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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| Published in: | Computers & fluids Vol. 39; no. 10; pp. 1893 - 1908 |
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| Format: | Journal Article |
| Language: | English |
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01.12.2010
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| ISSN: | 0045-7930, 1879-0747 |
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| Abstract | 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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| AbstractList | 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. |
| Author | Cinnella, P. Hercus, S.J. |
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| Keywords | 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 |
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| SubjectTerms | 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 |
| Title | Robust optimization of dense gas flows under uncertain operating conditions |
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