Turbulent Jet Diffusion Flames Research Articles

Turbulence-radiation interaction in large eddy simulations of turbulent reactive flows: a review

Turbulence-radiation interaction (TRI) arises from the highly non-linear coupling between fluctuations of radiative intensity and fluctuations of temperature and chemical composition of the medium. It is well recognized that neglecting turbulent fluctuations of the latter scalars when computing the mean radiation field, as is standard in Reynolds-Averaged Navier-Stokes (RANS) simulations, leads to mispredictions of the radiative heat fluxes and the radiant fraction in as much as one order of magnitude. Conversely, the importance of TRI in large eddy simulations (LES)—i.e., the effect of subfilter-scale (SFS) fluctuations on the filtered radiation field—has only started to be investigated in the last decade. The present text reviews the knowledge gathered so far on the subject. The instantaneous and the spatially filtered forms of the radiative transfer equation (RTE) are presented, and the unresolved correlations resulting from the filtering operation are discussed. Models for accounting for SFS-TRI are surveyed, with the most popular ones (namely, probability density function-based methods and empirical approximations) reviewed in detail. An appraisal of the literature on the importance and characteristics of SFS-TRI is performed, with special attention given to turbulent jet diffusion flames and pool fires, which are the two configurations for which SFS-TRI has been studied the most. The main findings of these studies are summarized, and recommendations are given on directions of future works on the subject.

Reconstruction Method of 3D Turbulent Flames by Background-Oriented Schlieren Tomography and Analysis of Time Asynchrony

Background-oriented Schlieren tomography (BOST) is widely used for 3D reconstruction of turbulent flames. Two major concerns are associated with 3D reconstruction. One is the time asynchrony within the data acquisition of the high-speed camera. The other is that the ray tracing process requires significant computational consumption. This study proposes a ray tracing optimization method based on the k-d tree. The study results show that the average search nodes for each ray are only 0.018% of 3D flame with 3.07 million grid nodes. In addition, a parameter estimation method of the unknown azimuth power spectrum function is proposed. First, a typical Sandia turbulent jet diffusion flame dataset was built and validated accordingly, with experiments. The algorithm’s applicability to the 3D reconstruction of temperature and density fields is discussed on this basis. The root-mean-square error (RMSE) of the cross-section density for 3D reconstruction is below 0.1 kg/m3. In addition, the RMSE of the cross-section temperature is below 270 K. Finally, an uncertainty analysis of the flame reconstruction based on a physical model is performed by optimizing the ray tracing method. For the time asynchronous variance of 1 ms, the density uncertainty of the 3D reconstruction is below 1.6 × 10−2 kg/m3, and the temperature uncertainty is below 70 K. The method can provide an essential basis for the design of BOST systems and the 3D reconstruction of turbulent flames.

The importance of accurately modelling soot and radiation coupling in laminar and laboratory-scale turbulent diffusion flames

Ultrafine and highly light absorbing soot particles have been known to be harmful to human health and a major climate forcer. Understanding the mechanisms of soot formation and developing reliable and robust soot models are essential to design efficient and clean combustion systems. Thermal radiation is often the dominant heat transfer mode in both turbulent and laminar flames and affects soot formation through its influence on temperature. Significant progress has been made in modelling soot formation, thermal radiation transfer, and turbulence radiation interaction in hydrocarbon flames over the last three decades. However, the optically thin approximation has still been used to simplify thermal radiation transfer modelling and turbulence radiation interaction has often been neglected in soot formation modelling without proper justification. In this paper, soot models developed for laminar and laboratory scale turbulent jet diffusion flames, non-gray radiative heat transfer models, and turbulence radiation interaction models are reviewed. The aim of this review is to highlight the importance of thermal radiation heat transfer in both laminar and turbulent diffusion flames and turbulence radiation interaction in turbulent jet diffusion flames. Use of the optically thin approximation is often not justified even in laminar diffusion flames. This is particularly true in diffusion flames at elevated pressures or microgravity. Although the prediction of soot in both laminar and turbulent flames is primarily influenced by soot kinetic model, it is also significantly affected by aerosol dynamic model, thermal radiation heat transfer model, and turbulence radiation interaction model. It is important to accurately model soot aerosol dynamics and non-gray thermal radiation transfer in laminar diffusion flames and additionally to adequately model turbulence radiation interaction in turbulent flames for the purpose of validating reaction mechanisms and soot kinetic models.

