Robust optimization of dense gas flows under uncertain operating conditions

被引：23

作者：

Cinnella, P. ^{[1
]}

Hercus, S. J. ^{[1
]}

机构：

[1] Lab DynFluid Arts & Metiers ParisTech, F-75013 Paris, France

来源：

COMPUTERS & FLUIDS | 2010年 / 39卷 / 10期

关键词：

Uncertainty quantification; Polynomial chaos; Robust optimization; Dense gas dynamics; ORGANIC RANKINE-CYCLES; TRANSONIC-FLOWS; FLUIDS; PROPAGATION; PERFORMANCE; RECOVERY;

D O I：

10.1016/j.compfluid.2010.06.020

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

A robust optimization procedure based on a multi-objective genetic algorithm (MOGA) is used to generate airfoil profiles for transonic inviscid flows of dense gases, subject to uncertainties in the upstream thermodynamic conditions. The effect of the random variations on system response is evaluated using a non-intrusive polynomial chaos (PC) based method known as the probabilistic collocation method (PCM). After initial PCM simulations which showed that the dense gas system was highly sensitive to input parameter variation, a multi-objective genetic algorithm coupled to the PCM produced a Pareto front of optimized individual geometries which exhibited improvements in mean performance and/or stability over the baseline NACA0012 airfoil. This type of analysis is essential in improving the feasibility of organic Rankine cycle (ORC) turbines, which are typically designed to recover energy from variable sources such as waste heat from industrial processes. (C) 2010 Elsevier Ltd. All rights reserved.

引用

页码：1893 / 1908

页数：16

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