Dynamic Adaptive Chemistry Research Articles

A chemistry load balancing model for OpenFOAM

Efficient simulation tools are crucial for studying complex systems such as reacting flows where computational costs of computing chemical reaction rates can vastly exceed the costs for the integration of the convective and diffusive transport terms. Load imbalance in parallel computing poses a significant challenge for massively parallel reacting flow simulations. In response, a novel load balancing library has been developed to enhance OpenFOAM's solver performance in parallel environments. This library seamlessly integrates with OpenFOAM, offering ease of use and applicability to any OpenFOAM reacting solver incorporating finite-rate chemistry. In addition, it supports the standard and the dynamic adaptive chemistry model (TDAC) of OpenFOAM.The newly developed load-balanced standard and TDAC models address significant load imbalances by exchanging information between processes via MPI calls and tracking ODE solution times on a cell level. The TDAC model introduces dual tables on each core and enables immediate addition of computed solutions, enhancing computational efficiency. Validation on various test cases, including simulations on the HLRS Hawk supercomputer with up to 8000 cores, confirms identical results compared to the original unbalanced models, with notable speed-up factors of up to 6 for the standard and 5 for the TDAC model. Despite non-linear scaling at lower cell count per processor, load-balanced models consistently outperform unbalanced counterparts, making them the preferred choice for reacting flow simulations in OpenFOAM.

Combustion simulations of scramjet combustor using reduced mechanism of surrogate fuel for regenerative cooling pyrolysis products

Taking the surrogate fuel (64% ethylene and 36% methane in mole percentage) for regenerative cooling pyrolysis products used in HIFiRE-2 scramjet combustor as an example, present work systematically explores the workflow of the integrated mechanism reduction for surrogate fuel of pyrolysis products, the kinetic performance verification of the preferred reduced mechanism, and the combustion simulation application of the reduced mechanism in scramjet combustor. A static integrated reduction strategy is performed to obtain reduced mechanism for the surrogate fuel with the NUIGMech1.2 as detailed mechanism under wide conditions for temperature range of 900 - 1800 K, pressure range of 1 - 4 atm, and equivalence ratio range of 0.25 - 5.0. A reduced mechanism (34 species and 181 reactions)with remarkably reduced size is obtained, which presents favorable performance incomprehensive kinetic validations. With this compact and high-fidelity reduced mechanism, the combustion simulations for the scramjet combustor are carried out combining with tabulation of dynamic adaptive chemistry for run-time speed-up. The simulation results of static pressure profiles obtained for cold and hot states match well with the experimental measurements for the two conditions with flight Mach number of 5.84 and 6.50. Meanwhile, the flow and combustion characteristics of the two conditions are investigated based on simulation results. The integrated reduction strategy and systematic kinetic verification used in present work provide reference values for the application of more complex surrogate fuel mechanisms in scramjet combustor combustion simulation.

Application of static integrated skeletal reduction and tabulation of dynamic adaptive chemistry in the combustion simulation of ethylene-fueled scramjet combustor.

The high-fidelity reduced mechanism is one of the key elements in the combustion simulation of scramjet combustors to reveal their combustion and flow phenomena. In the present work, the hierarchically constructed NUIGMech1.2 (2857 species and 11 814 reactions) is applied to the combustion simulation of an ethylene-fueled scramjet combustor using the method of static integrated skeletal reduction and tabulation of dynamic adaptive chemistry (TDAC). The integrated skeletal reduction strategy successively consists of species elimination using the revised directed relation graph with error propagation method of fixed species scheme and improved sensitivity analysis method, and reactions elimination based on computational singular perturbation importance index. A preferred ethylene skeletal mechanism (26 species and 117 reactions) is obtained through the integrated skeletal reduction strategy under target working conditions of temperature range of 900-1800 K, pressure range of 1-4 atm, and equivalence ratio range of 0.25-5.0. The compact skeletal mechanism is comprehensively validated against the experimental results of ignition delay times, laminar flame speeds, and key species concentration profiles. Meanwhile, it shows consistent results with the detailed mechanism on the adiabatic flame temperature profiles and "S"-curves. When applying this skeletal mechanism to combustion simulations of ethylene-fueled scramjet combustor with double parallel cavities, the path flux analysis method and in situ adaptive tabulation algorithm of TDAC is further utilized to speed up the chemical reaction solution process at run-time. Under the scramjet and ramjet modes, the corresponding simulation results in terms of flame luminosity images, schlieren images, and static pressure distributions, coincide well with those of experimental measurements. The combustion and flow characteristics of the two modes are investigated and analyzed comparatively based on above results and combustion performance parameters. Present work contributes to the application of fuel kinetic mechanisms in scramjet combustor combustion simulation.

