Large Kinetic Mechanisms Research Articles

Transitory sensitivity in automatic chemical kinetic mechanism analysis

AbstractDetailed chemical kinetic mechanisms are necessary for resolving many important chemical processes. As the chemistry of smaller molecules has become better grounded and quantum chemistry calculations have become cheaper, kineticists have become interested in constructing progressively larger kinetic mechanisms to model increasingly complex chemical processes. These large kinetic mechanisms prove incredibly difficult to refine and time‐consuming to interpret. Traditional sensitivity analysis on a large mechanism can range from inconvenient to practically impossible without special techniques to reduce the computational cost. We first present a new time‐local sensitivity analysis we term transitory sensitivity analysis. Transitory sensitivity analysis is demonstrated in an example to accurately identify traditionally sensitive reactions at an 18,000x speed up over traditional sensitivities. By fusing transitory sensitivity analysis with more traditional time‐local branching, pathway, and cluster analyses, we develop an algorithm for efficient automatic mechanism analysis. This automatic mechanism analysis at a time point is able to identify the reactions a target is most sensitive to using transitory sensitivity analysis and then propose hypotheses why the reaction might be sensitive using branching, pathway, and cluster analyses. We implement these algorithms within the reaction mechanism simulator (RMS) package, which enables us to report the automatic mechanism analysis results in highly readable text formats and in molecular flux diagrams.

A novel machine learning based lumping approach for the reduction of large kinetic mechanisms for plasma-assisted combustion applications

The development of skeletal mechanisms has become essential for multi-dimensional simulations of plasma-assisted combustion (PAC). However, reduction tools developed for traditional combustion applications are not always applicable to PAC, due to the complex interplay between non-equilibrium plasma and combustion kinetics. Plasma direct relation graph with error propagation (P-DRGEP) is a recent plasma-specific reduction method developed in order to incorporate plasma energy branching in the reduction. In the first part of this work, the applicability of P-DRGEP to large kinetic mechanisms is investigated. A detailed isooctane/air plasma mechanism containing 2805 species and 18457 reactions is reduced to 415 species and 4716 reactions, keeping errors on ignition time within 3% for a wide range of initial conditions: from 750 K to 1200 K, 10 atm and equivalence ratios from 0.75 to 1.50. The second part focuses on isomer lumping, which is another reduction technique widely used in combustion. When applied to PAC, it is shown that the resulting lumped mechanism produces poor results. A novel plasma-specific isomer lumping strategy using machine learning is proposed instead. With the supervised algorithm of gradient boosting, predictive regression models are generated, which describe rate coefficients of lumped reactions adequately. These models are trained with simulation data. Leveraging this newly proposed lumping approach on the reduced mechanism, allows for an additional 28% reduction in the number of species and 19% reduction in the number of reactions. Two different versions are presented: in the first one the models are trained using one input feature (1D), while in the second one, two input features are selected (2D). The resulting lumped mechanism is shown to produce accurate predictions of PAC over the entire parameter space of interest, while significantly decreasing the computational time. Indicatively, with the 1D version the maximum error on ignition time in this range of conditions is 6%. The 2D approach produces even lower errors, which do not exceed 3%.Novelty and significance statementIn this work, a novel approach for isomer lumping, in plasma-assisted combustion mechanisms, is demonstrated. This plasma-specific approach, uses predictive machine learning regression models to describe the complex evolution of lumped reaction rate coefficients. Combining it with the plasma direct relation graph with error propagation, a powerful reduction framework is created, which is successfully demonstrated on a detailed isooctane/air plasma kinetic mechanism, via zero-dimensional ignition simulations. This framework constitutes a useful tool towards the creation of highly accurate skeletal mechanisms, which significantly reduce the computational costs of simulations.

