Component-Based Geometry Manipulation for Aerodynamic Shape Optimization with Overset Meshes

Abstract

Mesh generation for high-fidelity CFD simulation and aerodynamic shape optimization is a timeconsuming task. We can model complex geometries accurately using overset meshes where multiple high-quality structured meshes corresponding to different aircraft components overlap to model the full aircraft configuration. Nevertheless, from the geometry manipulation standpoint, most methods operate on the entire geometry rather than on each component, which diminishes the advantages of overset meshes. To address this issue, we introduce a geometry module that operates on individual components and automatically computes their intersections to automate the overset mesh updates during optimization. This method is also differentiated to compute derivatives with respect to component-based design variables and is integrated within an optimization framework. Using these automatically updated meshes and the corresponding derivatives, we perform aerodynamic shape optimization including the wing-body intersection for the DLR-F6 geometry and achieve a reduction of 15 drag counts (5%) compared to the baseline design.

Flexible Formulation of Spatial Integration Constraints in Aerodynamic Shape Optimization

“…MACH is a set of Python-, FORTRAN-, and C++-based tools and utilities that provide all necessary geometry, aerodynamics, and structural analyses to perform high-fidelity aircraft MDO. Individual components of MACH are described in detail in previous publications [1,22], but we briefly review the overall methodology here.…”

“…IDWarp accomplishes this mesh deformation using an inverse distance mesh warping method similar to the method developed by Luke et al [30]. The initial volume mesh itself is generated using pyHyp [22], which uses a hyperbolic volume mesh marching scheme [31].…”

High-Fidelity Aerodynamic Shape Optimization of a Full Configuration Regional Jet

“…In this process, interpolation cells are designated to transfer information between the individual meshes. This enables a more modular meshing process and also opens up the possibility of optimizing the intersections between components, as done by Secco et al [12]. The wing shape is parametrized with 7 twist variables and 272 shape variables.…”

“…Significant improvements can be made when optimizing with respect to a single discpline as well. Aerodynamic shape optimization has been applied to the design of wings [8,9], winglets [10,11], and wing-body intersections [12,13]. Additionally, novel configurations such as the blended-wing-body [14,15] and D8 double-bubble [16] have been optimized to improve aerodynamic performance.…”

An Object-oriented Framework for Rapid Discrete Adjoint Development using OpenFOAM

“…The adjoint method was first introduced in fluid mechanics by Pironneau [1] and then extended for aerodynamic shape optimization by Jameson [2]. Since then, the adjoint method has been widely used in gradient-based optimization for various applications, including aerodynamics [3][4][5][6][7][8][9][10], hydrodynamics [11,12], heat transfer [13,14], structures [15], as well as the coupling of the above disciplines [16][17][18][19]. There are two different approaches for implementing the adjoint method: the continuous approach and the discrete approach.…”