel proportions were set for investigating their effects on the combustion using a simplex centroid mixture design. CFD simulations consist of a standard k-epsilon viscosity flow model, Euler multiphase fluid flow model, and a simplified biomass-char combustion reaction model. The predictive responses of the model are bed temperature, heat flux, and carbon dioxide (CO2) and carbon monoxide (CO) mass flows. The simulation results showed that the operation with coal gave the highest temperature and heat flux, followed by wood chips and bark. On the other hand, the operation with biomass releases fewer gas emissions, CO2 and CO, than those of the operation with coal. The type and proportion of boiler fuel can be easily determined using two methods: predictive equation and ternary contour plot graph, which can be likened to the digital twin of biomass/coal co-firing boiler.

Simulation study of DBD discharge characteristics and active particle-assisted methane combustion

This paper established a model of the coaxial dielectric barrier discharge (DBD). Ar-CH4 is fed into the mixture as a working gas. Using 10.0 kHz as power research in different voltage and dielectric materials and medium thickness has influences on producing active particle mole fraction. The simulation results show that when the voltage increase, the molar concentration of O3 and H energetic particles increase obviously. When the thickness of the medium increases, the molar concentration of O3 and H decreases. When the dielectric constant of the dielectric material increases, the molar concentration of O3 and H also increases. The molar concentration of OH active particles also changed, but the law was unclear. At the same time, when the discharge voltage is 15 kV, the dielectric thickness is 2 mm, the dielectric constant is 3.75, and the DBD discharge is uniform and stable, the molar concentration of O3 and H is 0.069% and 0.056% respectively. The corresponding concentrations of O3 and H active particles were introduced into the established methane combustion reaction model. The study found that the joining of O3 and H energetic particles make the reaction begin to move significantly ahead of time. That has a promoting effect on the rate of the methane combustion reaction. In the methane combustion reaction model with different equivalence ratios, O3 with a molar concentration of 0.069% and H with a molar concentration of 0.056% introduced, it is concluded that these two active particles also have a tremendous promoting effect on the expansion of the thin burning limit of methane.

Study on Plasma Combustion in Aeroengine Combustor

To explore the plasma combustion affect the performance of the aircraft engine combustor, 16 components in the aviation kerosene 25-step reaction mechanism on the basis of considering step 9 simplify the plasma reaction mechanism, set up considering plasma electron collision reaction excited state relaxation and excited states participate in the process of chemical reaction kerosene combustion reaction model, The numerical calculation of the combustion process in the combustor is carried out, and the numerical calculation results of the combustion process with or without plasma combustion are compared and analyzed. The results show that the core reaction zone of the combustion chamber fuel is relatively forward under the conventional mechanism condition, and the average temperature of the combustion chamber outlet is 1910K. The temperature and high-temperature zone of the combustor where the plasma mechanism is applied are mainly distributed in the combustor outlet, mixing hole and main combustion hole, and the average temperature is 2050K. Meanwhile, the kerosene combustion efficiency also increases from 82.4% to 90.1%, increasing by about 7.7%. The results show that the addition of a plasma mechanism can deepen the chemical reaction degree of kerosene and release more heat to improve the combustion efficiency of kerosene.

The Effect of High-Velocity Air-Fuel WC-12Co Coatings on the Wear and Corrosion Resistance of TC18 Titanium Alloy