Computational investigation of NOx emission in biogas combustion.

Landfills and anaerobic digesters in the waste treatment processes generate biogas. Biogas can be used as a fuel and excess biogas is typically burned in a flare to reduce the greenhouse effect. However, burning biogas produces several pollutants, including CO2, NOx, and SO2. To minimize these emissions, the amount of excess air used in the combustion process needs to be considered, which has a significant impact on NOx emissions. This study developed a Computational Fluid Dynamics (CFD) model to simulate a small-scale biogas combustion system and analyses the effect of excess air on heat output and NOx emissions during biogas combustion. The GRI-Mech reaction mechanism was used to simulate reactions, and the model was validated by comparing it to experimental data from the DLR-Stuttgart CH4/H2/N2 Jet Flame. To reduce computational costs, a Tabulation of Dynamic Adaptive Chemistry (TDAC) algorithm was used to dynamically adapt the reaction mechanism in real time. Turbulence in the DLR flame was simulated using Reynolds-Averaged Navier-Stokes (RANS). The CFD model used a co-flow of a natural draft to provide additional air, while the air was premixed with fuel. The CFD model was used to simulate various premixed equivalent ratios, and the resulting emissions and heat outputs were compared. The study found that the optimal premixed equivalent ratio for the studied system was between 0.85 and 1.1, as this range produced the highest temperature and lowest NOx emissions. This model facilitates emission analysis of gas-phase combustion systems.

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.

Assessment of dynamic adaptive chemistry with tabulated reactions for the simulation of unsteady multiregime combustion phenomena

Solving chemistry is an integral part of reacting flow simulations, usually dominating the computational cost. Among the different strategies to accelerate the solution of chemistry and to achieve realizable simulations, the use of Dynamic Adaptive Chemistry (DAC) stands out among other methods. DAC methods are based on the use of reduced mechanisms generated from local conditions. The reduction process is computationally expensive and strategies for reducing the frequency of reduction and the re-utilization of the generated reduced mechanisms are key in making DAC methods computationally affordable. In this study, a new method hereby referred as Tabulated Reactions for Adaptive Chemistry (TRAC) is proposed to correlate chemical states with their reduced mechanisms in order to reduce both the frequency of reduction and to allow for re-utilization of reduced chemical schemes. TRAC introduces a mechanism tabulation strategy based on the use of a low-dimensional space that defines the thermo-chemical conditions for which specific reduced reaction mechanisms are stored. Chemistry reduction is achieved by the use of Path Flux Analysis (PFA) with a reaction rate-sensitivity method to achieve further reduction in the reaction mechanisms. The new TRAC proposal is applied to various canonical transient problems and the results are compared with reference solutions obtained from detailed chemistry calculations. A speedup of about 4x was achieved with TRAC while maintaining an error under 3% in the prediction of the major and minor species, flame structure, and flame propagation.