Efficient numerical methods for the optimisation of large kinetic reaction mechanisms

Optimisation of detailed combustion mechanisms means that the corresponding kinetic model is fitted to experimental data via optimising their important rate and thermodynamic parameters within their domain of uncertainty. Typically, several dozen parameters are fitted to several hundred to several thousand data points. Many numerical optimisation methods have been used, but the efficiency of these methods has not been compared systematically. In this work, parameters of an H2/O2/NO x mechanism (214 reaction steps of 35 species) were fitted to 1552 indirect (ignition delay times measured in shock tubes and concentrations measured in flow reactors) and 755 direct measurements. Three test cases were investigated: (1) fitting the Arrhenius parameters of 2 reaction steps to 732 data points; (2) fitting the Arrhenius parameters of 4 reaction steps to 1077 data points; (3) fitting the Arrhenius parameters of 10 reaction steps to 2307 data points. All 3 cases were investigated in 2 ways: fitting the A-parameters only and fitting all Arrhenius parameters (5, 11 and 29 parameters, respectively). A series of global (FOCTOPUS, genetic algorithm, simulated annealing, particle swarm optimisation, covariance matrix adaptation evolutionary strategy (CMA-ES)) and local (simplex, pattern search, interior-point, quasi-Newton, BOBYQA, NEWUOA) optimisation methods were tested on these cases, some of them in two variants. The methods were compared in terms of the final error function value and number of error function evaluations. The main conclusions are that the FOCTOPUS resulted in the lowest final error value in all cases, but this method required relatively many error function evaluations. As the task became more difficult, more and more methods failed. A variant of the BOBYQA method looked stable and efficient in all cases.

Principal component analysis based combustion model in the context of a lifted methane/air flame: Sensitivity to the manifold parameters and subgrid closure

The present work advances the PC-transport approach in the context of Large Eddy Simulation (LES) of turbulent combustion. Accurate modeling of combustion systems requires large kinetic mechanisms. However, realistic high-fidelity simulations of turbulent reacting flows still represent a big challenge on the current computational tools. Therefore, a parameterization of the thermo-chemical state-space using a reduced number of variables is needed. To this end, the potential offered by Principal Component Analysis (PCA) in identifying low-dimensional manifolds is very appealing. The present paper extends the PC-transport approach, coupled with Gaussian Process Regression (GPR), to a lifted methane/air flame in LES. Previous investigations by the authors showed the great potential of the PC-GPR model in the context of Sandia flames. This study investigated some key features of the model: the sensitivity to the training data set and the scaling methods . To this end, two different canonical reactors were used: unsteady counter-flow laminar flames (CFLF) and unsteady perfectly stirred reactor (PSR). Moreover, the authors proposes an approach to address the issue of data density inherent to large numerical data sets, by means of a kernel density weighting of the data set before applying PCA. Finally, a subgrid scale (SGS) closure model was coupled to the PC-transport approach to treat complex turbulence/chemistry interactions.

Master equation lumping for multi-well potential energy surfaces: A bridge between ab initio based rate constant calculations and large kinetic mechanisms

Ab initio transition state theory-based master equation methodologies for the calculation of rate constants have gained enormous popularity in the past decades. Nevertheless, introducing these rate constants into large kinetic schemes is a non-trivial task when large potential energy surfaces (PESs) are investigated. To determine proper phenomenological rate constants it is in fact necessary to account for the formation of all the thermodynamically stable wells considered in the master equation (ME), even if most wells do not exhibit significant secondary reactivity. Moreover, reactions involving intermediates with lifetimes comparable to the rovibrational relaxation timescale can exhibit discontinuities both in the rate constants and in the number of thermodynamically stable wells across the investigated temperature and pressure ranges. In this work, we address these problems with a “master equation-based lumping” (MEL) approach specifically designed to process the output of ME calculations of multi-well PESs. Simple kinetic simulations allow identifying both intermediate wells with limited lifetime and isomers with similar reactivity. Then, equivalent rate constants for a smaller set of pseudospecies are derived so as to reproduce the kinetics of the detailed mechanism. Our methodology is independent of any experimental data or experience-based assumptions. The power of MEL is demonstrated with three case studies of increasing complexity, namely the PES for CH3COOH decomposition, and the portions of the C5H5OH and C10H10/C10H9 PESs accessed from C5H5 + OH and C5H5 + C5H5 recombination. This work constitutes the first systematic step addressing the robust integration of rate constants derived from ME simulations into global kinetic schemes and provides a useful approach for the entire chemical kinetics community filling the gap between detailed theoretical investigations of complex PESs and the development of detailed kinetic models.

