Natural Laminar Flow Optimization of Transonic Nacelle Based on Differential Evolution Algorithm
Publication: Journal of Aerospace Engineering
Volume 32, Issue 4
Abstract
Natural laminar flow (NLF) design is widely used to reduce skin friction drag to improve aircraft aerodynamic performance. In this paper, a differential evolution (DE) algorithm was applied to a NLF-designed transonic nacelle. The class shape transformation (CST) method was tested in terms of accuracy before being adopted as the geometry parameterization method that describes three longitudinal profiles constructing the nacelle surface. The purpose of this optimization is to extend the laminar length of each longitudinal profile of the nacelle while maintaining pressure drag under a preset limit. A high-fidelity computational fluid dynamics (CFD) solver was used for accurate laminar/turbulence transition prediction. It was tested in terms of pressure distribution and particularly laminar transition prediction. The whole process was executed via a Python version 3 script automatically. The laminar length was extended on longitudinal profiles after DE operation. The laminar area of the optimized nacelle surface was increased by 16.64% and total drag coefficient was decreased by 11.6 counts.
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Acknowledgments
The authors are grateful for the funding by Integrated design technique of civil aircraft: jet engine (considering thrust) and airframe (GXB[2016]92): natural laminar flow nacelle aerodynamic design. The authors are grateful for the support by United Innovation Program of Shanghai Commercial Aircraft Engine. The program was founded by Shanghai Municipal Commission of Economy and Informatization, Shanghai Municipal Education Commission and AECCC Commercial Aircraft Engine Co., Ltd. (No. AR909). The authors owe a great debt to Alannah Manson for her very kind support in amending the original manuscript. The first author would like to express his thanks to Han Jiang for his guidance in relation to Python 3 scripting, and to Zhiming Han (engineer of AECC Commercial Aircraft Engine Co., Ltd.) and Yan Yan (engineer of Shanghai Academy of Spaceflight Technology) for their advice on the optimization platform.
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Published In
Journal of Aerospace Engineering
Volume 32 • Issue 4 • July 2019
Copyright
©2019 American Society of Civil Engineers.
History
Received: Jul 20, 2018
Accepted: Jan 2, 2019
Published online: Apr 19, 2019
Published in print: Jul 1, 2019
Discussion open until: Sep 19, 2019
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