Dynamic Adaptive Chemistry Method Research Articles

Tangential stretching rate analysis of DRGEP-based automated target species selection dynamic adaptive chemistry method

Automated target species selection dynamic adaptive chemistry (automated DAC) based on the directed relation graph with error propagation is a dynamic mechanism reduction method that can alleviate the huge computational overhead of high-precision large eddy simulations. However, the accuracy of this algorithm is often assessed based on the temperature and ignition delay time, which are a posteriori results and do not reflect the physicochemical nature. Because of this, we proposed a new criterion based on the tangential stretching rate (TSR) value to study the automated DAC method from the perspective of chemical reactions, which can characterize the most energetic reaction mode. First, we tested the new criterion by applying it to the analyses of the simulations adopting the detailed mechanism and automated DAC. The results verified the performance of the new criterion and showed that a new TSR value oscillation error phenomenon was discovered by it. This error is caused by the inappropriate cut-off of the important species and chemical reactions in the DAC-generate skeleton mechanisms. Second, we studied the measures to eliminate this newly emerged error. We found that reducing the search threshold, increasing the number of target species of the automated DAC method, and modifying the automated DAC to the time-correlated automated DAC by adding a time correlation term can alleviate this TSR value oscillation error. In addition, constant pressure autoignition simulations of methane/air were implemented to support this study. The TSR-computational singular perturbation participation indices were also introduced for assistance.

Dynamic adaptive chemistry with mechanisms tabulation and in situ adaptive tabulation (ISAT) for computationally efficient modeling of turbulent combustion

To deal with the major challenges of combustion simulation with detailed chemical kinetics, a new dynamic adaptive chemistry method with mechanisms tabulation (DAC-ST: DAC with single tabulation) and a method in which DAC-ST is combined with the ISAT (In Situ Adaptive Tabulation) approach (DAC-DT: DAC with double tabulation) to accelerate numerical calculation of large chemical mechanisms in turbulent combustion flows are proposed. Compared to the conventional DAC, DAC-ST expedites calculations by tabulating and reusing the reduced mechanisms, and DAC-DT further expedites the calculations by reducing the number of direct ordinary differential equations (ODEs) integrations and tabulating and retrieving the solutions. The correlation of the two mechanisms is evaluated by a pre-specified criterion comprising temperature, pressure, fuel, oxygen, OH, CH2O, and HO2, and can be updated by a user-specified threshold value. The proposed methods were validated in a piloted partially premixed methane/air diffusion flame (Sandia Flame D) with the 53-species GRI-Mech 3.0 and a hydrogen fueled model scramjet combustor (DLR) with 9 species and 27 reactions. The results indicate that the two proposed methods can accurately capture the flame structure and that the relative percentage errors of temperature and species concentration are well-controlled and proportional to the threshold values. Further, compared to the conventional DAC, detailed diagnostics show that DAC-ST and DAC-DT reduce the computational overhead of mechanism reduction by factors of 41 and 96 for Flame D and 165 and 365 for DLR, respectively. They also speed up ODE integration by factors of 3.9 and 7.8 for Flame D and 1.6 and 4.0 for DLR, respectively. The successful validation demonstrates that the two proposed methods can be efficiently used in the simulation of reactive flow for detailed kinetic mechanisms, especially for the case with a large number of cells, e.g., DNS.

On the application of tabulated dynamic adaptive chemistry in ethylene-fueled supersonic combustion

The demands for extending the limiting operation conditions and enhancing the combustion efficiency of scramjets raise new challenges to the research of reliable robust and controllable flame stabilization in supersonic flows. In the present study, Large Eddy Simulation of flame stabilization in a realistic supersonic combustor, employing the tabulation of dynamic adaptive chemistry (TDAC) method were conducted, in comparisons with other relevant chemistry treatment methods, i.e., dynamic adaptive chemistry (DAC), global skeletal mechanism, and detailed mechanism. The wall pressures, the pseudo one-dimensional metrics, combustor global performance and flame structures are all well reproduced by the DAC/TDAC methods compared with the experimental measurements and the benchmark predictions by the detailed mechanism, while the global skeletal mechanism fails to predict the flame stabilization characteristics. The reason for the discrepancy induced by the skeletal mechanism in the flame stabilization simulation was further illustrated through reaction path analyses. Regarding the computational efficiency, the DAC method shows high efficiency for complex reaction systems, with an almost linear increasing speedup factor with the increase of species number. The TDAC method almost doubly further improves the DAC efficiency. The DAC/TDAC methods show great potential of alleviating the huge computational cost while improving the chemistry fidelity for supersonic combustion especially for flame stabilization modeling.

