Fast Prediction of HCCI and PCCI Combustion with an Artificial Neural Network-Based Chemical Kinetic Model

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We have added the capability to look at in-cylinder fuel distributions using a previously developed ignition model within a fluid mechanics code (KIVA3V) that uses an artificial neural network (ANN) to predict ignition (The combined code: KIVA3V-ANN). KIVA3V-ANN was originally developed and validated for analysis of Homogeneous Charge Compression Ignition (HCCI) combustion, but it is also applicable to the more difficult problem of Premixed Charge Compression Ignition (PCCI) combustion. PCCI combustion refers to cases where combustion occurs as a nonmixing controlled, chemical kinetics dominated, autoignition process, where the fuel, air, and residual gas mixtures are not necessarily as homogeneous as ... continued below

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Piggott, W T; Aceves, S M; Flowers, D L & Chen, J Y September 26, 2007.

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We have added the capability to look at in-cylinder fuel distributions using a previously developed ignition model within a fluid mechanics code (KIVA3V) that uses an artificial neural network (ANN) to predict ignition (The combined code: KIVA3V-ANN). KIVA3V-ANN was originally developed and validated for analysis of Homogeneous Charge Compression Ignition (HCCI) combustion, but it is also applicable to the more difficult problem of Premixed Charge Compression Ignition (PCCI) combustion. PCCI combustion refers to cases where combustion occurs as a nonmixing controlled, chemical kinetics dominated, autoignition process, where the fuel, air, and residual gas mixtures are not necessarily as homogeneous as in HCCI combustion. This paper analyzes the effects of introducing charge non-uniformity into a KIVA3V-ANN simulation. The results are compared to experimental results, as well as simulation results using a more physically representative and computationally intensive code (KIVA3V-MPI-MZ), which links a fluid mechanics code to a multi-zone detailed chemical kinetics solver. The results indicate that KIVA3V-ANN produces reasonable approximations to the more accurate KIVA3V-MPI-MZ at a much reduced computational cost.

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PDF-file: 14 pages; size: 0.3 Mbytes

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  • Presented at: Western States Section Meeting of the Combustion Institute, Livermore, CA, United States, Oct 16 - Oct 17, 2007

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  • Report No.: UCRL-CONF-235084
  • Grant Number: W-7405-ENG-48
  • Office of Scientific & Technical Information Report Number: 922102
  • Archival Resource Key: ark:/67531/metadc893997

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Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

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  • September 26, 2007

Added to The UNT Digital Library

  • Sept. 27, 2016, 1:39 a.m.

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  • Nov. 28, 2016, 2:45 p.m.

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Piggott, W T; Aceves, S M; Flowers, D L & Chen, J Y. Fast Prediction of HCCI and PCCI Combustion with an Artificial Neural Network-Based Chemical Kinetic Model, article, September 26, 2007; Livermore, California. (digital.library.unt.edu/ark:/67531/metadc893997/: accessed December 18, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.