The analysis of temperature and air entrainment rate for the turbulence diffusion jet flame of propane and carbon dioxide gas mixture

A few of studies have investigated the temperature profile and air entrainment of the turbulent jet diffusion flame produced by the combustion of a gas mixture. In this study, the air entrainment rate and temperature distribution along the axis of a turbulent jet diffusion flame of propane mixed with different concentrations of carbon dioxide were therefore investigated, which using a series of experiments conducted with 2, 3, and 4 mm nozzles. The results show that the flame temperature increased in the continuous flame region and decreased in fire plume region as the carbon dioxide concentration increased under the same heat release rate. A virtual origin model was then developed to analyze the relationship between carbon dioxide concentration and flame temperature. The air entrainment rate of the fire plume region was influenced by the gas flow speed from the nozzles, and the total air entrainment rate was inversely proportional to the carbon dioxide concentration. A correlation among the flame temperature, carbon dioxide concentration, and air entrainment rate was then established, providing a valuable resource for the risk assessment of turbulent jet diffusion flames.

Role of Buoyancy Induced Vortices in a Coupled-Mode of Oscillation in Laminar and Turbulent Jet Diffusion Flames

Role of Buoyancy Induced Vortices in a Coupled-Mode of Oscillation in Laminar and Turbulent Jet Diffusion Flames

Modeling and simulation of a turbulent jet diffusion flame of a biodiesel surrogate composed of MD, n-Hept, MC and EtOH

The more properties of biodiesel one wishes to represent, the more complex the surrogate mixture becomes. Often the aim is to represent the H/C and O/C ratios and molecular weight using a small number of pure components. In this article, a turbulent jet diffusion flame of a biodiesel surrogate, composed of 50% Methyl Decanoate, 40% n-heptane, 9% methyl crotonate and 1% ethanol is modeled and simulated. The strategy used is to apply the Directed Relation Graph technique for a first reduction, and Layerless Neural Network to define the main chain and obtain a skeletal mechanism. The results obtained for temperature and mass fractions of CO2, CO and H2O agree reasonably with literature data for a mechanism of 147 reactions and 45 species. The small number of species facilitates the simulation of the coupling between turbulence and chemical kinetics, a desirable feature of reduced mechanisms.

Improving the mixing of a turbulent jet diffusion flame of methane via linear quadratic regulator control

The purpose of this article is to show the improvement of the mixture of a turbulent jet diffusion flame of methane by means of linear quadratic regulator control. The model is based on low Mach and high Damköhler numbers. Large eddy simulation is used to simulate a turbulent flow with the Smagorinsky submodel for turbulent viscosity. The control employs a pair of actuators that act transversally to the flow direction on the walls of the burner. They consist of rectangular openings added along the depth of the duct. Numerical tests were performed by changing the position of one of these actuators. The results show that the controllers alter the jet diffusion flame, generating vortices of different sizes and dispersion patterns, which quickly leads to highly complex flow patterns.