Study of SiCl4/H2/O2 chemical kinetics and its application to fused silica glass synthesis

ABSTRACTA comprehensive computational model is first presented, which is capable of describing multispecies turbulent reacting flow and particle formation and deposition. Then a detailed mechanism for SiCl4/H2/O2 reaction system is proposed and applied to the prediction of a counterflow laminar diffusion flame. Good agreement in flame temperature is obtained between numerical results and experimental data, and the augmented 30-reaction SiCl4 submechanism in the detailed mechanism has a significant impact on the SiO2 formation rate. To employ the detailed mechanism in three-dimensional simulations, the reduction of reaction mechanism is performed to eliminate inconsequential species and reactions. The physiochemical process in a silica glass furnace is first simulated with the detailed mechanism to obtain realistic sample compositions. Then, the directed relation graph (DRG) method is employed to construct three comprehensive skeletal mechanisms by considering all accessed compositions in the fused silica glass synthesis, and further analyzethe governing chemical kinetics. The predicted total SiO2 deposition on glass ingot is found to depend on mechanism reduction, and the species SiCl and HOCl are important for the formation of SiO2 and OH. While only two species and seven reactions are removed from the detailed mechanism even though the reduction threshold error of 0.15 has been employed. It is also found that the number of important chemical species and reactions is significant only in a narrow region, which indicates a great potential in local adaptive mechanism reduction. Therefore, the dynamic adaptive chemistry (DAC) is used to generate a small locally valid skeletal mechanism. For the same accuracy in temperature and SiO2 mass fraction, DAC method obtains higher speedup factor than that of DRG method.

Computational acceleration of multi-dimensional reactive flow modelling using diesel/biodiesel/jet-fuel surrogate mechanisms via a clustered dynamic adaptive chemistry method

This study proposes a clustered dynamic adaptive chemistry (CDAC) method, which uses an iterative K-means algorithm to partition the computational cells into different groups according to their similarity in terms of the temperature and significant species compositions. Taking advantage of the clustered cells, the averaged thermo-chemical properties of the cells in the respective clusters are then used to identify the adaptive dynamic reduced chemistry through the method of direct relation graph with error propagation (DRGEP). Moreover, the integration of the chemical source term, which commonly dominates the computational effort in reactive flow simulations, is now performed using the dynamic adaptive reduced chemistry at the cluster level instead of the cell level. With this CDAC method, the on-the-fly DRGEP process as well as the chemistry integration only needs to be conducted at the cluster level, dramatically reducing the unnecessary repeated computation for similar computational cells. In addition, the adaptive dynamic reduced chemistry further accelerates the chemistry integration process due to less ordinary differential equations (ODEs) to be solved for each cluster. This newly proposed CDAC method was tested in multi-dimensional homogeneous charged compression ignition engine (HCCI), direct injection compression ignition (DICI) engine and constant volume chamber combustion (CVCC) fueled with diesel, biodiesel and kerosene through the use of their respective surrogate fuel mechanisms under different operating conditions. The performance of CDAC in terms of accuracy and efficiency were extensively analyzed and discussed using different user-defined parameters, mechanisms with different surrogate components and number of species as well as different meshes with different number of grid cells. Based on this analysis, the error tolerances in DRGEP and the error tolerances of the temperature and significant species’ mass fraction are recommended as: εd ≤ 0.001, εT ≤ 20 K and εY ≤ 0.01 to achieve less than 0.1% integral error. With these recommended user-defined tolerances, it can be observed that the current CDAC method is able to accurately predict the in-cylinder pressure as well as the species profile in HCCI, DICI and the flame lift-off length in CVCC when compared with the full chemistry calculations as well as the experimental results. Moreover, with the recommended user defined error tolerances and a chemical kinetic model of 112 species, this CDAC method is capable of achieving a computational time speed-up factor of more than 3 compared to the conventional DAC and of almost 5 compared to the full chemistry. Finally, the CDAC method coupled CFD was applied to simulate a diesel DICI engine under three different engine speeds with a detailed primary reference fuel (PRF) mechanism of more than 1000 species. The 3-D simulation could be finished in acceptable CPU time while being able to well capture the experimental in-cylinder pressure and heat release rate for all the three engine speeds.

