Low-order modelling and sensor-based prediction of stalled airfoils at moderate Reynolds number
Low-order modelling and sensor-based prediction of stalled airfoils at moderate Reynolds number
We present analysis from planar time-resolved particle image velocimetry (PIV) fields in the stream-wise surface-normal plane of turbulent flow surrounding NACA 0012 and NACA 65-410 airfoils at 𝑅𝑒𝑐 ≈ 7 × 104 focusing on static stall phenomena at intermediate to high angles of attack 𝛼. Dominant flow frequencies, identified from the lift spectra obtained from a load cell mounted to the foils, highlight the presence of bluff-body shedding (𝑓★/𝛼 = 𝑓 𝑐/𝑈∞sin 𝛼 of O(10−1) where 𝑓 is the frequency, c the airfoil chord, and 𝑈∞ the freestream velocity) for all cases and prominent low frequencies (𝑓 ★/𝛼 of O(10−2)) for cases in transient stall (TS). A data-driven modelling framework via the proper orthogonal decomposition (POD) reveals the low-frequencies are associated to flow separation and reattachment driven by underlying expansion and contraction normal to the suction surface. Further, the ability to predict the low-order features from (pseudo) pressure probes at the leading, mid-chord, and trailing edges for both TS and deep stall (DS) cases is quantified using linear stochastic estimation (LSE).The framework pinpoints the centroid of the region of reverse flow with error on the order of 5 and 20% for DS and TS regimes, respectively. Notably, it is found that LSE coefficients governing the low-order POD correlations do not strongly depend on the airfoil geometry.This is demonstrated by the comparative performance of training the LSE using the probes of the NACA 0012 cases to predict the NACA 65-410 velocity fields and vice-versa. This work demonstrates the insight afforded by the POD on the low-order features of turbulent stalled airfoils as well as the similarity of such features across geometries towards predicting flow features for potentially any airfoil geometry without the need to re-train an LSE library.
Carter, Douglas
75fd127b-b918-4bd3-9ada-6e1c7e1ad69d
Ganapathisubramani, Bharathram
5e69099f-2f39-4fdd-8a85-3ac906827052
Carter, Douglas
75fd127b-b918-4bd3-9ada-6e1c7e1ad69d
Ganapathisubramani, Bharathram
5e69099f-2f39-4fdd-8a85-3ac906827052
Carter, Douglas and Ganapathisubramani, Bharathram
(2023)
Low-order modelling and sensor-based prediction of stalled airfoils at moderate Reynolds number.
AIAA Journal.
(doi:10.2514/1.J062475).
(In Press)
Abstract
We present analysis from planar time-resolved particle image velocimetry (PIV) fields in the stream-wise surface-normal plane of turbulent flow surrounding NACA 0012 and NACA 65-410 airfoils at 𝑅𝑒𝑐 ≈ 7 × 104 focusing on static stall phenomena at intermediate to high angles of attack 𝛼. Dominant flow frequencies, identified from the lift spectra obtained from a load cell mounted to the foils, highlight the presence of bluff-body shedding (𝑓★/𝛼 = 𝑓 𝑐/𝑈∞sin 𝛼 of O(10−1) where 𝑓 is the frequency, c the airfoil chord, and 𝑈∞ the freestream velocity) for all cases and prominent low frequencies (𝑓 ★/𝛼 of O(10−2)) for cases in transient stall (TS). A data-driven modelling framework via the proper orthogonal decomposition (POD) reveals the low-frequencies are associated to flow separation and reattachment driven by underlying expansion and contraction normal to the suction surface. Further, the ability to predict the low-order features from (pseudo) pressure probes at the leading, mid-chord, and trailing edges for both TS and deep stall (DS) cases is quantified using linear stochastic estimation (LSE).The framework pinpoints the centroid of the region of reverse flow with error on the order of 5 and 20% for DS and TS regimes, respectively. Notably, it is found that LSE coefficients governing the low-order POD correlations do not strongly depend on the airfoil geometry.This is demonstrated by the comparative performance of training the LSE using the probes of the NACA 0012 cases to predict the NACA 65-410 velocity fields and vice-versa. This work demonstrates the insight afforded by the POD on the low-order features of turbulent stalled airfoils as well as the similarity of such features across geometries towards predicting flow features for potentially any airfoil geometry without the need to re-train an LSE library.
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AIAA__Data_Driven_Model_of_Stalled_Aerofoils__Author_Version
- Accepted Manuscript
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Accepted/In Press date: 11 March 2023
Identifiers
Local EPrints ID: 476136
URI: http://eprints.soton.ac.uk/id/eprint/476136
ISSN: 0001-1452
PURE UUID: 0251c868-14ee-4aee-84c3-3dc078443764
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Date deposited: 12 Apr 2023 16:47
Last modified: 18 Jul 2023 04:01
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Author:
Douglas Carter
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