A dynamic load balancing model coupled with DAC and ISAT for a stochastic turbulent combustion model

Due to the composition-dependent stiffness of chemistry, simulations of reactive turbulent flows may present computational load imbalance among parallel processes when spatial decomposition is used for parallelisation, causing high CPU idle time and waste of computational resources. To increase computational efficiency, a dynamic load balancing (DLB) model is proposed to redistribute computational load among computing cores. The DLB model exploits a decomposition in the mixture fraction space with two dynamic adjusting decomposition strategies to realise load redistribution. The DLB model is suitable for the integration of chemistry on stochastic particles in hybrid Eulerian/Lagrangian turbulent combustion models in which the Eulerian field is conventionally decomposed statically in physical space in a way that balances the computational load for the solution of the Navier-Stokes equation but which does not generally lead to balanced load for the computation of the composition fields. Here it is tested using an OpenFOAM-based platform, mmcFoam, which is a comprehensive object-orientated C++ library for stochastic turbulent combustion modelling. Apart from direct integration (DI) for chemistry, the DLB model is also coupled with dynamic adaptive chemistry (DAC) and in situ adaptive tabulation (ISAT), which allows for extra speedup. The performance of the coupled models is validated and assessed for two laboratory flame conditions that exhibit different levels of computational load imbalance. Overall, the DLB model effectively balances the computational load distribution and increases the effective usage of computing power, shortening the simulation wall time required. Moreover, a strong scaling test is carried out using up to 512 cores. Although all approaches have sub-ideal scalability, the scalability of each with DLB is significantly better than without DLB. While DLB-ISAT has relatively poor scalability compared to the DI- and DAC-based methods, DLB-ISAT still ranks the fastest among the algorithms in all scaling trials.

Use of dynamic adaptive chemistry and dynamic cell clustering in computational fluid dynamics to accelerate calculation of combustion simulation of diesel engine

Based on the theory of dynamic adaptive chemistry and dynamic cell clustering, a 3D model for fuel combustion in the cylinder is constructed to study the fast calculation algorithm to accelerate the engine combustion simulation and analyze the impacts of the basic structure and the main parameters of dynamic adaptive chemistry and dynamic cell clustering on diesel engine simulation. It is found that the error threshold value of the dynamic adaptive chemistry algorithm should be set appropriately to ensure the accuracy of the simulation, and the dynamic adaptive chemistry algorithm should employ different initial search species and threshold values to meet different research objectives. Regarding the dynamic cell clustering algorithm, the data accuracy in the overall spatial range can be guaranteed, but the local location information will be missing, which will have a great impact on the prediction of pollutants. Therefore, suitable cell clustering conditions should be selected to meet the requirements of different targets.

Comprehensive Analyses on the Influential Factors in Supersonic Combustion Simulation Using Dynamic Adaptive Chemistry Method

ABSTRACT To overcome the major challenge of reactive flow simulation for chemical kinetics dominated flame dynamics in supersonic combustion, on-the-fly mechanism reduction for high fidelity simulation of scramjet becomes mandatory. For dynamic adaptive chemistry (DAC) methodology, there are three major factors controlling the accuracy and efficiency of the overall simulation, namely, the mechanism reduction method, error threshold value ε DAC , and search initiating species (SIS). In the present work, systematic investigations of the three influential factors were conducted for large eddy simulation of ethylene-fueled supersonic combustion within a unified DAC framework. The results show that all the four mechanism reduction methods, i.e., DRG, DRGEP, PFA, and DAC-L, are adequate for the combustor’s global performance prediction regarding the wall pressure, stable combustion productions, and temperature. However, for intricate flame stabilization characteristics, the DRG, DRGEP, and DAC-L methods yield comparable prediction accuracy in radical distributions, whereas the PFA method leads to relatively large discrepancies compared to direct integration with the detailed mechanism. The DRGEP method obtains the best balance between numerical accuracy and computational efficiency among the four methods, while the PFA method is the most computationally demanding one. Regarding the mechanism reduction error threshold value, the relative errors in physical property predictions increase as the relaxation of the error threshold value. And the comparative study suggests that the ε DAC should not exceed 10 − 4 for high fidelity simulations of supersonic combustion. Furthermore, the stable species combination, namely, fuel, O2, and N2 incurs larger relative errors in radical mass fraction prediction than the combination including fuel and intermediate species HO2 and CO. Nevertheless, the latter is less computationally efficient than the former as it requires 15% more CPU time to solve the stiff ODE system of the resultant skeletal mechanism. It should be noted that the computational overheads for mechanism reduction under various ε DAC values and SIS combinations are almost the same, and the overall computational efficiency is mainly determined by the CPU time for solving the stiff ODE system of the size-reduced skeletal mechanisms.

