用可逆神经网络生成氢气燃烧器，一键设计低氮氧化物方案。
Generative Design of a Gas Turbine Combustor Using Invertible Neural Networks
- 通过可逆神经网络学习几何参数与性能的关系
- 输入性能目标即可生成满足条件的多个设计方案
- 适合需要快速迭代燃烧器设计的工程师和研究者
为实现高效燃气轮机在预混模式下100%氢气燃烧并保持低NOx排放，需对燃烧系统进行全新设计以确保稳定运行且无回火现象。由于功率范围从4兆瓦至600兆瓦的所有发动机机型均受影响，设计工作量巨大。为降低这一负担，特别是促进不同机型间知识迁移，本研究采用最新生成式人工智能技术，训练一个可逆神经网络（INN），基于可扩展的几何参数化燃烧器设计数据库及其仿真性能标签。利用INN的逆向推导能力，可生成满足特定性能要求的设计方案，显著提升设计效率。
原文摘要 · Abstract (English)
The need to burn 100% H2 in high efficient gas turbines featuring low NOx combustion in premix mode require the complete redesign of the combustion system to ensure stable operation without any flashback. Since all engine frames featuring a power range from 4 MW up to 600 MW are affected, a huge design effort is expected. To reduce this effort, especially to transfer knowledge between the different engine classes, generative design methods using latest AI technology will provide promising potential. In this work, this challenge is approached utilizing the current advances in generative artificial intelligence. We train an Invertible Neural Network (INN) on an expandable database of geometrically parameterized combustor designs with simulated performance labels. Utilizing the INN in its inverse direction, multiple design proposals are generated which fulfill specified performance labels.
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