Full-Space Approach to Aerodynamic Shape Optimization

伴随梯度气动外形优化中间断Galerkin方法的研究

Investigation of discontinuous Galerkin methods in adjoint gradient-based aerodynamic shape optimization

本研究开发了基于高阶间断Galerkin方法的伴随梯度气动外形优化框架，发现其梯度包含高阶矩修正项，在同等计算成本下能提供更精确的伴随梯度，并提升设计空间探索能力以获得更优气动外形。

Machine Learning in Aerodynamic Shape Optimization

Machine learning (ML) has been increasingly used to aid aerodynamic shape optimization (ASO), thanks to the availability of aerodynamic data and continued developments in deep learning. We review the applications of ML in ASO to date and provide a perspective on the state-of-the-art and future directions. We first introduce conventional ASO and current challenges. Next, we introduce ML fundamentals and detail ML algorithms that have been successful in ASO. Then, we review ML applications to ASO addressing three aspects: compact geometric design space, fast aerodynamic analysis, and efficient o…

The analytical solution to Newton's aerodynamic problem in the class of bodies with vertical plane of symmetry and developable side boundary

The method of Hessian measures is used to find the differential equation that defines the optimal shape of nonrotationally symmetric bodies with minimal resistance moving in a rare medium. The synthesis of optimal solutions is described. A theorem on the optimality of the obtained solutions is proved.

带自适应增广的尾缘变体飞行器气动优化控制分配

Control Allocation with Adaptive Augmentation for Aerodynamic Optimization of Trailing Edge Morphing Aircraft

针对尾缘变体飞行器，提出带自适应增广的控制分配框架，在不确定性下保证稳定，并优化机翼形状以实现椭圆升力分布，数值验证有效。

Enhanced data efficiency using deep neural networks and Gaussian processes for aerodynamic design optimization

Adjoint-based optimization methods are attractive for aerodynamic shape design primarily due to their computational costs being independent of the dimensionality of the input space and their ability to generate high-fidelity gradients that can then be used in a gradient-based optimizer. This makes them very well suited for high-fidelity simulation based aerodynamic shape optimization of highly parametrized geometries such as aircraft wings. However, the development of adjoint-based solvers involve careful mathematical treatment and their implementation require detailed software development. Fu…

Combining Sobolev Smoothing with Parameterized Shape Optimization

On the one hand, Sobolev gradient smoothing can considerably improve the performance of aerodynamic shape optimization and prevent issues with regularity. On the other hand, Sobolev smoothing can also be interpreted as an approximation for the shape Hessian. This paper demonstrates, how Sobolev smoothing, interpreted as a shape Hessian approximation, offers considerable benefits, although the parameterization is smooth in itself already. Such an approach is especially beneficial in the context of simultaneous analysis and design, where we deal with inexact flow and adjoint solutions, also call…

考虑强气动力的可重复使用火箭动力着陆的浓缩PIPG序列凸优化

Condensed PIPG Sequential Convex Optimization for Reusable-Rocket Powered Landing with Strong Aerodynamics

针对强气动力下可重复使用火箭动力着陆问题，提出带精确降维架构的浓缩PIPG序列凸优化方法，经验证可获紧最优解，辅以多种加速器提升性能。

Deep convolutional neural network for shape optimization using level-set approach

This article presents a reduced-order modeling methodology via deep convolutional neural networks (CNNs) for shape optimization applications. The CNN provides a nonlinear mapping between the shapes and their associated attributes while conserving the equivariance of these attributes to the shape translations. To implicitly represent complex shapes via a CNN-applicable Cartesian structured grid, a level-set method is employed. The CNN-based reduced-order model (ROM) is constructed in a completely data-driven manner thus well suited for non-intrusive applications. We demonstrate our ROM-based sh…

Generative Thermal Design Through Boundary Representation and Multi-Agent Cooperative Environment

Generative design has been growing across the design community as a viable method for design space exploration. Thermal design is more complex than mechanical or aerodynamic design because of the additional convection-diffusion equation and its pertinent boundary interaction. We present a generative thermal design using cooperative multi-agent deep reinforcement learning and continuous geometric representation of the fluid and solid domain. The proposed framework consists of a pre-trained neural network surrogate model as an environment to predict heat transfer and pressure drop of the generat…

Deep neural operators can serve as accurate surrogates for shape optimization: A case study for airfoils

Deep neural operators, such as DeepONets, have changed the paradigm in high-dimensional nonlinear regression from function regression to (differential) operator regression, paving the way for significant changes in computational engineering applications. Here, we investigate the use of DeepONets to infer flow fields around unseen airfoils with the aim of shape optimization, an important design problem in aerodynamics that typically taxes computational resources heavily. We present results which display little to no degradation in prediction accuracy, while reducing the online optimization cost…