Application of a 1-D Predicting Tool to the Analysis of Pressure Oscillations in an Industrial Gas Turbine Combustor: GE10 Machine

Abstract

The development of current industrial gas turbines is strictly constrained by legislative requirements for low polluting emissions. Lean Premixed combustion technology has become through the years the necessary standard to meet such requirements. Premixed technology introduces a new range of problems: combustion instabilities in many operating conditions. Specifically, lean premixed flames pose the threat of pressure oscillations. This phenomenon is the effect of the strong interaction between combustion heat-release and fluid dynamics aspects. The prediction of acoustic oscillations and combustion instabilities is generally difficult because of the complexity of real combustor geometries. As a result, the design phase is usually performed as a trial-and-error task: a specific design is constructed, tested and modified, in a process that continues until acceptable results are found. A specific tool was developed by GE Energy to help predicting the acoustic behaviour of newly designed partially-premixed combustors, avoiding the traditional trial-and-error process: the tool allows the designer to analyze the problem of combustion instabilities since the early design phase, limiting subsequent testing efforts. A mono-dimensional tool based on the 1-D acoustic model was developed by GE Energy and was applied to the single-can combustor of the GE10 machine (a gas turbine in the 10MW class). All the main geometrical features of the GE10 machine, including fuel line geometry, were considered and modeled in a one-dimensional scheme, in order to build an equivalent model for the linear tool analysis. The main frequencies, measured during tests on the GE10 machine, were compared to the numerical results of the tool, showing good agreement between numerical and experimental results and confirming the predictive capability. This good agreement demonstrates that the model can be used for predicting the effects of design changes, with a reduced need of tests.