Ravi Samtaney

We perform direct numerical simulation of flow past the NACA (National Advisory Committee for Aeronautics) 0012 airfoil with partially covered wavy roughness elements near the leading edge. The Reynolds number Rec based on the freestream velocity (U0) and airfoil chord length (C), and the angle of attack (AoA) is fixed, i.e., Rec=5×104, AoA=10°. The leading edge roughness elements are characterized by streamwise sinusoidal-wave geometry that is uniform in the spanwise direction with fixed height, whereas varying wave numbers (k) from 0 to 12. Based on the validation of the smooth baseline case (k = 0), the roughness effects on the aerodynamic performance are evaluated in terms of the lift and drag coefficients. The drag coefficient decreases monotonically with k, while the variation of the lift coefficient with k is similar to the “drag crisis” phenomenon observed in cylinder flows. The sharp variations of lift and drag coefficients from k = 6 to 8 indicate that k = 8 is a critical case, beyond which massive separation occurs and almost covers the airfoil's suction side and dominates the airfoil aerodynamic performance. To further reveal the underlying mechanism, the flow structures, pressure, skin friction coefficients, shear layer transition onset, and boundary layer separation are quantified to investigate the roughness effects. The roughness elements strongly modify the separation behavior, whereas they have little effect on the transition onset. The unsteady interactions and convections of separation bubbles downward the trailing edge also change the wake evolution. Based on the scaling of the asymmetric wake profiles, we find that the wake defect is gently decreasing with k, but the increase in the wake width is much stronger. This scaling analysis gains a better insight into the roughness effects on the momentum deficit flow rate, which confirms the drag mechanism with different roughness wave numbers.

Nonlinear wave interactions in magnetohydrodynamics (MHD), such as shock refraction at an inclined density interface, lead to a plethora of wave patterns with numerous wave types. Identification of different types of MHD waves is an important and challenging task in such complex wave patterns. Moreover, owing to the multiplicity of solutions and their admissibility for different systems, especially for intermediate-type MHD shock waves, the identification of MHD wave types is complicated if one relies on the Rankine–Hugoniot jump conditions. MHD wave detection is further exacerbated by nonphysical smearing of discontinuous shock waves in numerical simulations. This paper proposes two MHD wave detection methods based on convolutional neural network to enable wave classification and identify their locations. The first method separates the output into regression (location prediction) and classification problems, assuming the number of waves for each training data is fixed. In contrast, the second method does not specify the number of waves a priori, and the algorithm predicts wave locations and classifies types using only regression. We use one-dimensional input data (density, velocity, and magnetic fields) to train the two models that successfully reproduce a complex two-dimensional MHD shock refraction structure. The first fixed output model efficiently provides high precision and recall, achieving total neural network accuracy up to 99%, and the classification accuracy of some waves approaches unity. The second detection model has relatively low performance, with more sensitivity to the setting of parameters, such as the number of grid cells Ngrid and the thresholds of confidence score and class probability, etc. The detection model achieves better than 90% accuracy with F1 score >0.95. The proposed two methods demonstrate very strong potential for MHD wave detection in complex wave structures and interactions.