Hardware-in-the-Loop Testing of Modern On-Board Power Systems Using Digital Twins
Bibliographic record
Abstract
Simulation has always played an important role in the development, integration and deployment of aircraft, land vehicles and naval ships. Ever-increasing system design complexity also increased the necessity for more stringent testing and integration capabilities of these new topologies. Real-time simulators can be very useful tools to test, validate and integrate these complex devices. Maintenance and subsystem upgrades, common issues in such complex systems, cannot be easily done on the real systems, especially on larger systems like those in navy ships. This is when a real-time digital replica with Hardware-In-the-Loop capability is very useful. This type of system is also known as a ‘Digital Twin’. This approach is compatible with model-based design; a design philosophy that is based entirely on simulation models, from the specifications to release and field commissioning. In this paper, we describe the Digital Twin approach and explain it in the context of navy ships. Such systems usually integrate many subsystems, such as traction systems, power generation and auxiliary systems, all connected through various communication links. The test and integration requirements for such vehicle or land systems affect several levels of the control hierarchy; from low-level power electronic converters used for propulsion and auxiliary systems to high-level supervisory controls. In this paper, we will describe a HIL test made on a simplified zonal power system of a navy ship.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".