Autoignition model optimized based on simple artificial brain

Ken Naitoh, Tairo Ise

研究成果: Conference article査読


A well-known auto-ignition model for gasoline, which was proposed by Halstead et al, is automatically optimized on computers by using a simple artificial brain including genetic algorithm as learning theory and an intuition model. Arbitrary constants inside the mathematical equations of highly-nonlinear chemical reaction processes can be fitted by using the experimental time-evolutions of several components. Thus, ignition delay, the interval from compression start to ignition occurrence, can be accurately calculated for different types of fuel, production regions, and engine test benches. The intuition model clarifies whether the arbitrary constants are optimized or not. The present approach will be important for building up several types of virtual engines, which are based on zero-dimensional thermodynamic models, ensemble-averaged flow simulators, and large eddy simulation (LES).

ジャーナルSAE Technical Papers
出版ステータスPublished - 2003 1 1
イベントPowertrain and Fluid Systems Conference and Exhibition - Pittsburgh, PA, United States
継続期間: 2003 10 272003 10 30

ASJC Scopus subject areas

  • Automotive Engineering
  • Safety, Risk, Reliability and Quality
  • Pollution
  • Industrial and Manufacturing Engineering

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