TY - JOUR
T1 - Design of non-axisymmetric endwall of a stator to improve the efficiency of a high pressure turbine
T2 - 13th European Turbomachinery Conference on Turbomachinery Fluid Dynamics and Thermodynamics, ETC 2019
AU - Rehman, Abdul
AU - Liu, Bo
AU - Song, Zhaoyun
N1 - Publisher Copyright:
Copyright © by the Authors.
PY - 2019
Y1 - 2019
N2 - This paper presents an optimization method to improve the efficiency of a high pressure turbine by constructing non-axisymmetric endwalls on the hub and the shroud of the stator of a high pressure turbine. The numerical investigation was conducted through commercial tool FineTM/Turbo. The optimization was quantified by using optimization algorithms based on the pseudo-objective function. The objective was to increase total-to-total efficiency with the constraint on the mass flow rate equal to the design point value. In order to ensure that global optimum had been achieved, the function of parameters was first approximated through the artificial neural network, and then optimum was achieved by implementing the genetic algorithm. It was adopted through the design and optimization environment of FineTM/Design3D. The endwall of the hub and the endwall of the shroud of the stator were optimized together. The result of the investigation showed that the optimized shape of the endwalls can significantly help to reduce the transverse pressure gradient. The total pressure loss coefficient and spanwise mass averaged entropy were reduced. The design of the optimized turbine under steady simulations was confirmed through unsteady simulations.
AB - This paper presents an optimization method to improve the efficiency of a high pressure turbine by constructing non-axisymmetric endwalls on the hub and the shroud of the stator of a high pressure turbine. The numerical investigation was conducted through commercial tool FineTM/Turbo. The optimization was quantified by using optimization algorithms based on the pseudo-objective function. The objective was to increase total-to-total efficiency with the constraint on the mass flow rate equal to the design point value. In order to ensure that global optimum had been achieved, the function of parameters was first approximated through the artificial neural network, and then optimum was achieved by implementing the genetic algorithm. It was adopted through the design and optimization environment of FineTM/Design3D. The endwall of the hub and the endwall of the shroud of the stator were optimized together. The result of the investigation showed that the optimized shape of the endwalls can significantly help to reduce the transverse pressure gradient. The total pressure loss coefficient and spanwise mass averaged entropy were reduced. The design of the optimized turbine under steady simulations was confirmed through unsteady simulations.
KW - Artificial neural network
KW - Efficiency
KW - Genetic algorithm
KW - High pressure turbine
KW - Non-axisymmetric endwall
KW - Optimization
UR - http://www.scopus.com/inward/record.url?scp=85199012647&partnerID=8YFLogxK
M3 - 会议文章
AN - SCOPUS:85199012647
SN - 2313-0067
JO - European Conference on Turbomachinery Fluid Dynamics and Thermodynamics, ETC
JF - European Conference on Turbomachinery Fluid Dynamics and Thermodynamics, ETC
Y2 - 8 April 2019 through 12 April 2019
ER -