A computationally-efficient method for flamelet calculations

A new open-source code for the simulation of the diffusion flamelet equations is proposed. Emphasis is placed on using an approximate Jacobian to reduce the computational cost of the matrix operations. Performance of the proposed solvers is tested by performing flamelet calculations with kinetic mechanisms of varying sizes. For the unity Lewis number equations, the present iterative Newton solver using an approximate Jacobian greatly outperforms direct Newton solvers using exact Jacobians. The computation cost scales linearly with the number of species, leading to a reduction in solution times by two orders of magnitude for mechanisms containing thousands of species. The applicability of the Jacobian approximations to the solution of the non-unity Lewis number flamelet equations is assessed. The approximations are generally inadequate to solve the full non-unity Lewis number equations but can be used in some applications depending on the balance of terms in the flamelet equations. As an example, the flamelet solver is applied to the study of sooting tendencies in laminar co-flow diffusion flames where modified non-unity Lewis number flamelet equations, previously shown to accurately reproduce experimentally-measured Yield Sooting Indices (YSI), are solved. The accelerated flamelet solver is well suited for sensitivity analysis and uncertainty quantification with large detailed kinetic mechanisms, tasks for which the computational cost was previously prohibitive.

Sparse, iterative simulation methods for one-dimensional laminar flames

Sparse, iterative simulation methods for one-dimensional laminar flames are proposed. The resulting steady and unsteady flame solvers exploit approximate Jacobians to greatly reduce the computational cost associated with matrix operations. The constant, non-unity Lewis number assumption is introduced to further reduce the computational cost. The solvers are also parallelized to reduce time-to-solution on distributed memory computer systems. Computed laminar flame speeds and species profiles for a range of chemical mechanisms (from 10 to 2878 species) are compared against a well-validated commercial code and are found to be consistent within solver tolerances. The computation times of both the unsteady and steady solutions increase only linearly with the number of species, which is a significant improvement over the quadratic or cubic scaling of existing steady-state flame solvers. For the largest mechanism tested, the steady-state flame solver is two orders of magnitude faster than commonly-used codes. The use of an approximate Jacobian is shown to reduce the rate of convergence for the steady-state solver, but does not significantly affect the domain of convergence. The steady-state solver with approximate Jacobian is thus well suited for computationally efficient laminar flame speed sweeps with large kinetic mechanisms.

Principal component analysis acceleration of rovibrational coarse-grain models for internal energy excitation and dissociation.

The present work introduces a novel approach for obtaining reduced chemistry representations of large kinetic mechanisms in strong non-equilibrium conditions. The need for accurate reduced-order models arises from compression of large ab initio quantum chemistry databases for their use in fluid codes. The method presented in this paper builds on existing physics-based strategies and proposes a new approach based on the combination of a simple coarse grain model with Principal Component Analysis (PCA). The internal energy levels of the chemical species are regrouped in distinct energy groups with a uniform lumping technique. Following the philosophy of machine learning, PCA is applied on the training data provided by the coarse grain model to find an optimally reduced representation of the full kinetic mechanism. Compared to recently published complex lumping strategies, no expert judgment is required before the application of PCA. In this work, we will demonstrate the benefits of the combined approach, stressing its simplicity, reliability, and accuracy. The technique is demonstrated by reducing the complex quantum N2(Σg+1)-N(Su4) database for studying molecular dissociation and excitation in strong non-equilibrium. Starting from detailed kinetics, an accurate reduced model is developed and used to study non-equilibrium properties of the N2(Σg+1)-N(Su4) system in shock relaxation simulations.