TC18 titanium alloy is an essential material for aircraft landing gear. To reveal the wear and corrosion mechanisms of landing gear in service, a WC-12Co coating on a TC18 substrate was prepared by High-Velocity Air-Fuel (HVAF) spraying based on optimized process parameters, and an analysis of the microscopic characterization results for the materials involved was performed. Based on the computational fluid dynamics (CFD) method, the combustion reaction and discrete phase models of HVAF spraying were established. The flame characteristics under compressible turbulence and the flight temperature and velocity of particles were calculated. The effect of the spraying parameters on the flight temperature and velocity of particles was evaluated based on the response surface method (RSM) through multiple groups of orthogonal experiments, and the optimized process parameters were determined. The mass flow rate of reactants was 0.051 kg/s, the oxygen/fuel ratio was 2.83, the mass flow rate of the nitrogen was 0.000325 kg/s, the pressure of oxygen and fuel inlet was 1 MPa, the pressure at the particles inlet was 0.6 MPa and the maximum temperature and velocity of spraying particles were 1572 K and 417 m/s, respectively. The coatings prepared with the optimized process were subjected to the Scanning Electron Microscope (SEM), Energy Dispersive Spectroscopy (EDS), X-Ray Diffraction (XRD), wear, hardness, artificial seawater soaking and neutral salt spray experiments. The results showed that the mean hardness of the TC18 substrate was 401.2 HV0.3, the mean hardness of the WC-12Co coating was 1121 HV0.3, the friction coefficient between the TC18 substrate and the Si3N4 ceramic ball was 0.55 and the friction coefficient between the WC-12Co coating and the Si3N4 ceramic ball was 0.4. Compared to the TC18 substrate, the hardness of the WC-12Co coating was increased by 720 HV0.3, the friction coefficient with the Si3N4 ceramic ball decreased by 0.11, the corrosion resistance significantly improved and the maximum depth of the corrosion pits was 5 μm. The properties of the TC18 titanium alloy were effectively improved by the WC-12Co coating. The results of this study provide guidance for surface protection technologies of aircraft landing gear.

Neural network approach to response surface development for reaction model optimization and uncertainty minimization

We examine the state-of-the-art neural network (NN) approach and its flexible implementations in combustion reaction model uncertainty quantification (UQ), optimization, and uncertainty minimization (UM). The work is motivated by addressing the problem of limited scalability of the traditional polynomial response surface methodology in handling large size of rate parameters and target data sets. Features of the NN training, accuracy, and trade-offs in several key aspects of the NN application are discussed. We show that for high-dimensional reaction model optimization and UM, a shallow NN with only one hidden layer is more robust and accurate than the polynomial response methodology. Further, we demonstrate that NN allows for adaptive training. New neural networks that augment new input parameters or updates in a trial reaction model can be adapted from the existing networks with much smaller training efforts. In addition, deep neural networks are capable of covering functional dependencies of initial thermodynamic conditions and boundary conditions, thus yielding generalized response surfaces with rate parameters and thermodynamic/mixture conditions as the input for a given combustion property. The NN approach can be readily integrated into the framework of the Method of Uncertainty Minimization using Polynomial Chaos Expansions (MUM-PCE) developed earlier. We present a test case that uses the trial Foundational Fuel Chemistry Model Version 2.0 (FFCM-2, a model consisting of 96 species and 1054 reactions for combustion of relevant C0–4 species), optimizing it against FFCM-1 targets, to illustrate the efficiency and accuracy of the NN method.

Supersonic Shrouding Methane Mixtures for Supersonic Combustion Coherent Jets

A coherent jet oxygen supply plays a key role in the process of electric arc furnace steelmaking: it provides the necessary oxygen for the smelting of molten steel and promotes the flow of the molten pool. Compared with coherent jets in current use, the supersonic combustion coherent jet shrouded in supersonic methane gas could improve the impact capacity and stirring intensity of the molten pool. In order to reduce the smelting cost, the characteristics of the supersonic combustion coherent jet shrouding the supersonic methane and nitrogen mixtures must be studied. Computational fluid dynamics software is used to simulate the supersonic combustion coherent jet under various methane–nitrogen mixing conditions. The six-component combustion mechanism of methane and the Eddy Dissipation Concept combustion reaction model are selected. In agreement with thermal experiments, the simulation results show that the inclusion of a small amount of nitrogen has little effect on the combustion of supersonic shrouding methane gas. However, as the nitrogen content increases, the combustion region of supersonic shrouding gas becomes shorter in length, resulting in decreases in the lengths of the high-temperature, low-density region, and the high-turbulence-intensity region. These effects weaken the ability of the shrouding gas to envelop the main oxygen jet. The potential core length of the main oxygen jet decreases significantly; this decrease first accelerates and then decelerates. These results demonstrate the feasibility of including a small amount of nitrogen (about 10 wt%) in the supersonic shrouding methane gas without substantial negative impacts on the characteristics of the supersonic combustion coherent jet.

