Hai-Wen Ge

Acceleration of the chemistry solver for modeling DI engine combustion using dynamic adaptive chemistry (DAC) schemes

Acceleration of the chemistry solver for engine combustion is of much interest due to the fact that in practical engine simulations extensive computational time is spent solving the fuel oxidation and emission formation chemistry. A dynamic adaptive chemistry (DAC) scheme based on a directed relation graph error propagation (DRGEP) method has been applied to study homogeneous charge compression ignition (HCCI) engine combustion with detailed chemistry (over 500 species) previously using an R-value-based breadth-first search (RBFS) algorithm, which significantly reduced computational times (by as much as 30-fold). The present paper extends the use of this on-the-fly kinetic mechanism reduction scheme to model combustion in direct-injection (DI) engines. It was found that the DAC scheme becomes less efficient when applied to DI engine simulations using a kinetic mechanism of relatively small size and the accuracy of the original DAC scheme decreases for conventional non-premixed combustion engine. The present study also focuses on determination of search-initiating species, involvement of the NOx chemistry, selection of a proper error tolerance, as well as treatment of the interaction of chemical heat release and the fuel spray. Both the DAC schemes were integrated into the ERC KIVA-3v2 code, and simulations were conducted to compare the two schemes. In general, the present DAC scheme has better efficiency and similar accuracy compared to the previous DAC scheme. The efficiency depends on the size of the chemical kinetics mechanism used and the engine operating conditions. For cases using a small n-heptane kinetic mechanism of 34 species, 30% of the computational time is saved, and 50% for a larger n-heptane kinetic mechanism of 61 species. The paper also demonstrates that by combining the present DAC scheme with an adaptive multi-grid chemistry (AMC) solver, it is feasible to simulate a direct-injection engine using a detailed n-heptane mechanism with 543 species with practical computer time.

Computational Optimization of a Down-Scaled Diesel Engine Operating in the Conventional Diffusion Combustion Regime Using a Multi-Objective Genetic Algorithm

Computational optimization of a high-speed diesel engine, combined with diesel engine size-scaling, is presented. A multi-objective genetic algorithm was employed to simultaneously optimize fuel consumption and engine-out emissions of the down-scaled version of a previously optimized baseline engine. By separating the design parameters into hardware parameters (e.g., the piston bowl geometry) and controllable parameters (e.g., injection pressure and timings), multiple operating conditions were optimized simultaneously. A new variable was introduced to evaluate the convergence of the optimization, defined as the ratio of the number of Pareto designs and the number of valid designs in each generation. Particular interest was placed on the effect of injection pressure on the optimization of the engine and whether the previously optimized baseline engine design holds for different engine sizes. For 32 generations, totaling 1024 designs, no better design than the initial optimum, which was generated for the baseline engine, was found. This indicates that the current engine size-scaling model works well.

Scaling Laws for Diesel Combustion Systems

Scaling laws are developed to guide the transfer of combustion system designs between diesel engines of different sizes using simple formulations. In this chapter, the concepts and formulation of scaling laws are presented. A practical example is provided to study a light-duty and a heavy-duty production diesel engines using the established scaling laws.

Assessment of Optimization and Regression Methods for Engine Optimization

Engine optimization problems by nature are multi-objective problems, which involve simultaneously optimizing multiple design parameters. Based on the review of optimization methods in Chap. 2, it was determined that multi-objective genetic algorithms (MOGA) are an appropriate optimization method. This chapter assesses the performance of different MOGAs for engine optimization problems. The assessment was conducted using three popular MOGAs [μ-GA (Coello Coello and Pulido 2001), NSGA II (Deb et al. 2002), ARMOGA (Sasaki and Obayashi 2005)] applied to a heavy-duty diesel engine operated at a high-load condition. In addition to this assessment, the niching technique of NSGA II was also evaluated. Convergence and diversity metrics of MOGAs were defined to complete the assessment of different niching techniques. Regression analysis was then conducted on the design datasets that were obtained from the optimizations with two niching strategies

Acceleration of Multi-Dimensional Engine Simulation with Detailed Chemistry

Detailed chemistry is necessary for kinetics-controlled combustion processes, such as HCCI and low-temperature combustion. However, the use of detailed chemistry can lead to significantly increased computational costs. This chapter summarizes several different strategies in order to reduce computational costs when detailed chemistry is solved in the simulations.

A Two-Zone Multigrid Model for SI Engine Combustion Simulation Using Detailed Chemistry

An efficient multigrid (MG) model was implemented for spark-ignited (SI) engine combustion modeling using detailed chemistry. The model is designed to be coupled with a level-set-G-equation model for flame propagation (GAMUT combustion model) for highly efficient engine simulation. The model was explored for a gasoline direct-injection SI engine with knocking combustion. The numerical results using the MG model were compared with the results of the original GAMUT combustion model. A simpler one-zone MG model was found to be unable to reproduce the results of the original GAMUT model. However, a two-zone MG model, which treats the burned and unburned regions separately, was found to provide much better accuracy and efficiency than the one-zone MG model. Without loss in accuracy, an order of magnitude speedup was achieved in terms of CPU and wall times. To reproduce the results of the original GAMUT combustion model, either a low searching level or a procedure to exclude high-temperature computational cells from the grouping should be applied to the unburned region, which was found to be more sensitive to the combustion model details.