The formation of pollutant emissions in jet engines is closely related to the fuel distribution inside the combustor. Hence, the characteristics of the spray formed during primary breakup are of major importance for an accurate prediction of the pollutant emissions. Currently, an Euler–Lagrangian approach for droplet transport in combination with combustion and pollutant formation models is used to predict the pollutant emissions. The missing element for predicting these emissions more accurately is well defined starting conditions for the liquid fuel droplets as they emerge from the fuel nozzle. Recently, it was demonstrated that the primary breakup can be predicted from first principles by the Lagrangian, mesh-free, Smoothed Particle Hydrodynamics (SPH) method. In the present work, 2D Direct Numerical Simulations (DNS) of a planar prefilming airblast atomizer using the SPH method are presented, which capture most of the breakup phenomena known from experiments. Strong links between the ligament breakup and the resulting spray in terms of droplet size, trajectory and velocity are demonstrated. The SPH predictions at elevated pressure conditions resemble quite well the effects observed in experiments. Significant interdependencies between droplet diameter, position and velocity are observed. This encourages to employ such multidimensional interdependence relations as a base for the development of primary atomization models.

This paper presents results from an extensive experimental study on the rubbing behavior of labyrinth seal fins (SFs) and a honeycomb liner. The objective of the present work is to improve the understanding of the rub behavior of labyrinth seals by quantifying the effects and interactions of sliding speed, incursion rate, seal geometry, and SF rub position on the honeycomb liner. In order to reduce the complexity of the friction system studied, this work focuses on the contact between a single SF and a single metal foil. The metal foil is positioned in parallel to the SF to represent contact between the SF and the honeycomb double foil section. A special test rig was set up enabling the radial incursion of a metal foil into a rotating labyrinth SF at a defined incursion rate of up to 0.65 mm/s and friction velocities up to 165 m/s. Contact forces, friction temperatures, and wear were measured during or after the rub event. In total, 88 rub tests including several repetitions of each rub scenario have been conducted to obtain a solid data base. The results show that rub forces are mainly a function of the rub parameters incursion rate and friction velocity. Overall, the results demonstrate a strong interaction between contact forces, friction temperature, and wear behavior of the rub system. The presented tests confirm basic qualitative observations regarding blade rubbing provided in literature.

In this paper, the complex two-phase flow during oil-jet impingement on a rotating spur gear is investigated using the meshless smoothed particle hydrodynamics (SPH) method. On the basis of a two-dimensional setup, a comparison of single-phase SPH to multiphase SPH simulations and the application of the volume of fluid method is drawn. The results of the different approaches are compared regarding the predicted flow phenomenology and computational effort. It is shown that the application of single-phase SPH is justified and that this approach is superior in computational time, enabling faster simulations. In the next step, a three-dimensional single-phase SPH setup is exploited to predict the flow phenomena during the impingement of an oil-jet on a spur gear for three different jet inclination angles. The oil’s flow phenomenology is described and the obtained resistance torque is presented. Thereby, a significant effect of the inclination angle on the oil spreading and splashing process as well as the resistance torque is identified.

Internal coolant passages of gas turbine vanes and blades have various orientations relative to the external hot gas flow. As a consequence, the inflow of film cooling holes varies as well. To further identify the influencing parameters of film cooling under varying inflow conditions, the present paper provides detailed experimental data. The generic study is performed in a novel test rig, which enables compliance with all relevant similarity parameters including density ratio. Film cooling effectiveness as well as heat transfer of a 10–10–10 deg laidback fan-shaped cooling hole is discussed. Data are processed and presented over 50 hole diameters downstream of the cooling hole exit. First, the parallel coolant flow setup is discussed. Subsequently, it is compared to a perpendicular coolant flow setup at a moderate coolant channel Reynolds number. For the perpendicular coolant flow, asymmetric flow separation in the diffuser occurs and leads to a reduction of film cooling effectiveness. For a higher coolant channel Reynolds number and perpendicular coolant flow, asymmetry increases and cooling effectiveness is further decreased. An increase in blowing ratio does not lead to a significant increase in cooling effectiveness. For all cases investigated, heat transfer augmentation due to film cooling is observed. Heat transfer is highest in the near-hole region and decreases further downstream. Results prove that coolant flow orientation has a severe impact on both parameters.

Predictive maintenance is considered a proactive approach that capitalizes on advanced sensing technologies and data analytics to anticipate potential equipment malfunctions, enabling cost savings and improved operational efficiency. For journal bearings, predictive maintenance assumes critical significance due to the inherent complexity and vital role of these components in mechanical systems. The primary objective of this study is to develop a data-driven methodology for indirectly determining the wear condition by leveraging experimentally collected vibration data. To accomplish this goal, a novel experimental procedure was devised to expedite wear formation on journal bearings. Seventeen bearings were tested and the collected sensor data were employed to evaluate the predictive capabilities of various sensors and mounting configurations. The effects of different downsampling methods and sampling rates on the sensor data were also explored within the framework of feature engineering. The downsampled sensor data were further processed using convolutional autoencoders (CAEs) to extract a latent state vector, which was found to exhibit a strong correlation with the wear state of the bearing. Remarkably, the CAE, trained on unlabeled measurements, demonstrated an impressive performance in wear estimation, achieving an average Pearson coefficient of 91% in four different experimental configurations. In essence, the proposed methodology facilitated an accurate estimation of the wear of the journal bearings, even when working with a limited amount of labeled data.

In this study, we propose a population-based, data-driven intelligent controller that leverages neural-network-based digital twins for hypothesis testing. Initially, a diverse set of control laws is generated using genetic programming with the digital twin of the system, facilitating a robust response to unknown disturbances. During inference, the trained digital twin is utilized to virtually test alternative control actions for a multi-objective optimization task associated with each control action. Subsequently, the best policy is applied to the system. To evaluate the proposed model predictive control pipeline, experiments are conducted on a multi-mode heat transfer test rig. The objective is to achieve homogeneous cooling over the surface, minimizing the occurrence of hot spots and energy consumption. The measured variable vector comprises high dimensional infrared camera measurements arranged as a sequence (655,360 inputs), while the control variable includes power settings for fans responsible for convective cooling (3 outputs). Disturbances are induced by randomly altering the local heat loads. The findings reveal that by utilizing an evolutionary algorithm on measured data, a population of control laws can be effectively learned in the virtual space. This empowers the system to deliver robust performance. Significantly, the digital twin-assisted, population-based model predictive control (MPC) pipeline emerges as a superior approach compared to individual control models, especially when facing sudden and random changes in local heat loads. Leveraging the digital twin to virtually test alternative control policies leads to substantial improvements in the controller’s performance, even with limited training data.