Aero-thermal design and analysis of gas turbine combustion systems - Current status and future direction

Mongia, H. C.

doi:10.2514/6.1998-3982

Cited by 34 publications

(14 citation statements)

References 32 publications

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“…The elementary interaction between a vortex and flame often represents a key process in the description of turbulent reactive flows [21]. Consequently, the combustor modeled for this design optimization study is the one used by Keller et al [22] in an experimental study of mechanisms of instabilities in turbulent combustion leading to flashback.…”

Section: Computational Modelmentioning

confidence: 99%

Combustor Design Optimization Using Co-Kriging of Steady and Unsteady Turbulent Combustion

Wankhede

Bressloff

Keane

2011

Journal of Engineering for Gas Turbines and Power

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In the gas turbine industry, computational fluid dynamics (CFD) simulations are often used to predict and visualize the complex reacting flow dynamics, combustion environment and emissions performance of a combustor at the design stage. Given the complexity involved in obtaining accurate flow predictions and due to the expensive nature of simulations, conventional techniques for CFD based combustor design optimization are often ruled out, primarily due to the limits on available computing resources and time. The design optimization process normally requires a large number of analyses of the objective and constraint functions which necessitates a careful selection of fast, reliable and efficient computational methods for the CFD analysis and the optimization process. In this study, given a fixed computational budget, an assessment of a co-Krlging based optimization strategy against a standard Kriging based optimization strategy is presented for the design of a 2D combustor using steady and unsteady Reynolds-averaged Navier Stokes (RANS) formulation. Within the fixed computational budget, using a steady RANS formulation, the Kriging strategy successfully captures the underlying response: however with unsteady RANS the Kriging strategy fails to capture the underlying response due to the e.xistence of a high level of noise. The co-Kriging strategy is then applied to two design problems, one using two levels of grid resolutions in a steady RANS formulation and the other using steady and unsteady RANS formulations on the same grid resolution. With the co-Kriging strategy, the multifidelity analysis is expected to flnd an optimum design in comparatively less time than that required using the high-fldelity model alone since ¡ess high-fidelity function calls should be required. However, using the applied computational setup for co-Kriging, the Kriging strategy beats the co-Kriging strategy under the steady RANS formulation whereas under the unsteady RANS formulation, the high level of noise stalls the co-Kriging optimization process, [

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Section: Computational Modelmentioning

confidence: 99%

Combustor Design Optimization Using Co-Kriging of Steady and Unsteady Turbulent Combustion

Wankhede

Bressloff

Keane

2011

Journal of Engineering for Gas Turbines and Power

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“…For combustor applications, numerical optimisation can be used after the preliminary design phase and during detailed design as part of the fine-tuning process. The preliminary design uses empirical and semi-empirical tools to achieve design tasks quickly [39].…”

Section: Mathematical Optimisationmentioning

confidence: 99%

Optimization of Gas Turbine Combustor Mixing for Improved Exit Temperature Profile

Motsamai

Snyman

Meyer

2010

Heat Transfer Engineering

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“…Despite these challenges, many design tools, among which is computational fluid dynamics (CFD), have been used with varying success (Mongia 1998). The conflicting nature of the combustor performance objectives has forced some researchers to consider designs such as staged combustion (Lefebvre 1998), in which no attempt is made to achieve all the performance objectives in a single combustion zone.…”

Section: Introductionmentioning

confidence: 99%

Multi-disciplinary design optimization of a combustor

Motsamai

Visser

Morris

2008

Engineering Optimization

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A technique for design optimization of a combustor is presented. This technique entails the use of computational fluid dynamics (CFD) and mathematical optimization to minimize the combustor exit temperature profile. The empirical and semi-empirical correlations commonly used for optimizing combustor exit temperature profile do not guarantee optimum. As an experimental approach is time consuming and costly, use is made of numerical techniques. However, using CFD without mathematical optimization on a trial and error basis does not guarantee optimal solutions. A better approach, which is often viewed as too expensive, is a combination of the two approaches, thus incorporating the influence of the variables automatically. In this study the combustor exit temperature profile is optimized. The optimum (uniform) combustor exit temperature profile mainly depends on the geometric parameters. Combustor parameters have been used as optimization variables. The combustor investigated is an experimental liquid-fuelled atmospheric combustor with a turbulent diffusion flame. The CFD simulations use the Fluent code with a standard k-ε model. The optimization is carried out using the Dynamic-Q algorithm, which is specifically designed to handle constrained problems where the objective and constraint functions are expensive to evaluate. The optimization leads to a more uniform combustor exit temperature profile compared with the original.

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Aero-thermal design and analysis of gas turbine combustion systems - Current status and future direction

Cited by 34 publications

References 32 publications

Combustor Design Optimization Using Co-Kriging of Steady and Unsteady Turbulent Combustion

Combustor Design Optimization Using Co-Kriging of Steady and Unsteady Turbulent Combustion

Optimization of Gas Turbine Combustor Mixing for Improved Exit Temperature Profile

Multi-disciplinary design optimization of a combustor

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