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Non-intrusive uncertainty quantification in the simulation of turbulent spray combustion using Polynomial Chaos Expansion: A case study
Combustion and Flame ( IF 4.4 ) Pub Date : 2020-03-01 , DOI: 10.1016/j.combustflame.2019.11.021
Benedict Enderle , Bastian Rauch , Felix Grimm , Georg Eckel , Manfred Aigner

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.

中文翻译:

使用多项式混沌展开模拟湍流喷雾燃烧的非侵入式不确定性量化:案例研究

摘要 湍流喷雾燃烧模拟中输入不确定性的一个主要来源是由于需要指定初级破碎后液体喷雾的状态,即拉格朗日输运方程的喷雾边界条件。为了进一步提高此类模拟的可信度和预测能力,除了感兴趣的数量外,还应报告输出不确定性。因此,本文介绍了根据喷雾边界条件和数值近似误差的规范对不确定性进行全面量化的结果。通过使用详细的有限速率化学的 Euler-Lagrange 模拟框架,研究了表征良好的实验室规模喷​​雾火焰。由于模拟模型的直接蒙特卡罗采样是禁止的,非侵入式多项式混沌扩展 (PCE) 用于不确定性的前向传播。在筛选研究中优先考虑不确定的输入参数,这可以减少参数空间。概率界限的计算揭示了确定性参考模拟周围的广泛不确定性区域。在后验敏感性分析中,这种差异的大部分可追溯到雾化器的不确定喷雾锥角。输入不确定性的显式计算最终允许评估所考虑情况下的总预测不确定性。概率界限的计算揭示了确定性参考模拟周围的广泛不确定性区域。在后验敏感性分析中,这种差异的大部分可追溯到雾化器的不确定喷雾锥角。输入不确定性的显式计算最终允许评估所考虑情况下的总预测不确定性。概率界限的计算揭示了确定性参考模拟周围的广泛不确定性区域。在后验敏感性分析中,这种差异的大部分可追溯到雾化器的不确定喷雾锥角。输入不确定性的显式计算最终允许评估所考虑情况下的总预测不确定性。
更新日期:2020-03-01
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