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- 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".