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The urgent need for a global energy transition from traditional fossil fuels to renewable sources has been underscored by the alarming rise in global temperatures and growing concerns about air pollution. Light-duty internal combustion engines, despite years of technological advances, continue to contribute significantly to greenhouse gas emissions and harmful pollutants, necessitating further progress in engine technologies for a multi-faceted solution to combat climate change and improve air quality. This cumulative dissertation addresses the critical challenge of understanding and controlling the cycle-to-cycle variability in engine combustion that directly impacts emissions. It extensively investigates the influence of in-cylinder flows on combustion phenomena in an optically accessible spark-ignition engine with the overarching goal of reducing cycle-to-cycle variations. The study includes five peer-reviewed articles and unpublished work, providing a comprehensive analysis of the relationship between engine flows and combustion processes from simplified to realistic engine configurations. High-speed diagnostics and robust analysis techniques are employed to advance the development of predictive combustion models that contribute to improved engine design. This work extends existing measurement techniques by combining cutting-edge equipment with novel analysis approaches, culminating in the development of phenomenological models. In addition, it provides a vast database of accurate boundary conditions and multi-parameter vector and scalar data to benefit the engine research community and improve numerical simulations and models. The thesis is structured in two parts, with each publication building on the previous one and increasing in complexity. The first part focuses on the fundamental processes of intake flows and their interaction with direct injection sprays. A designed flow bench integrated into the engine test cell is used to study turbulent intake flows o

In this work, the Engine Combustion Network Spray G injector was mounted in the Darmstadt optical-accessible engine to study phenomena typical of multi-hole, early direct-injection events in spark-ignition engines characterized by tumble flow charge motion. Dedicated experimental measurements of both in-cylinder spray morphology and flow velocities before and after the injection process were carried out to assess the adopted numerical setup under real engine conditions. A dynamic secondary breakup model from the literature was coupled with an atomization multi-motion regime model. The model was validated against state-of-the-art ECN Spray G experiments for a constant-volume chamber under low evaporating condition. Then, the simulation of the spray injection in the engine was carried out and the achieved results were compared against the experimental data. Overall, good agreement between experiments and simulations was observed for the spray morphology and velocity fields in both cases. With reference to engine calculations the intake flow was seen to induce spray asymmetry. A partial vortex generated during the intake phase on the tumble plane interacts with the spray, developing into a full vortex which induces an upward flow that stabilizes the spray. The upward flows below the intake valve increase the dilution of the plume outside the tumble plane, which therefore exhibits reduced penetration. Moreover, the intake valves protect from the energetic intake flow the recirculation vortex generated at the tip of the plumes that lie outside the tumble plane. The intake flow helps fuse the vapor fuel clouds of the individual plumes near the injector tip, obtaining a vapor fuel with a shape like that generated by a horseshoe multi-hole injector. Finally, a phenomenological model of the interaction between the multi-hole injector jets and the engine intake flow was introduced to describe the spray evolution in a typical DISI engine.

A well-characterized multi-hole gasoline injector, the Engine Combustion Network’s (ECN) Spray G injector, was investigated in an optically accessible research engine under four motored operating conditions with early injection. The experiments were conducted at intake pressures of 0.4 bar and 0.95 bar, nearly matching the ECN’s standard early injection operating conditions, Spray G2 (flash boiling) and Spray G3 (early injection), respectively. This was combined with two engine speeds at 800 rpm and 1500 rpm. Using particle image velocimetry and volumetric Mie scatter imaging, the in-cylinder flows were evaluated and the effects on the spray morphology were characterized. The in-cylinder flow was evaluated to understand the spray-flow interaction, including the turbulent kinetic energy. Little effect on turbulent energy was observed in the region examined near the exit of the fuel injector nozzle shortly after injection. Mie scatter imaging was used to characterize the spray morphology and wall wetting was clearly visible on the spark plug. Cyclic variability of the sprays was found to be insignificant; and major differences in spray morphology are attributed to the in-cylinder velocity and intake pressure at the time of injection. Decreasing the bulk gas density by decreasing the intake pressure had a number of effects on the evolution of the spray including faster evaporation, increased axial liquid penetration, and decreased spray angle. Increasing the in-cylinder flow magnitudes by increasing the engine speed had a similar effect on spray morphology by also increasing the evaporation rate, increasing the axial penetration, and decreasing the spray opening angle. Comparison of the motored spray cases with a no-flow case (when the fuel is sprayed into the engine without the piston present) further illustrated the extent to which the intake flow influenced the spray shape.

