Transportation Energy Conversion

GE Research will develop design optimization tools for the laser powder bed fusion based additive manufacturing of turbomachinery components. The team will integrate the latest advances in multi-physics topology optimization with fast machine learning-based producibility evaluations extracted from large training datasets comprising high-fidelity physics-based simulations and experimental validation studies. The integrated methodology will be used to demonstrate simultaneous improvements in the producibility and thermodynamic efficiency of a multi-physics turbomachinery component.

The National Renewable Energy Laboratory (NREL) will develop a novel wind turbine design capability that enables designers to explore advanced technology concepts at a lower cost. This capability will harness the power of a deep neural network (DNN)-based inverse design methodology. To overcome challenges with the use of traditional DNNs in this application, NREL will develop innovative techniques to sparsify the neural network using active subspaces that will ensure that the model is invertible and can quickly zoom in on relevant designs at minimal cost.

The Massachusetts Institute of Technology (MIT) will develop machine learning (ML) enhanced tools to accelerate the development of catalysts that promote the oxygen evolution reaction (OER) or the oxygen reduction reaction (ORR). These reactions are critical to the cost-effective generation (OER) or oxidation (ORR) of hydrogen. Available catalysts for promoting these reactions include scarce and costly precious metals like platinum. Hence, their practical applications are limited by high cost and low abundance in addition to moderate stability.

GE Global Research will develop a probabilistic inverse design machine learning (ML) framework, Pro-ML IDeAS, to take performance and requirements as input and provide engineering designs as output. Pro-ML IDeAS will calculate the design explicitly without iteration and overcome the challenges of ill-posed inverse problems. Pro-ML IDeAS will use GE’s Bayesian hybrid modeling with multi-fidelity intelligent design and analysis of computer experiments and a novel probabilistic invertible neural network (INN). The proposed framework can be applied to general complex design problems.