Ali Aldawood

REAL-TIME EVALUATION OF A DETAILED CHEMISTRY HCCI ENGINE MODEL USING A TABULATION TECHNIQUE

A storage/retrieval scheme has been implemented for a Stochastic Reactor Model (SRM) for Homogeneous Charge Compression Ignition (HCCI) engines which enables fast evaluation in transient multi-cycle simulations. The SRM models combustion, turbulent mixing, and convective heat transfer during the closed-volume part of the engine cycle employing detailed chemical kinetics. In contrast to previously developed storage/retrieval techniques which tabulate chemistry only, our method stores, retrieves, and interpolates output quantities of the entire internal combustion engine model, i.e. the SRM. These quantities include ignition timing, cumulative heat release, maximum pressure rise rate, and emissions of CO, CO2, unburnt hydrocarbons, and NOx, as functions of equivalence ratio, octane number, and inlet temperature for instance. The new tool is intended to be used for performing a variety of otherwise exceedingly expensive computational tasks such as multi-cycle multi-cylinder simulations, transient operation and control, optimization of engine operating parameters, design of experiments, and identification of parameters for achieving stable HCCI operation over a wide range of conditions. Using transient control as an example, we show that, when coupled to a commercial 1D CFD engine modelling package, the tabulation scheme makes such simulations feasible and convenient.

HCCI Combustion Control Using Dual-Fuel Approach: Experimental and Modeling Investigations

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.

Multi-Objective Optimization of a Kinetics-Based HCCI Model Using Engine Data

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.