Modeling Unsteady Hydrodynamic Cross-Coupling in Horizontal Submarine Maneuvers
Bibliographic record
Abstract
A modified indicial response model for predicting transient hydrodynamics in horizontal submarine maneuvers is presented. To facilitate the analysis of unsteady cross-coupling effects, the existing formulation is modified to incorporate translation and rotation effects into a single set of indicial responses. The formulation takes advantage of a submarines tendency to pivot about a point just aft of the nose, which limits the realistic kinematic space that the vehicle will operate in. This approach is used to study the relative contribution of hydrodynamic cross-coupling on the unsteady response of the BB2 submarine, and is used to predict the hydrodynamic loads on the submarine in prescribed horizontal oscillation maneuvers simulated with a computational fluid dynamics method. Particular emphasis is placed on the out-of-plane hydrodynamics, which exhibit highly non-linear hysteresis characteristics not predicted by traditional quasi-steady coefficient models. The hydrodynamic predictions for transient horizontal oscillations at extreme operational frequencies are improved with the addition of the indicial response model as compared to a traditional quasi-steady coefficient model. However, the out-of-plane hydrodynamics exhibit highly non-linear characteristics at high accelerations that the IRM is unable to predict.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".