On Artificial Intelligence for Simulation and Design Space Exploration in Gas Turbine Design

Gas turbine design is a process that requires designing many interrelated subsystems, e.g., performance, secondary air system, air compression, or combustion. Subsystem models are created by various engineering design tools. During the design process there exists an extraordinary amount of generated data resulting from created models, simulation, and engine field tests. This data can be leveraged by artificial intelligence techniques such as machine learning to help accelerate the exploration of the large design spaces existing in the complex system of a gas engine. This paper presents a vision and road map of integrating such AIs and preliminary ideas on relevant AI models for such use cases. We explore increasing the realistic nature of existing simulations, approximating simulations to avoid excess computation, and cumulative effect modeling.