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© 2018 SAE International. All Rights Reserved. Digital engineering workflows, involving physico-chemical simulation and advanced statistical algorithms, offer a robust and cost-effective methodology for model-based internal combustion engine development. In this paper, a modern Tier 4 capable Cat® C4.4 engine is modelled using a digital workflow that combines the probability density function (PDF)-based Stochastic Reactor Model (SRM) Engine Suite with the statistical Model Development Suite (MoDS). In particular, an advanced multi-zonal approach is developed and applied to simulate fuels, in-cylinder combustion and gas phase as well as particulate emissions characteristics, validated against measurements and benchmarked with respect to the predictive power and computational costs of the baseline model. The multi-zonal SRM characterises the combustion chamber on the basis of different multi-dimensional PDFs dependent upon the bulk or the thermal boundary layer in contact with the cylinder liner. In the boundary layer, turbulent mixing is significantly weaker and heat transfer to the liner alters the combustion process. The integrated digital workflow is applied to perform parameter estimation based on the in-cylinder pressure profiles and engine-out emissions (i.e. NOx, CO, soot and unburnt hydrocarbons; uHCs) measurements. Four DoE (design-of-experiments) datasets are considered, each comprising measurements at a single load-speed point with various other operating conditions, which are then used to assess the capability of the calibrated models in mimicking the impact of the input variable space on the combustion characteristics and emissions. Both model approaches predict in-cylinder pressure profiles, NOx, and soot emissions satisfactorily well across all four datasets. Capturing the physics of emission formation near the cylinder liner enables the multi-zonal SRM approach to provide improved predictions for intermediates, such as CO and uHCs, particularly at low load operating points. Finally, fast-response surrogates are generated using the High Dimensional Model Representation (HDMR) approach, and the associated global sensitivities of combustion metrics and emissions are also investigated.

A Stochastic Reactor Model (SRM) has been used to simulate the transition from Spark Ignition (SI) mode to Homogeneous Charge Compression Ignition (HCCI) mode in a four cylinder in-line four-stroke naturally aspirated direct injection SI engine with cam profile switching. The SRM is coupled with GT-Power, a onedimensional engine simulation tool used for modelling engine breathing during the open valve portion of the engine cycle, enabling multi-cycle simulations. The model is initially calibrated in both modes using steady state data from SI and HCCI operation. The mode change is achieved by switching the cam profiles and phasing, resulting in a Negative Valve Overlap (NVO), opening the throttle, advancing the spark timing and reducing the fuel mass as well as utilising a pilot injection. Experimental data is presented along with the simulation results. The model is used to investigate key control parameters and their effects on parameters that are difficult to measure experimentally. The effect of the spark in the first HCCI cycles is found to have a major impact on the stability of the transition.

A previously developed Stochastic Reactor Model (SRM) is used to simulate combustion in a four cylinder in-line four-stroke naturally aspirated direct injection Spark Ignition (SI) engine modified to run in Homogeneous Charge Compression Ignition (HCCI) mode with a Negative Valve Overlap (NVO). A portion of the fuel is injected during NVO to increase the cylinder temperature and enable HCCI combustion at a compression ratio of 12:1. The model is coupled with GT-Power, a one-dimensional engine simulation tool used for the open valve portion of the engine cycle. The SRM is used to model in-cylinder mixing, heat transfer and chemistry during the NVO and main combustion. Direct injection is simulated during NVO in order to predict heat release and internal Exhaust Gas Recycle (EGR) composition and mass. The NOx emissions and simulated pressure profiles match experimental data well, including the cyclic fluctuations. The model predicts combustion characteristics at different fuel split ratios and injection timings. The effect of fuel reforming on ignition timing is investigated along with the causes of cycle to cycle variations and unstable operation. A detailed flux analysis during NVO unearths interesting results regarding the effect of NOx on ignition timing compared with its effect during the main combustion. © 2009 SAE International.

A dual-fuel approach to control combustion in HCCI engine is investigated in this work. This approach involves controlling the combustion heat release rate by adjusting fuel reactivity according to the conditions inside the cylinder. Experiments were performed on a single-cylinder research engine fueled with different ratios of primary reference fuels and operated at different speed and load conditions, and results from these experiments showed a clear potential for the approach to expand the HCCI engine operation window. Such potential is further demonstrated dynamically using an optimized stochastic reactor model integrated within a MATLAB code that simulates HCCI multi-cycle operation and closed-loop control of fuel ratio. The model, which utilizes a reduced PRF mechanism, was optimized using a multi-objective genetic algorithm and then compared to a wide range of engine data. The optimization objectives, selected based on relevance to this control study, were the cylinder pressure history, pressure rise rate, and gross indicated mean effective pressure (IMEPg). The closed-loop control of fuel ratio employed in this study is based on a search algorithm, where the objective is to maximize the gross work rather than directly controlling the combustion phasing to match preset values. This control strategy proved effective in controlling pressure rise rate and combustion phasing while not needing any prior knowledge or preset information about them. It also ensured that the engine was always delivering maximum work at each operation condition. This is in a sense analogous to the use of maximum brake torque timing in spark-ignition engines. The dynamic model allowed for convenient examination of the dual-fuel approach beyond the limits tested in the experiments, and thus helped in performing an overall assessment of the approach’s potential and limitations.