Principal component analysis coupled with nonlinear regression for chemistry reduction

Large kinetic mechanisms are required in order to accurately model combustion systems. If no parameterization of the thermo-chemical state-space is used, solution of the species transport equations can become computationally prohibitive as the resulting system involves a wide range of time and length scales. Parameterization of the thermo-chemical state-space with an a priori prescription of the dimension of the underlying manifold would lead to a reduced yet accurate description. To this end, the potential offered by Principal Component Analysis (PCA) in identifying low-dimensional manifolds is very appealing. The present work seeks to advance the understanding and application of the PC-transport approach by analyzing the ability to parameterize the thermo-chemical state with the PCA basis using nonlinear regression. In order to demonstrate the accuracy of the method within a numerical solver, unsteady perfectly stirred reactor (PSR) calculations are shown using the PC-transport approach. The PSR analysis extends previous investigations to more complex fuels (methane and propane), showing the ability of the approach to deal with relatively large kinetic mechanisms. The ability to achieve highly accurate mapping through Gaussian Process based nonlinear regression is also shown. In addition, a novel method based on local regression of the PC source terms is also investigated which leads to improved results.

A multi-timescale and correlated dynamic adaptive chemistry and transport (CO-DACT) method for computationally efficient modeling of jet fuel combustion with detailed chemistry and transport

A correlated dynamic adaptive chemistry and transport (CO-DACT) method is developed to accelerate numerical simulations with detailed chemistry and transport properties in a reactive flow. Different sets of phase parameters, which govern the transport properties and chemical reaction pathways, respectively, are proposed to identify the correlated groups for transport properties and reaction pathways in both temporal and spatial coordinates. The correlated transport properties and reduced chemical mechanisms in phase space are dynamically updated by different user-specified threshold values. For the calculation of detailed transport properties, the mixture-averaged diffusion model is employed. For the on-the-fly generation of reduced chemistry, the multi-generation path flux analysis (PFA) method is used. In the present method, the chemical reduction and transport properties calculation are only conducted once for all the computation cells in the same correlated group within the pre-specified thresholds. Therefore, without sacrificing accuracy within the range of uncertainty of mechanisms and transport properties, the CO-DACT method can eliminate all redundant chemistry reductions and transport properties calculations in temporal and spatial coordinates when the transport properties and chemical reaction pathways are correlated due to the similarities in phase space. The CO-DACT method is further integrated with the hybrid multi-timescale (HMTS) method to achieve efficient integration of chemistry. Simulations of outward propagating spherical premixed flames and one dimensional (1D) diffusion ignitions of a jet fuel surrogate mixture, as well as an unsteady spherical propagating diffusion flame of a DME/air mixture are conducted to validate the present algorithm. The impact of the selection of threshold values as well as the dependence of numerical errors on pressure and equivalent ratio are also examined. The results demonstrate that the CO-DACT method can increase the computation efficiency for transport properties by at least two-order of magnitudes. Moreover, it is robust, accurate, and improves the overall computation efficiency involving a large kinetic mechanism.

Parallel on-the-fly adaptive kinetics in direct numerical simulation of turbulent premixed flame

A new numerical framework for direct numerical simulation (DNS) of turbulent combustion is developed employing on-the-fly adaptive kinetics (OAK), correlated transport (CoTran), and a point-implicit ODE solver (ODEPIM). The new framework is tested on a canonical turbulent premixed flame employing a real conventional jet fuel mechanism. The results show that the new framework provides a significant speed-up of kinetics and transport computation, which allows DNS with large kinetic mechanisms, and at the same time maintains high accuracy and good parallel scalability. Detailed diagnostics show that calculation of the chemical source term with ODEPIM is 17 times faster than with a pure implicit solver in this test. OAK utilizes a path flux analysis (PFA) method to reduce the large kinetic mechanism to a smaller size for each location and time step, and it can further speed up the chemical source calculation by 2.7 times in this test. CoTran uses a similar correlation method to make the calculation of mixture-averaged diffusion (MAD) coefficients 72 times faster in this test. Compared to conventional DNS, the total CPU time of the final framework is 20 times faster, kinetics is 46 times faster, and transport is 72 times faster in this test.

