Transition prediction and sensitivity analysis for a natural laminar flow wing glove flight experiment
Open access
Date
2021-08Type
- Journal Article
Abstract
Natural laminar flow technology can significantly reduce aircraft aerodynamic drag and has excellent technical appeal for transport aircraft development with high aerodynamic efficiency. Accurately and efficiently predicting the laminar-to-turbulent transition and revealing the maintenance mechanism of laminar flow in a transport aircraft's flight environment are significant for developing natural laminar flow wings. In this research, we carry out natural laminar flow flight experiments with different Reynolds numbers and angles of attack. The critical N-factor is calibrated as 9.0 using flight experimental data and linear stability theory from a statistical perspective, which makes sure that the relative error of transition location is within 5%. We then implement a simplified eN transition prediction method with a similar accuracy compared with linear stability theory. We compute the sensitivity information for the simplified eN method with an adjoint-based method, using the automatic differentiation technique (ADjoint). The impact of Reynolds numbers and pressure distributions on TS waves is analyzed using the sensitivity information. Through the sensitivity analysis, we find that: favorable pressure gradients not only suppress the development of TS waves but also decrease their sensitivity to Reynolds numbers; there exist three special regions which are very sensitive to the pressure distribution, and the sensitivity decreases as the local favorable pressure gradient increases. The proposed sensitivity analysis method enables robust natural laminar flow wings design. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000490642Publication status
publishedExternal links
Journal / series
Chinese Journal of AeronauticsVolume
Pages / Article No.
Publisher
ElsevierSubject
eN method; Flight experiment; Natural laminar flow; Sensitivity analysis; Transition predictionMore
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