Abstract
A major source of input uncertainties in the simulation of turbulent spray combustion is introduced by the need to specify the state of the liquid spray after primary breakup, i.e. a spray boundary condition for the lagrangian transport equations. To further enhance the credibility and predictive capabilities of such simulations, output uncertainties should be reported in addition to the quantities of interest. Therefore, this paper presents results from a comprehensive quantification of uncertainties from the specification of a spray boundary condition and numerical approximation errors. A well characterized lab-scale spray flame is studied by means of an Euler-Lagrange simulation framework using detailed finite rate chemistry. As direct Monte Carlo sampling of the simulation model is prohibitive, non-intrusive Polynomial Chaos expansion (PCE) is used for forward propagation of the uncertainties. Uncertain input parameters are prioritized in a screening study, which allows for a reduction of the parameter space. The computation of probabilistic bounds reveals an extensive uncertainty region around the deterministic reference simulation. In an a posteriori sensitivity analysis, the majority of this variance is traced back to the uncertain spray cone angle of the atomizer. The explicit computation of input uncertainties finally allows for an evaluation of total predictive uncertainty in the case considered.
Highlights
With the ever increasing availability of high performance computing capacities, high fidelity numerical combustion simulation is emerging as a powerful tool for the design, analysis and optimization of gas turbine combustors and associated combustion processes
The present study successfully demonstrated the use of nonintrusive Polynomial Chaos Expansion for forward uncertainty quantification and sensitivity analysis in the simulation of turbulent spray combustion
Profiles of temperature over the reaction zone in a laboratory scale spray flame were considered as output quantities of interest
Summary
With the ever increasing availability of high performance computing capacities, high fidelity numerical combustion simulation is emerging as a powerful tool for the design, analysis and optimization of gas turbine combustors and associated combustion processes. This trend is motivated by the need for a reduction in tournaround time and cost in the design process as well as a detailed understanding of physical mechanisms in order to reduce pollutant emissions. Among the various approaches guiding the validation process [5,6,7], uncertainty quantification (UQ) methods have the potential to help understand sensitivities of simulation results to modeling uncertainties and improve the models towards better physical representation [2]
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