Experimental Investigation of Spoiler Deployment on Wing Stall
Bibliographic record
Abstract
Upper surface wing flaps, known as spoiler, are typically used to reduce lift and increase drag at touchdown; however spoilers have been shown to increase lift and reduce drag at near-stall conditions. The purpose of this experiment was to determine the spoilers’ impact on lift, drag, moment, and aerodynamic efficiency of a NACA 2412 airfoil at angles of attack (α) from −8 ° to 32 °. The experiment was conducted in the Ryerson Low-Speed Wind Tunnel (closed-circuit, 1 m × 1 m test section) at Re=783761, Ma=0.136. The lift coefficient (Cl), drag coefficient (Cd), moment coefficient about the quarter-chord () were captured with a changing spoiler deflection angle (δ) and spoiler length (b in percent chord). It was found that deflecting the spoiler resulted in an increase maximum lift of up to 2.497%. It was found that deflecting the spoiler by 8° was optimal for the b=10 cases. Any larger deflection reduced the lift gain, and a deflection of 25° caused the maximum lift to be 2.786% less than the clean configuration. In the b=15 case, δ=15° was optimal (1.760% maximum lift coefficient increase). The b=10 cases increased maximum lift coefficient between 0.35% and 2.10% higher than the b=15 cases. The source of the lift gain at high angles of attack is apparent in an analysis of the airfoil pressure distribution. The spoiler increased the suction peak on the airfoil surface upstream of the spoiler, and increased the pressure downstream. However the suction increase upstream is larger than the pressure increase downstream, resulting in a net increase in lift. The spoiler increased the stall angle 37.658% to 87.658% higher than the clean configuration. Stall angle increased with both δ and with an increased spoiler length. The spoiler airfoil produced less drag than the clean configuration at high angles of attack. The combination of the increased lift, and reduced drag resulted in an increase in aerodynamic efficiency at high angle of attack.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".