Curve matching, a generalized framework for models/experiments comparison: An application to n-heptane combustion kinetic mechanisms

The increasing number of experimental data, accurate thermodynamic and reaction rate parameters drive the extension, revision, and update of large size kinetic mechanisms. Despite these detailed mechanisms (i.e. the models) generally allow good predictive capabilities, their management and update are critical. The usual validation procedure of a kinetic scheme consists in graphically comparing numerical simulations with the widest set of experimental data, with the goal of proving the model predictive capabilities over a broad range of temperature, pressure, and dilution conditions. At every iteration the model needs to be automatically evaluated through a quantitative methodology, without relying upon a standard graphical visualization.This work aims at proposing a method, named Curve Matching (CM), to evaluate the agreement between models and experimental data. The approach relies on the transformation of discrete experimental data and the relative numerical predictions in two different continuous functions. In this way, CM allows not only to compare the errors (i.e. the differences between the experimental and calculated values), but also the shapes of the measured and numerical curves (i.e. their first derivatives) and possible shifts along the x-axis. These features allow to overcome the limitations of Sum of Squared Error based methods. The present approach is discussed by means of a few models/experiment comparisons in ideal reactors and laminar flames. Due to the large number of both experimental data and kinetic mechanisms available in the literature, n-heptane was selected as the test fuel.

OpenSMOKE++: An object-oriented framework for the numerical modeling of reactive systems with detailed kinetic mechanisms

OpenSMOKE++ is a general framework for numerical simulations of reacting systems with detailed kinetic mechanisms, including thousands of chemical species and reactions. The framework is entirely written in object-oriented C++ and can be easily extended and customized by the user for specific systems, without having to modify the core functionality of the program. The OpenSMOKE++ framework can handle simulations of ideal chemical reactors (plug-flow, batch, and jet stirred reactors), shock-tubes, rapid compression machines, and can be easily incorporated into multi-dimensional CFD codes for the modeling of reacting flows. OpenSMOKE++ provides useful numerical tools such as the sensitivity and rate of production analyses, needed to recognize the main chemical paths and to interpret the numerical results from a kinetic point of view. Since simulations involving large kinetic mechanisms are very time consuming, OpenSMOKE++ adopts advanced numerical techniques able to reduce the computational cost, without sacrificing the accuracy and the robustness of the calculations.In the present paper we give a detailed description of the framework features, the numerical models available, and the implementation of the code. The possibility of coupling the OpenSMOKE++ functionality with existing numerical codes is discussed. The computational performances of the framework are presented, and the capabilities of OpenSMOKE++ in terms of integration of stiff ODE systems are discussed and analyzed with special emphasis. Some examples demonstrating the ability of the OpenSMOKE++ framework to successfully manage large kinetic mechanisms are eventually presented. Program summaryProgram title: OpenSMOKE++Catalogue identifier: AEVY_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEVY_v1_0.htmlProgram obtainable from: CPC Program Library, Queen’s University, Belfast, N. IrelandLicensing provisions: GNU General Public License, version 3No. of lines in distributed program, including test data, etc.: 146353No. of bytes in distributed program, including test data, etc.: 4890534Distribution format: tar.gzProgramming language: C++.Computer: Any computer that can run a C++ Compiler.Operating system: Tested on Microsoft Windows 7, Ubuntu 14.4.RAM: From a few Mb to several Gb depending on the size of the system being simulated.Classification: 22.External routines: Eigen, Boost C++ Libraries, RapidXMLNature of problem: Evolution of reacting gas mixtures with detailed description of thermodynamic, kinetic and transport data.Solution method: Stiff systems of Ordinary differential Equations, whose solution is obtained using methods based on the Backward Differentiation Formulas (BDF) (LU factorization of dense matrices is required).Additional comments: The code was specifically conceived for managing homogeneous, reacting mixtures including thousands of species and reactions.Running time: Problem-dependent, from seconds (small kinetics) to hours

Multi-timescale and correlated dynamic adaptive chemistry modeling of ignition and flame propagation using a real jet fuel surrogate model

