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UNCERTAINTY QUANTIFICATION OF NOX EMISSION DUE TO OPERATING CONDITIONS AND CHEMICAL KINETIC PARAMETERS IN A PREMIXED BURNER

[+] Author and Article Information
Sajjad Yousefian

Mechanical Engineering, Combustion Chemistry Centre, and Ryan Institute, National University of Ireland Galway, Ireland; Research Centre for Marine and Renewable Energy, Galway, Ireland
s.yousefian2@nuigalway.ie

Gilles Bourque

Siemens Canada Ltd, 9545 Cote de Liesse Road, Montreal QC H9P 1A5, Canada; Department of Mechanical Engineering, McGill University, Montréal, Québec, H3A 0C3, Canada
gilles.bourque@siemens.com

Rory Monaghan

Mechanical Engineering, Combustion Chemistry Centre, and Ryan Institute, National University of Ireland Galway, Ireland; Research Centre for Marine and Renewable Energy, Galway, Ireland
rory.monaghan@nuigalway.ie

1Corresponding author.

ASME doi:10.1115/1.4040897 History: Received June 21, 2018; Revised July 02, 2018

Abstract

Many sources of uncertainty exist when emissions are modelled for a gas turbine combustion system. They originate from uncertain inputs, boundary conditions, calibration, or lack of sufficient fidelity in the model. In this paper, a non-intrusive polynomial chaos expansion (NIPCE) method is coupled with a chemical reactor network (CRN) model using Python to quantify uncertainties of NOx emission in a premixed burner rigorously. The first objective of the uncertainty quantification (UQ) in this study is the development of a global sensitivity analysis method based on NIPCE to capture aleatory uncertainty due to the variation of operating conditions and input parameters. The second objective is uncertainty analysis of Arrhenius parameters in the chemical kinetic mechanism to study the epistemic uncertainty in the modelling of NOx emission. A two-reactor CRN consisting of a perfectly stirred reactor (PSR) and a plug flow reactor (PFR) is constructed in this study using Cantera to model NOx for natural gas at the relevant operating conditions for a benchmark premixed burner. The results of uncertainty and sensitivity analysis using NIPCE based on point collocation method (PCM) are then compared with the results of advanced Monte Carlo simulation (MCS). Surrogate models are also developed based on the NIPCE approach and compared with the forward model in Cantera to predict NOx emissions. The results show the capability of NIPCE approach for UQ using a limited number of evaluations to develop a UQ-enabled emission prediction tool for gas turbine combustion systems.

Copyright (c) 2018 by ASME
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