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Laminar burning velocity (LBV) is a crucial parameter in many practical combustion simulations, and the acquisition of LBVs is a critical step for evaluating potential alternative fuels and/or suitable surrogates. Ethanol is regarded as one of the most promising alternative fuels, particularly for spark ignition engines, as it may be produced from renewable biomass. While there exists data showing the pressure dependence on ethanol’s LBV compared to iso-octane and n-heptane (primary reference fuels (PRFs)), the relative magnitude of the pressure dependence of LBV between ethanol and toluene has not been established. The inclusion of toluene is important because commercial gasoline has significant levels of aromatics. In this work, common gasoline surrogate components of iso-octane, n-heptane, toluene, and ethanol were studied as pure compounds and in equal volume binary, ternary, and quaternary mixtures. Experiments are conducted in a spherical combustion bomb at elevated temperatures (380 K and 450 K) and pressures (1 to 4 bar). Under these conditions, ethanol and n-heptane present faster LBVs than iso-octane and toluene, particularly at 450 K. Ethanol and iso-octane present strong pressure dependence, with this effect accentuated at higher temperature conditions. As equal volume mixtures were studied, the effect of these distinct behaviours was examined in comparison to chemical kinetic predictions and widely used mixing rules.

This study presents the first demonstration of Marshall and Hinton’s empirical correlation for laminar burning velocity (LBV) applied to iso-octane/ethanol blends. The “Oxford” correlation is first validated for neat iso-octane and ethanol combustion against the outputs of three different chemical kinetics mechanisms and experimental data from the literature. The effects of three different mixing rules, simple energy fraction, modified Le Châtelier, and modified version of the more complex mixing law of Hirasawa, on the predicted LBVs of iso-octane/ethanol blends are evaluated. The modified Le Châtelier mixing law is used with the Oxford correlation to predict blend LBVs at various temperature and pressure conditions. The results are validated against previously unpublished data for iso-octane/ethanol binary fuels at elevated temperature and pressure (up to 10 bar) both from this group, and from the earlier experimental studies of Varea et al. (CORIA) and Broustail et al. (PRISME). The Oxford correlation is shown to perform well across a wide range of temperature and pressure conditions (respectively 298–640 K and 1–10 bar) for the single component fuels. Used in conjunction with a modified Le Châtelier mixing law, the LBVs predicted by the correlation provide an excellent match to the available experimental data across the full range of ethanol concentrations examined (25, 50, and 75% v/v).

In this article, different manifold reduction techniques are implemented for the post-processing of Particle Image Velocimetry (PIV) images from a Spark Ignition Direct Injection (SIDI) engine. The methods are proposed to help make a more objective comparison between Reynolds-averaged Navier-Stokes (RANS) simulations and PIV experiments when Cycle-to-Cycle Variations (CCV) are present in the flow field. The two different methods used here are based on Singular Value Decomposition (SVD) principles where Proper Orthogonal Decomposition (POD) and Kernel Principal Component Analysis (KPCA) are used for representing linear and non-linear manifold reduction techniques. To the authors’ best knowledge, this is the first time a non-linear manifold reduction technique, such as KPCA, has ever been used in the study of in-cylinder flow fields. Both qualitative and quantitative studies are given to show the capability of each method in validating the simulation and incorporating CCV for each engine cycle. Traditional Relevance Index (RI) and two other previously developed novel indexes: the Weighted Relevance Index (WRI) and the Weighted Magnitude Index (WMI), are used for the quantitative study. The results indicate that both POD and KPCA show improvements in capturing the main flow field features compared to ensemble-averaged PIV experimental data and single cycle experimental flow fields while capturing CCV. Both methods present similar quantitative accuracy when using the three indexes. However, challenges were highlighted in the POD method for the selection of the number of POD modes needed for a representative reconstruction. When the flow field region presents a Gaussian distribution, the KPCA method is seen to provide a more objective numerical process as the reconstructed flow field will see convergence with an increasing number of modes due to its usage of Gaussian properties. No additional criterion is needed to determine how to reconstruct the main flow field feature. Using KPCA can, therefore, reduce the amount of analysis needed in the process of extracting the main flow field while incorporating CCV.

Accurate modeling of the initial transient period of spray development is critical within diesel engines, as it impacts on the amount of vapor penetration and hence the combustion characteristics of the spray. In addition, in multiple injection schemes shorter injections will be mostly, if not totally, within the initial transient period. This paper investigates how two different commercially available Computational Fluid Dynamics (CFD) codes (hereafter noted as Code 1 and Code 2) simulate transient diesel spray atomization, in a non-combusting environment. The case considered for comparison is a single-hole injection of n-dodecane representing the Engine Combustion Network’s ‘Spray A’ condition. It was identified that the different spray break-up models used by the codes (Reitz-Diwakar for Code 1, Kelvin-Helmholtz/Rayleigh-Taylor (KH-RT) for Code 2) had a significant impact on the transient liquid penetration. From differing initial base setups, Code 1’s case was then matched as closely as possible to Code 2’s case, applying the KH-RT break-up model in Code 1 with the same constants for the break-up and turbulence models as in Code 2. Despite the nominal equivalence between the two simulations, there existed a discrepancy in liquid length prediction throughout injection between codes. This was thought to be caused by differing implementations of the KH-RT model in both codes. Therefore, a new implementation of the KH-RT model was input into Code 1 in order to allow correct matching of the liquid length to experimental data throughout the injection period. This was achieved utilizing a numerical switch based on the Ohnesorge number. Results from the new model are shown and compared to the previous implementation, showing an improved ability to match to experimental data. Differences between the results from the modified KH-RT model in Code 1 and the standard implementation in Code 2 are also discussed.

A novel combustion modelling approach is proposed here to 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 regularisation 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 successful application of CSE 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 the paper 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.

This article considers the application and refinement of artificial neural network methods for the prediction of NO x emissions from a high-speed direct injection diesel engine over a wide range of engine operating conditions. The relative computational cost and performance of two backpropagation algorithms, Levenberg–Marquardt and Bayesian regularization, for this application are compared, with the Levenberg–Marquardt algorithm demonstrating a significant cost advantage. This work also assesses the performance of two alternative filtering approaches, a p-value test and the Pearson correlation coefficient, for reducing the required number of input variables to the model. The p-value test identified 32 input parameters of significance, whereas the Pearson correlation test highlighted 14 significant parameters while additionally providing a ranking of their relative importance. Finally, the article compares the predictive performance of the models generated by the two filtering methods. Overall, both models show good agreement to the experimental data with the model created using the Pearson correlation test showing improved performance in the low-NO x region.