Sensitivity analysis, uncertainty quantification, and optimization for thermochemical properties in chemical kinetic combustion models

2019 ◽  
Vol 37 (1) ◽  
pp. 771-779 ◽  
Author(s):  
Florian vom Lehn ◽  
Liming Cai ◽  
Heinz Pitsch
Keyword(s):  
Sensitivity Analysis ◽  
Uncertainty Quantification ◽  
Chemical Kinetic ◽  
Thermochemical Properties ◽  
Combustion Models

Uncertainty Quantification of NOx Emission Due to Operating Conditions and Chemical Kinetic Parameters in a Premixed Burner

2018 ◽  
Vol 140 (12) ◽  
Author(s):  
Sajjad Yousefian ◽  
Gilles Bourque ◽  
Rory F. D. Monaghan
Keyword(s):  
Sensitivity Analysis ◽  
Uncertainty Quantification ◽  
Gas Turbine ◽  
Flow Reactor ◽  
Epistemic Uncertainty ◽  
Plug Flow ◽  
Chemical Kinetic ◽  
Operating Conditions ◽  
Nox Emission ◽  
Gas Turbine Combustion

Many sources of uncertainty exist when emissions are modeled for a gas turbine combustion system. They originate from uncertain inputs, boundary conditions, calibration, or lack of sufficient fidelity in a model. In this paper, a nonintrusive 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. The first objective of uncertainty quantification (UQ) in this study is development of a global sensitivity analysis method based on the NIPCE method to capture aleatory uncertainty on NOx emission due to variation of operating conditions. The second objective is uncertainty analysis (UA) of NOx emission due to uncertain Arrhenius parameters in a chemical kinetic mechanism to study epistemic uncertainty in emission modeling. 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 emission in a benchmark premixed burner under gas turbine operating conditions. The results of uncertainty and sensitivity analysis (SA) using NIPCE based on point collocation method (PCM) are then compared with the results of advanced Monte Carlo simulation (MCS). A set of surrogate models is 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.

Uncertainty Quantification of NOx Emission due to Operating Conditions and Chemical Kinetic Parameters in a Premixed Burner

2018 ◽  
Author(s):  
Sajjad Yousefian ◽  
Gilles Bourque ◽  
Rory F. D. Monaghan
Keyword(s):  
Sensitivity Analysis ◽  
Uncertainty Quantification ◽  
Gas Turbine ◽  
Flow Reactor ◽  
Epistemic Uncertainty ◽  
Plug Flow ◽  
Chemical Kinetic ◽  
Operating Conditions ◽  
Nox Emission ◽  
Gas Turbine Combustion

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 rigorously quantify uncertainties of NOx emission in a premixed burner. The first objective of the uncertainty quantification (UQ) in this study is 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. UQ is performed through the use of a number of packages in Python. 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.

06-02 Methods for Uncertainty Quantification, Sensitivity Analysis, and Prediction Sessions

2021 ◽  
Author(s):  
Michael Prime ◽  
Gavin Jones ◽  
Vicente Romero ◽  
Justin Winokur ◽  
Benjamin Schroeder
Keyword(s):  
Sensitivity Analysis ◽  
Uncertainty Quantification

Review of Hybrid Emissions Prediction Tools and Uncertainty Quantification Methods for Gas Turbine Combustion Systems

2017 ◽  
Author(s):  
Sajjad Yousefian ◽  
Gilles Bourque ◽  
Rory F. D. Monaghan
Keyword(s):  
Uncertainty Quantification ◽  
Gas Turbine ◽  
Kinetic Modelling ◽  
Computational Cost ◽  
Chemical Kinetic ◽  
Design Development ◽  
Deterministic Models ◽  
Prediction Tools ◽  
Gas Turbine Combustion ◽  
Combustion Systems

There is a need for fast and reliable emissions prediction tools in the design, development and performance analysis of gas turbine combustion systems to predict emissions such as NOx, CO. Hybrid emissions prediction tools are defined as modelling approaches that (1) use computational fluid dynamics (CFD) or component modelling methods to generate flow field information, and (2) integrate them with detailed chemical kinetic modelling of emissions using chemical reactor network (CRN) techniques. This paper presents a review and comparison of hybrid emissions prediction tools and uncertainty quantification (UQ) methods for gas turbine combustion systems. In the first part of this study, CRN solvers are compared on the bases of some selected attributes which facilitate flexibility of network modelling, implementation of large chemical kinetic mechanisms and automatic construction of CRN. The second part of this study deals with UQ, which is becoming an important aspect of the development and use of computational tools in gas turbine combustion chamber design and analysis. Therefore, the use of UQ technique as part of the generalized modelling approach is important to develop a UQ-enabled hybrid emissions prediction tool. UQ techniques are compared on the bases of the number of evaluations and corresponding computational cost to achieve desired accuracy levels and their ability to treat deterministic models for emissions prediction as black boxes that do not require modifications. Recommendations for the development of UQ-enabled emissions prediction tools are made.

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