![]() The FIXED_CL_MODE works by updating the angle of attack (AoA) during the simulation run such that the resulting CL matches the TARGET_CL value. In this particular case we are running at a constant lift value, in order to set that feature the relevant part of the configuration file is presented below. % Optimization constraint functions with pushing factors (affects its value, not the gradient in the python scripts), separated by semicolons % To include quadratic penalty function: use OPT_CONSTRAINT option syntax within the OPT_OBJECTIVE list. % Optimization objective function with scaling factor, separated by semicolons. A number of objective functions/constraitns are implemented in SU2, and we recommend that you check the config_template.cfg file in the root directory for a list of those that are available. First, we note that we are choosing a drag objective and pitching moment constraint. ![]() Several of the key configuration file options for this simulation are highlighted here. The mesh can be seen in Figure (1).įigure (1): Zoom view of the initial computational mesh. The mesh consists of a far-field boundary and a Navier-Stokes wall (non-slip) along the airfoil surface. Remember that the free-stream pressure is computed from this values (assuming perfect gas). ![]() lift objective function and pitching moment constraint) equations on the RAE 2822 airfoil at a lift coefficient value of 0.724 using air with the following free-stream conditions: This problem will solve the RANS and adjoint RANS (drag at cte. The goal of the design process is to minimize the coefficient of drag at a constant lift value by changing the shape of the airfoil and everything reducing the pitching moment and keeping a minimum thickness. The flow conditions of this numerical experiment are such that a transonic shock appear on the upper surface causing shock induced separation, which causes drag. Many useful output files will be available to you at the conclusion. This iterative design loop will proceed until a minimum is found or until reaching a maximum number of optimizer iterations. As in the NACA 0012 case, we will use Hicks-Henne bump functions to parameterize the shape (design variables).īy launching the shape_optimization.py script, a gradient-based optimizer will orchestrate the design cycle consisting of the flow solver, adjoint solver, geometry evaluation, and geometry/mesh deformation tools available in SU2. In terms of constraints we will use pitching moment and airfoil thickness. For this tutorial, drag coefficient at a constant lift coefficient will be our objective function. We start with a baseline geometry (RAE 2822) and grid as input to our design cycle, along with a chosen objective function, constraints and set of design variables. And do not forget to compile with the adjoint mode capability. #Airfoil shape 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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