Non-intrusive uncertainty quantification in the simulation of turbulent spray combustion using Polynomial Chaos Expansion: A case study

被引:14
|
作者
Enderle, Benedict [1 ]
Rauch, Bastian [1 ]
Grimm, Felix [1 ]
Eckel, Georg [1 ]
Aigner, Manfred [1 ]
机构
[1] German Aerosp Ctr DLR, Pfaffenwaldring 38-40, D-70569 Stuttgart, Germany
关键词
Uncertainty quantification; Sensitivity analysis; Polynomial Chaos Expansion; Spray combustion; Finite-rate chemistry; GLOBAL SENSITIVITY-ANALYSIS; LARGE-EDDY SIMULATION; TEMPERATURE-MEASUREMENTS; VAPORIZATION MODEL; FLAME STRUCTURE; LES; VALIDATION; ENGINE; DILUTE;
D O I
10.1016/j.combustflame.2019.11.021
中图分类号
O414.1 [热力学];
学科分类号
摘要
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. (C) 2019 The Author(s). Published by Elsevier Inc
引用
收藏
页码:26 / 38
页数:13
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