Genetic Algorithm Optimization of a High-Pressure Turbine Vane Pressure Side Film Cooling Array

Johnson, Jamie J.; King, Paul I.; Clark, John P.; Ooten, Michael K.

doi:10.1115/1.4023470

Cited by 26 publications

(7 citation statements)

References 12 publications

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“…In the present study, the cooling optimization has been done based on the Genetic algorithm (GA). For film cooling applications, satisfactory solutions can be given quickly by GA [20,24]. Darwin's theory of evolution is the motivation of the Genetic algorithms.…”

Section: Resultsmentioning

confidence: 99%

“…Lee and Kim [19] used coupling Kriging model and sequential quadratic programming to optimize a shaped hole. Johnson et al [20] applied the genetic algorithm to optimize the film cooling array of a high-pressure turbine on the vane pressure side. A multi-objective technique is performed by Lee et al [21] to optimize a shape of one row of fan shape holes.…”

Section: Introductionmentioning

confidence: 99%

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Multi-objective optimization of three rows of film cooling holes by genetic algorithm

2021

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Aero-thermal optimization on multi-rows of film cooling over a flat plate has been performed to optimize the inclination angles. Hence three cylindrical holes with injection angles of ?, ?, and ? have been considered. The cooling hole has a 3 mm diameter and an inclined angle between 25 to 35 degrees. Numerical simulations were performed at a fixed density ratio of 1.25 and blowing ratio of 0.5. The control-volume method with a SIMPLEC algorithm has been used to solve the steady-state RANS equations with SST k-? turbulent model. The injection angles of the holes are selected as the design variables to perform the optimization of three rows of film cooling. In order to evaluate the performance of holes arrangement, two objective functions are defined based on aerodynamic losses and adiabatic film cooling effectiveness. The curve fitting method (CFM) is used to find the optimal point of objective functions. The optimizations have been performed using the genetic algorithm (GA) method. Results of the present study show that the best performance of three rows of cooling holes was achieved in inclined angles 25.45, 32.85 and 33.1.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization of three rows of film cooling holes by genetic algorithm

2021

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“…By means of statistical analysis, they tried to identify the most influencing parameters among blowing ratio, density ratio, hole pitch and trench depth to diameter ratio for a round hole embedded in a trench. Johanson et al [37] went some steps beyond parametric study and used Genetic Algorithm to optimize a high-pressure turbine vane pressure side cooling. They showed that by means of an efficient form of CFD an improved film cooling array could be redesigned from a baseline case.…”

Section: Computational Fluid Dynamics Studiesmentioning

confidence: 99%

On Film Cooling of Turbine Guide Vanes : From Experiments and CFD-Simulations to Correlation Development

Najafabadi¹

2015

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To achieve high thermal efficiency in modern gas turbines, the turbine-inlet temperature has to be increased. In response to such requisites and to prevent thermal failure of the components exposed to hot gas streams, the use of different cooling techniques, including film cooling, is essential. Finding an optimum film cooling design has become a challenge as it is influenced by a large number of flow and geometrical parameters. This study is dedicated to some important aspects of film cooling of a turbine guide vane and consists of three parts. The first part is associated with an experimental investigation of the suction and pressure side cooling by means of a transient IR-Thermography technique under engine representative conditions. It is shown that the overall film cooling performance of the suction side can be improved by adding showerhead cooling if fan-shaped holes are used, while cylindrical holes may not necessarily benefit from a showerhead. According to the findings, investigation of an optimum cooling design for the suction side is not only a function of hole shape, blowing ratio, state of approaching flow, etc., but is also highly dependent on the presence/absence of showerhead cooling as well as the number of cooling rows. In this regard, it is also discussed that the combined effect of the adiabatic film effectiveness (AFE) and the heat transfer coefficient (HTC) should be considered in such study. As for the pressure side cooling, it is found that either the showerhead or a single row of cylindrical cooling holes can enhance the HTC substantially, whereas a combination of the two or using fan-shaped holes indicates considerably lower HTC. An important conclusion is that adding more than one cooling row will not augment the HTC and will even decrease it under certain circumstances. In the second part, computational fluid dynamics (CFD) investigations have shown that film cooling holes subjected to higher flow acceleration will maintain a higher level of AFE. Although this was found to be valid for both suction and pressure side, due to an overall lower acceleration for the pressure side, a lower AFE was achieved. Moreover, the CFD results indicate that fan-shaped holes with low area ratio (dictated by design constraints for medium-size gas turbines), suffer from cooling jet separation and hence reduction in AFE for blowing ratios above unity. Verification of these conclusions by experiments suggests that CFD can be used more extensively, e.g. for parametric studies. The last part deals with method development for deriving correlations based on experimental data to support engineers in the design stage. The proposed method and the ultimate correlation model could successfully correlate the laterally averaged AFE to the downstream distance, the blowing ratio and the local pressure coefficient representing the effect of approaching flow. The applicability of the method has been examined and the high level of predictability of the final model demonstrates its suitability to be used for design purposes...