A new correlated dynamic adaptive chemistry (CO-DAC) method is developed and integrated with the hybrid multi-timescale (HMTS) method for computationally efficient modeling of ignition and unsteady flame propagation of real jet fuel surrogate mixtures with a detailed and comprehensively reduced kinetic mechanism. A concept of correlated dynamic adaptive chemistry (CO-DAC) method in both time and space coordinates is proposed by using a few key phase parameters which govern the low, intermediate, and high temperature chemistry, respectively. Correlated reduced mechanisms in time and space are generated dynamically on the fly from the detailed kinetic mechanism by specifying thresholds of phase parameters of correlation and using the multi-generation path flux analysis (PFA) method. The advantages of the CO-DAC methods are that it not only provides the flexibility and accuracy of kinetic model and chemistry integration but also avoids redundant model reduction in time and space when the chemistry is frequently correlated in phase space. To further increase the computational efficiency in chemistry integration, the hybrid multi-timescale (HMTS) method is integrated with the CO-DAC method to solve the stiff ordinary differential equations (ODEs) of the reduced chemistry generated on the fly by CO-DAC. The present algorithm is compared and validated against the conventional VODE solver, DAC and HMTS/DAC methods for simulating ignition and unsteady flame propagation of real jet fuel surrogate mixtures consisting of four component fuels, n-dodecane, iso-octane, n-propyl benzene, and 1,3,5-trimethyl benzene. The results show the present HMTS/CO-DAC algorithm is not only computationally efficient but also robust and accurate. Moreover, it is shown that compared to the DAC and HMTS/DAC methods, the computation time of model reduction in CO-DAC is almost negligible even for a large kinetic mechanism involving hundreds of species. In addition, the results show that computation efficiency of CO-DAC increases from homogeneous ignition to one-dimensional flame propagation for both the first and second generation PFA reduction. Therefore, the present HMTS/CO-DAC method can enable high-order model reduction and achieve higher computation efficiency for multi-dimensional numerical modeling.

Faster solvers for large kinetic mechanisms using adaptive preconditioners

The adaptive preconditioners developed in this paper substantially reduce the computational cost of integrating large kinetic mechanisms using implicit ordinary differential equation (ODE) solvers. For a well-stirred reactor, the speedup of the new method is an order of magnitude faster than recent approaches based on direct, sparse linear system solvers. Moreover, the new method is up to three orders of magnitude faster than traditional implementations of the ODE solver where the Jacobian information is generated automatically via finite differences, and the factorization relies on standard, dense matrix operations. Unlike mechanism reduction strategies, the adaptive preconditioners do not alter the underlying system of differential equations. Consequently, the new method achieves its performance gains without any loss of accuracy to within the local error controlled by the ODE solver. Such speedup allows higher fidelity mechanism chemistry to be coupled with multi-dimensional fluid dynamics simulations.

N-Butanol droplet combustion: Numerical modeling and reduced gravity experiments

Recent interest in alternative and bio-derived fuels has emphasized butanol over ethanol as a result of its higher energy density, lower vapor pressure and more favorable gasoline blending properties. Numerous efforts have examined the combustion of butanol from the perspective of low dimensional gas-phase transport configurations that facilitate modeling and validation of combustion kinetics. However, fewer studies have focused on multiphase butanol combustion, and none have appeared on isolated droplet combustion that couples experiments with robust modeling of the droplet burning process. This paper presents such an experimental/numerical modeling study of isolated droplet burning characteristics of n-butanol. The experiments are conducted in an environment that simplifies the transport process to one that is nearly one-dimensional as promoted by burning in a reduced gravity environment. Measurements of the evolution of droplet diameter (Do=0.56–0.57mm), flame standoff ratio (FSR≡Df/D) and burning rate (K) are made in the standard atmosphere under reduced gravity and the data are compared against numerical simulation. The detailed model is based on a comprehensive time-dependent, sphero-symmetric droplet combustion simulation that includes spectrally resolved radiative heat transfer, multi-component diffusive transport, full thermal property variations and detailed chemical kinetic. The simulations are carried out using both a large order kinetic mechanism (284 species, 1892 reactions) and a reduced order mechanism (44 species, 177 reactions). The results show that the predicted burning history and flame standoff ratios are in good agreement with the measurements for both the large and reduced order mechanisms. Additional simulations are conducted for varying oxygen concentration to determine the limiting oxygen index and to elucidate the kinetic processes that dictate the extinction of the flame at these low oxygen concentrations.

