The results reported that calculated values obtained for developed compositions turned out to be slightly higher than for baseline gasolines. The key hypothesis of the merit function was improved gasoline efficiency and reduce exhaust emissions when operating gasoline engines at various conditions. The vast expertise of this laboratory work has obviously reported that fuel compositions samples contained the following concentrations by weight percentage, i.e., light condensate naphtha – 46–56, isopentane fraction – up to 4, aromatic components – up to 20, MTBE –14–15, isoolefins hydrocarbons – 15–16, and isooctane – up to 20. Physical and chemical characteristics of these innovative gasoline recipes were investigated in accordance with standard test methods regulations. In addition, preliminary mathematical digital model for evaluating a comprehensive merit function of automotive gasoline for newly proposed fuel motor compositions was developed to maximize gasoline performance and decrease emissions when operating gasoline engines at various conditions. This paper addresses producing new recipes of environmentally friendly high octane gasoline fuel grades RONs 92 and 95.
One of these requirements is introducing merit function to assess the advantages of innovative motor gasoline formulations, regarding their anticipated influence on performance for future engines. New fuels composition should be corresponded with modern engine technologies requirements. Gasoline engines have an even greater potential for optimization. This last result demonstrates the ability of the LES tool to study the cycle-to-cycle variability observed in CV2 but further work is now required. Finally, the numerical pressure signal and wall heat flux obtained from the simulation of a sequence of ten cycles is compared to the experimental data. The numerical results are then used to describe the flow during the successive phases, i.e injection, combustion and exhaust, of one specific cycle. A qualitative comparison of the numerical results with other measurements, i.e PIV, flame visualization and MIE tomography, is also presented. For the experimental pressure peaks, the values obtained are 17.8, 14 and 14.6 bars which shows the validity of the modeling strategy. The values obtained for the calculated pressure peaks respectively for the first three reactive cycles are 17.4, 13.1 and 14.4 bars. The calculated pressure profiles are in very good agreement with the experimental results. The chamber pressure and temperature of air is 1 bar and 307K for the initial cycle. A quantitative comparison of the results and computational data is performed based on the pressure and heat flux signals in the case of propane-air combustion. The sensitivity of the numerical results to the values of key modeling parameters such as injected air mass mair, ejected mass mout, segregation rate S ∈, wall heat losses parameter κT ∈ is studied. A detailed time-function for the inlet and outlet mass flow rates and a new blending function for the wall heat fluxes are also introduced. Specific modeling developments are provided in this paper for turbulent combustion in the presence of residual burnt gases and the database takes into account the self-similar nature of the thermochemical properties. Consequently, the wide spectrum of unresolved scales in the presence of so many correlated physical mechanisms leads to the introduction of several modeling parameters. However, the LES of CV2 presents numerous modeling difficulties due to the interactions of turbulent flow with fast and slow chemical reactions, compressible effects, walls and heat losses. Developing a modeling framework compatible with coarse meshes for which the computational cost is reasonable is the first step necessary to simulate multiple cycles. In addition, high-fidelity simulation of all previous cycles is too costly.
The main difficulty is that, no matter how good the models used or the mesh refinement, specifying the boundary and initial conditions for the simulation of a specific cycle independently of the previous cycles requires an unattainable amount and accuracy of experimental data. The development of a computational tool capable of simulating and completing the experimental data obtained on a constant volume cyclic combustion (CV2) bench available at the Pprime Institute is the major objective of this study.
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