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.

The pollutant emissions of aircraft engines are strongly affected by the fuel injection into the combustion chamber. Hence, the precise description of the fuel spray is required in order to predict these emissions more reliably. The characteristics of a spray is determined during the atomization process, especially during primary breakup in the vicinity of the atomizer nozzle. Currently, Euler-Lagrangian approaches are used to predict the droplet trajectories in combustor simulations along with reaction and pollutant formation models. To be able to reliably predict pollutant emissions in the future, well-defined starting conditions of the liquid fuel droplets close to the atomizer nozzle are necessary. In the present work, Euler-Lagrangian simulations of a generic airblast atomizer are presented. The starting conditions of the droplets are varied in the simulations by means of a primary breakup model, which takes into account the local gas velocity when predicting the droplet diameter. The objective of this work is to determine the optimal parameters of the probability density functions for the starting position and the starting velocity of the droplets. Spray properties observed in the simulations are used to qualitatively evaluate the major effects of the distribution parameters on the spray and the suitability of the primary breakup model being applied. Hence, the spatial distribution of an experimental spray can be reproduced using a statistical model for the droplet starting conditions.

With the increasing demand for efficient and accurate numerical simulations of spray combustion in jet engines, the necessity for robust models to enhance the capabilities of spray models has become imperative. Existing approaches often rely on ad hoc determinations or simplifications, resulting in information loss and potentially inaccurate predictions for critical spray characteristics, such as droplet diameters, velocities, and positions, especially under extreme operating conditions or temporal fluctuations. In this study, we introduce a novel approach to modeling multivariate spray characteristics using Gaussian mixture models (GMM). By applying this approach to spray data obtained from numerical simulations of the primary atomization in air-blast atomizers, we demonstrate that GMMs effectively capture the spray characteristics across a wide range of operating conditions. Importantly, our investigation reveals that GMMs can handle complex non-linear dependencies by increasing the number of components, thereby enabling the modeling of more complex spray statistics. This adaptability makes GMMs a versatile tool for accurately representing spray characteristics even under extreme operating conditions. The presented approach holds promise for enhancing the accuracy of spray combustion modeling, offering an improved injection model that accurately captures the underlying droplet distribution. Additionally, GMMs can serve as a foundation for constructing meta models, striking a balance between the efficiency of low-order approaches and the accuracy of high-fidelity simulations.

The carriage of portable electronic devices (PED) powered by lithium-ion batteries in the aircraft cabin today is a fact. Passengers carry several such batteries in mobile phones, tablets, laptops, e-cigarettes, power banks, etc. Even though rare, there is a remaining risk that a Li-ion battery experiences thermal runaway. This typically results in the emission of smoke and gas as well as the emergence of flames and fire, thus posing a threat to safe operation. To meet this challenge, procedures have been defined, and additional mitigation means have emerged on the market. This study presents an anonymized assessment of additional mitigation means. For this, manufacturers provided samples of their product on a voluntary basis to test the potential to contain a Li-ion battery fire. Furthermore, handling was evaluated by a panel of cabin crew members. As a result, a series of recommendations for additional mitigation means and procedures was derived.

Logos Verlag Berlin, Simon Holz Multivariate Statistische Modellierung der Tropfenstartbedingungen für Euler-Lagrange-Simulationen von Triebwerksbrennkammern, Reihe: Forschungsberichte aus dem Institut für Thermische Strömungsmaschinen, Bd. 93/2023