Data-driven optimization of a gas turbine combustor: A Bayesian approach addressing NOx emissions, lean extinction limits, and thermoacoustic stability

Abstract

The design of gas turbine combustors for optimal operation at different power ratings is a multifaceted engineering task, as it requires the consideration of several objectives that must be evaluated under different test conditions. We address this challenge by presenting a data-driven approach that uses multiple probabilistic surrogate models derived from Gaussian process regression to automatically select optimal combustor designs from a large parameter space, requiring only a few experimental data points. We present two strategies for surrogate model training that differ in terms of required experimental and computational efforts. Depending on the measurement time and cost for a target, one of the strategies may be preferred. We apply the methodology to train three surrogate models under operating conditions where the corresponding design objectives are critical: reduction of NO x emissions, prevention of lean flame extinction, and mitigation of thermoacoustic oscillations. Once trained, the models can be flexibly used for different forms of a posteriori design optimization, as we demonstrate in this study.

Keywords

• Bayesian statistics
• data-driven optimization
• emission reduction
• gas turbine combustion
• surrogate modeling
• thermoacoustics