Boundary-layer Suction and Aerodynamic Shape Optimization for Hybrid Laminar Flow Control on a Fin

Haoyang Wu, Wenping Song, Han Nie, Zhonghua Han

Research output: Contribution to journalConference articlepeer-review

Abstract

Aerodynamic drag reduction is essential for improving flight efficiency and reducing emissions of transport aircraft, and is of great significance for achieving the goal of green aviation. Application of hybrid laminar flow control (HLFC) system on fins has been proved of great potentials in drag reduction. How to design a good aerodynamic shape and boundary-layer suction distribution to reduce the sum of pressure drag and viscous friction drag by extending the laminar flow region under complex geometric constraints is still a challenge for HLFC on a highly swept fin. To address the problem, this article develops a surrogate-based HLFC fin design optimization method. The surrogate-based optimization is conducted based on an in-house software SurroOpt, and the aerodynamic characteristics of fins are calculated with a RANS solver PMNS3D coupled with automatic transition prediction by a NTS-NCF eN method. Validation of the developed method is conducted on a 40°-swept fin at Ma=0.785 and Re=2.6×107, setting three suction areas over the fin surface at 0c~0.2c. The objective of the design optimization is to enlarge laminar flow region and reduce drag at a reasonable boundary-layer suction flow rate. Compared to the baseline fin with suction, whose laminar flow area is 41.25% and total drag is 35.46 counts at cruise state, the optimized fin has reached a larger laminar flow area of 57.7% and lower drag of 31.74 counts, and is more robust at ±2° sideslip angles, suggesting that our method is effective for HLFC design on highly-swept fins.

Original languageEnglish
JournalICAS Proceedings
StatePublished - 2024
Event34th Congress of the International Council of the Aeronautical Sciences, ICAS 2024 - Florence, Italy
Duration: 9 Sep 202413 Sep 2024

Keywords

  • e method
  • fin
  • Hybrid laminar flow control
  • Kriging model
  • surrogate-based optimization

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