Researches on Detailed Numerical Simulation of Submersible Ballast Tank High-Pressure Air Blowing Based on Adaptive Runge–Kutta Method
Bibliographic record
Abstract
Abstract In this study, a detailed numerical simulation of the high-pressure air blowing (HP air blowing system) accurate expected process for submersible ballast tanks was conducted, motivated by the urgent need for accurate numerical simulation models to optimize the performance of this system, which is critical for the safety and maneuverability of the boat. The study involved a series of experiments on a test bench to validate the numerical simulation model. A comprehensive numerical simulation model was developed, incorporating various influencing factors. The model was based on the Laval spray theory, one-dimensional (1D) air flow theory, van der Waals equation, Bernoulli equation, and isothermal compression of the air cushion, with the adaptive Runge–Kutta (RK) computation method proposed for the computations. The results of the study indicated that the relative errors of the main parameters between the simulation and the experimental submersible were below 5%. It was observed that increasing the sea-valve area and reducing the blowing duration could lower the cost of high-pressure air. Conversely, increasing the air pipe length resulted in a prolonged blowing duration and a decreased drainage rate of the ballast tank. The findings of this research suggest that the proposed model is a promising strategy of the accurate expected behavior prediction for the HP air blowing system of submersibles or submarines. Conclusions drawn from the experiments are appropriate for assessments of engineering design, providing valuable technical support for further research and development in this field.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".