Electrification of transport, together with the decarbonization of energy production are suggested by the European Union for the future quality of air. However, in the medium period, propulsion systems will continue to dominate urban mobility, making mandatory the retrofitting of thermal engines by applying combustion modes able to reduce NOx and PM emissions while maintaining engine performances. Low Temperature Combustion (LTC) is an attractive process to meet this target. This mode relies on premixed mixture and fuel lean in-cylinder charge whatever the fuel type: from conventional through alternative fuels with a minimum carbon footprint. This combustion mode has been subject of numerous modelling approaches in the engine research community. This study provides a theoretical comparative analysis between multi-zone (MZ) and Transported probability density function (TPDF) models applied to LTC combustion process. The generic thermo-kinetic balances for both approaches have been analyzed in term of similarities. Only onion-skin for MZ models have been considered in this study. The governing assumptions linked to sub-models for each approach to describe mixing process for TPDF and interzonal heat and mass transport for MZ are discussed. This step identifies the calibrated model parameters for each approach and their effects on the accuracy in predicting LTC mode simulations. This work shows that the transported probability density function model has fewer parameters to calibrate compared to multi-zone model. Transported probability density function seems easier to use for LTC process.


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    Title :

    Theoretical Analysis of Multi-Zone and Transported Probability Density Function Approaches Applied to Low Temperature Combustion Process


    Additional title:

    Sae Technical Papers



    Conference:

    16th International Conference on Engines & Vehicles ; 2023



    Publication date :

    2023-08-28




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English