Aircraft icing represents a serious hazard in aviation which has caused a number of fatal accidents over the years. In addition, it can lead to substantial increase in drag and weight, thus reducing the aerodynamics performance of the airplane. The process of ice accretion on a solid surface is a complex interaction of aerodynamic and environmental variables. The complex relationship makes machine learning-based methods an attractive alternative to traditional numerical simulation-based approaches. In this study, we introduce a purely data-driven approach to find the complex pattern between different flight conditions and aircraft icing severity prediction. The supervised learning algorithm Extreme Gradient Boosting (XGBoost) is applied to establish the prediction framework which makes prediction based on any set of observations. The input flight conditions for the proposed prediction framework are liquid water content, droplet diameter and exposure time. The proposed approach is demonstrated in three cases: maximum ice thickness prediction, icing area prediction and icing severity level evaluation. Performance comparison studies and error analysis are also conducted to verify the effectiveness and performance of the proposed method. Results show that the proposed method has reasonable capability in evaluating aircraft icing severity.

Ce papier décrit les progrès récents concernant l'étude de l'interaction entre les jets propulsifs et les tourbillons de sillage, dans le champ proche d'un avion de transport. Une attention particulière a été portée sur les effets des jets sur la dynamique des tourbillons de sillage et l'influence des ces derniers à la fois sur la dispersion des émissions aussi bien que sur les transformations microphysiques et chimiques, qui peuvent avoir un impact sur l'environnement. Nous discutons les résultats de simulations numériques basées sur des approches DNS et LES, de la dynamique et du mélange à petite échelle ainsi que les transformations chimiques et la formation des traînées de condensation. Pour citer cet article : R. Paoli, F. Garnier, C. R. Physique 6 (2005).

A method to simulate ice accretion on an aircraft wing using a three-dimensional compressible Navier-Stokes solver, a Eulerian droplet flow field model, a mesh morphing model, and a thermodynamic model, is presented in this paper. The above models are combined together into one solver and implemented in OpenFOAM. Two-way coupling is achieved between airflow field calculation and ice simulation. The density-based solver rhoEnergyFoam is used to calculate the airflow field. The roughness wall function is proposed to simulate the roughness effect caused by ice accretion. For droplet flow field calculation, the Eulerian model is applied and the permeable wall boundary condition is used on the wing to simulate the droplet impingement. The icing thermodynamic model is built based on the Messinger model. The mesh morphing model adjusts the wing’s shape every time step based on the amount of accreted ice so that the airflow field is updated during the simulation. The effect of the ice accretion on the airflow is studied by comparing the aerodynamic performance—with and without ice. The ice accretion on the ONERA M6 wing model under a specific condition has been simulated to validate the solver’s performance and investigate the effect of the accreted ice on the aerodynamic performance.

The fog-basking behavior of the Onymacris unguicularis, a beetle species living in the coastal regions of the Namibian desert, has recently caught the attention of the engineering community, as suggesting a viable biomimetic approach to address the problem of harvesting water in arid regions of the globe. Previous research has focused on observation and analysis of the beetle’s elytron properties and how these affect fog-collection rates. The head stance taken by the Onymacris unguicularis when fog basking is well documented. However, how this stance affects droplet collection has not been studied up to now. The present paper addresses this problem from a computational fluid dynamics perspective, where three-dimensional numerical simulations are used to characterize the fog flow properties around a simplified geometry mimicking the beetle’s body. The simulations employ two-way coupling between the gas flow and the dispersed fog phase to account for feedback effects of fog droplets on the carrier fluid (air), and assume that droplets are captured after hitting the elytron surface. The study considers several combinations of free-stream velocity and droplet volume fraction. The analysis reveals that there is a range of head-stance angles, corresponding to an inclination of the beetle between 35 deg and 45 deg with respect to the horizon, that maximizes water collection on the beetle’s back, in qualitative agreement with observations in nature and laboratory experiments. A rationale is proposed to explain this phenomenon, finding that the specific head stance corresponds to the maximum residence time of fluid particles above the beetle’s elytron surface. This, in turn, designates the maximum likelihood for water droplets to be captured in the boundary layer developing over the beetle and subsequently hit the surface where they get captured. The results reveal the importance of the fluid flow pattern around the beetle’s body in addition to the microphysical properties of the elytron when reliable predictions of the water droplet collection efficiency are sought.

Compressible density-based solvers are widely used in OpenFOAM, and the parallel scalability of these solvers is crucial for large-scale simulations. In this paper, we report our experiences with the scalability of OpenFOAM’s native rhoCentralFoam solver, and by making a small number of modifications to it, we show the degree to which the scalability of the solver can be improved. The main modification made is to replace the first-order accurate Euler scheme in rhoCentralFoam with a third-order accurate, four-stage Runge-Kutta or RK4 scheme for the time integration. The scaling test we used is the transonic flow over the ONERA M6 wing. This is a common validation test for compressible flows solvers in aerospace and other engineering applications. Numerical experiments show that our modified solver, referred to as rhoCentralRK4Foam, for the same spatial discretization, achieves as much as a 123.2% improvement in scalability over the rhoCentralFoam solver. As expected, the better time resolution of the Runge–Kutta scheme makes it more suitable for unsteady problems such as the Taylor–Green vortex decay where the new solver showed a 50% decrease in the overall time-to-solution compared to rhoCentralFoam to get to the final solution with the same numerical accuracy. Finally, the improved scalability can be traced to the improvement of the computation to communication ratio obtained by substituting the RK4 scheme in place of the Euler scheme. All numerical tests were conducted on a Cray XC40 parallel system, Theta, at Argonne National Laboratory.