Aero-Thermal shape optimization for the discrete film cooling of a turbine airfoil

Carole El Ayoubi, Wahid Ghaly, Ibrahim Hassan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A multiple-objective shape optimization is implemented for two staggered rows of discrete film cooling holes on the suction surface of a turbine vane. The optimization aims to maximize the film cooling performance while minimizing the corresponding aerodynamic penalty. The cooling performance is assessed using the adiabatic film cooling effectiveness, while the aerodynamic penalty is measured with a mass-Averaged total pressure loss coefficient. The conical expansion angle, the compound angle, and the length to diameter ratio of the nonexpanded portion of the hole are selected as geometric design variables. The effect of varying the geometric variables on the adiabatic film cooling effectiveness and the aerodynamic penalty is analyzed using the optimization method and threedimensional Reynolds-Averaged Navier-Stokes (RANS) simulations. A non-dominated sorting genetic algorithm (NSGA-II) is coupled with an artificial neural network (ANN) to perform the multiple-objective optimization. RANS simulations are employed to construct the ANN network which produces low-fidelity predictions of the objective functions during the optimization. The Pareto front of optimum solutions is generated. Two optimum designs, denoted as the aerodynamic, and thermal optimums are chosen from the Pareto front and evaluated through RANS simulations. The optimum designs present improved performance in comparison to the reference design, which consists of cylindrical holes.

Original languageEnglish
Title of host publicationHeat Transfer and Thermal Engineering
PublisherAmerican Society of Mechanical Engineers (ASME)
Volume8B-2015
ISBN (Electronic)9780791857502
DOIs
Publication statusPublished - 2015
EventASME 2015 International Mechanical Engineering Congress and Exposition, IMECE 2015 - Houston, United States
Duration: 13 Nov 201519 Nov 2015

Other

OtherASME 2015 International Mechanical Engineering Congress and Exposition, IMECE 2015
CountryUnited States
CityHouston
Period13/11/1519/11/15

Fingerprint

Shape optimization
Airfoils
Turbines
Cooling
Aerodynamics
Neural networks
Sorting
Genetic algorithms
Hot Temperature
Optimum design

ASJC Scopus subject areas

  • Mechanical Engineering

Cite this

Ayoubi, C. E., Ghaly, W., & Hassan, I. (2015). Aero-Thermal shape optimization for the discrete film cooling of a turbine airfoil. In Heat Transfer and Thermal Engineering (Vol. 8B-2015). American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/IMECE2015-51871

Aero-Thermal shape optimization for the discrete film cooling of a turbine airfoil. / Ayoubi, Carole El; Ghaly, Wahid; Hassan, Ibrahim.

Heat Transfer and Thermal Engineering. Vol. 8B-2015 American Society of Mechanical Engineers (ASME), 2015.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Ayoubi, CE, Ghaly, W & Hassan, I 2015, Aero-Thermal shape optimization for the discrete film cooling of a turbine airfoil. in Heat Transfer and Thermal Engineering. vol. 8B-2015, American Society of Mechanical Engineers (ASME), ASME 2015 International Mechanical Engineering Congress and Exposition, IMECE 2015, Houston, United States, 13/11/15. https://doi.org/10.1115/IMECE2015-51871
Ayoubi CE, Ghaly W, Hassan I. Aero-Thermal shape optimization for the discrete film cooling of a turbine airfoil. In Heat Transfer and Thermal Engineering. Vol. 8B-2015. American Society of Mechanical Engineers (ASME). 2015 https://doi.org/10.1115/IMECE2015-51871
Ayoubi, Carole El ; Ghaly, Wahid ; Hassan, Ibrahim. / Aero-Thermal shape optimization for the discrete film cooling of a turbine airfoil. Heat Transfer and Thermal Engineering. Vol. 8B-2015 American Society of Mechanical Engineers (ASME), 2015.
@inproceedings{3220483f0bc64dd1b786c78db26786c3,
title = "Aero-Thermal shape optimization for the discrete film cooling of a turbine airfoil",
abstract = "A multiple-objective shape optimization is implemented for two staggered rows of discrete film cooling holes on the suction surface of a turbine vane. The optimization aims to maximize the film cooling performance while minimizing the corresponding aerodynamic penalty. The cooling performance is assessed using the adiabatic film cooling effectiveness, while the aerodynamic penalty is measured with a mass-Averaged total pressure loss coefficient. The conical expansion angle, the compound angle, and the length to diameter ratio of the nonexpanded portion of the hole are selected as geometric design variables. The effect of varying the geometric variables on the adiabatic film cooling effectiveness and the aerodynamic penalty is analyzed using the optimization method and threedimensional Reynolds-Averaged Navier-Stokes (RANS) simulations. A non-dominated sorting genetic algorithm (NSGA-II) is coupled with an artificial neural network (ANN) to perform the multiple-objective optimization. RANS simulations are employed to construct the ANN network which produces low-fidelity predictions of the objective functions during the optimization. The Pareto front of optimum solutions is generated. Two optimum designs, denoted as the aerodynamic, and thermal optimums are chosen from the Pareto front and evaluated through RANS simulations. The optimum designs present improved performance in comparison to the reference design, which consists of cylindrical holes.",
author = "Ayoubi, {Carole El} and Wahid Ghaly and Ibrahim Hassan",
year = "2015",
doi = "10.1115/IMECE2015-51871",
language = "English",
volume = "8B-2015",
booktitle = "Heat Transfer and Thermal Engineering",
publisher = "American Society of Mechanical Engineers (ASME)",

