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From fuel properties to engine performance

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Combustion Engines Finland

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From fuel properties to engine performance

  1. 1. FromFuel Properties to Engine Performance Michal Wojcieszyk 21 March 2019
  2. 2. 2 Agenda 1. ADVANCEFUEL project in brief 2. Alternative fuels and their properties vs light-duty engine performance 3. Approach and data selection 4. Modeling methodology 5. Example of results 6. Fuel Blend Property Calculator 7. Key observations
  3. 3.  Part of EU Horizon 2020  Coordination and Support Action of EU Commission  Facilitating market roll-out of advanced liquid biofuels in transportation sector between 2020 and 2030 and beyond 3 Partners: Stakeholders:
  4. 4. 4 Importance of the research • Increased market acceptance and end-use of renewable fuels • Support for decision makers and fuel producers • Assessment of future potential of alternative fuels • Providing market stakeholders with state-of-art knowledge and sophisticated, user-friendly tools with integrated calculators, standards, and recommendations.
  5. 5. Structure of the problem 5
  6. 6. 6 Analyzed SI fuels: • Gasoline • Methanol (CH3OH) • Ethanol (C2H5OH) • Butanol (C4H9OH) Analyzed CI fuels: • Diesel • HVO • FAME (biodiesel) • BTL/GTL • DME Selected SI input properties: • Octane Number • Heat of Vaporization • Net Calorific Value • Auto Ignition Temperature • Carbon content Selected CI input properties: • Net Calorific Value • Cetane Number • Density • Viscosity • Oxygen content • Carbon content Alternative fuels and properties
  7. 7. Driving conditions vs fuel properties – CI engine
  8. 8. Engine data selection NEDC
  9. 9. 9 • Character of the data: multi input, single output • Approach: data-driven black-box modeling • Mathematical methodology: multilinear regression • Validation: residual analysis and cross-validation • Input and output parameters represented as relative (%) changes in reference to standard diesel/gasoline. Modeling methodology = + + + ( ) - fuel consumption [%] X – alternative fuel concentration [%] A(X)...D(X) – fuel property [%] a...d – model coefficients.
  10. 10. How to select input properties ? • Properties that are measured in the literature sources. • Statistical significance analysis (t-test, p-value for t- test). All input properties have to be mathematically significant and justified.
  11. 11. Model proposal – CI fuel consumption (FC) 2 – Fuel Consumption – Density – Viscosity – Cetane number – Lower heating value 2 – Oxygen content [Units – % changes] Note: Model under continuous development!
  12. 12. 12 Model proposal – SI fuel consumption (FC) Note: Model under continuous development!
  13. 13. Fuel Blend Property Calculator - FBPC
  14. 14. 14 Key observations • Engine performance is influenced both by fuel properties and driving conditions. • Driving cycle (WLTC, NEDC) approach for light-duty engines seems to be the most suitable one from the end-user point of view. • While studying an effect of fuel properties on engine performance, the black- box modeling was applied and multilinear regression executed. • Developed models both for SI and CI case represent the impact of fuel properties on engine performance with high accuracy (FC and CO2). Models are under continuous development. • Fuel consumption of alternative fuel or its blends can be predicted based on known set of fuel properties. It is highly dependent on NCV, density and CN in CI case. NCV, density, oxygen content and RON matter for SI engine.
  15. 15. Thank you for your attention ! Michal Wojcieszyk michal.wojcieszyk@aalto.fi Yuri Kroyan yuri.kroyan@aalto.fi Martti Larmi martti.larmi@aalto.fi • Energy Conversion Research Group https://vimeo.com/321946937

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