A numerical investigation on the injection timing of boot injection rate-shapes in a kerosene-diesel engine with a clustered dynamic adaptive chemistry method

In this study, we conducted a numerical investigation on the effect of injection timing of boot injection rate shape on the combustion and emission characteristics in a direct injection compression ignition (DICI) engine fueled with kerosene/diesel blending. Considering the complex surrogate in kerosene chemical mechanisms and the huge computational workload in multi-dimensional engine simulations, we employed a clustered dynamic adaptive chemistry method (CDAC) to accelerate the chemistry integration process. This study firstly specified the user-defined parameters in this CDAC method by sensitivity analysis in a HCCI and DICI engine with different user-defined parameter combinations. With these specified parameters, CDAC is then validated by comparing its predicted in-cylinder pressure with the full chemistry ones. It is found that the current CDAC method could reduce the computational time by more than 60% compared with the full chemistry CPU time. CDAC, subsequently, is used to conduct the numerical investigation on the injection timing of boot injection rate shapes. Four different boot injection rate shapes are simulated and compared with the normal rectangular injection. The effect injection timing of the boot injection rate on the engine performance and combustion/emission characteristic is then analyzed in detail. It is found that the change of start of injection (SOI) in boot injection has little influence of the ignition delay in the DICI engine fuelled with diesel and kerosene blending due to the high cetane number of diesel and better volatility of kerosene. In addition, with kerosene addition into the diesel combustion, it is observed that the CO emission could be reduced at all the varied SOI.

A Bundled Dynamic Adaptive Chemistry Method toward Accelerating Combustion Chemistry Integration

A bundled dynamic adaptive chemistry (BDAC) method is proposed to accelerate the combustion chemistry integration. Based on the basic dynamic adaptive chemistry (DAC) method (which dynamically reduces the chemical mechanism in the simulation within every time step in combustion simulations), this BDAC method bundles the similar conditions and applies DAC on the bundled computational domain both spatially and temporally. In this way, the cost of the chemistry reduction process could be greatly saved. This method was coupled with a semi-implicit integration scheme and a conventional implicit solver and then tested in methane/air auto-ignition. The performance of BDAC in terms of accuracy and efficiency is subsequently compared with the original solver and the DAC method. It is shown that this method is able to dramatically reduce the computational cost, while still maintain acceptable accuracy. This method is very promising to be employed in multidimensional CFD simulations to reduce computational cost.

Effects of the turbulence model and the spray model on predictions of the n-heptane jet fuel–air mixing and the ignition characteristics with a reduced chemistry mechanism

Reynolds-averaged Navier–Stokes simulations with an improved spray model and a realistic chemistry mechanism are performed for turbulent spray flames under diesel-like conditions in a constant-volume chamber. Comprehensive numerical analyses including two turbulence models (the renormalisation group k– ε model and the standard two-equation k– ε model) with different model coefficients are made. The distribution of the fuel mixture fractions is a very important factor affecting the combustion process. In this study, we also use the entrainment gas-jet model, modifications of the the spray model coefficient and two turbulence models to investigate extensively the influence of the gas-jet theory model on the fuel–air mixture process. First, a non-reacting case is validated by comparing the liquid-phase penetration and the vapour-phase penetration and also the mixture fractions at different axis positions. Second, approriate methods are confirmed according to accurate mixture fraction distributions to validate the combustion process. Because of the large number of species and reactions, the calculation of chemically reacting flows is unaffordable, particularly for three-dimensional simulations. Hence, the dynamic adaptive chemistry method for efficient chemistry calculations is extended in this work to reduce the computational cost of the spray combustion process when a reduced chemistry mechanism is used. The results show that, in the evaporation case, the gas-jet theory model can be used to obtain a relatively accurate fuel vapour penetration length with different influential factors and that improved numerical methods can effectively reduce the mesh dependence for the spray evaporation process. It is demonstrated that the Schmidt number Sc and the turbulence models significantly influence the mixture fraction distribution. Very good agreement with available experimental data is found concerning the ignition delay time and the flame lift-off length for different oxygen concentrations owing to the accurate fuel mixture fraction.