A multi-objective optimization scheme based on stochastic global search is developed and used to examine the performance of an HCCI model containing a reduced chemical kinetic mechanism, and to study interrelations among different model responses. A stochastic reactor model of an HCCI engine is used in this study, and dedicated HCCI engine experiments are performed to provide reference for the optimization. The results revealed conflicting trends among objectives normally used in mechanism optimization, such as ignition delay and engine cylinder pressure history, indicating that a single best combination of optimization variables for these objectives does not exist. This implies that optimizing chemical mechanisms to maintain universal predictivity across such conflicting responses will only yield a predictivity tradeoff. It also implies that careful selection of optimization objectives increases the likelihood of better predictivity for these objectives. This may have a particular importance in those practical applications where a high degree of predictivity for a limited number of responses is needed, but only a reasonable computational expense can be afforded. These insights are utilized here to develop a highly predictive HCCI model of engine cylinder pressure history, and to evaluate the model’s ability to predict exhaust emissions. The insight provided by multi-objective optimization on the interplay among different model responses could be of great help for guiding mechanism reduction process and for customizing models based on specific needs.

An established Stochastic Reactor Model (SRM) is used to simulate the transition from Spark Ignition (SI) to Homogeneous Charge Compression Ignition (HCCI) combustion mode in a four cylinder in-line four-stroke naturally aspirated direct injection SI engine with cam profile switching. The SRM is coupled with GT-Power, a one-dimensional engine simulation tool used for modelling engine breathing during the open valve portion of the engine cycle, enabling multi-cycle simulations. The mode change is achieved by switching the cam profiles and phasing, resulting in a Negative Valve Overlap (NVO), opening the throttle, advancing the spark timing and reducing the fuel mass as well as using a pilot injection. A proven technique for tabulating the model is used to create look-up tables in both SI and HCCI modes. In HCCI mode several tables are required, including tables for the first NVO, transient valve timing NVO, transient valve timing HCCI and steady valve timing HCCI and NVO. This results in the ability to simulate the transition with detailed chemistry in very short computation times. The tables are then used to optimise the transition with the goal of reducing NO x emissions and fluctuations in IMEP. Copyright © 2010 SAE International.

A detailed chemical model was implemented in the KIVA3V two dimensional CFD code to investigate the effects of the spray cone angle and injection timing on the PCCI combustion process and emissions in an optical research diesel engine. A detailed chemical model for Primary Reference Fuel (PRF) consisting of 157 species and 1552 reactions was used to simulate diesel fuel chemistry. The model validation shows good agreement between the predicted and measured pressure and emissions data in the selected cases with various spray angles and injection timings. If the injection is retarded to -50° ATDC, the spray impingement at the edge of the piston corner with 100° injection angle was shown to enhance the mixing of air and fuel. The minimum fuel loss and more widely distributed fuel vapor contribute to improving combustion efficiency and lowering uHC and CO emissions in the engine idle condition. Finally, the coupling of CFD and multi-zone Stochastic Reactor Model (SRM) was demonstrated to show improvement in CO and uHC emissions prediction.

Two-stage fuel direct injection (DI) has the potential to expand the operating region and control the autoignition timing in a Diesel fuelled homogeneous charge compression ignition (HCCI) engine. In this work, to investigate the dual-injection HCCI combustion, a stochastic reactor model, based on a probability density function (PDF) approach, is utilized. A new wall-impingement sub-model is incorporated into the stochastic spray model for direct injection. The model is then validated against measurements for combustion parameters and emissions carried out on a four stroke HCCI engine. The initial results of our numerical simulation reveal that the two-stage injection is capable of triggering the charge ignition on account of locally rich fuel parcels under certain operating conditions, and consequently extending the HCCI operating range. Furthermore, both simulated and experimental results on the effect of second injection timing on combustion indicate that there exists an optimal second injection timing to gain maximum engine output work for a given fuel split ratio.

We numerically simulate a Homogeneous Charge Compression Ignition (HCCI) engine fuelled with a blend of ethanol and diethyl ether by means of a stochastic reactor model (SRM). A 1D CFD code is employed to calculate gas flow through the engine, whilst the SRM accounts for combustion and convective heat transfer. The results of our simulations are compared to experimental measurements obtained using a Caterpillar CAT3401 single-cylinder Diesel engine modified for HCCI operation. We consider emissions of CO, CO2 and unburnt hydrocarbons as functions of the crank angle at 50% heat release. In addition, we establish the dependence of ignition timing, combustion duration, and emissions on the mixture ratio of the two fuel components. Good qualitative agreement is found between our computations and the available experimental data. The performed numerical simulations predict that the addition of diethyl ether to ethanol neither spreads out the combustion nor lowers light-off temperatures significantly, both in accordance with experimental observations.