Numerical modelling for diesel spray combustion

Diesel engines are widely known for their high thermal efficiency, high torque, reliability, durability, fuel economy, and low carbon dioxide emissions in various industrial applications, such as power generation, mass transportation and off-road applications. However, the combination of a stratified air-fuel mixture and a non-premixed flame gives rise to nitrogen oxides (NOx) and particulate matter (PM) emissions for diesel combustion. Studies have shown that diesel engines can achieve very low NOx and PM emissions at high efficiency through the use of a range of low-temperature combustion (LTC) strategies and this thesis seeks to investigate the turbulent mixing influence on spray atomization and combustion processes encountered in compression ignition diesel engines under low temperature conditions. Recent studies indicate that end-of-injection (EOI) processes may support ignition recession back to the injector nozzle, thereby helping to reduce emissions. The first part of the thesis contributes to the physical understanding of this EOI phenomenon, combustion recession, using computational fluid dynamics studies at LTC conditions. Simulations are performed on a single-hole injection of n-dodecane under a range of the Engine Combustion Network's (ECN) "Spray A" conditions. The primary objective of this part is to assess the ability of a Flamelet Generated Manifold (FGM) combustion model in predicting and characterizing the combustion recession. All simulations are performed under the Reynolds-Averaged Navier-Stokes (RANS) framework in a grid-converged Lagrangian spray scenario. The simulation of combustion recession is qualitatively validated against experimental data from the literature, and the efficacy of each model in predicting combustion recession is evaluated. Overall, it was found that the FGM model was able to capture the combustion recession phenomenon well --- showing particular strength in predicting distinct auto-ignition events in the near nozzle region.
Following the validation of the FGM model in predicting combustion recession, the importance of the chosen chemical mechanism in predicting diesel fuel spray combustion is also investigated. Studies were again performed under the RANS framework using the Flamelet Generated Manifold (FGM) model with four different chemical mechanisms for n-dodecane that are commonly used in the engine simulation communities - including recently developed reduced chemistry mechanisms. The flamelet database for each of the chemical mechanism is generated using two distinct methods: 0D homogeneous reactor (HR) ignition flamelets and 1D igniting counterflow diffusion (ICDF) flamelets. The effect of different tabulation approaches is investigated first following the discussion of the impact of chemical mechanisms on the prediction of combustion recession. Further discussions include an evaluation of the performance of the chemical mechanisms in predicting the most relevant reacting spray characteristics compared to the ECN experimental database: ignition delay time (IDT), flame lift-off length (LOL) and the flame reactive region. Results show that the choice of both the tabulation method and chemical mechanism plays a significant role in initial flame stabilization and end of injection (EOI) transient processes. In general, both tabulation techniques were able to qualitatively capture the flame characteristics before EOI; however, ICDF tabulation is better suited for the FGM approach in order to capture the combustion recession. Furthermore, the chemical mechanisms studied indicate that mechanisms with stronger low temperature chemistry predictions are more likely to promote combustion recession under an FGM framework.
In addition, a novel combustion modelling approach is proposed here to further study the transient effects of diesel spray. Conditional Source-term Estimation (CSE) is a combustion model which invokes the Conditional Moment Closure (CMC) hypothesis to provide an approximation of the mean chemical source term in an averaged transport equation. Unlike CMC, where transport equations are solved for conditional moments, CSE recovers these conditional moments through the solution of an inverse problem. Integral equations are inverted for the conditional moments, by assuming spatial homogeneity in the conditional averages where Tikhonov regularization is applied. Previous CSE studies have shown that the model is able to predict the flame characteristics successfully for both premixed and non-premixed combustion modes. However, most of these investigations were based on methane flames. This study will be the first CSE application to a complex hydrocarbon fuel, n-dodecane, under the Engine Combustion Network's (ECN) "Spray A" conditions. Detailed chemistry is included in tabulated form using the Flamelet Generated Manifold (FGM) methodology. The predictions of this study include both the Favre averaged conditional mass fraction of reactive species and temperature. The results are compared with available experimental data and previous numerical results. Both RANS and LES simulations are performed under the same condition. The objectives of this part of the thesis are (i) assessment of the application of CSE on igniting diesel spray (ii) comparison of the CSE numerical results with available experimental results and previous numerical simulations. Overall, the combination of a chemical mechanism that has been tuned to predict "Spray A" conditions with the CSE-FGM model is able to successfully predict autoignition delay time and lift-off length of n-dodecane spray within the scatter of the experimental data. CSE-FGM offers a feasible tool for detailed combustion analysis of diesel spray flames. Both RANS and LES can give reasonably good global predictions of the flame. The LES approach is more data-rich, given the opportunity to explore more local and unsteady phenomenon present in a transient diesel jet.

Keywords: diesel spray
combustion
conditional source-term estimation
numerical modelling
computational fluid dynamics
manifold reduction
Advisor: Davy, M