Autoignition model optimized based on simple artificial brain

Ken Naitoh, Tairo Ise

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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).

Original languageEnglish
Title of host publicationSAE Technical Papers
DOIs
Publication statusPublished - 2003
Externally publishedYes
EventPowertrain and Fluid Systems Conference and Exhibition - Pittsburgh, PA
Duration: 2003 Oct 272003 Oct 30

Other

OtherPowertrain and Fluid Systems Conference and Exhibition
CityPittsburgh, PA
Period03/10/2703/10/30

Fingerprint

Brain
Ignition
Engines
Large eddy simulation
Gasoline
Chemical reactions
Simulators
Genetic algorithms
Thermodynamics

ASJC Scopus subject areas

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

Cite this

Autoignition model optimized based on simple artificial brain. / Naitoh, Ken; Ise, Tairo.

SAE Technical Papers. 2003.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Naitoh, K & Ise, T 2003, Autoignition model optimized based on simple artificial brain. in SAE Technical Papers. Powertrain and Fluid Systems Conference and Exhibition, Pittsburgh, PA, 03/10/27. https://doi.org/10.4271/2003-01-3229
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