Effect of Application of Electric Force to Diesel Fuel Droplets Charged Through Millikan Oil-Drop Experiment on Physical and Thermal Characteristics of Flame

Abstract This paper proposes a new feasible method to allow continuous change in the primary injection spray cone angle of liquid fuel droplets, which are injected from nozzles in liquid fuel combustion systems, to control the flame shape and thermal characteristics of the flame. The method is based on electric force applied to fuel droplets charged through frictional effects between the internal surface of the nozzle and the fuel flow as the liquid fuel is sprayed (based on the Millikan oil-drop experiment). A sprint computational fluid dynamics code was developed to investigate the effect of application of electric force to charged diesel fuel droplets, which were injected from a pressure swirl atomizer, on physical and thermal characteristics of a two-dimensional axisymmetric turbulent jet diffusion flame. The results show that an electric field applied to charged fuel droplets (electric force) changes the spatial distribution of the liquid fuel droplets in the flame reaction zone. An applied electric force in (−y) direction diverts the fuel droplets towards the axis centerline of the furnace and, consequently, decreases the primary injection cone angle and increases the concentration of the evaporated droplets around the axis centerline, which enhances the fuel-oxidant mixing rate and raises the flame temperature. Unlike an applied electric force in (−y) direction, an applied electric force in (+y) direction decreases the flame temperature. However, as the primary injection cone angle is decreased, an applied electric force in (+y) direction increases the flame temperature.

Assessment of weighted-sum-of-gray-gases models for gas-soot mixture in jet diffusion flames

The global radiative property models are widely used in combustion simulation due to its high computational efficiency. This study assesses the weighted sum of gray gases (WSGG) models for gas-soot mixture in the simulations of turbulent jet diffusion flame at atmospheric pressure with fully coupled combustion and radiation heat transfer. Non-adiabatic tabulated chemistry approach and the method of moments were employed for combustion modeling and soot particle dynamics. The radiative transfer equations are solved by the discrete ordinate model. Two kinds of WSGG model based on mixture modeling (MM) and superposition method (SM), and their combination with the gray and non-gray soot radiative property models are involved in the comparison. First, the radiative property models are evaluated on the one-dimensional parallel plate system using line-by-line model as the benchmark, and then the WSGG models are evaluated on the basis of the FSCK model in the combustion simulation. The results show that MM and SM provide similar prediction accuracy, while MM has much lower computational cost. The MM combined with the non-gray soot radiative property model (MMNS) provides the best accuracy for gas-soot mixture. Moreover, the relative error of MM combined with gray soot radiative property model (MMGS) is less than 10% for radiation source term, which is acceptable in engineering simulation. The gray implementation of WSGG model has low accuracy, which indicates that it is necessary to use the non-gray implementation of WSGG model in CFD simulations.

An integrated approach to predict scalar fields of a simulated turbulent jet diffusion flame using multiple fully connected variational autoencoders and MLP networks

A novel integrated deep learning approach for data-driven surrogate modelling of combustion computational fluid dynamics (CFD) simulations is presented. It combines variational autoencoders (VAEs) with deep neural networks (DNNs) to predict detail cell-by-cell two-dimensional distributions of temperature, velocity and species mass fractions from high level inputs such as velocity and fuel and air mass fractions. The VAE model is used to generate low dimensional encodings of the CFD data and the DNN is used in turn to map boundary conditions to the encodings. The results show that regularization is required during all training phases. Sufficiently accurate results were achieved for the reproduced species mass fractions with mean average errors below 0.3 [%wt.]. The validation mean average percentage errors for the temperature and velocity fields are 1.7% and 7.1% respectively. It is therefore possible to predict detail two-dimensional contours of CFD solution data with adequate generalizability and accuracy.

The experimental study of flame height and lift-off height of propane diffusion flames diluted by carbon dioxide

The flame height and lift-off height of the turbulent jet diffusion flame of gas mixture at ambient temperature and pressure were studied. Different diameter nozzles (2, 3 and 4 mm), heat release rates, and concentrations of carbon dioxide (CO2) were considered. The flow rates of propane (C3H8) and CO2 were controlled by two rotameters. The experimental results showed that the flame height firstly increased with an increase in the concentration of CO2 in the gas mixture under the same heat release rate, and then decreased with a further increase in the concentration of CO2. The flame height decreased with an increase in the concentration of CO2 when the heat release rate was sufficiently large. The lift-off height of the gas mixture increased with an increase in the concentration of CO2 under the same heat release rate and nozzle diameter. Considering the stoichiometric air fuel ratio and laminar flame speed, two correlations of flame height and lift-off height for a mixture of C3H8 and CO2 have been proposed, respectively.

