Optimization of an Autonomous Underwater Vehicle Using a Gradient-Based Approach
Bibliographic record
Abstract
In this research, an adjoint method is employed to optimize the nose and tail of an Autonomous Underwater Vehicle (AUV).The drag force, which has a significant impact on energy consumption, is considered as an objective function to be minimized, while the partial volume is chosen as a constraint.The entire procedure is carried out utilizing two open-source softwares: Salome (CAD and mesh generator) and OpenFOAM v2206 (CFD solver and optimizer).Reynolds-averaged Navier-Stokes equations with the k-ω SST turbulence model are used to simulate the turbulent flow around the AUV.Besides, the constrained optimization is performed using the adjointOptimisationFoam, a 3D steady-state adjoint Navier-Stokes incompressible solver in OpenFOAM v2206.The drag force obtained from this study is validated against experimental results, indicating a good agreement (a 0.58% discrepancy).According to the results, the optimized AUV indicates a 3.25% reduction in drag force over the baseline after nine optimization cycles.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".