Optimization of a fan-shaped hole to improve film cooling performance by RBF neural network and genetic algorithm

Wang, Chunhua; Zhang, Jingzhou; Zhou, Junhui

doi:10.1016/j.ast.2016.08.004

Cited by 82 publications

(17 citation statements)

References 18 publications

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“…Unlike their work, in our study, both the external and internal boundary conditions were affected by the optimization of film hole diameters that metered the coolant flow. Wang et al [31] used neural and genetic algorithms to optimize film hole shape. Nowak et al [30] optimized the interior structures of a steam turbine airfoil and found that it was computationally demanding.…”

Section: Conjugate Heat Transfer Configurationmentioning

confidence: 99%

Nonlinear Optimization of Turbine Conjugate Heat Transfer with Iterative Machine Learning and Training Sample Replacement

Dutta

Smith

2020

Energies

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A simple yet effective optimization technique is developed to solve nonlinear conjugate heat transfer. The proposed Nonlinear Optimization with Replacement Strategy (NORS) is a mutation of several existing optimization processes. With the improvements of 3D metal printing of turbine components, it is feasible to have film holes with unconventional diameters, as these holes are created while printing the component. This paper seeks to optimize each film hole diameter at the leading edge of a turbine vane to satisfy several optimum thermal design objectives with given design constraints. The design technique developed uses linear regression-based machine learning model and further optimizes with strategic improvement of the training dataset. Optimization needs cost and benefit criteria are used to base its decision of success, and cost is minimized with maximum benefit within given constraints. This study minimizes the coolant flow (cost) while satisfying the constraints on average metal temperature and metal temperature variations (benefits) that limit the useful life of turbine components. The proposed NORS methodology provides a scientific basis for selecting design parameters in a nonlinear design space. This model is also a potential academic tool to be used in thesis works without demanding extensive computing resources.

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Section: Conjugate Heat Transfer Configurationmentioning

confidence: 99%

Nonlinear Optimization of Turbine Conjugate Heat Transfer with Iterative Machine Learning and Training Sample Replacement

Dutta

Smith

2020

Energies

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“…They improved the film cooling effectiveness over the surface while reducing the aerodynamic losses. Wang et al [23] optimized of a fan-shaped hole by coupling the RBF neural network and genetic algorithm. In general, Previous studies are focused only to optimize the averaged film cooling effectiveness through the hole shape [21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

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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“…Different studies have investigated the effect of these parameters on film cooling, but limited studies are available on how these parameters are interrelated. Radial basis function neural network and genetic algorithms has been implemented to optimize a fan-shaped hole to improve film cooling performance [15]. The current study is implementing the RSM to optimize a confined jet flow that forms a cool air blanket film over a flat plate.…”

Section: Introductionmentioning

confidence: 99%

“…The effect of different hole shape has been explored in literature [23] and it is reported that fan-shaped hole shows improved film cooling effectiveness when compared to a cylindrical hole. To sum up, the main outcome from the previous studies [15][16][17][18][19][20][21][22] is that for a specific turbulent intensity value at a specific angle, increasing the blowing ratio increases the film cooling effectiveness (FCE) up to a critical blowing ratio. When this critical value is reached, the coolant jet fails to stay attached to the wall and it will penetrate to the mainstream causing the FCE to fall drastically.…”

Section: Introductionmentioning

confidence: 99%

Optimization of a Confined Jet Geometry to Improve Film Cooling Performance Using Response Surface Methodology (RSM)

2020

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This study investigates the interrelated parameters affecting heat transfer from a hot gas flowing on a flat plate while cool air is injected adjacent to the flat plate. The cool air forms an air blanket that shield the flat plate from the hot gas flow. The cool air is blown from a confined jet and is simulated using a two-dimensional numerical model under three variable parameters; namely, blowing ratio, jet angle and density ratio. The interrelations between these parameters are evaluated to properly understand their effects on heat transfer. The analyses are conducted using ANSYS-Fluent, and the performance of the air blanket is reported using local and average adiabatic film cooling effectiveness (AFCE). The interrelation between these parameters and the AFCE is established through a statistical method known as response surface methodology (RSM). The RSM model shows that the AFCE has a second order relation with the blowing ratio and a first order relation with both jet angle and density ratio. Also, it is found that the highest average AFCE is reached at an injection angle of 30 degree, a density ratio of 1.2 and a blowing ratio of 1.8.

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Optimization of a fan-shaped hole to improve film cooling performance by RBF neural network and genetic algorithm

Cited by 82 publications

References 18 publications

Nonlinear Optimization of Turbine Conjugate Heat Transfer with Iterative Machine Learning and Training Sample Replacement

Nonlinear Optimization of Turbine Conjugate Heat Transfer with Iterative Machine Learning and Training Sample Replacement

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

Optimization of a Confined Jet Geometry to Improve Film Cooling Performance Using Response Surface Methodology (RSM)

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