GE Research has secured a contract for the Phase I of $2.1m project to build an AI-driven invertible neural network capable of directly translating performance metrics into optimised designs for industrial gas turbine (IGT) aerodynamic components.

The contract for the two-year project has been awarded through US Advanced Research Projects Agency–Energy’s (ARPA-E) Design Intelligence Fostering Formidable Energy Reduction and Enabling Novel Totally Impactful Advanced Technology Enhancements (DIFFERENTIATE ) programme.

Project aims to reduce design cycle times for gas turbines by 30-50%

The project aims to achieve a reduction in design cycle times by 30-50%, or from 1 year to a few months.

Together with GE’s Gas Power business and the University of Notre Dame, GE researchers will develop and demonstrate a new AI and ML- enabled design framework that reduces the time.

GE Research probabilistic design lead engineer and project leader Sayan Ghosh said that the team is building a probabilistic inverse design machine learning framework, named Pro-ML IDeAS.

The framework uses an AI-driven invertible neural network to address multiple design iterations and challenges.

Ghosh added: “This will essentially create a paradigm shift in gas turbine design by enabling us to explore and discover new learning curves not previously possible.

“We believe that the Pro-ML IDeAS, powered by AI and ML, will allow us to break free from the traditional design constraints and let us achieve more optimal designs in significantly less time versus the current state-of-the-art.

Additionally, the project will result in the creation of an inverse design process to optimise the design of a gas turbine blade component and reduce the design cycle time.

The framework is planned to be extended to other applications such as aviation turbine engines, aero-derivative engines, wind turbines, and hydro turbines, GE said.

Recently, GE Renewable Energy, COBOD and LafargeHolcim have teamed up to develop wind turbines with optimised 3D printed concrete bases in a bid to increase renewable energy production and use.