Dynamic modeling and simulation of the MUN Explorer autonomous underwater vehicle with a fuel cell system
Bibliographic record
Abstract
The actual power system of the MUN Explorer Autonomous Underwater Vehicles (AUVs) uses 11 Lithium-ion (Li-ion) batteries as a main energy source. The batteries are directly connected into the BLDC motor to run the MUN Explorer for the desired operating sequence. This paper presents a dynamic model of the MUN Explorer AUV including a fuel cell system to run under the same operating conditions as suggested by its manual. A PI controller was applied into the dynamic model to maintain the operating conditions such as motor speed, DC bus voltage and the load torque, due to its advantages and simplicity for tuning technique. The MUN Explorer AUV dynamic model with a fuel cell is a proposed system to increase the power capacity, it is better to use a simple controller to see the system behaviors. The simulation of the entire system dynamics model along with the proportional-integral (PI) controller is done in MATLAB / Simulink. The simulation results are included in the paper. The DC bus voltage is measured at 48 V, and the motor speed is 20 (rad/s), which is equivalent to 190 (rpm). The power profile of the fuel cell and battery are presented and plotted against time. The PI controller gives satisfactory results in terms of maintaining the same operating conditions of the MUN Explorer AUV with a fuel cell.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".