Effects of carbon dioxide addition to fuel on flame radiation fraction in propane diffusion flames

The flame radiation fraction of the turbulent jet diffusion flame of propane and carbon dioxide mixed gas was studied under the ambient temperature of 300 K and the pressure of 1 bar. The diameters of the nozzle were 2, 3, and 4 mm. Different flow rates of propane and carbon dioxide were controlled to obtain different concentration ratios. The results showed that for a given heat release rate, the flame heat radiation fraction decreases with increasing the concentration of carbon dioxide in the gas mixture. Theories of soot formation and laminar flame speed were used to explain the evolution of the radiation fraction under different volume fractions of carbon dioxide. A correlation model about the relationship among the radiation fraction, heat release rate, and fuel equivalent ratio was proposed.

Application of generative deep learning to predict temperature, flow and species distributions using simulation data of a methane combustor

A data-driven surrogate modelling methodology of CFD simulation data is proposed.It uses convolutional variational autoencoders (VAE) and multi-layer perceptron (MLP) neural networks in an integrated manner to predict temperature, velocity and species mass fraction profiles on a cell-by-cell basis. The VAE performs feature extraction and data compression, while significantly reducing the number of network interconnections and ensuring physically realistic predictions.The MLP maps the CFD boundary condition values to the encodings generated with the VAE encoder network. The approach is demonstrated via application to a 2D axisymmetric methane-fired turbulent jet diffusion flame. The integrated network model produced average normalized mean absolute errors (NMAE) of 2.41% for the temperature predictions, 0.99% for velocity and 0.925% for species mass fractions. It is, therefore, possible to predict 2D CFD data fields with reasonable accuracy and generalizability, although high NMAE values were observed for certain cells confined to a small region near the centreline of the burner. The methodology lays the foundation for application to larger industrial problems to visualize processes inside equipment, deploy virtual sensors, perform quick what-if analysis, explore the design space, link to optimization routines to effectively control equipment, detect anomalies, or to form part of lower-dimensional system simulations.

An experimental study on thermal runaway characteristics of lithium-ion batteries with high specific energy and prediction of heat release rate

Understanding the potential thermal hazards of lithium-ion batteries (LIBs) during thermal runaway (TR) is helpful to assess the safety of LIB during storage, transport and use. This paper presents a comprehensive analysis of the thermal runaway (TR) characteristics of type 21700 cylindrical LIBs with a specific energy of 266 W∙h/kg. The batteries with both 30% state of charge (SOC) and 100% SOC were triggered to TR by uniform heating using a flexible heater in a laboratory environment. Three high definition cameras and one high-speed camera were placed to capture TR behavior and flame evolution from different viewpoints. Correlation between the heat release rate (HRR) and the mean flame height of turbulent jet diffusion flame were used to estimate the HRRs of LIBs. Additional characteristics of cell failure (for cells with 100% and 30% SOC) were also noted for comparison, including: number of objects ejected from the cell; sparks and subsequent jet fires. An approach has been developed to estimate the HRRs from TR triggered fires and results compared with previous HRR measurements for type 18650 cylindrical cells with a similar cathode composition.

