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DG TREN Particulates Consortium

DG TREN Particulates Consortium. Lemnos, 11-12 September 2003. Comments on: 1) Data Analysis 2) Deliverable 14. Neville Thompson, Technical Coordinator, CONCAWE FEMG. Data Analysis.

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DG TREN Particulates Consortium

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  1. DG TREN Particulates Consortium Lemnos, 11-12 September 2003 Comments on:1) Data Analysis2) Deliverable 14 Neville Thompson, Technical Coordinator, CONCAWE FEMG

  2. Data Analysis • Particulates data being examined by Shell statistician with experience from EPEFE, also chairman of CEC Statistics Group • Wealth of data available • Only summary parameters for main test cycles examined so far • Full analysis carried out on CONCAWE and AVL data • Screening only on main database • First step is to review data for consistency, trends and outliers • Raw data plots prior to developing means and statistics • Several details need clarification in next update to the data-base • Statistician will need to work closer with LAT/partners

  3. Approach to data analysis • First analysis carried out on CONCAWE (Shell) and AVL data • Data used for Deliverable 14 • 3 repeats carried out on all fuels/engines tested using block design • Arithmetic means used for mass data, geometric means for number counts • Error bars contructed based on pooled SDs and 1.4*SE as in EPEFE • For diesel, where error bars do not overlap we can be 95% confident that there is a difference between the fuels/engines • Gasoline data is currently plotted on the same basis for comparison but this is optimistic in view of lower number of tests • Main data-base • Differences in data – number of repeats, order of testing, different tasks of different partners, cycles tested, outliers make statistical treatment a challenge • Additional spreadsheet developed to enable overview of data by engine and fuel • Initial plots prepared based on mean, min and max data

  4. AVL data – HD Diesel - ESC reg PM

  5. CONCAWE data – LD Diesel - NEDC reg PM

  6. AVL data – ESC ELPI Total N, stages 1-7

  7. CONCAWE data – NEDC ELPI Total N, stages 1-7

  8. AVL data – ESC SMPS data N<30 and N>30

  9. CONCAWE data – 120 km/h - SMPS data N<30 and N>30

  10. Instrument comparison - Summary statisticsAVL HD data

  11. Instrument comparison - Summary statistics CONCAWE LD data

  12. MAIN DATA

  13. Variation in number of repeats carried out by different labs

  14. Order of testing varied between labs

  15. Full database – LD Diesel - NEDC - PM • LD data covers well the newer technology vehicles • Weak on older technologies as a result of INRETS dropping out

  16. Full database – LD Diesel - NEDC - ELPI

  17. Full database – LD Gasoline - NEDC - ELPI

  18. Full database – HD - ESC reg PM

  19. Full database – HD - ETC reg PM

  20. Full database – HD - ESC - ELPI

  21. Deliverable 14 - Effects on particulate emissions and relevance of fuel quality for emission factors • First draft of Deliverable 14 was prepared and circulated in July based on data then available from CONCAWE (Shell) and AVL • Simple means, no statistics • Provided an outline structure for the Deliverable and preliminary conclusions • Only comment was from LAT to update with data from other partners who had tested fuel effects, when available • CONCAWE and AVL data analysis now updated with error bars • Arithmetic means for mass data, geometric means for number counts • Error bars contructed based on pooled SDs and 1.4*SE as in EPEFE • For diesel, where error bars do not overlap we can be 95% confident that there is a difference between the fuels/engines • Gasoline data is currently shown on the same basis for comparison but this is optimistic in view of lower number of tests • Deliverable also updated with mean results from other partners • Fuel trends seem to be broadly consistent, but some additional parameters • Several details on the data still to be resolved • Statistics will follow when the overall data-base is finalised

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