Generative Design of a Gas Turbine Combustor Using Invertible Neural Networks

TL;DR

This paper presents a generative AI approach using invertible neural networks to design gas turbine combustors capable of burning 100% hydrogen with low NOx emissions, aiming to streamline the redesign process across engine classes.

Contribution

It introduces a novel application of invertible neural networks for inverse design of combustors, enabling efficient generation of designs meeting specified performance criteria.

Findings

Successfully trained INN on a database of combustor designs and performance data.

Generated multiple design proposals that meet target performance specifications.

Demonstrated potential to accelerate combustor redesign across different engine sizes.

Abstract

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.