Assessment of engineering gas radiative property models in high pressure turbulent jet diffusion flames

This article aims to determine the most relevant engineering global method for gas radiative property modeling to be applied in the simulations of combustion problems. Two versions of the full-spectrum correlated k (FSCK) model, the Rank-Correlated full-spectrum k-distribution/ Spectral-Line-Weighted-sum-of-gray-gases (RC-FSK/RC-SLW) and a new version of the Weighted-Sum-of-Grey-Gases (WSGG) model are compared with the Narrow-Band CK (NBCK) model in four turbulent axisymmetric jet diffusion flames fueled either by hydrogen or methane at atmospheric and higher pressures. These comparisons are performed in decoupled radiative heat transfer calculations with the thermal fields being prescribed. The databases and coefficients associated to these different models are determined from a unique Line-By-Line database in order to allow a relevant comparison. Model results suggest that the SLW/FSCK methods coupled to the so-called improved scheme proposed by Cai and Modest or the Rank-Correlated SLW/FSK model, along with k-g distributions generated from accurate high-resolution high-temperature databases are the most mature gas radiative property models to be implemented in CFD codes dealing with combustion problems involving gas-soot mixtures.

DNS-driven analysis of the Flamelet/Progress Variable model assumptions on soot inception, growth, and oxidation in turbulent flames

Modeling suites for Large-Eddy Simulations of soot evolution in turbulent flames include a number of submodels describing the chemistry of soot precursors, particle dynamics, heterogeneous soot chemistry, turbulent mixing, and combustion. To understand the reasons for model failure and to enhance the overall model performance, it is necessary to identify and subsequently improve model components with a leading order effect on the overall error. In this work, errors in soot predictions associated with flamelet-based combustion models are isolated and quantified in a combined a-priori and partial a-posteriori analysis using large-scale Direct Numerical Simulation (DNS) data of a sooting turbulent jet diffusion flame. Gas-phase quantities entering the calculation of the soot source terms and hence coupling the combustion model to the soot model are analyzed in the DNS first. The performance of a Flamelet/Progress Variable model with respect to these quantities is then analyzed a-priori for two DNS cases employing a mixture-averaged transport model and unity Lewis numbers. Then, the soot evolution along Lagrangian trajectories extracted from the DNS is re-computed using rate coefficients directly taken from the DNS and from the flamelet table. In the context of this partial a-posteriori analysis, flamelet-induced errors are also compared to errors induced by the chemical soot model. The soot surface growth and oxidation rate coefficients are reasonably well predicted by the flamelet library. Considerably larger errors in the model-predicted soot mass originate from the tabulated quantities entering the calculation of PAH-based soot growth rates. However, these errors can be reduced to a few percent if the rate is appropriately scaled with the mass fraction of polycyclic aromatic hydrocarbons (PAH). However, this requires the solution of a transport equation for the PAH mass fraction, and modeling the source term in this equation is shown to be challenging. Overall, the largest uncertainties can be attributed to the chemical mechanism for PAH formation and the model for the PAH source term.

Low-dissipation finite element strategy for low Mach number reacting flows

The present paper extends the conservative finite element convective scheme proposed by Charnyi et al.(Journal of Computational Physics 337, 2017, 289–308) originally formulated for incompressible flows to the low Mach regime. Similar to Lehmkuhl et al.(Journal of Computational Physics 390, 2019, 51–65) stabilisation is only introduced for the continuity equation by means of a non-incremental fractional-step method, modified in order to account for variable density flows. The final scheme preserves momentum and angular momentum for variable density flows. The error of kinetic energy conservation is of order O(δthk+1), thus dissipation is limited. Standard stabilised finite elements are used for the scalars. Time integration is carried out by means of an explicit third order Runge-Kutta scheme for all equations. The proposed strategy is tested on a set of relevant cases with available reference data using large-eddy simulations. First, an anisothermal turbulent channel flow is assessed. Later, a technically premixed turbulent flame in a swirl-stabilized configuration is considered. And finally, a turbulent jet diffusion flame in a low-velocity co-flow has been studied. In all cases the performance of the presented low Mach formulation is fairly good, showing better accuracy than skew-symmetric like strategies.

