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Stanford, Bret K.

Publications and source records attributed to Stanford, Bret K..

38 records · Page 3

Aerostructural Level Set Topology Optimization for a Common Research Model Wing

The purpose of this work is to use level set topology optimization to improve the design of a representative wing box structure for the NASA common research model. The objective is to minimize the total compliance of the structure under aerodynamic and body force loading, where the aerodynamic loading is coupled to the structural deformation. A taxi bump case was also considered, where only body force loads were applied. The trim condition that aerodynamic lift must balance the total weight of the aircraft is enforced by allowing the root angle of attack to change. The level set optimization method is implemented on an unstructured three-dimensional grid, so that the method can optimize a wing box with arbitrary geometry. Fast matching and upwind schemes are developed for an unstructured grid, which make the level set method robust and efficient. The adjoint method is used to obtain the coupled shape sensitivities required to perform aerostructural optimization of the wing box structure.

Dunning, Peter D.↗

Structural Assessment of Advanced Composite Tow-Steered Shells

The structural performance of two advanced composite tow-steered shells, manufactured using a fiber placement system, is assessed using both experimental and analytical methods. The fiber orientation angles vary continuously around the shell circumference from 10 degrees on the shell crown and keel, to 45 degrees on the shell sides. The two shells differ in that one shell has the full 24-tow course applied during each pass of the fiber placement system, while the second shell uses the fiber placement system s tow drop/add capability to achieve a more uniform shell wall thickness. The shells are tested in axial compression, and estimates of their prebuckling axial stiffnesses and bifurcation buckling loads are predicted using linear finite element analyses. These preliminary predictions compare well with the test results, with an average agreement of approximately 10 percent.

Wu, K. Chauncey↗