N.C. A&T received 21 grants totaling $1.45 million in February. One highlight of February’s s funding was a grant worth $115,258 from the National Institute of Aerospace to Dr. Nail Yamaleev of the Department of Mathematics.
The project: Improvements of Unstructured Finite Volume Solutions for Turbulent Flows
The issue: Current computational fluid dynamics solutions provide insufficient accuracy in predicting complex turbulent flows involving, for example, flow separation and shear layers. The flow separation entails significant energy losses and limits the performance of many aerodynamic systems. Reliable prediction and control of flow separation is absolutely critical for meeting targeted vehicle aerodynamic efficiency, especially at off-design conditions. Widely recognized limitations of current computational and optimization approaches are deterioration of accuracy of gradients and finite-volume solutions on curved highly anisotropic grids typical for high-Reynolds-number flow computations and large computational and storage costs associated with solution of the primary and adjoint flow equations.
Accurate and efficient prediction and optimization of separated flows can lead to significant reduction in the lift-to-drag ratio, thus improving performance, reducing fuel consumption, extending the flight envelope, and enhancing aircraft survivability. Improved accuracy and efficiency of turbulent flow solutions will lead to practical computational tools that are capable of capturing the complex physics present in various aerodynamic applications that are of interest to NASA including rotary and fixed-wing vehicles across all speed regimes.
Abstract: Research to be performed on this project will be directed to enhance the state-of-the art unstructured Computational Fluid Dynamics (CFD) simulation and optimization methodologies implemented in NASA’s code, FUN3D. The overall efforts focus on improving accuracy and efficiency of unstructured finite-volume methods and reduced-order models.
In Task 1, novel effective computational approaches will be studied to provide significant improvements in accuracy of gradient reconstruction and finite-volume solutions with no appreciable increase in complexity.
Task 2 will be concerned with development of efficient and scalable convergence acceleration methods that significantly reduce the turn-around time required for practical large-scale aerodynamic simulations.
Task 3 is aimed at reduction of the storage and computational costs by extending reduced-order models based the proper orthogonal decomposition to unsteady flows with shocks.