Structured mesh computational fluid dynamic solvers are inherently faster than unstructured solvers, which is particularly advantageous for aerodynamic design optimization, where hundreds of flow solutions are required. However, generating body-fitted multiblock meshes for complex geometries is challenging and is a time consuming task. The overset mesh technique greatly reduces the manual effort required to generate meshes over complex geometries by overlapping a series of simpler meshes. However, generating the necessary connectivity information between meshes in a robust and computationally efficient manner remains a challenge. We address this challenge by developing an efficient parallel overset grid assembly technique based on implicit hole cutting that is fully automatic. The method is fully parallel and scales to hundreds of processors. Several optimizations of the Common Research Model wing-body-tail configuration are performed using the meshes generated by our technique. We compare the best drag reduction obtained from multiblock and overset meshes using two different artificial dissipation schemes. The smooth, highly orthogonal overset meshes produce better results than the multiblock meshes, by up to 3 drag counts. An application to rotorcraft design is also presented. The demonstrated meshing flexibility and accurate transonic solutions make the overset mesh technique ideally suited for aerodynamic shape optimization.

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Turbulent flows involving adverse pressure gradients, curvature and mild separation are analyzed and data-driven augmentations are developed for predictive models. Large eddy simulations are performed over a set of parametric flow configurations and the impact of varying curvature, boundary layer thickness and Reynolds number is assessed. The first step in the data-driven methodology involves the determination of functional discrepancies in existing models using inverse modeling. The inferred discrepancy is reconstructedas a function of locally non-dimensional flow features-using a machine learning algorithm. This machinelearned discrepancy is embedded within a k − ω turbulence model. The impact of the choice of data used for the inverse modeling on the predicted velocity field and Reynolds stresses is analyzed in detail. The dataaugmented turbulence model, trained using a very small subset of flows and limited data is shown to yield much-improved predictions of flow properties, over the entire set of configurations. This work represents a key step toward the development of more general data-augmented turbulence models.