Automatic Differentiation Adjoint of the Reynolds-Averaged Navier-Stokes Equations with a Turbulence Model

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106452/1/AIAA2013-2581.pd

Multiobjective Design Optimization using Nash Games

International audienceIn the area of pure numerical simulation of multidisciplinary coupled systems, the computational cost to evaluate a configuration may be very high. A fortiori, in multi- disciplinary optimization, one is led to evaluate a number of different configurations to iterate on the design parameters. This observation motivates the search for the most in- novative and computationally efficient approaches in all the sectors of the computational chain : at the level of the solvers (using a hierarchy of physical models), the meshes and geometrical parameterizations for shape, or shape deformation, the implementation (on a sequential or parallel architecture; grid computing), and the optimizers (deterministic or semi-stochastic, or hybrid; synchronous, or asynchronous). In the present approach, we concentrate on situations typically involving a small number of disciplines assumed to be strongly antagonistic, and a relatively moderate number of related objective functions. However, our objective functions are functionals, that is, PDE-constrained, and thus costly to evaluate. The aerodynamic and structural optimization of an aircraft configuration is a prototype of such a context, when these disciplines have been reduced to a few major objectives. This is the case when, implicitly, many subsystems are taken into account by local optimizations. Our developments are focused on the question of approximating the Pareto set in cases of strongly-conflicting disciplines. For this purpose, a general computational technique is proposed, guided by a form of sensitivity analysis, with the additional objective to be more economical than standard evolutionary approaches

Enhancement of Adjoint Design Methods via Optimization of Adjoint Parameters

Three approaches of enhancing the Euler adjoint design methods using a non-linear gradient-based optimization package, SNOPT, have been investigated. First, the convergence of Euler and adjoint solutions was accelerated by optimizing the coefficients of the residual smoothing scheme and the CFL number. Second, the input parameters of Euler adjoint design methods were optimized such that the best aerodynamic shape can be achieved in a given number of design iterations. Finally the SNOPT software has also been used to provide line searches of the shape optimization parameters at each step and improved the robustness of the design methods. The numerical results showed the feasibility of integration of the SNOPT package and the adjoint software in order to speed-up, improve, and stabilize the performance of the design methods. I