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Model guided application for investigating particle number (PN) emissions in GDI spark ignition engines

Amit Bhave; Kok Foong Lee; David Ooi; Jiawei Lai; Nick Eaves; Sebastian Mosbach; Andreas Manz; Jan Niklas Geller; Jennifer Anna Noble; Dumitru Duca; Christian Focsa


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  <identifier identifierType="DOI">10.5281/zenodo.2592302</identifier>
  <creators>
    <creator>
      <creatorName>Amit Bhave</creatorName>
      <affiliation>CMCL Innovations</affiliation>
    </creator>
    <creator>
      <creatorName>Kok Foong Lee</creatorName>
      <affiliation>CMCL Innovations</affiliation>
    </creator>
    <creator>
      <creatorName>David Ooi</creatorName>
      <affiliation>CMCL Innovations</affiliation>
    </creator>
    <creator>
      <creatorName>Jiawei Lai</creatorName>
      <affiliation>CMCL Innovations</affiliation>
    </creator>
    <creator>
      <creatorName>Nick Eaves</creatorName>
      <affiliation>University of Cambridge</affiliation>
    </creator>
    <creator>
      <creatorName>Sebastian Mosbach</creatorName>
      <affiliation>University of Cambridge</affiliation>
    </creator>
    <creator>
      <creatorName>Andreas Manz</creatorName>
      <affiliation>Robert Bosch GmbH</affiliation>
    </creator>
    <creator>
      <creatorName>Jan Niklas Geller</creatorName>
      <affiliation>Robert Bosch GmbH</affiliation>
    </creator>
    <creator>
      <creatorName>Jennifer Anna Noble</creatorName>
      <affiliation>University of Lille</affiliation>
    </creator>
    <creator>
      <creatorName>Dumitru Duca</creatorName>
      <affiliation>University of Lille</affiliation>
    </creator>
    <creator>
      <creatorName>Christian Focsa</creatorName>
      <affiliation>University of Lille</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Model guided application for investigating particle number (PN) emissions in GDI spark ignition engines</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <dates>
    <date dateType="Issued">2019-03-26</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/2592302</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.2592301</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Abstract&lt;/p&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;

&lt;p&gt;Model guided application (MGA) combining physico-chemical internal combustion engine simulation with advanced analytics offers a robust framework to develop and test particle number (PN) emissions reduction strategies. The digital engineering workflow presented in this paper integrates the &lt;em&gt;k&lt;/em&gt;inetics &amp;amp; SRM Engine Suite with parameter estimation techniques applicable to the simulation of particle formation and dynamics in gasoline direct injection (GDI) spark ignition (SI) engines. The evolution of the particle population characteristics at engine-out and through the sampling system is investigated. The particle population balance model is extended beyond soot to include sulphates and soluble organic fractions (SOF). This particle model is coupled with the gas phase chemistry precursors and is solved using a sectional method. The combustion chamber is divided into a wall zone and a bulk zone and the fuel impingement on the cylinder wall is simulated. The wall zone is responsible for resolving the distribution of equivalence ratios near the wall, a factor that is essential to account for the formation of soot in GDI SI engines. In this work, a stochastic reactor model (SRM) is calibrated to a single-cylinder test engine operated at 12 steady state load-speed operating points. First, the flame propagation model is calibrated using the experimental in-cylinder pressure profiles. Then, the population balance model parameters are calibrated based on the experimental data for particle size distributions from the same operating conditions. Good agreement was obtained for the in- cylinder pressure profiles and gas phase emissions such as NOx. The MGA also employs a reactor network approach to align with the particle sampling measurements procedure, and the influence of dilution ratios and temperature on the PN measurement is investigated. Lastly, the MGA and the measurements procedure are applied to size-resolved chemical characterisation of the emitted particles.&lt;/p&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/724145/">724145</awardNumber>
      <awardTitle>Portable Nano-Particle Emission Measurement System</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
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