Influence of biomass on multi-component reaction model and combustion products of coal gangue

The effects of biomass on coal gangue combustion characteristics, combustion products and combustion reaction model are studied in this paper. Based on the thermogravimetric experimental data, a multi-component combustion reaction model for coal gangue and biomass was established. The CG combustion reaction mechanism can be well explained by the two-component combustion reaction model. The participation degree of organic matter and inorganic matter is 90% and 10%, respectively. C3P1, C1P1 and C1P3 can be better explained by the five-component combustion reaction model. The reaction model corresponding to PS is a four-component combustion reaction model. Moreover, it is found that the addition of biomass can promote the combustion reaction of coal gangue and the production of gas products using Fourier transform infrared spectroscopy technology. Through combustion, the proportion of F4 mode of Cr, Cd, Pb, Cu, and Ni for CG sample increased from 92%, 60%, 52%, 28% and 60% to 95%, 90%, 82%, 91% and 94%, respectively. The toxicity reduction effect for Cd, Pb, Cu, and Ni is obvious, and the proportion of F4 mode is increased by more than 30%. Compared with other mixed samples, the ash formed by C1P1 is less harmful.

Effects of ammonia addition on combustion characteristics in partially-premixed swirling ammonia/methane/air flame

Ammonia combustion has received intense research interest recently for its potential to reduce CO2 emission. This study aims to investigate the turbulent combustion characteristics in a bluff-body burner for CH4/NH3 mixtures with different ammonia blending ratios (15%, 30%, and 45% by mole fraction) through large eddy simulation and experiments. The simulations are conducted using openFOAM with a low Mach number solver and the partially stirred reactor combustion model with a detailed reaction mechanism. The flow field of one typical case is measured using the particle image velocimetry technique to verify the accuracy of the numerical results.The combustion characteristics are discussed. As the ammonia blending ratio increases, the flame height shortens, the flame color gradually changes from blue to orange, and the intermittent local quenching zone moves upstream, indicating that the combustion is becoming unstable. Meanwhile, the flow fields exhibit similar characteristics though the ammonia concentration varies greatly. The CO and NO emissions are also discussed. The CO emission decreases and the NO emission increases as the ammonia blending ratio increases

Development of a combustion reaction model for lubricant synthetic base oil by experimental and numerical methods

The synthetic base oil (SyBO) is the main component of lubricant oil, determining its basic physical and chemical properties, as well as impacting the pre-ignition, in-cylinder combustion and emission characteristics during the engine applications. Yet the involvement of SyBO in the engine combustion process is rarely investigated, primarily due to the lack of a proper combustion reaction model. The objective of the present study was to develop a fundamentally based combustion reaction model for SyBO, through the joint experimental and theoretical methods. A comprehensive set of experimental techniques was used to characterize the physicochemical properties of SyBO. Results showed that SyBO consisted of 13 large hydrocarbon molecules, ranging from C21 to C54, with mostly linear or lightly branched alkanes. A kinetic modeling approach was developed to systematically construct the reaction model for each component, which were then combined to create the full-component reaction model of SyBO. The accuracy of the thus-developed combustion reaction model was verified by a theoretically calculated ignition delay (ID) data and experimental measurements. The full-component model of SyBO was then reduced by the directed relation graph with error propagation (DRGEP) method to contain 87 species and 647 reactions. To further reduce the size of kinetic model as well as predict the physiochemical properties of SyBO, one surrogate molecule, i-C39H80, through the quantitative structure-property relationship (QSPR) of functional groups in SyBO, as well as one surrogate component, n-C54H110, were proposed. Again, the reaction model for the surrogate molecule was developed, verified, and then reduced to contain 73 species and 530 reactions, which might be applicable to computational fluid dynamics (CFD) simulations for practical engine systems.

