<div class="section abstract"><div class="htmlview paragraph">Autonomous takeoff and landing maneuvers of an unmanned aerial vehicle (UAV) from/on a moving ground vehicle (GV) have been an area of active research for the past several years. For military missions requiring repeated flight operations of the UAV, precise landing ability is important for autonomous docking into a recharging station, since such stations are often mounted on a ground vehicle. The development of precise and efficient control algorithms for this autonomous maneuvering has two key challenges; one is related to flight aerodynamics and the other is related to a precise detection of the landing zone. The aerodynamic challenges include understanding the complex interaction of the flows over the UAV and GV, potential ground effects at the proximity of the landing surface, and the impact of the variations in the surrounding wind flow and ambient conditions. While a large body of work in this area can be found on the control aspect of the UAV landing and takeoff maneuvers, research on the aerodynamic aspects of such maneuvers is non-existent. This paper presents an in-depth computational fluid dynamics (CFD) based aerodynamic characterization of the transient flow fields associated with the landing of a hobby-model quadcopter (the UAV) on an idealized road vehicle (the GV), the 35-degree slant angle Ahmed body. Transient improved delayed detached eddy simulations (IDDES) are carried out using the commercial CFD code STAR-CCM+. Our study indicates that the pressure field is the first flow property that gets impacted by the proximity of the UAV to the GV.</div></div>

<div class="section abstract"><div class="htmlview paragraph">This article discusses an application of Machine Learning (ML) tools to improve the prediction accuracy of Computational Fluid Dynamics (CFD) for external aerodynamic workflows. The Reynolds Averaged Navier-Stokes (RANS) approach to CFD has proved to be one of the most popular simulation methodologies due to its quick turnaround times and acceptable level of accuracy for most applications. However, in many cases the accuracy for the RANS models can prove to be suboptimal that can be significantly improved with model closure coefficient tuning. During the original turbulence model creation, these closure coefficients were chosen by somewhat ad hoc methods using simple canonical flows that do not transfer well to flows involving more complex objects, like the automotive bodies used in this work. This work presents a novel method of applying ML tools to CFD to optimize the turbulence closure coefficients by using model explainability tools such as Shapley Values, Shapley Additive exPlanations (SHAP), and ML surrogate models. The 25-degree slant Ahmed body model was used to obtain sampling data to tune closure coefficient in the Menter Shear Stress Transport (SST) turbulence model implemented in the open source CFD code, OpenFOAM v2012. Shapley additive values were then calculated using the samples which showed that <i>β</i>^∗ has the strongest influence over the model predictions of lift and drag. ML surrogate models were then applied alongside SHAP providing a better overall sampling efficiency with Shapley additive values and more complete explanations of the model. The SHAP explanations showed that <i>β</i>^∗ had the most influence on the force predictions followed by <i>σ</i>_<i>ω</i>2, while <i>σ</i>_<i>ω</i>1, <i>σ</i>_<i>k</i>1, and <i>σ</i>_<i>k</i>2 were shown to have little impact. The surrogate model was then used along with its explanations to provide optimized coefficients that reduced the error in the drag and lift predictions to -3.67% and -2.49% respectively, from -9.67% and -75.8%.</div></div>