Nonequilibrium Effects in Hypersonic Combustion Modeling

A zonal nonequilibrium model (ZNM) based on the concept of zonal representation of the nonequilibrium relaxation times is proposed. ZNM, together with dynamic zone flamelet model, zonal dynamic adaptive chemistry, and zonal in situ adaptive tabulation, forms the complete framework for nonequilibrium combustion modeling. The framework is then applied to the modeling of a high- scramjet operating at Mach 12 based on improved delayed detached eddy simulation with a total of 92.52 million cells. The nonequilibrium effects exerted noticeable influences on the flow characteristics and engine performance through two main mechanisms, i.e., cooling and radical farming. Under a reduced flame temperature, the mixing efficiency increases due to the lower viscosity, whereas both the combustion efficiency and thrust recede. The Borghi diagram incorporating the compressibility effect shows that nearly 2/3 of the zones are in the flamelet mode, and the flamelet mode distributes wider under nonequilibrium.

Numerical study on the effect of injection pressure on high-pressure diesel spray

This paper analyses the effect of injection pressures on spray and flame structures under high-pressure diesel spray conditions using a three-dimensional Reynolds-Average Navier-Stokes (RANS) model. The simulations are conducted using three kinetic mechanisms known as Luo (106 species), Cai (57 species) and Yao (54 species). Tabulation and Dynamic Adaptive Chemistry (TDAC) solver is adopted to reduce the computational cost. The reacting results obtained using the Luo kinetic mechanism demonstrate the best agreement with the experiment. The Cai and Yao chemistry models overpredict and underpredict the ignition delay time respectively, whereas both mechanisms overpredict the flame lift-off length, particularly at higher injection pressures. It is found that the error for the ignition delay time is larger at higher injection pressures while the opposite is true for the flame lift-off length. The results reveal that at lower injection rates, the cool flame, represented by the CH2O, occurs at richer mixture closer to the nozzle. On the other hand, the heat release rate is lower while the CO emission is higher. Detailed analysis on scalar fields shows that the Cai and Yao mechanisms lead to more consistent results in capturing the temperature field in terms of high-temperature reaction zone while Yao and Luo mechanisms result in a more consistent trend in producing the C2H2 species.

Evaluation of different models for turbulent combustion of hydrogen-air mixtures. Large Eddy Simulation of a LOVA sequence with hydrogen deflagration in ITER Vacuum Vessel

• LES with TFM model was validated vs H2 experiments. • Validated model was used to simulate ITER VV LOVA sequence with H2 combustion. • In LOVA sequence burning speed increased 1–3.5 times due to flame acceleration. • Maximum average VV pressure was 4–5.5 times to the initial one. The modelling of premixed combustion in the presence of a turbulent field in three-dimensional (3-D) confined scenarios was studied in this work, and applied to hydrogen combustion within ITER vacuum vessel (VV). Two different combustion approaches were tested with Large Eddy Simulation: a Flamelet Progress Variable (LES-FPV) and a Thickened Flame Model (LES-TFM). For the case of LES-TFM modelling, Dynamic Adaptive Chemistry (DAC) with a detailed kinetic mechanism for hydrogen combustion and in-situ adaptive tabulation (ISAT) methods were employed. Moreover, an adaptive meshing technique was used with the aim of tracking the flame front to ensure an adequate spatial resolution in this region. Experimental validation was performed to assess the ability of the different studied approaches to predict the flame burning speed, flame acceleration, and pressure evolution for lean H 2 -Air volume percent mixtures from 16 to 28 % propagating within a turbulent field. Results revealed that both approaches led to accurate predictions in terms of flame burning speed. When considering DAC and ISAT methods with detailed chemistry, LES-TFM model was found to be a cost-efficient solution. This model was used to analyse two loss of vacuum accident (LOVA) sequences within ITER VV. Results showed that turbulence might increase the flame burning speed by a factor of up to 3.5 for the case of big breaches (0.15 m 2 ) but it would not affect in case of breaches of 0.02 m 2 . Besides, results showed that autoignition with 2 kg of H 2 within the VV at 13.35 kPa might degenerate in detonation with average wall pressure levels ∼70 kPa.

