Abstract
Recently, some approaches based on genetic algorithms and neural networks have been applied for optimizing arbitrary constants in fluid-dynamic, chemical, and physical models. Here, combination of genetic algorithm and the intuition theory proposed by K. Naitoh automatically optimizes the auto-ignition model proposed by Halstead et al. First, the differential equations describing highly nonlinear chemical reaction processes of gasoline are optimized with genetic algorithm (GA). In some cases, ignition delay, the interval from compression start to ignition occurrence, can be accurately calculated by using the optimized constants. The intuition theory also clarifies whether the arbitrary constants are optimized by GA or not. It is understood by using this intuition theory that some of the arbitrary constants cannot be optimized. There are so many types of computational models predicting engine performance, which are based on zero-dimensional thermodynamic models, ensemble averaged flow simulators, and large eddy simulations. Arbitrary constants in the chemical reaction models must be varied for each computational model. The present automatic technique on optimization will bring us an important role for realizing virtual engines.
