Aerostructural wing shape optimization assisted by algorithmic differentiation

Abstract

With more efficient structures, last trends in aeronautics have witnessed an increased flexibility of wings, calling for adequate design and optimization approaches. To correctly model the coupled physics, aerostructural optimization has progressively become more important, being nowadays performed also considering higher-fidelity discipline methods, i.e., CFD for aerodynamics and FEM for structures. In this work a model for high-fidelity gradient-based aerostructural optimization of wings, assisted by algorithmic differentiation and including aerodynamic and structural nonlinearities, is presented. First, the model is illustrated: a key feature lies in its enhanced modularity. Each discipline solver, employing algorithmic differentiation for the evaluation of adjoint-based sensitivities, is interfaced at high-level by means of a wrapper to both solve the aerostructural primal problem and evaluate discrete-consistent gradients of the coupled problem. Second, to demonstrate the feasibility of the method, a framework is ad-hoc set up, within the open-source SU2 multiphysics suite, with the inclusion of a

Toward the End-to-End Optimization of Particle Physics Instruments with Differentiable Programming: a White Paper

“…While first attempts to numerically improve the shape of airfoils [30,31] were based on finite differences, the analytic derivation of sensitivity equations [32][33][34] proved much more efficient when the number of design parameters is large. During the last two decades, automatic differentiation 1 (AD) has been integrated into various CFD codes [35][36][37] and enabled a large number of design optimization studies [37][38][39][40].…”

Discrete and Continuous Adjoint-Based Aerostructural Wing Shape Optimization of a Business Jet

“…During the last two decades, high-fidelity CFD and computational structural mechanics (CSM) tools have almost exclusively been used. For instance, in [10,11] Euler CFD codes were employed, while the use of CFD models involving the solution of the Reynolds-Averaged Navier-Stokes (RANS) equations into aerostructural shape optimization problems can be found in [12][13][14][15][16][17][18]. More accurate fluid flow models, such as large-eddy and direct numerical simulations, can be used too.…”

“…A (discrete) adjoint-based aerodynamic shape optimization that coupled the RANS solver with a commercial finite element solver and a thermodynamic engine cycle analysis tool was used for the gradient-based aeroelastic optimization of a full aircraft with powered engines in [14]. In [16], a framework, within SU2, for the gradient-based aerostructural optimization of wings, assisted by algorithmic differentiation, was tested on the ONERA M6 and NASA CRM wings. The results reconfirmed the importance of including aerostructural coupling in shape optimization.…”