Analysis and comparison of in-situ combustion chemical reaction models

In-situ combustion (ISC) is a promising enhanced oil recovery method for heavy oil, late stage, or very deep reservoirs. One persistent challenge in modeling ISC is determining the correct chemical reaction model for heavy oil combustion. Reaction models containing more reactions or hydrocarbon pseudocomponent species are thought to model better the combustion of heavy oil, but smaller models are preferable for upscaling. Furthermore, most reaction models in prior work have been chosen on an ad hoc basis, and no work has performed a comparative analysis between oil samples and reaction models. Here we apply a reaction model optimization workflow to calibrate seven different ISC reaction models to experimental data for combustion of two heavy oil samples. We show that both the stoichiometry and kinetic parameters for a given model can vary substantially between oil samples. Results also show that a greater number of parameters, reactions, or pseudocomponent species do not necessarily improve the performance of the reaction model. On the other hand, a greater number of pseudocomponent intermediate coking stages appears to improve the accuracy. Overall, our findings suggest that reaction models with fewer reactions or parameters describe heavy oil combustion as well or better than comparatively larger models and that the number of stages of pseudocomponent decomposition is the most important aspect of a reaction model. These results allow for better selection from existing ISC reaction models as well as provide heuristics for future design of improved models for heavy oil combustion.

Modeling of thermal and kinetic processes in non-equilibrium plasma ignition applied to a lean combustion engine

In recent years novel ignition systems have been developed to enable stable and efficient engine operations with lean mixtures. Among them, radio-frequency corona ignition systems create discharges that involve a much wider region compared to traditional spark, and produce non equilibrium plasma with high levels of active radicals and excited species. These devices considerably increase the early flame growth speed and extend stable operating limits. With the aim of expanding the knowledge on high efficiency lean-burn SI engines, this paper investigates and compares the combustion development generated by spark and corona ignitions through computational fluid dynamics, within the Reynolds–Averaged Navier–Stokes framework for turbulence modeling. In order to simultaneously take thermal and chemical effects into account, the Perfectly Stirred Reactor combustion model is used. Experimental data are also collected for validation in an optical access engine, for different mixture levels, from stoichiometric to very lean. The faster burn rate generated by the corona system in the initial stage of the combustion is well predicted by the simulations, in all the relative air–fuel ratio conditions. Remarkably, as the mixture becomes lean, simulations are able to capture the non-linear transition from fast to slow kernel growth, before a self-sustainable flame propagation is established. This correlates very well with the measured engine cyclic variability and the corresponding steep change in the duration of the flame kernel formation. Ultimately, this study highlights the important role of the atomic oxygen, as active radical, in promoting and enhancing the combustion initiated by a corona discharge, in addition to the volumetric ignition effect. By contrast, the validated simulations allow to explain that the high-temperature thermal plasma generated in a traditional spark discharge is insensitive to kinetic aspects.

Large Eddy Simulation of Lean Mixed-Mode Combustion Assisted by Partial Fuel Stratification in a Spark-Ignition Engine

Abstract Partial fuel stratification (PFS) is a promising fuel injection strategy to improve the stability of lean combustion by applying a small amount of pilot injection right before spark timing. Mixed-mode combustion, which makes use of end-gas autoignition following conventional deflagration-based combustion, can be further utilized to speed up the overall combustion. In this study, PFS-assisted mixed-mode combustion in a lean-burn direct injection spark-ignition (DISI) engine is numerically investigated using multi-cycle large eddy simulation (LES). A previously developed hybrid G-equation/well-stirred reactor combustion model for the well-mixed operation is extended to the PFS-assisted operation. The experimental spray morphology is employed to derive spray model parameters for the pilot injection. The LES-based model is validated against experimental data and is further compared with the Reynolds-averaged Navier–Stokes (RANS)-based model. Overall, both RANS and LES predict the mean pressure and heat release rate traces well, while LES outperforms RANS in capturing the cycle-to-cycle variation (CCV) and the combustion phasing in the mass burned space. Liquid and vapor penetrations obtained from the simulations agree reasonably well with the experiment. Detailed flame structures predicted from the simulations reveal the transition from a sooting diffusion flame to a lean premixed flame, which is consistent with experimental findings. LES captures more wrinkled and stretched flames than RANS. Finally, the LES model is employed to investigate the impacts of fuel properties, including heat of vaporization (HoV) and laminar burning speed (SL). Combustion phasing is found more sensitive to SL than to HoV, with a larger fuel property sensitivity of the heat release rate from autoignition than that from deflagration. Moreover, the combustion phasing in the PFS-assisted operation is shown to be less sensitive to SL compared with the well-mixed operation.

