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In this study the structured laser illumination planar imaging (SLIPI) technique is combined with gas phase phosphor thermometry to measure quasi-instantaneously two-dimensional temperature fields with reduced bias from multiple scattering. Different reconstruction strategies are implemented, evaluated and compared, including a two-pulse and one-pulse SLIPI approach. A gradient-based threshold algorithm for particle detection is applied to conventional planar light sheet imaging as an alternative to reduce the bias caused by multiple scattering in seeding-free regions. As a demonstration, measurements are performed in a canonical flow configuration, consisting of a heated, turbulent, air jet surrounded by an ambient co-flow. Both air flows are seeded with the thermographic phosphor BaMgAl₁₇O₁₇:Eu²⁺. Conventional light sheet imaging in the context of gas phase phosphor thermometry suffers from multiple scattering causing a significant temperature bias and low temperature sensitivity. Applying the gradient threshold algorithm removes areas without any seeding particles which improves accuracy, precision and temperature sensitivity. However, multiple scattering influences are still present and may cause an increasing bias particularly for higher seeding density. One pulse (1p) SLIPI exhibits high accuracy at intermediate precision. Multiply scattered luminescence is not fully removed and spatial resolution is lowered. Two pulse (2p) SLIPI is recommended for high temperature sensitivity and accuracy, removing impact of multiple scattering furthermost. However, 2p-SLIPI exhibits reduced temperature precision.

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.

Cycle-to-cycle variations in an optically accessible four-stroke direct injection spark-ignition gasoline engine are investigated using high-speed scanning particle image velocimetry and in-cylinder pressure measurements. Particle image velocimetry allows to measure in-cylinder flow fields at high spatial and temporal resolution. Binary classifiers are used to predict combustion cycles of high indicated mean effective pressure based on in-cylinder flow features and engineered tumble features obtained during the intake and the compression stroke. Basic in-cylinder flow features of the mid-cylinder plane are sufficient to predict combustion cycles of high indicated mean effective pressure as early as 180 degree crank angle before the top dead center at 0 degree crank angle. Engineered characteristic tumble features derived from the flow field are not superior to the basic flow features. The results are independent of the tested machine learning method (multilayer perceptron and boosted decision trees) and robust to hyper-parameter selection.

A single-cylinder full-metal engine with a real combustion chamber geometry was used to investigate particulate number emissions resulting from transient engine operation. The formation of particulate number emissions depends on mixture formation influenced by the in-cylinder flow and injection, and the formation of fuel films on the in-cylinder walls. For the investigation of this multi-parameter process, simultaneous endoscopic PIV and combustion visualization were applied. Hence, the measurement techniques allowed the investigation of in-cylinder flow, flame propagation, and soot formation. The test rig was modified to apply a generic load step and a realistic tip-in with Miller cycle. The reproducibility of the engine parameters during the transient allowed statistical analysis and the comparison between steady-state operating points. Cause-and-effect chains concerning the formation of soot are concluded by correlation analysis of parameters extracted from the flow field, the flame propagation and the soot luminosity.

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.

While existing engineering tools enable us to predict how homogeneous surface roughness alters drag and heat transfer of near-wall turbulent flows to a certain extent, these tools cannot be reliably applied for heterogeneous rough surfaces. Nevertheless, heterogeneous roughness is a key feature of many applications. In the present work we focus on spanwise heterogeneous roughness, which is known to introduce large-scale secondary motions that can strongly alter the near-wall turbulent flow. While these secondary motions are mostly investigated in canonical turbulent shear flows, we show that ridge-type roughness — one of the two widely investigated types of spanwise heterogeneous roughness — also induces secondary motions in the turbulent flow inside a combustion engine. This indicates that large scale secondary motions can also be found in technical flows, which neither represent classical turbulent equilibrium boundary layers nor are in a statistically steady state. In addition, as the first step towards improved drag predictions for heterogeneous rough surfaces, the Reynolds number dependency of the friction factor for ridge-type roughness is presented.

Transient engine operation of direct-injection spark ignition engines can result in high particulate number emissions. To investigate the causes of soot formation, an engine test rig was developed to perform detailed measurements of real transient operation. For this purpose, a single-cylinder full-metal engine with a real combustion chamber geometry was equipped with minimally invasive optical accesses. Simultaneous high-speed endoscopic PIV, spray visualization, and combustion imaging were applied to investigate the in-cylinder processes in detail. Endoscopic PIV was first compared in the central symmetry plane with classical PIV performed at the equivalent optical engine at steady-state operation for verification. Then the engine parameters of a tip-in performed by the corresponding four-cylinder engine, which led to high particle number emissions, were applied to the single-cylinder engine. The engine parameters were in a good agreement and particle number emissions due to the maneuver were within the same range of several 10⁶ #/cm³. In total, 19 repetitions of the tip-in maneuver were analyzed with respect to the in-cylinder processes and repeatability of engine parameters. Furthermore, the in-cylinder flow field during the late compression stroke, flame propagation, and soot luminosity of single cycles during the tip-in indicated cause-and-effect chains for the formation of pool fire and soot at the injector tip. The direction of the flow below the spark plug influenced the direction of flame propagation. An early arrival of the flame enhanced the formation of soot from fuel films formed on the piston surface or at the injector. In engine applications, counter measures can be applied to reduce the particle number emissions when accounting for these indicated cause-and-effect chains.