Degree centrality of combustion reaction networks for analysing and modelling combustion processes

Combustion research still needs more advanced fundamental understanding of combustion chemistry and dynamics from molecule scale to particle. The latter is also needed for soot and nanoparticles formation and combustion system control such as homogeneous charge compression ignition engine, and flame regimes and instability. The complex interactions between hundreds of species linked within thousands of reactions continue to be a challenge to analyse and model. The focus on this paper is to develop a method to facilitate the modelling and analysing of the detailed kinetics chemistry of fuels combustion. Through the use of combustion reaction networks (CRNs) analysis of degree centrality, principal species are identified during a combustion process by exploiting the introduced definition of principal species. A principal, central or the more active species of a combustion process, at a specific time step or cell of area/volume mesh, is the more tied up to other species in the CRN and so have the largest value of degree centrality. The accuracy of the dynamic identification of principal species, locally adapted to the thermochemical conditions at each time step/cell of the simulated combustion process, used by the employed directed relation graphs method of mechanisms reduction, is proved. The simulations were carried out using an adjusted dynamic adaptive chemistry approach of detailed chemistry implementing. It is demonstrated that an ‘active’ species in a combustion system would not necessary be considered as a part of important species set needed for its predictive simulations.

An efficient finite-rate chemistry model for a preconditioned compressible flow solver and its comparison with the flamelet/progress-variable model

An efficient finite-rate chemistry (FRC) model is developed for a preconditioned compressible flow solver. The model uses a point-implicit stiff ODE solver and a correlated dynamic adaptive chemistry algorithm. With respect to the conventional FRC model using the double precision variable coefficient stiff ODE solver, the present work achieves an 8.6 times speed-up for chemistry calculation, and 6.4 times for total computation, when using a 20-species kinetics mechanism for methane/air flames. As an example problem, a piloted partially premixed methane/air jet flame (Sandia Flame D), with a relatively low level of local extinction and re-ignition, is considered, and both the new FRC-large eddy simulation (LES) and flamelet/progress-variable (FPV)-LES are conducted. The FRC-LES approach predicts larger time-averaged flame length, and better agrees with the measured value. This is because the instantaneous high-temperature zone for the FPV-LES case is significantly smaller than it's FRC-LES counterpart, especially in the downstream region. For spatial distribution of time-averaged statistics, the FPV-LES result agrees with the experimental data better. For conditional statistics in the mixture fraction space, the FRC-LES approach provides significantly better predictions. Near the stoichiometric region, in comparison with experimental data and the FRC-LES results, the FPV-LES approach predicts higher radical generation, but lower CO generation and heat release.

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.

