Application of a Stochastic Reactor Model Approach for Prediction of Gas Turbine Engine Emissions

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Siemens Canada Limited (SCL) is a manufacturer of industrial aero-derivative gas turbines for power generation. Given ever increasing effort by SCL to develop combustion that meet the most stringent emissions standards, there is a need to continually improve how new innovative low emission concepts can be design and brought to market rapidly. At the early stages of the design process, simplified models are utilized to understand global implications on emission levels of any alterations. This reliance on simplified modeling reduces the cost versus complex models or experimentation; however, there are large degrees of uncertainty in these simplified models. Understanding the uncertainty would allow for better decisions as to what designs are pursued further in the development process, avoiding costs increased from longer than expected development time. One of the largest sources of uncertainties in the simplified models is the inability to capture turbulence-chemistry interaction (TCI) with adequate fidelity to ensure reliable analysis. Developing of a computationally-efficient simplified modeling technique for gas turbines that can capture TCI will reduce the cost of and time required for developing new engine designs, enhancing the competitiveness of SCL. Currently, such simplified modeling techniques for gas turbines have not achieve the same level of maturity as the high end commercially available simulation tools like CFD. The proposed research will apply an existing Stochastic Reactor Model (SRM) software to modeling gas turbine emissions to address SCL emissions modeling challenges. The project is focused on a "proof of concept", demonstrating that a SRM approach is not only feasible for modeling gas turbine emissions, but also provide a robust and cost effective approach. The SRM approach is unique in that it can capture TCI while having low computational costs.The major milestones and deliverables are: (1) code modifications to apply the SRM approach to gas turbines, (2) determination of initial model parameters for gas turbine modeling, (3) initial proof of concept simulation of gas turbine emissions using the SRM approach, (4) assessment of the SRM approach using experimental data, and (5) a report on the findings of the proof of concept study.