An investigation of the interaction between the in-cylinder flow and the spray topology in two spray-guided direct injection optical engines is reported. The bulk flow field in the combustion chamber is characterized using particle image velocimetry. Geometrical parameters such as the axial penetration and the spray angle of the liquid spray are measured using Mie scatter imaging and/or diffuse back-illumination. The measured parameters are compared with data from a constant volume chamber available in the literature. For a late injection strategy, the so-called ECN Spray G standard condition, the mean values of the spray penetration do not seem to be significantly perturbed by the in-cylinder flow motion until the plumes approach the piston surface. However, spray probability maps reveal that cycle-to-cycle fluctuations of the spatial distribution of the liquid spray are affected by the magnitude of the in-cylinder flow. Particle image velocimetry during injection shows that the flow field in the vicinity of the spray plumes is heavily influenced by air entrainment, and that an upward flow in-between spray plumes develops. Consistent with previous research that demonstrated the importance of the latter flow structure for the prevention of spray collapse, it is found that increased in-cylinder flow magnitudes due to increased intake valve lifts or engine speeds enhance the spray-shape stability. Compared with cases without injection, the influence of the spray on the in-cylinder flow field is still noticeable approximately 2.5 ms after the start of injection.

Machine learning (ML) models based on a large data set of in-cylinder flow fields of an IC engine obtained by high-speed particle image velocimetry allow the identification of relevant flow structures underlying cycle-to-cycle variations of engine performance. To this end, deep feature learning is employed to train ML models that predict cycles of high and low in-cylinder maximum pressure. Deep convolutional autoencoders are self-supervised-trained to encode flow field features in low dimensional latent space. Without the limitations ascribable to manual feature engineering, ML models based on these learned features are able to classify high energy cycles already from the flow field during late intake and the compression stroke as early as 290 crank angle degrees before top dead center (-290° CA) with a mean accuracy above chance level. The prediction accuracy from -290° CA to -10° CA is comparable to baseline ML approaches utilizing an extensive set of engineered features. Relevant flow structures in the compression stroke are revealed by feature analysis of ML models and are interpreted using conditional averaged flow quantities. This analysis unveils the importance of the horizontal velocity component of in-cylinder flows in predicting engine performance. Combining deep learning and conventional flow analysis techniques promises to be a powerful tool for ultimately revealing high-level flow features relevant to the prediction of cycle-to-cycle variations and further engine optimization.

This is the author's version of a work that was accepted for publication in International Journal of Engine Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published as https://doi.org/10.1177/1468087419881535., [EN] An investigation of the interaction between the in-cylinder flow and the spray topology in two spray-guided direct injection optical engines is reported. The bulk flow field in the combustion chamber is characterized using particle image velocimetry. Geometrical parameters such as the axial penetration and the spray angle of the liquid spray are measured using Mie scatter imaging and/or diffuse back-illumination. The measured parameters are compared with data from a constant volume chamber available in the literature. For a late injection strategy, the so-called ECN Spray G standard condition, the mean values of the spray penetration do not seem to be significantly perturbed by the in-cylinder flow motion until the plumes approach the piston surface. However, spray probability maps reveal that cycle-to-cycle fluctuations of the spatial distribution of the liquid spray are affected by the magnitude of the in-cylinder flow. Particle image velocimetry during injection shows that the flow field in the vicinity of the spray plumes is heavily influenced by air entrainment, and that an upward flow in-between spray plumes develops. Consistent with previous research that demonstrated the importance of the latter flow structure for the prevention of spray collapse, it is found that increased in-cylinder flow magnitudes due to increased intake valve lifts or engine speeds enhance the spray-shape stability. Compared with cases without injection, the influence of the spray on the in-cylinder flow field is still noticeable approximately 2.5 ms after the start of injection.

In this paper, we compare the capabilities of two open source near-wall-modeled large eddy simulation (NWM-LES) approaches regarding prediction accuracy, computational costs and ease of use to predict complex turbulent flows relevant to internal combustion (IC) engines. The applied open source tools are the commonly used OpenFOAM, based on the finite volume method (FVM), and OpenLB, an implementation of the lattice Boltzmann method (LBM). The near-wall region is modeled by the Musker equation coupled to a van Driest damped Smagorinsky-Lilly sub-grid scale model to decrease the required mesh resolution. The results of both frameworks are compared to a stationary engine flow bench experiment by means of particle image velocimetry (PIV). The validation covers a detailed error analysis using time-averaged and root mean square (RMS) velocity fields. Grid studies are performed to examine the performance of the two solvers. In addition, the differences in the processes of grid generation are highlighted. The performance results show that the OpenLB approach is on average 32 times faster than the OpenFOAM implementation for the tested configurations. This indicates the potential of LBM for the simulation of IC engine-relevant complex turbulent flows using NWM-LES with computationally economic costs.