Updated Aerodynamic Analysis of Outboard Horizontal Stabilizers
Bibliographic record
Abstract
The present study examines the aerodynamics of Outboard Horizontal Stabilizer (OHS) configurations using lifting line theory and computational fluid dynamics. This work builds on past efforts that relied heavily on simplified approaches and an assumed behavior of the OHS concept. Thus, this study aims to utilize a mixture of approaches to quantify and describe the OHS concept and potential performance gains. A modern non-linear lifting-line theory, including a vortex decay model, is used for a fast low-fidelity model. This analysis is subsequently supported through Reynolds Averaged Navier Stokes simulations completed in OpenFOAM®. Both the lifting line method and CFD approaches were validated using experimental data obtained from the literature. A specific OHS configuration being developed by the present authors was used as a case study. The new results obtained in this work clearly show a substantial increase in performance for the OHS of 19% increased Cl/Cd when compared to a traditional configuration. In order to examine this further, the lifting line approach was utilized to explore the lift and drag performance of the chosen OHS case study when considering the impact of static stability and trim. The results show that even when considering the required negative lift on the horizontal stabilizer to achieve balance, the OHS version performs substantially better. Additionally, it was shown that the performance of the OHS is significantly improved at a negative static margin where the aircraft is statically unstable, but a positive lift on the tailplane can be achieved.
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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 | 0.001 | 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".