Industrial gas turbine (IGT) aerodynamic components
GE Integrating AI to Enable Performance-Informed Gas Turbine Inverse Design
Aiming to let new performance metrics be the principal driver in the design of cleaner, more efficient aerodynamic energy systems, GE Research, the technology development arm for GE, has been awarded Phase I of a two -year, $2.1 million project through ARPA-E’s DIFFERENTIATE (Design Intelligence Fostering Formidable Energy Reduction and Enabling Novel Totally Impactful Advanced Technology Enhancements) program to build an AI-driven invertible neural network that can direct translate these metrics into optimized designs.
Today, complex aerodynamic energy components such as gas turbine blades have extremely long design cycle times of more than a year that require compromise between cost, performance and reliability.
GE researchers, together with GE’s Gas Power business and the University of Notre Dame, are aiming to develop and demonstrate a new AI and ML- enabled design framework that takes half the time and is dictated almost entirely by the desired performance metrics to take the design of aerodynamic energy components to a whole new level.