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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.

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.

A new reflected shock tube facility, the Cold Driven Shock Tube (CDST), has been designed, built and commissioned at the University of Oxford for investigating IC engine fuel spray physics and chemistry. Fuel spray and chemical kinetics research requires its test gas to be at engine representative pressures and temperatures. A reflected shock tube generates these extreme conditions in the test gas for short durations (order milliseconds) by transiently compressing it through a reflected shock process. The CDST has been designed for a nominal test condition of 6 MPa, 900 K slug of air (300 mm long) for a steady test duration of 3 ms. The facility is capable of studying reacting mixtures at higher pressures (up to 150 bar) than other current facilities, whilst still having comparable size (100 mm diameter) and optical access to interrogate the fuel spray with high speed imaging and laser diagnostics. Future data gathered will support fundamental research for IC engine and fuel technologies leading to even higher thermal efficiency along with a reduction in emissions, and provide high quality, repeatable validation data for advanced model development. This paper describes the scope of the facility's capabilities, aspects of its design, details of the instrumentation, and the axially mounted single hole diesel injector.