Bibliographic record
Abstract
The objective of this thesis was to conduct a two dimensional Compuational Fluid Dyanmic analysis on wing-spoiler lift effectiveness in delaying stall effects using the NACA 2412 airfoil section. The project was divided into three sub-areas; grid-independent study, the baseline study and spoiler performance study. The grid independent study was carried out for the purpose of mesh optimization, i.e. to determine the point at which computed solutions had little or no change in value with increasing number of mesh nodes. This study was conducted at an angle of attack of 16 degrees as it served as both a high pitch angle value as well as a pre-stall point in which unsteady effects were not a determining influence that may have served as a deterrent in resolving potential grid error. The baseline study was conducted to establish a data foundation to be used as a comparison to the spoiler study to effectively determine its effects on the lift performance. Results for the baseline study were shown to match experimental values most closely using the Transition SST Turbulence Model at a Mach number of 0.17. Therefore to remain consistent the spoiler study was carried out for the same Mach value with the viscosity of 1.84E-5 [kg/ms] and Temperature of 300 [K], which produced a Reynolds number of approximately 3.79E6. Therefore, using the same flight conditions employed in the baseline study, the spoiler grids were generated in ANSYS ICEM CFD and imported into Fluent and solved using the Transition SST turbulence model. Results for spoiler deflections of 4, and 10 degrees were carried out across spoiler locations of 60%, 65 and 70% leading edge chord-wise locations. Results were shown to be optimal for 4 degree spoiler deflections across all locations tested, with highest values for lift effectiveness recorded at 70% leading edge. The findings of this thesis provides much potential for utilizing spoilers as lift enhancing devices and adds an alternate perspective in improving aircraft performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".