}

TY - GEN

T1 - Aero-Thermal shape optimization for the discrete film cooling of a turbine airfoil

AU - Ayoubi, Carole El

AU - Ghaly, Wahid

AU - Hassan, Ibrahim

PY - 2015

Y1 - 2015

N2 - A multiple-objective shape optimization is implemented for two staggered rows of discrete film cooling holes on the suction surface of a turbine vane. The optimization aims to maximize the film cooling performance while minimizing the corresponding aerodynamic penalty. The cooling performance is assessed using the adiabatic film cooling effectiveness, while the aerodynamic penalty is measured with a mass-Averaged total pressure loss coefficient. The conical expansion angle, the compound angle, and the length to diameter ratio of the nonexpanded portion of the hole are selected as geometric design variables. The effect of varying the geometric variables on the adiabatic film cooling effectiveness and the aerodynamic penalty is analyzed using the optimization method and threedimensional Reynolds-Averaged Navier-Stokes (RANS) simulations. A non-dominated sorting genetic algorithm (NSGA-II) is coupled with an artificial neural network (ANN) to perform the multiple-objective optimization. RANS simulations are employed to construct the ANN network which produces low-fidelity predictions of the objective functions during the optimization. The Pareto front of optimum solutions is generated. Two optimum designs, denoted as the aerodynamic, and thermal optimums are chosen from the Pareto front and evaluated through RANS simulations. The optimum designs present improved performance in comparison to the reference design, which consists of cylindrical holes.

AB - A multiple-objective shape optimization is implemented for two staggered rows of discrete film cooling holes on the suction surface of a turbine vane. The optimization aims to maximize the film cooling performance while minimizing the corresponding aerodynamic penalty. The cooling performance is assessed using the adiabatic film cooling effectiveness, while the aerodynamic penalty is measured with a mass-Averaged total pressure loss coefficient. The conical expansion angle, the compound angle, and the length to diameter ratio of the nonexpanded portion of the hole are selected as geometric design variables. The effect of varying the geometric variables on the adiabatic film cooling effectiveness and the aerodynamic penalty is analyzed using the optimization method and threedimensional Reynolds-Averaged Navier-Stokes (RANS) simulations. A non-dominated sorting genetic algorithm (NSGA-II) is coupled with an artificial neural network (ANN) to perform the multiple-objective optimization. RANS simulations are employed to construct the ANN network which produces low-fidelity predictions of the objective functions during the optimization. The Pareto front of optimum solutions is generated. Two optimum designs, denoted as the aerodynamic, and thermal optimums are chosen from the Pareto front and evaluated through RANS simulations. The optimum designs present improved performance in comparison to the reference design, which consists of cylindrical holes.

UR - http://www.scopus.com/inward/record.url?scp=84974725605&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84974725605&partnerID=8YFLogxK

U2 - 10.1115/IMECE2015-51871

DO - 10.1115/IMECE2015-51871

M3 - Conference contribution

VL - 8B-2015

BT - Heat Transfer and Thermal Engineering

PB - American Society of Mechanical Engineers (ASME)

ER -