Success Stories

Data-driven Kinetics Modeling of Chemical Vapor Infiltration

Oak Ridge National Laboratory

General Electric | Oak Ridge National Laboratory

Success Stories | GE and ORNL Slide

General Electric and Oak Ridge National Laboratory used a neural network to create a surrogate model that can efficiently represent computationally intensive CFD calculations obtained from high performance computing, allowing the exploration of vast parameter space of the chemical vapor infiltration process and understanding of chemical kinetics.


iconUse of CMC components in the hot section of an engine offers an increase of up to 165°C (300°C) in temperature capability.
iconThe higher temperature capability will lead to fuel efficiency benefits in aviation gas turbines.
iconCombination of computational fluid dynamics (CFD) and data driven modeling allows the vast parameter space of CVI process to be explored.

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