Multi-scale modeling of dynamics and ignition to flame transitions of high pressure stratified n-heptane/toluene mixtures

The transitions from ignition to flames as well as the combustion dynamics in stratified n-heptane and toluene mixtures are numerically modeled by a correlated dynamic adaptive chemistry method coupled with a hybrid multi-timescale method (HMTS/CO-DAC) in a one-dimensional constant volume chamber. The study attempts to answer how the kinetic difference between n-alkanes and aromatics leads to different ignition to flame transitions and knocking-like acoustic wave formation at low temperature and engine pressure conditions with fuel stratification. It is found that the low temperature chemistry (LTC) and fuel stratification of n-heptane leads to the formation of multiple ignition fronts. Four different combustion wave fronts, a low temperature ignition (LTI) front followed by a high temperature ignition (HTI) front, a premixed flame (PF) front, and a diffusion flame (DF) front, are demonstrated. The fast LTI and HTI wave front propagation leads to a shock-like strong acoustic wave propagation, thus strongly modifying the dynamics of the subsequent diffusion and premixed flame fronts. On the other hand, for the toluene mixture, due to the lack of LTC, only two combustion wave fronts are formed, a HTI front and a premixed flame front, exhibiting stable flow field and no formation of shock-like acoustic wave. The dynamics of transition from combustion to shock waves is further analyzed by using a modified Burgers’ equation. The analysis for n-heptane/air mixture indicates that both the onset of LTI and the strong dependency of HTI on the equivalence ratio can either promote or attenuate the transition from strong acoustic wave to shock wave. However, the toluene/air mixture exhibits no coupling with acoustic wave, suggesting that the rich LTC reactivity with fuel stratification, specific to the n-alkane chemistry, can lead to knocking and acoustic formation.

Dynamic adaptive chemistry with operator splitting schemes for reactive flow simulations

A numerical technique that uses dynamic adaptive chemistry (DAC) with operator splitting schemes to solve the equations governing reactive flows is developed and demonstrated. Strang-based splitting schemes are used to separate the governing equations into transport fractional substeps and chemical reaction fractional substeps. The DAC method expedites the numerical integration of reaction fractional substeps by using locally valid skeletal mechanisms that are obtained using the directed relation graph (DRG) reduction method to eliminate unimportant species and reactions from the full mechanism. Second-order temporal accuracy of the Strang-based splitting schemes with DAC is demonstrated on one-dimensional, unsteady, freely-propagating, premixed methane/air laminar flames with detailed chemical kinetics and realistic transport. The use of DAC dramatically reduces the CPU time required to perform the simulation, and there is minimal impact on solution accuracy. It is shown that with DAC the starting species and resulting skeletal mechanisms strongly depend on the local composition in the flames. In addition, the number of retained species may be significant only near the flame front region where chemical reactions are significant. For the one-dimensional methane/air flame considered, speed-up factors of three and five are achieved over the entire simulation for GRI-Mech 3.0 and USC-Mech II, respectively. Greater speed-up factors are expected for larger chemical kinetics mechanisms.

A dynamic adaptive chemistry scheme for reactive flow computations

An on-the-fly kinetic mechanism reduction scheme, referred to as dynamic adaptive chemistry (DAC), has been developed to incorporate detailed chemical kinetics into reactive flow computations with high efficiency and accuracy. The procedure entails reducing a detailed mechanism to locally and instantaneously accurate sub-mechanisms at each hydrodynamic time step of the calculation, and consequently no a priori information regarding simulation conditions is needed. The reduction utilizes an extended version of the directed relation graph (DRG) method in which the edges are weighted by a value that measures the dependence of the tail species (vertex) on the head species. An R-value is then defined at each vertex as the maximum of the products of these weights along all paths to that vertex from an initiating species. Active species are identified by their R-values exceeding a threshold value, εR, using a modified breadth-first search (BFS) that starts from a pre-defined set of initiating species. Chemical kinetics equations are then formulated with respect to the active species, with the inactive species considered only as third body collision partners. The DAC method is implemented into CHEMKIN and tested by simulating homogeneous charge compression ignition (HCCI) combustion using detailed and pre-reduced n-heptane mechanisms (578 species and 178 species, respectively) as the full mechanisms. The DAC scheme reproduces with high accuracy the pressure curves and species mass fractions obtained using the full mechanisms. The on-the-fly mechanism reduction scheme introduces minimal computational overhead and achieves more than 30-fold time reduction in calculations using the 578-species mechanism.