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.

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.

Pressure gain combustion (PGC) has been conceived to convert fuel's chemical energy into thermal energy and mechanical energy, thereby reducing the entropy production in the process. Recent research has shown that the rotating detonation combustor (RDC) can provide excellent specific thrust, specific impulse, and pressure gain within a small volume through rapid energy release by continuous detonation in the circumferential direction. The RDC as a PGC system for power generating gas turbines in combined cycle power plants could provide significant efficiency gains. However, few past studies have employed fuels that are relevant to power generation turbines, since RDC research has focused mainly on propulsion applications. In this study, we present experimental results from RDC operated on methane and oxygen-enriched air to represent reactants used in land-based power generation. The RDC is operated at a high pressure by placing a back-pressure plate downstream of the annular combustor. Past studies have focused mainly on probe measurements inside the combustor, and thus, little information is known about the nature of the products exiting the RDC. In particular, it is unknown if chemical reactions persist outside the RDC annulus, especially if methane is used as the fuel. In this study, we apply two time-resolved optical techniques to simultaneously image the RDC products at framing rate of 30 kHz: (1) direct visual-imaging to identify the overall size and extent of the plume, and (2) OH* chemiluminescence imaging to detect the reaction zones if any. Results show dynamic features of the combustion products that are consistent with the probe measurements inside the rotating detonation engine (RDE). Moreover, presence of OH* in the products suggests that the oblique shock wave and reactions persist downstream of the detonation zone in the RDC.

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.

Cyclic variability is investigated in an optically accessible single-cylinder spark-ignition research engine by introducing artificial exhaust gas in controlled amounts to the homogenous air–fuel mixture before ignition. A skip-fire scheme ensures the absence of internal exhaust gas recirculation (EGR) and allows the engine to be fired continuously for acquisition of large statistics. Four operating conditions ranging from a stable 0% EGR case up to a highly unstable extreme EGR case are analyzed to examine the increasing effects of homogeneous EGR on the cycle performance. To that end, high-speed measurements of the velocity field via particle image velocimetry and flame imaging in the tumble plane allow the determination of phenomena leading to various flame positions and sizes as well as faster and slower combustion cycles. Through extensive conditional statistical and multivariate correlation techniques, flames are found to be heavily influenced by large-scale velocity motion, especially with the presence of greater EGR which leads to lower flame speeds. The greater sensitivity of slower flames to variations in the velocity field manifests itself in an exponential increase in cyclic variability of the maximum in-cylinder pressure and causes misfire cycles where the flame is blown off or quenched at the cylinder roof. In the most extreme cycles at the highest EGR level, the state of the large-scale velocity structures at the time of ignition determines whether the flame propagates towards the center of the cylinder (and is blown off or quenched) or if the flame sustains growth by propagating within the lingering tumble vortex.