Recent progress and challenges in fundamental combustion research

More than 80% of world energy is converted by combustion. Development of efficient next generation advanced engines by using alternative fuels and operating at extreme conditions is one of the most important solutions to increase energy sustainability. To realize the advanced engine design, the challenges in combustion research are therefore to advance fundamental understanding of combustion chemistry and dynamics from molecule scales to engine scales and to develop quantitatively predictive tools and innovative combustion technologies. This review will present the recent progresses and technical challenges in fundamental combustion research in seven areas including advanced engine concepts using low temperature fuel chemistry, new combustion phenomena in extreme conditions, alternative and surrogate fuels, multi-scale modeling, high pressure combustion kinetics, experimental methods and advanced combustion diagnostics Firstly, new engine concepts such as the Homogeneous Charge Compression Ignition (HCCI), Reactivity Controlled Compression Ignition (RCCI), and pressure gain combustion will be introduced. The impact of low temperature combustion chemistry of fuels on combustion in advanced engines will be demonstrated. This is followed by the discussions of the needs of fundamental combustion research for new engine technologies. Secondly, combustion phenomena and flame regimes involving new combustion concepts such as fuel and thermal stratifications, plasma assisted combustion, and cool flames at extreme conditions will be analyzed. Thirdly, alternative fuels and methodologies to formulate surrogate fuel mixtures to model the target combustion properties of real fuels will be presented. A new concept of radical index and transport weighted enthalpy will be introduced to rank the fuel reactivity and to assess the impact of molecular structure on combustion properties The success and limitations of the current surrogate fuel models will be discussed by using jet fuels and biodiesels as examples. Fourthly, the difficulty of modeling large kinetic mechanism of real fuel will be discussed The multi-time scale (MTS) method and the correlated dynamic adaptive chemistry (CO-DAC) method for kinetic model reduction and computationally efficient modeling will be compared and analyzed. Fifthly, the progress and challenges of high pressure combustion kinetics for hydrogen and larger hydrocarbons will be discussed. The important pressuredependent reaction pathways and key intermediate species at high pressure will be analyzed. Fundamental experimental methods for combustion and their uncertainties in acquiring combustion properties for the validation of kinetic mechanism will be discussed. Finally, recent progress in diagnostics of HO2, H2O2, RO2, ketohydroperoxide, and other key intermediate species for high pressure kinetic mechanism development will be summarized. Conclusions and opportunities of future combustion research will be made.

A Comparative Computational Fluid Dynamics Study on Flamelet-Generated Manifold and Steady Laminar Flamelet Modeling for Turbulent Flames

The laminar flamelet model (LFM) (Peters, 1986, “Laminar Diffusion Flamelet Models in Non-Premixed Combustion,” Prog. Energy Combust. Sci., 10, pp. 319–339; Peters, “Laminar Flamelet Concepts in Turbulent Combustion,” Proc. Combust. Inst., 21, pp. 1231–1250) represents the turbulent flame brush using statistical averaging of laminar flamelets whose structure is not affected by turbulence. The chemical nonequilibrium effects considered in this model are due to local turbulent straining only. In contrast, the flamelet-generated manifold (FGM) (van Oijen and de Goey, 2000, “Modeling of Premixed Laminar Flames Using Flamelet-Generated Manifolds,” Combust. Sci. Technol., 161, pp. 113–137) model considers that the scalar evolution; the realized trajectories on the thermochemical manifold in a turbulent flame are approximated by the scalar evolution similar to that in a laminar flame. This model does not involve any assumption on flame structure. Therefore, it can be successfully used to model ignition, slow chemistry, and quenching effects far away from the equilibrium. In FGM, 1D premixed flamelets are solved in reaction-progress space rather than physical space. This helps better solution convergence for the flamelets over the entire mixture fraction range, especially with large kinetic mechanisms at the flammability limits (ANSYS FLUENT 14.5 Theory Guide Help Document, http://www.ansys.com). In the present work, a systematic comparative study of the FGM model with the LFM for four different turbulent diffusion/premixed flames is presented. The first flame considered in this work is methane-air flame with dilution air at the downstream. The second and third flames considered are jet flames in a coaxial flow of hot combustion products from a lean premixed flame called Cabra lifted H2 and CH4 flames (Cabra, et al., 2002, “Simultaneous Laser Raman-Rayleigh-LIF Measurements and Numerical Modeling Results of a Lifted Turbulent H2/N2 Jet Flame in a Vitiated Coflow,” Proc. Combust. Inst., 29(2), pp. 1881–1888; Lifted CH4/Air Jet Flame in a Vitiated Coflow, http://www.me.berkeley.edu/cal/vcb/data/VCMAData.html) where the reacting flow associated with the central jet exhibits similar chemical kinetics, heat transfer, and molecular transport as recirculation burners without the complex recirculating fluid mechanics. The fourth flame considered is a Sandia flame D (Barlow et al., 2005, “Piloted Methane/Air Jet Flames: Scalar Structure and Transport Effects,” Combust. Flame, 143, pp. 433–449), a piloted methane-air jet flame. It is observed that the simulation results predicted by the FGM model are more physical and accurate compared to the LFM in all the flames presented in this work. The autoignition-controlled flame lift-off is also captured well in the cases of lifted flames using the FGM model.

