CFD Aided Ship Design and Helicopter Operation
Bibliographic record
Abstract
In support of Canadian industrial and defence ship design and offshore helicopter operations, a series of Ship–Helicopter Operational Limits Analysis and Simulation (SHOLAS) projects are being conducted at the National Research Council Canada (NRC) in collaboration with Defence Research and Development Canada (DRDC). This study presents a brief overview of a Canadian in-house ship airwake simulation capability combining in-house high-fidelity wind-tunnel tests, full-scale sea trials, high-order computational fluid dynamics (CFD) tools, and realistic engineering-oriented flight simulators. This paper reports challenges and lessons learned during the course of the study, discusses the current capabilities and limitations of the CFD tools and the infrastructure required, and evaluates the gaps and barriers in industry adoption by focusing on how they could be overcome based on our current practice. After validating the CFD results of an updated version of a simplified frigate shape (SFS2) and the real-world Canadian Patrol Frigate (CPF), which are in reasonable agreement with the available in-house wind-tunnel and sea-trial data, the developed approach was recently applied to the design of an undisclosed Canadian ship. Among other applications, CFD airwake results were used with confidence as input to produce representative airwake features in industrial high-fidelity piloted flight simulators.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".