Assessing the thermal safety of solid propellant charges based on slow cook-off tests and numerical simulations

A method is proposed to assess the thermal safety of solid propellant charges by measuring the temperature and pressure of propellant specimens during cook-off tests and determining the reaction kinetics. In this work, two propellants—ammonium perchlorate/hydroxy-terminated polybutadiene/aluminum (AP/HTPB/Al), and ammonium perchlorate/hydroxy-terminated polyether/aluminum (AP/HTPE/Al)—are taken as examples to demonstrate this method in detail. Specifically, by implementing two kinds of cook-off tests—viz., multipoint temperature monitoring, and combustion pressure measurement—the thermal reaction temperature before ignition and the combustion pressure after ignition of the two propellants were respectively determined. Then, the kinetic parameters of the thermal decomposition reaction model and the parameters of the combustion reaction model were numerically simulated and calibrated to achieve mathematical descriptions of the entire process of cook off of two solid propellant charges. On this basis, the technology of grid-node separation calculation was employed to simulate and predict the rupture of a solid rocket motor case, thereby quantitatively describing the severity of the cook-off reaction. The results demonstrate that, at a heating rate of 1 K min−1, the ignition positions of the two propellant charges in the solid rocket motor are located in the annular area where the side wall and the front of the casing are joined. Compared with those of the AP/HTPE/Al charge, the ignition time of the AP/HTPB/Al charge is longer (12,714 s for AP/HTPB/Al vs. 9701 s for AP/HTPE/Al), the temperature of the casing before ignition is higher (501.5 K for AP/HTPB/Al vs. 466.2 K for AP/HTPE/Al), the reaction after ignition is more intense, and the deformation of the casing is more serious.

Analysis and neural network prediction of combustion stability for industrial gases

Combustion of industrial gases has gained increasing attention in the past decades. Great challenges for reliable industrial operation are posed by combustion instability. In this study, the oscillating combustion of industrial gases are classified into four typical types according to different causes for the first time. Typical causes of oscillating combustion include intrinsic mixing/kinetics/heat loss interaction, single inflowing fluctuation, inflowing fluctuation superposition and fuel switching. All the four typical oscillating combustion have been systematically analyzed with the unsteady perfectly stirred reactor combustion model and chemical explosive mode analysis. The physical reasons for typical combustion stabilities have been further revealed with the kinetic importance of dependent variable and chemical reaction. The short-term and long-term prediction models have been established within a wide range of system parameters using the generalized functional form of NARMAX and neural network method. The short-term prediction model aims to predict the short-term local physical information, and the long-term prediction model, which uses the clustered prediction method, aims to be a general prediction model for all the four typical types to meet the requirement of existing combustion instability controller. These prediction models making full use of the robustness of neural networks for nonlinear functions provide validation and data support for the design of controller and actuator.

Large eddy simulation on combustion noise in a non-premixed turbulent free flame: Effect of oxygen enhancement

Present work examines the effect of a low-level oxygen concentration enhancement in the oxidizer stream, on the generation of direct combustion noise in a non-premixed turbulent free flame. A hybrid approach utilizing a large eddy simulation, partially stirred reactor combustion model, and Lighthill analogy was employed to predict the sound pressure level in the farfield of the flame. Contributions of different noise sources, including heat release rate and, mole consumption and production rate fluctuations are calculated to predict the sound pressure level in the farfield. Results show that, in comparison to the flame burning with air, the higher temperature in oxygen enhanced flame leads to an increase in the heat release rate and an increase in the rate of consumption and production of the species. The total sound pressure level found to increase by adding oxygen to the oxidizer stream, especially in low frequency ranges. Enhancement of molar oxygen by 10%, increases the noise contribution of the heat release rate fluctuations about 20 dB in low frequency range, and about 10 dB in high frequencies. Additionally, the noise contribution from the mole consumption and production rate decreases about 5 dB in low frequency range and 10 dB in high frequencies.