Aerodynamic Shape Optimization by Physics Based Surrogates
"The main objective of the research project is to develop computationally efficient and robust procedures and algorithms for aerodynamic shape optimization using high-fidelity computational fluid dynamic (CFD) simulators that woul...
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Descripción del proyecto
"The main objective of the research project is to develop computationally efficient and robust procedures and algorithms for aerodynamic shape optimization using high-fidelity computational fluid dynamic (CFD) simulators that would go beyond the current state of the art and its limitations. The developed methodology will handle both two-dimensional and three dimensional aerodynamic/hydrodynamic surfaces, such as airfoils, wings, wing/fuselage, wing/fuselage/nacelle/pylon, turbomachinery blades, and submersibles. The critical task is to obtain satisfactory designs at a reasonable computational cost (in terms of the number of high-fidelity CFD simulations). To achieve the objective physics based surrogate optimization technique that combines the speed of the low-fidelity models with the accuracy of the CPU-intensive high-fidelity ones will be adopted. A special emphasis will be on finding ways to fully employ the embedded knowledge within the physics-bases surrogates to achieve a low computational cost of the overall design optimization. In particular, this will involve the development of new methodologies for generating cheap and robust low-fidelity models, and, equally important, reliable and accurate response correction techniques for aerodynamic responses. The developed procedures will be implemented in a software package for automated aerodynamic design . The programming environment used in the project is Matlab. The aerodynamic design procedures, as well as their software implementation, will be subjected to extensive numerical verification using a set of benchmark problems involving 2D and 3D cases for subsonic and transonic conditions. The goal will be to verify the ability of the procedures and algorithms to yield satisfactory and feasible designs at low computational cost. The performance of our algorithms will be compared to state-of-the-art methodologies described in literature. Then the methodologies will be applied to relevant industry design problems."