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.

We have developed an accelerated multi-zone model for engine cycle simulation (AMECS) of homogeneous charge compression ignition (HCCI) combustion. This model incorporates chemical kinetics and is intended for use in system-level simulation software. A novel methodology to capture thermal stratification in the multi-zone model is proposed. The methodology calculates thermal stratification inside the cylinder based on a single computational fluid dynamics (CFD) calculation for motored conditions. CFD results are used for tuning zone heat loss multipliers that characterize wall heat loss from each individual engine zone based on the assumption that these heat loss multipliers can then be used at operating conditions different from those used in the single CFD run because the functional form of thermal stratification is more dependent on engine geometry than on operating conditions. The model is benchmarked against detailed CFD calculations and fully coupled HCCI CFD chemical kinetics calculations. The results indicate that the heat loss multiplier approach accurately predicts thermal stratification during the compression stroke and (therefore) HCCI combustion. The AMECS model with the thermal stratification methodology and reduced gasoline chemical kinetics shows good agreement with boosted gasoline HCCI experiments over a range of operating conditions, in terms of in-cylinder pressure and heat release rate predictions. The computational advantage of this method derives from the need for only a single motoring CFD run for a given engine, which makes the method very well suited for rapid HCCI calculations in system-level codes such as GT-Power, where it is often desirable to evaluate consecutive engine cycles.

Large reaction mechanisms are often used to describe the combustion behavior of transportation-relevant fuels like gasoline, where these are typically represented by surrogate blends, e.g., n-heptane/iso-octane/toluene. We describe efforts to quantify the uncertainty in the predictions of such mechanisms at realistic engine conditions, seeking to better understand the robustness of the model as well as the important reaction pathways and their impacts on combustion behavior. In this work, we examine the importance of taking into account correlations among reactions that utilize the same rate rules and those with multiple product channels on forward propagation of uncertainty by Monte Carlo simulations. Automated means are developed to generate the uncertainty factor assignment for a detailed chemical kinetic mechanism, by first uniquely identifying each reacting species, then sorting each of the reactions based on the rate rule utilized. Simulation results reveal that in the low temperature combustion regime for iso-octane, the majority of the uncertainty in the model predictions can be attributed to low temperature reactions of the fuel sub-mechanism. The foundational, or small-molecule chemistry (C 0-C 4) only contributes significantly to uncertainties in the predictions at the highest temperatures (Tc=900 K). Accounting for correlations between important reactions is shown to produce non-negligible differences in the estimates of uncertainty. Including correlations among reactions that use the same rate rules increases uncertainty in the model predictions, while accounting for correlations among reactions with multiple branches decreases uncertainty in some cases. Significant non-linear response is observed in the model predictions depending on how the probability distributions of the uncertain rate constants are defined. It is concluded that care must be exercised in defining these probability distributions in order to reduce bias, and physically unrealistic estimates in the forward propagation of uncertainty for a range of UQ activities.

This study investigates the autoignition behavior of two gasoline surrogates doped with an alkyl nitrate cetane enhancer, 2-ethy-hexyl nitrate (2EHN) to better understand dopant interactions with the fuels, including influences of accelerating kinetic pathways and enhanced exothermicity. A primary reference fuel (PRF) blend of n-heptane/iso-octane, and a toluene reference fuel (TRF) blend of n-heptane/iso-octane/toluene are used where the aromatic fraction of the latter is set to 20% (liquid volume), while the content of n-heptane is adjusted so that the overall reactivity of the undoped fuels is similar, e.g., Anti-Knock Index (AKI) of ~91, Cetane Number (CN) ~25. Doping levels of 0.1, 1.0 and 3.0% (liquid volume basis) are used where tests are conducted within a rapid compression machine (RCM) at a compressed pressure of 21 bar, covering temperatures from 675 to 1025 K with stoichiometric fuel-oxygen ratios at O 2 = 11.4%.At the experimental conditions, it is found that the doping effectiveness of 2EHN is fairly similar between the two fuels, though 2EHN is more effective in the aromatic blend at the lowest temperatures, while it is slightly more effective in the non-aromatic blend at intermediate temperatures. Kinetic modeling of the experiments indicates that although some of the reactivity trends can be captured using a detailed model, the extents of predicted Cetane Number enhancement by 2EHN are too large, while differences in fuel interactions for the two fuels results in excessive stimulation of the non-aromatic blend. Sensitivity analysis using the kinetic model indicates that the CH 2 O and CH 3 O 2 chemistry are very sensitive to the dopant at all conditions. The rate of 2EHN decomposition is only important at low temperatures where its decomposition rate is slow due to the high activation energy of the reaction. At higher temperatures, dopant-derived 3-heptyl radicals are predicted to play an important role stimulating ignition. Finally, nitrogen chemistry becomes important only at the highest doping levels, primarily through the formation of methyl and ethyl nitrite, and nitric acid.

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