Shabudin Mat Tholudin Mat Lazim
Bibliographic record
Abstract
This paper presents the aerodynamic estimation carried out on a three-dimensional aircraft model by conducting wind tunnel tests and Computational Fluid Dynamics (CFD) simulation. The test model is a 15 % scaled down from a two-seater light aircraft that is closed to the Malaysian made SME MD3-160 aircraft. This aircraft model has been tested at two different low speed tunnels, namely at Universiti Teknologi Malaysia’s tunnel (UTM-LST) test section sized of 2.0 x 1.5 m2, and at Institute Aerodynamic Research, National Research Council of Canada (IAR-NRC) sized 3.0 x 2.0 m2 tunnel. The speed during testing at UTM-LST and IAR-NRC tunnels was up to 70 m/s, which corresponds to Reynolds Number of 1.3 x 10 6. The longitudinal and lateral directional aerodynamic characteristics of the aircraft such as coefficients of pressure, forces (lift, drag and side force) and moments (roll, pitch and yaw moment) had been experimentally measured either using direct force measurement or pressure measurement method. The data reduction methods included the strut support interference factor using dummy image and the blockage correction had been applied in this wind tunnel tests. On top of the experimental study, simulation was also performed using a commercial CFD code, FLUENT. Experimental works at UTM-LST and IAR–NRC tunnel showed that the aerodynamic characteristics of this light aircraft were in good agreement with each other. Simultaneously, the aerodynamic forces obtained from the experimental works and CFD simulations had been compared. The results proved that they were agreeable especially at a low 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.107 | 0.072 |
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".