Performance Analysis and Visualization of Canard and Fuselage Effects on Flow Patterns around a Straight-Body-Type-Fuselage (SBTF) Fighter Models with Experimental Method in Water Tunnel
Bibliographic record
Abstract
Performance analysis and visualization of canard and fuselage effects on flow patterns around an SBTF fighter model have been conducted. An SBTF fighter model with an experimental method in a water tunnel would easily reveal visually the vortex dynamics phenomenon that occurred. Testing has been done, with some form of non-canard and canard aircraft in a water tunnel, on vortex dynamics and aerodynamic power on the aircraft model. The stream demonstration used a fluid speed of 0.1 m/s and Reynolds number 6.577x103 on a 1:110 scale model and measured using dye injection method with a print ink type with a mixture ratio of 1:8. The result of the research using GAMA water tunnel showed the aerodynamic force and vortex dynamics phenomenon that happened on the model aircraft. The research on the benefits of the canard is also done by examining the performance and the flow visualization of an SBTF fighter without fuselage, with the symmetrical plane and with the regular fuselage in a water tunnel. The results showed that the use of canard affected the lift, drag, and flow pattern around the wings. The use of canard can enhance the lift of the plane and function to delay the stall by restoring the flow above the wing which should have experienced turbulence back into laminar so that the fighter can maneuver to a higher angle of attack (AoA) without stalling. It is identified that canard and fuselage play significant roles on the design of a fighter. The design choice of the fuselage and the employment of canard are fundamental to support the ability of maneuver and agility of the fighter.
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 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".