A CFD-based Methodology for Accurate Estimation of the Hydrodynamic Forces and Moments on an Explorer Class AUV
Bibliographic record
Abstract
Accurate estimation of the hydrodynamic forces and moments acting on an Autonomous Underwater Vehicle (AUV) is essential for optimizing their motion behavior and developing effective control strategies. In the present study, these hydrodynamic forces and moments acting on an Explorer class AUV are predicted using the Computational Fluid Dynamics (CFD) and Semi-Empirical (SE) methods for various angles of attacks (AoA) and operational speeds. Validation is done by comparing the CFD and SE results for the benchmark DARPA SUBOFF submarine model with experimental results available in the literature. The research also evaluates the applicability of SE equations for two different bare hull configurations - Explorer AUV and the DARPA SUBOFF. By comparing CFD and SE results, the study assesses errors in SE estimates at various angles of attack (AoA) ranging from 0° to 30°. SE equations are known to be effective for axisymmetric bodies up to an AoA of 12°. Beyond this range, the accuracy of SE equations in predicting forces and moments remains unexplored and is a key investigation area in the present study. As SE methods do not provide expressions for fully appended configurations, the current study employs a component build-up approach, summing the semi-empirically obtained drag contributions from the hull and control planes for the Explorer AUV. These component build-up-based SE results are then compared with CFD outcomes to evaluate their accuracy for fully appended configurations of the Explorer AUV.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.001 |
| 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".