Design, Test and CFD Analysis of Drone for Foreign Object Debris
Bibliographic record
Abstract
Runway inspections are regularly conducted by airports to identify and prevent foreign object debris (FOD) that can potentially cause significant damage and pose risks to aircraft.There are an outstanding number of sources that could cause FOD to appear in operational areas, which makes prevention and detection very difficult and time consuming.These sources can come from the environment through wildlife and weather, operating equipment, and personnel on the runways, and even from the airport infrastructure itself.The aim of this project is to assist airports in enhancing runway safety by drones to improve the runway inspection process.SolidWorks, a computer-aided design (CAD) software, was used to create five designs, Given the high stresses that drones must endure during flight, it was decided to use onyx, a composite material made of nylon and carbon fibre, to construct the drone's chassis.Onyx has a strength of up to 40 MPa, The selection of SolidWorks Flow Simulation was employed as the method for conducting the CFD propeller analysis.Subsequent rounds of CFD testing led to the attainment of an average force value of 11.313 N. The outcome closely approximates the manually computed thrust value for the 7035 propellers at an RPM of 13000.The flow simulation findings represent the required thrust magnitude essential for operating the drone at the maximum permissible thrust limit.
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 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".