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“…Johnson et al. 3 performed a GA optimization of a nozzle guide vane. The objective was to minimize the heat load on the vane pressure side by redistributing arrays of cooling holes across the surface as well as varying injection angle, compound angle, and cooling hole area.…”

Section: Introductionmentioning

confidence: 99%

Combined scalar tracking, computational fluid dynamics, and multi-objective genetic algorithm method to accelerate optimization of film cooling systems

Ornano

Povey

2020

Proceedings of the Institution of Mechanical Engineers, Part A:

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This paper presents a method to significantly accelerate optimization of film cooling systems. The method combines high-fidelity computational fluid dynamics with scalar tracking implemented, a proxy model (linear superposition model) initialized with the computational fluid dynamics solution, and a multi-objective evolutionary algorithm approach. The proposed method is structured as follows: the computational fluid dynamics solution is used to predict the (generally complex) flow domain for the film cooling system; the scalar tracking method identifies the contributions of individual holes to an overall cooling effectiveness distribution by associating a unique passive scalar variable to the flow associated with each hole, and solving an additional advection–diffusion (scalar transport) equation; the proxy model is a (generally linear) superposition model implemented – for example – in Matlab, which inherits the scalar values from the computational fluid dynamics solution, and allows extrapolation of solutions to new design points as part of an optimization process; the optimization process is handled with a multi-objective evolutionary algorithm approach which iterates the proxy model to optimize for a defined objective function. The process works with inner and outer convergence loops. The inner convergence loop is the multi-objective evolutionary algorithm interfacing with the proxy model, which achieves convergence against a design target. At the end of each inner loop cycle, a high-fidelity computational fluid dynamics simulation is run, and this is used to recalibrate the proxy model. Convergence for a given objective function is typically achieved with six outer-loop iterations (high-fidelity computational fluid dynamics runs) and 10,000 inner-loop iterations per outer-loop iteration. The significant advantage of the proposed method is that for certain optimization problems, the computational cost can be reduced by several orders of magnitude, replacing thousands of high-fidelity computational fluid dynamics runs with approximately six computational fluid dynamics runs. The process is demonstrated by applying the optimization method to the film cooling of a flat plate. In our example we have an objective function which maximizes the component life (related to the difference from an arbitrary target temperature distribution) and minimizes the mixing loss introduced by the films. The flow environment was moderately compressible. The optimization converged after six computational fluid dynamics runs. A 30% reduction in mixing loss, a 11% increase in component life, and a 30% reduction in cooling mass flow rate were achieved. The advantages and limitations of the proposed method are also discussed in detail.

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Genetic Algorithm Optimization of a High-Pressure Turbine Vane Pressure Side Film Cooling Array

Cited by 26 publications

References 12 publications

Multi-objective optimization of three rows of film cooling holes by genetic algorithm

Multi-objective optimization of three rows of film cooling holes by genetic algorithm

On Film Cooling of Turbine Guide Vanes : From Experiments and CFD-Simulations to Correlation Development

Combined scalar tracking, computational fluid dynamics, and multi-objective genetic algorithm method to accelerate optimization of film cooling systems

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