TY - GEN
T1 - Robust optimization of variable-camber continuous trailing-edge flap system action using stochastic kriging
AU - Liu, Yan
AU - Bai, Junqiang
AU - Livne, Eli
N1 - Publisher Copyright:
© 2016, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.
PY - 2015
Y1 - 2015
N2 - A robust optimization method is described in this paper, focused on the optimization of a Variable-Camber Continuous Trailing-Edge Flap (VCCTEF) system for drag reduction in transonic cruise. The VCCTEF, through the rich combination of shapes it can attain, has the potential to provide greater design and operation freedom than conventional flap systems and greater efficiency in drag reduction and performance improvement. The VCCTEF deformation shape optimization, however, when based on rigorous CFD simulations, presents a significant computational challenge because of the high cost of repetitive analyses required. The construction of surrogate models and their utilization in the optimization process is one common way for tackling this challenge. Here Stochastic Kriging (SK) is used, extending the commonly used Deterministic Kriging (DK). Accounting for the intrinsic uncertainty of data, Stochastic Kriging and the optimization based on it are especially suited for addressing uncertain flight conditions as well as math modeling limitations. An optimization engine is presented that couples geometric shape synthesis with CFD simulations and an automated optimization process focused on the VCCTEF system. A potential for drag reduction is shown even in the presence of uncertainties.
AB - A robust optimization method is described in this paper, focused on the optimization of a Variable-Camber Continuous Trailing-Edge Flap (VCCTEF) system for drag reduction in transonic cruise. The VCCTEF, through the rich combination of shapes it can attain, has the potential to provide greater design and operation freedom than conventional flap systems and greater efficiency in drag reduction and performance improvement. The VCCTEF deformation shape optimization, however, when based on rigorous CFD simulations, presents a significant computational challenge because of the high cost of repetitive analyses required. The construction of surrogate models and their utilization in the optimization process is one common way for tackling this challenge. Here Stochastic Kriging (SK) is used, extending the commonly used Deterministic Kriging (DK). Accounting for the intrinsic uncertainty of data, Stochastic Kriging and the optimization based on it are especially suited for addressing uncertain flight conditions as well as math modeling limitations. An optimization engine is presented that couples geometric shape synthesis with CFD simulations and an automated optimization process focused on the VCCTEF system. A potential for drag reduction is shown even in the presence of uncertainties.
UR - http://www.scopus.com/inward/record.url?scp=85067317612&partnerID=8YFLogxK
U2 - 10.2514/6.2015-2421
DO - 10.2514/6.2015-2421
M3 - 会议稿件
AN - SCOPUS:85067317612
SN - 9781624103636
T3 - 33rd AIAA Applied Aerodynamics Conference
BT - 33rd AIAA Applied Aerodynamics Conference
PB - American Institute of Aeronautics and Astronautics Inc, AIAA
T2 - 33rd AIAA Applied Aerodynamics Conference, 2015
Y2 - 22 June 2015 through 26 June 2015
ER -