#AIRFOIL SHAPES DOWNLOAD#
If you have yet to complete these requirements, please see the Download and Installation pages. The design loop is driven by the shape_optimization.py script, and thus Python along with the NumPy and SciPy Python modules are required for this tutorial. It is assumed that you have already obtained and compiled SU2_CFD, SU2_CFD_AD, SU2_DOT, SU2_DOT_AD, SU2_GEO, and SU2_DEF. The following tutorial will walk you through the steps required when performing shape design for the transonic turbulent airfoil using SU2 and the automatic differentiation tool. You will need the mesh file mesh_RAE2822_turb.su2, the config file turb_SA_RAE2822.cfg and initial solution files for the solver and adjoint solution_flow.dat, solution_adj_cd.dat, and solution_adj_cmz.dat. You can find the resources for this tutorial in the folder design/Turbulent_2D_Constrained_RAE2822 in the tutorial repository. We will walk through the shape design process and highlight several options related to the discrete adjoint (Automatic Differentiation) in SU2 and the configuration options for shape design.
shape_optimization.py - automates the entire shape design process by executing the SU2 tools and optimizer.SU2_GEO - evaluates the thickness of airfoil and its gradient with respect to each design variable.SU2_DEF - deforms the geometry and mesh with changes in the design variables during the shape optimization process.SU2_DOT_AD - projects the adjoint surface sensitivities into the design space to obtain the gradient.SU2_CFD_AD - performs the adjoint flow simulations using automatic differentiation.SU2_CFD - performs the direct flow simulations.The following SU2 tools will be showcased in this tutorial: The airfoil geometry chosen for this tutorial is a RAE2822 airfoil (AGARD Report AR 138, 1979) at transonic speed in viscous turbulent fluid and constant C L. Upon completing this tutorial, the SU2 user will be familiar with performing a constrained optimal shape design of a 2D airfoil geometry.
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