Experimental and kinetic modeling study of PAH formation in methane coflow diffusion flames doped with n-butanol

In order to understand the interactions between butanol and hydrocarbon fuels in the PAH formation, experimental and kinetic modeling investigations were combined to study methane laminar coflow diffusion flames doped with two inlet mole fractions of n-butanol (1.95% and 3.90%) in this work. Mole fractions of flame species along the flame centerline were measured using synchrotron VUV photoionization mass spectrometry. A detailed kinetic model of n-butanol combustion, extended from a recent published n-butanol model, was provided in this work to reproduce the fuel decomposition and the formation of benzene and PAHs in the investigated flames. Numerical simulations were performed with laminarSMOKE code, a CFD code specifically conceived to handle large kinetic mechanisms. The simulation results were able to follow the observed effects of n-butanol addition from the experimental results. In particular, unsaturated hydrocarbons, especially C6–C16 aromatics, were predicted satisfactorily. The reaction flux analysis revealed that benzene precursors, especially C3 radicals, increase significantly with increasing inlet mole fraction of n-butanol. This enhances the formation of phenyl and benzyl radicals, which are important PAH precursors. Reactions of benzyl, phenyl radicals and benzene with C2–C3 species are the major formation pathways for indene and naphthalene. And PAHs with more carbon atoms are dominantly formed from naphthyl and indenyl radicals.

A dynamic adaptive chemistry scheme with error control for combustion modeling with a large detailed mechanism

A new error controlled dynamic adaptive chemistry (EC-DAC) scheme is developed and validated for ignition and combustion modeling with large, detailed, and comprehensively reduced n-heptane and n-decane mechanisms. A fuel oxidation progress variable is introduced to determine the local model reduction threshold by using the mass fraction of oxygen. An initial threshold database for error control is created according to the progress variable in a homogeneous ignition system using a detailed mechanism. The threshold database tabulated by the fuel oxidation progress variable is used to generate a dynamically reduced mechanism with a specified error bound by using the Path Flux Analysis (PFA) method. The method leads to an error-controlled kinetic model reduction according to the local mixture reactivity and improves the computation efficiency. Numerical simulations of the homogeneous ignition of n-heptane/air and n-decane/air mixtures at different initial conditions are conducted with one detailed and one comprehensively reduced mechanism involving 1034 and 121 species, respectively. The results show that the present algorithm of error-controlled adaptive chemistry scheme is accurate. The computation efficiency is improved by more than one-order for both mechanisms. Moreover, unsteady simulations of outwardly propagating spherical n-heptane/air premixed flames demonstrate that the method is rigorous even when transport is included. The successful validation in both ignition and unsteady flame propagation for both detailed and reduced mechanisms demonstrates that this method can be efficiently used in the direct numerical simulation of reactive flow for large kinetic mechanisms.