Scalable Parallel Approach for High-Fidelity Steady-State Aeroelastic Analysis and Adjoint Derivative Computations

TLDR

Concurrent optimization of aerodynamic shape and structural sizing yields optimal aeroelastic performance, yet high‑fidelity models make this optimization difficult. This work introduces key improvements to enable such concurrent optimization with high‑fidelity aerodynamic and structural models. The authors present fully coupled Newton–Krylov solvers for aerostructural and adjoint systems, enhanced by refined computation of multidisciplinary terms. The coupled adjoint method accurately computes gradients for thousands of design variables, scales efficiently on a full aircraft with Euler CFD and detailed wing FEM, improves convergence rates, verifies gradient accuracy, and demonstrates the feasibility of optimizing full aircraft configurations with hundreds of shape and sizing variables.

Abstract

Aeroelastic systems achieve the best performance when the aerodynamic shape and structural sizing are optimized concurrently, but such an optimization is challenging when high-fidelity aerodynamic and structural models are required. This paper addresses this challenge through several significant improvements. Fully coupled Newton–Krylov methods are presented for the solution of aerostructural systems and for the corresponding adjoint systems. The coupled adjoint method presented can compute gradients with respect to thousands of multidisciplinary design variables accurately and efficiently. This is enabled by several improvements in the computation of the multidisciplinary terms in the coupled adjoint. The parallel scalability of the methods is demonstrated for a full aircraft configuration using an Euler computational fluid dynamics model with more than state variables and a detailed structural finite element model of the wing with more than degrees of freedom. The coupled Newton–Krylov methods are shown to improve the convergence rate of both the aerostructural solution and the coupled adjoint derivative computations. Gradient computations of aerodynamic and structural functions with respect to both aerodynamic shape and structural sizing variables are verified, and scaling is demonstrated to variables. The accuracy and scalability of the presented methods make it possible to perform aerostructural optimizations of full aircraft configurations with respect to hundreds of external shape and structural sizing design variables, leading to optimal aeroelastic tailoring.