Numerical Analysis of Combustion Process in the Dual Fuel Internal Combustion Engine

Abstract

<div class="section abstract"><div class="htmlview paragraph">Fully flexible dual fuel (DF) internal combustion (IC) engines, that can burn diesel and gas simultaneously, have become established among heavy-duty engines as they contribute significantly to lower the environmental impact of the transport sector. In order to gain better understanding of the DF combustion process and establish an effective design methodology for DFIC engines, high fidelity computational fluid dynamics (CFD) simulation tools are needed. The DF strategy poses new challenges for numerical modelling of the combustion process since all combustion regimes have to be modelled simultaneously. Furthermore, DF engines exhibit higher cycle-to-cycle variations (CCV) compared to the pure diesel engines. This issue can be addressed by employing large eddy simulation coupled with appropriate DF detailed chemistry mechanism. However, such an approach is computationally too expensive for today’s industry-related engine calculations. On the other hand, Reynolds-Averaged Navier-Stokes (RANS) based simulations coupled with combustion models lack the fidelity to accurately capture underlying flow physics. This work couples the Partially-Averaged Navier-Stokes (PANS) turbulence method with flamelet-generated manifold (FGM) tabulated chemistry approach for combustion modelling with the objective to provide an optimum between accuracy and computational cost. The PANS method is a scale resolving turbulence model which seamlessly vary from RANS to direct numerical simulation (DNS) depending upon the prescribed cut-off length. Two reaction mechanisms optimized for DF combustion process, using n-heptane and a mixture of methane/propane, were tested in order to find the most suitable mechanism for the generation of the FGM table. The feasibility of the methodology and ability to capture CCV is tested by simulating three different operating points of DF single cylinder research engine. The numerically obtained results are compared with the available experimental data.</div></div>