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.

Mid-level ethanol/gasoline blends can provide knock resistance benefits for stoichiometric spark-ignition engine operation, but previous studies have identified challenges associated with spray impingement and wall wetting, leading to excessive particulate matter emissions. At the same time, stratified-charge spark-ignition operation can provide increased thermal efficiency, but care has to be exercised to avoid excessive in-cylinder soot formation. In support of the use of mid-level ethanol/gasoline blends in advanced spark-ignition engines, this study presents spray and fuel-film measurements in a direct-injection spark-ignition engine operated with a 30 vol.%/70 vol.% ethanol/gasoline blend (E30). Crank-angle resolved fuel-film measurements at the piston surface are conducted using two different implementations of the refractive index matching technique. A small-angle refractive index matching implementation allows quantification of the wetted area, while a large-angle refractive index matching implementation enables semi-quantitative measurements of fuel-film thickness and volume, in addition to fuel-film area. The fuel-film measurements show that both the amount of fuel deposited on the piston and the shape of the fuel-film patterns are strongly influenced by the injection timing, duration, intake pressure, and coolant temperature. For combinations of high in-cylinder gas density and long injection duration, merging of the individual spray plumes, commonly referred to as spray collapse, can cause a dramatic change to the shape and thickness of the wall fuel films. Overall, the study provides guidance to engine designers aiming at minimizing wall wetting through tailored combinations of injection timings and durations.

In the present paper, direct numerical simulation (DNS) and particle image velocimetry (PIV) have been applied complementarily in order to generate a database of near-wall turbulence properties of a highly turbulent jet impinging on a solid surface under different inclination angles. Thereby, the main focus is placed on an impingement angle of 45°, since it represents a good generic benchmark test case for a wide range of technical fluid flow applications. This specific configuration features very complex flow properties including the presence of a stagnation point, development of the shear boundary layer and strong streamline curvature. In particular, this database includes near-wall turbulence statistics along with mean and rms velocities, budget terms in the turbulent kinetic energy equation, anisotropy invariant maps, turbulent length/time scales and near-wall shear stresses. These properties are useful for the validation of near-wall modeling approaches in the context of Reynolds-averaged Navier–Stokes (RANS) and large-eddy simulations (LES). From this study, in which further impingement angles (0°, 90°) have been considered in the experiments only, it turns out that (1) the production of turbulent kinetic energy appears negative at the stagnation point for an impingement angle other than 0° and is balanced predominantly by pressure-related diffusion, (2) quasi-coherent thin streaks with large characteristic time scales appear at the stagnation region, while the organization of the flow is predominantly toroidal further downstream, and (3) near-wall shear stresses are low at the stagnation region and intense in regions where the direction of the flow changes suddenly.

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.

We continue the investigation on the relations of QCDCL and QBF resolution systems. In particular, we introduce QCDCL versions that tightly characterise QU-Resolution and (a slight variant of) long-distance Q-Resolution. We show that most QCDCL variants - parameterised by different policies for decisions, unit propagations and reductions - lead to incomparable systems for almost all choices of these policies.

In the present work, accurate determination of single-particle ignition is focused on using high-speed optical diagnostics combined with machine learning approaches. Ignition of individual particles in a laminar flow reactor are visualized by simultaneous 10 kHz OH-LIF and DBI measurements. Two coal particle sizes of 90-125{\mu}m and 160-200{\mu}m are investigated in conventional air and oxy-fuel conditions with increasing oxygen concentrations. Ignition delay times are first evaluated with threshold methods, revealing obvious deviations compared to the ground truth detected by the human eye. Then, residual networks (ResNet) and feature pyramidal networks (FPN) are trained on the ground truth and applied to predict the ignition time.~Both networks are capable of detecting ignition with significantly higher accuracy and precision. Besides, influences of input data and depth of networks on the prediction performance of a trained model are examined.~The current study shows that the hierarchical feature extraction of the convolutions networks clearly facilitates data evaluation for high-speed optical measurements and could be transferred to other solid fuel experiments with similar boundary conditions., Comment: 9 pages, 6 figures, Mediterranean Combustion Symposium 2023