Numerical Investigation of a Gasoline-Like Fuel in a Heavy-Duty Compression Ignition Engine Using Global Sensitivity Analysis

Paper #:
  • 2017-01-0578

Published:
  • 2017-03-28
Abstract:
A fuel in the gasoline autoignition range (RON > 60) can be effectively used as an alternative to diesel fuel in a compression ignition engine. Such fuels allow more time for the mixing of fuel and oxygen before combustion starts, owing to longer ignition delay. Moreover, by controlling fuel injection timing, it can be ensured that the in-cylinder mixture is “premixed enough” before combustion occurs to prevent soot formation while remaining “sufficiently inhomogeneous” in order to avoid an excessive heat release rate. Gasoline compression ignition (GCI) has the potential to offer diesel-like efficiency at a lower cost, and can be achieved with fuels such as straight run gasoline with low octane numbers, which require significantly less processing in the refinery compared to today’s fuels. To aid the design and optimization of combustion system using such fuels, a global sensitivity analysis (GSA) was conducted in order to investigate the relative influence of various design parameters on specific targets of efficiency, emissions and heat release rate. The engine operating and design parameters included injection pressure, injection strategies, EGR levels, intake temperature and pressure and injector design, and these were varied to achieve various targets for efficiency, emissions and pressure rise rate. The baseline case was a three-dimensional computational fluid dynamics (CFD) simulation in a sector mesh under medium speed-load conditions. Eleven input parameters were chosen and uncertainty ranges were identified for each of these. These input variables were then perturbed in their respective uncertainty ranges using the Monte Carlo method to generate a set of 256 CFD simulations and multiple targets were calculated from the simulation results. GSA was then applied as a screening tool to identify the input parameters that had a significant impact on each target by quantifying the sensitivity of the target to each input parameter. Subsequently, a detailed analysis of the influence of important input parameters on target functions was carried out.
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