Propulsion aerodynamics and power estimation for a Mad Rock Marine Solution hydro craft using a Hoverhawk Warp Drive Propfan-No Nozzle Case
Bibliographic record
Abstract
Propulsion aerodynamics and power estimation for a Mad Rock Marine Solution hydro craft (or airboat), using a Hoverhawk Warp Drive propfan, were performed. With a given on-the-shelf controllable pitch propeller blade of the Warp Drive Propfan, measurement of geometry data was obtained, along with the airboat resistance data. In the current work, based on the available data, a series of virtual full-scale propellers was designed and built. These virtual full-scale propellers were then tested under 'open water' (open air) condition in a virtual wind tunnel, simulated by the in-house propeller software package, PROPELLA. A series of thrust coefficients KT and power coefficients KP were then obtained after a number of computational runs. These obtained coefficients from the virtual wind tunnel, as the basis, or the fundamental design charts, were then used for the design and optimization of the propulsion system of the airboat. Different from the traditional design methods that use Bp-d diagram or charts etc., a computer-aided design method was developed. This CAD method utilizes the advantage of spreadsheet software for generation of trend-line equations and then for interpolations for optimization. The design optimization was performed in terms of efficiency (minimum required power for the given airboat speed) for 4, 6 and 8 propfan blades. Mach number correction was included. The blade root chord section's spindle torque, in-plane and out-of-plane bending moments were also predicted by the virtual wind tunnel/cavitation tunnel, PROPELLA and a strength analysis of the blade root section was then performed based on the magnitudes of the moments, moment inertias of the section and the carbon-fiber material of the blade. Suggestions on the configuration and geometry of the propulsion system were made based on the above analysis.
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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 | 0.000 | 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".