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Record W4391330084 · doi:10.2514/6.2024-2336

Updated Aerodynamic Analysis of Outboard Horizontal Stabilizers

2024· article· en· W4391330084 on OpenAlexaff
Arjuna De Alwis, Ryan Ward, Schuyler Hinman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAerodynamicsComputer scienceAerospace engineeringAeronauticsMarine engineeringEnvironmental scienceGeologyEngineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.003
GPT teacher head0.207
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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