Predictions of droplet heating and evaporation: an application to biodiesel, diesel, gasoline and blended fuels

Mansour Al Qubeissi

    Research output: Contribution to journalArticlepeer-review

    24 Citations (Scopus)
    74 Downloads (Pure)


    This work is focused on the analyses of automotive fuel droplets’ heating and evaporation with application to biodiesel, diesel, gasoline, and blended diesel-biodiesel fuels. The analyses are made considering conditions representative of real internal combustion engines. The evolutions of droplet radii and temperatures for gasoline, diesel, and a broad range of biodiesel fuels and their selective diesel fuel blends have been predicted using the Discrete Component model (DCM). These mixtures are combined with up to 116 components of 98 hydrocarbons and 4-18 methyl esters. The results are compared with the predictions of the case when blended biodiesel/diesel fuel are represented by pure fossil and biodiesel fuels. In contrast to previous studies, it is shown that droplet evaporation time and surface temperature predicted for 100% biodiesel are not always close to those predicted for pure diesel fuel, but they are entirely dependent on the biodiesel fuel composition. Also, the previously introduced multi-dimensional quasi-discrete model and its application to the aforementioned fuels are discussed. The application of the latter model contributes to improving the CPU efficiency with up to 96% in computational time compared to the original approach (the DCM), with a minimal loss in the accuracy of the model predictions.
    Original languageEnglish
    Pages (from-to)260-267
    Number of pages8
    JournalApplied Thermal Engineering
    Issue numberC
    Early online date6 Mar 2018
    Publication statusPublished - 25 May 2018


    • Biodiesel
    • Diesel
    • Fuel blends
    • Gasoline
    • Droplet
    • Heating and evaporation

    ASJC Scopus subject areas

    • Automotive Engineering
    • Fuel Technology
    • Fluid Flow and Transfer Processes
    • Renewable Energy, Sustainability and the Environment
    • Pollution
    • Computational Mechanics


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