Parametric Deformation of Discrete Geometry for Aerodynamic Shape Design

We present a versatile discrete geometry manipulation platform for aerospace vehicle shape optimization. The platform is based on the geometry kernel of an open-source modeling tool called Blender and offers access to four parametric deformation techniques: lattice, cage-based, skeletal, and direct manipulation. Custom deformation methods are implemented as plugins, and the kernel is controlled through a scripting interface. Surface sensitivities are provided to support gradient-based optimization. The platform architecture allows the use of geometry pipelines, where multiple modelers are used in sequence, enabling manipulation difficult or impossible to achieve with a constructive modeler or deformer alone. We implement an intuitive custom deformation method in which a set of surface points serve as the design variables and user-specified constraints are intrinsically satisfied. We test our geometry platform on several design examples using an aerodynamic design framework based on Cartesian grids. We examine inverse airfoil design and shape matching and perform lift-constrained drag minimization on an airfoil with thickness constraints. A transport wing-fuselage integration problem demonstrates the approach in 3D. In a final example, our platform is pipelined with a constructive modeler to parabolically sweep a wingtip while applying a 1-G loading deformation across the wingspan. This work is an important first step towards the larger goal of leveraging the investment of the graphics industry to improve the state-of-the-art in aerospace geometry tools

State-of-the-art in aerodynamic shape optimisation methods

Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners

Aerodynamic Design of the RAE 2822 in Transonic Viscous Flow: Single- and Multi-Objective Optimization Studies

This paper addresses a benchmark aerodynamic design problem proposed by the AIAA Aerodynamic Design Optimization Discussion Group: Drag minimization of the RAE 2822 in transonic viscous flow at a fixed lift coefficient with constraints on the pitching moment coefficient and the cross-sectional area. The single-objective optimization (SOO) problem is solved using surrogate-based optimization (SBO) with the surrogates constructed through output space mapping and variable-resolution Reynolds-Averaged Navier-Stokes computational fluid dynamics models. Improving the implementation of our search algorithms enabled us to obtain the SOO optimal design four times faster than in our prior work in terms of CPU time. To explore the design space in the vicinity of the SOO optimal design, the problem is recast as a multi-objective optimization (MOO) one by treating the drag and pitching moment coefficients as the objectives while fulfilling the given constraints on the lift coefficient and the cross-sectional area. The MOO algorithm yields the Pareto front of the two conflicting objective functions in close proximity of the design obtained by the SOO formulation in the feasible and infeasible space of the original SOO problem