Robust optimization of dense gas flows under uncertain operating conditions

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...

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Bibliographic Details
Published in:Computers & fluids Vol. 39; no. 10; pp. 1893 - 1908
Main Authors: Cinnella, P., Hercus, S.J.
Format: Journal Article
Language:English
Published: Kidlington Elsevier Ltd 01.12.2010
Elsevier
Subjects:
Airfoils
Applied sciences
Chaos theory
Collocation methods
Computational methods in fluid dynamics
Computer simulation
Dense gas dynamics
Energy
Energy. Thermal use of fuels
Engines and turbines
Equipments for energy generation and conversion: thermal, electrical, mechanical energy, etc
Exact sciences and technology
Fluid dynamics
Fundamental areas of phenomenology (including applications)
Genetic algorithms
Optimization
Physics
Polynomial chaos
Rankine cycle
Robust optimization
Uncertainty quantification
Waste heat
Uncertainty quantification
Polynomial chaos
Robust optimization
Dense gas dynamics
Transonic flow
Aerodynamics
Stochastic method
Modeling
Organic Rankine cycle
Optimization
Genetic algorithm
Non viscous fluid
Dense gas
Numerical simulation
Organic Rankine engine
Aerofoil
Collocation method
ISSN:0045-7930, 1879-0747
Online Access:Get full text
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Description
Summary: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.
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ISSN:0045-7930
1879-0747
DOI:10.1016/j.compfluid.2010.06.020