This paper presents an engine sizing and cycle selection study of ultra high bypass ratio engines applied to a subsonic commercial aircraft in the N+2 (2025) timeframe. NASA has created the Environmentally Responsible Aviation (ERA) project to serve as a technology transition bridge between fundamental research (TRL 1–4) and potential commercial application (TRL 7). Specifically, ERA is focused on subsonic transport technologies that could reach TRL 6 by 2020 and can be integrated into an advanced vehicle concept to simultaneously meet the ERA project metrics for noise, emissions, and fuel burn. An important variable in exploring the technology trade space is the selection of the optimal engine cycle for use on the advanced aircraft. Previous literature demonstrated the cycle optimization using a design of experiments (DOE) to explore the engine cycle design space for a pre-defined technology package. However, since the optimal engine cycle is dependent upon the specific technology package, this process would have to be repeated to ensure optimal performance for each technology package. With more than 80 technologies to be analyzed, the combinatorial space of technology packages is enormous. As a result, executing a DOE to find the optimum engine cycle for each technology package is infeasible. To address this issue, it is proposed to use surrogate models that encompass the engine cycle and technology design space to enable fast and accurate optimization of the engine cycle for any given technology package.

This paper describes the generation and analysis of surrogate models used for technology assessment and cycle optimization of an ultra high bypass geared turbofan engine architecture. The first study in the paper shows that a single surrogate model can be used to accurately simulate both a technology and cycle design space. To demonstrate the proposed surrogate modeling approach, the cycle design space for three different technology packages was analyzed. This study demonstrated that when an optimal cycle is found within the constrained interior of a design space, the surrogate modeling approach is quite accurate. The study also established that the surrogate models can also be used to assess potential cycles at the boundaries or even outside of the region for which they were trained.

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