An Exponential Integrator with Schur–Krylov Approximation to accelerate combustion chemistry computation

The Exponential Integrator with Schur–Krylov Approximation (EISKA) algorithm was developed for combustion applications. This algorithm combines the advantages of the explicit large step advancement of the exponential schemes and the dimension reduction effect of the Krylov subspace approximation, and was improved by introducing the Schur decomposition to control the rounding error. The EISKA based on the SpeedCHEM (SC) package was implemented to simulate a methane partially stirred reactor (PaSR) with pair-wise mixing model by considering the mechanisms of Li et al., GRI-Mech 3.0 and USC Mech II. Accuracy and computational efficiency of EISKA are systematically compared with those of DVODE. In the case of the Li mechanism which is a priori sufficiently small to be handled directly in combustion simulations, the computations were accelerated by a factor of 1.99 without losing accuracy. In the cases of GRI-Mech 3.0 and USC Mech II which are significantly larger than the Li mechanism, chemical reduction methods, namely the Correlated Dynamic Adaptive Chemistry (CoDAC) and the Multi-timescale (MTS) method were coupled with either DVODE or EISKA. The results show that the EISKA is faster than DVODE either with or without chemical reduction methods. Model results show that the best strategy is to use EISKA without any reduction method which leads to the same accuracy as compared to DVODE and acceleration factors of 2.61 and 2.19 for GRI-Mech 3.0 and USC Mech II, respectively.

Dynamic adaptive acceleration of chemical kinetics with consistent error control

The incorporation of detailed chemistry in combustion simulations is challenging due to the large number of chemical species and the wide range of chemical timescales. The performance of acceleration methods such as tabulation/retrieval strategies may deteriorate dramatically when large variation in the accessed composition space is present. In this study, a dynamic adaptive acceleration method (DAAM) is proposed, in which in situ adaptive tabulation (ISAT) or dynamic adaptive chemistry (DAC) is dynamically selected for chemistry integration based on the encountered composition inhomogeneity. The principle component analysis (PCA) of instantaneous representative compositions is employed to identify a low-dimensional subspace, in which the composition inhomogeneity of the computational cells is quantified through reconstructing the histogram of composition. ISAT is invoked for cells being in composition regions with high cell/particle numbers to avoid unnecessary tabulations and DAC is employed for the remaining ones by invoking on-the-fly reduction to generate small skeletal mechanisms for local thermo-chemical conditions and therefore accelerates the chemistry integration. A heuristic approach that dynamically adjusts the DAC reduction threshold based on the user-defined ISAT error tolerance has been proposed for DAAM, which enables a single, intuitive error control parameter for the combined use of these two methods and more importantly enables rigorous local error control. DAAM have been demonstrated in internal combustion engine (ICE) model simulations and premixed charge compression ignition (PCCI) engine simulations of n-heptane/air mixture, respectively. DAAM can improve the acceleration performance up to 50% compared to standalone ISAT while maintaining the same level of accuracy in temperature and species. It also shows advantage in speedup performance over the fixed ISAT-DAC method at the same level of accuracy.

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.

Application of cell agglomeration algorithm coupled with dynamic adaptive chemistry for transient engine simulation of diesel fuel

Comprehensively understanding the characteristics of chemical reactions that occur during the combustion process is necessary to improve the design of modern internal combustion engine. However, huge computational cost limits the application of detailed chemical mechanism for practical combustion technologies. Therefore, the objective of this study is to improve the computational efficiency of the chemical reaction in practical engine simulations based on cell agglomeration (CA). In this study, in situ adaptive tabulation (ISAT), dynamic adaptive chemistry (DAC), and CA algorithms are implemented into the KIVA code. CA coupled with DAC and ISAT methods are carried out to validate the optimal acceleration method for practical engine simulation. The results show that CA can significantly reduce the cost of chemistry calculations by effectively reducing the number of CFD cells in which the chemistry calculation need to be performed during HCCI engine and diesel engine simulations. A maximum speed-up factor of 261-fold can be achieved using a 171-species mechanism. The error tolerance can affect the computational accuracy, particularly while using larger chemical mechanism. For non-premixed combustion, the selected coordinates in the reduced-space species composition can further affect the computational accuracy. Overall, the CA-DAC method can achieve the best computational efficiency without lowering the computational accuracy.

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.