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Pei, Yuanjiang
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A Machine Learning-Genetic Algorithm (ML-GA) Approach for Rapid Optimization Using High-Performance Computing

SAE International Journal of Commercial Vehicles

  • Journal Article
  • 2018-01-0190
Published 2018-04-03 by SAE International in United States
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CFD-Guided Heavy Duty Mixing-Controlled Combustion System Optimization with a Gasoline-Like Fuel

SAE International Journal of Commercial Vehicles

  • Journal Article
  • 2017-01-0550
Published 2017-03-28 by SAE International in United States
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A Progress Review on Soot Experiments and Modeling in the Engine Combustion Network (ECN)

SAE International Journal of Engines

  • Journal Article
  • 2016-01-0734
Published 2016-04-05 by SAE International in United States
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Assessing the Importance of Radiative Heat Transfer for ECN Spray A Using the Transported PDF Method

SAE International Journal of Fuels and Lubricants

  • Journal Article
  • 2016-01-0857
Published 2016-04-05 by SAE International in United States
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Understanding Fuel Stratification Effects on Partially Premixed Compression Ignition (PPCI) Combustion and Emissions Behaviors

Published 2019-04-02 by SAE International in United States
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Comparison of In-Nozzle Flow Characteristics of Naphtha and N-Dodecane Fuels

Published 2017-03-28 by SAE International in United States
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Evaluation of Shot-to-Shot In-Nozzle Flow Variations in a Heavy-Duty Diesel Injector Using Real Nozzle Geometry

SAE International Journal of Fuels and Lubricants

  • Journal Article
  • 2018-01-0303
Published 2018-04-03 by SAE International in United States
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Computing Statistical Averages from Large Eddy Simulation of Spray Flames

Published 2016-04-05 by SAE International in United States
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Global Sensitivity Analysis of a Gasoline Compression Ignition Engine Simulation with Multiple Targets on an IBM Blue Gene/Q Supercomputer

Published 2016-04-05 by SAE International in United States
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Reduced Gasoline Surrogate (Toluene/n-Heptane/iso-Octane) Chemical Kinetic Model for Compression Ignition Simulations

Published 2018-04-03 by SAE International in United States
This content contains downloadable datasets
Annotation ability available