Inverse aerodynamic design of turbo components for Carnot batteries by means of physics informed networks enhanced by generative learning

通过生成学习增强的物理信息网络对卡诺电池涡轮组件进行逆向空气动力学设计

• 批准号: 526152410
• 负责人: Professor Dr. Hanno Gottschalk
• 金额: --
• 依托单位: Lehrstuhl für Thermische Turbomaschinen und Flugtriebwerke
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/526152410?language=en
• 关键词: Inverse; aerodynamic; design; turbo; components

We develop new simulation methods based on deep neural networks that shorten computation times with respect to traditional CFD considerably. In particular, we apply physics informed neural networks for a rough representation of fluid flows in turbo-machinery components operating in Carnot batteries. Such rough solutions are refined by conditional generative adversarial networks (GAN) in order to create realistic fine structure of turbulent flows. in particular, we study the physical and the generalization properties of such deep learning based solutions to fluid flow with respect to changing boundary conditions and geometry. This enables us to rapidly evaluate designs under strongly changing boundary conditions, as they are typical for the discharging cycle of a Carnot battery. In this way, we provide an invaluable tool for the inverse design of turbocomponents for Carnot batteries.

我们开发了新的基于深度神经网络的模拟方法，大大缩短了传统CFD的计算时间。特别是，我们应用物理通知神经网络来粗略表示在卡诺电池中运行的涡轮机械部件中的流体流动。通过条件生成对抗网络（GAN）对这些粗糙解进行细化，从而得到真实的湍流精细结构。特别是，我们研究了这种基于深度学习的流体流动解决方案在改变边界条件和几何形状方面的物理和泛化性质。这使我们能够在强烈变化的边界条件下快速评估设计，因为它们是卡诺电池放电周期的典型特征。通过这种方式，我们为卡诺电池涡轮部件的逆向设计提供了宝贵的工具。