An Efficient Machine-Learning Approach for PDF Tabulation in Turbulent Combustion Closure

ABSTRACT Probability density function (PDF) based turbulent combustion modeling is limited by the need to store multi-dimensional PDF tables that can take up large amounts of memory. A significant saving in storage can be achieved by using various machine-learning techniques that represent the thermo-chemical quantities of a PDF table using mathematical functions. These functions can be computationally more expensive than the existing interpolation methods used for thermo-chemical quantities. More importantly, the training time can amount to a considerable portion of the simulation time. In this work, we address these issues by introducing an adaptive training algorithm that relies on multi-layer perception (MLP) neural networks for regression and self-organizing maps (SOMs) for clustering data to tabulate using different networks. The algorithm is designed to address both the multi-dimensionality of the PDF table as well as the computational efficiency of the proposed algorithm. SOM clustering divides the PDF table into several parts based on similarities in data. Each cluster of data is trained using an MLP algorithm on simple network architectures to generate ‘local’ functions for thermo-chemical quantities. The algorithm is validated for the so-called DLR-A turbulent jet diffusion flame using both RANS and LES simulations and the results of the PDF tabulation are compared to the standard linear interpolation method. The comparison yields a very good agreement between the two tabulation techniques and establishes the MLP-SOM approach as a viable method for PDF tabulation.

Single-shot two-dimensional measurement of nanoparticles in turbulent jet-diffusion flames using phase-selective laser-induced breakdown spectroscopy

In this work, we demonstrate a single-shot two-dimensional measurement of vanadic oxide (V2O5) nanoparticles in a turbulent jet-diffusion flame based on phase-selective laser-induced breakdown spectroscopy (PS-LIBS). By collecting the atomic spectra of vanadium near 439 nm from nano-sized plasmas within the ∼30 ns lifetime, particle information can be revealed without interference from elastic scattering or Bremsstrahlung emissions. As the laser intensity increases, the signal intensity first increases and then saturates when the laser intensity reaches 0.5 GW/cm2. In the saturation regime, a proportional correlation is established between the signal intensity and the particle volume fraction. Based on the parametric study in the laminar condition, we image the instantaneous distribution of particle volume fraction in the turbulent condition using single-shot PS-LIBS measurements. The snapshots show that vanadic oxide nanoparticles concentrate near the diffusion flame surface, which may be caused by the rapid formation of particles on the oxidizer/precursor side and quick dilution across the reaction layer. The Proper Orthogonal Decomposition (POD) analysis further indicates that the fluctuations of the particle volume fraction originate from the unsteady flame surface at the upstream positions and large-scale mixing at the downstream positions. The single-shot PS-LIBS measurement shows promising potential for resolving complex processes of particle formation in turbulent flame synthesis.

Numerical simulations of turbulent lifted jet diffusion flames in a vitiated coflow using the stochastic multiple mapping conditioning approach

Lifted turbulent jet diffusion flames of H2/N2 issued into a hot coflowing stream of combustion products from a lean premixed H2/air mixture are simulated using the stochastic multiple mapping conditioning (MMC) approach within the Reynolds-averaged Navier–Stokes (RANS) framework. The underlying turbulent flow is modeled using the two-equation k–ɛ model with modified constants. In the MMC approach, large-scale turbulent fluctuations are emulated by introducing suitable reference variables. A single reference variable is used here to describe the evolution of mixture fraction via a mapping function. The modified Curl's model has been adapted to model the micro-mixing term. A detailed chemical kinetic mechanism involving 9 species and 21 reversible reactions is used for H2/O2 combustion. The computed results from the present simulations are found to be in excellent agreement with the available experimental data. Numerical results are found to be sensitive to change in the minor dissipation timescale, which certainly controls the conditional fluctuations around the conditional mean. Also, the predicted flame lift-off heights obtained using the minor mixing time constant Cmin = 0.25 are found to be in close proximity with the experimentally observed values for the entire range of coflow temperatures.