Opportunities for Improved Warship Energy Efficiency: A Canadian Patrol Frigate’s Operational Energy Use Patterns
Bibliographic record
Abstract
This project explores a Canadian warship’s propulsion and electrical energy use patterns to define energy baselines and determine if the ship would be able to save energy without compromising mission capability. The study also aims to define the key factors preventing more efficient energy use, and suitable technical and behavioral options to reduce overall mission fuel consumption. The author postulates that improved energy efficiency can coincidentally improve mission, cost and environmental performance. \n\nThis study defines a Canadian Patrol Frigate’s energy baselines for a single warship between July 2015 and March 2016. HMCS VANCOUVER (VAN) machinery control system and bridge logbook data were combined to define the ship’s daily trends for both propulsion and electrical energy, and determine what opportunities were available to meet speed demands, using more efficient engine configurations. The ship’s new machinery control system also allowed for real-time data capture of the ship’s total electrical power demand, and monitoring of the operating trends of electrical motors that drive the ship’s array of pumps and fans. These data, coupled with equipment amperage load-checks, provided an estimate of various system’s electrical energy use, both at sea and in port. \n\nDuring approximately 70% of all operations, VAN would have had favorable engineering, operational and weather conditions to assume the most efficient engine configuration, without degrading mission effectiveness. The ship used an average of 40.6 m3 of fuel, each of the 71 days at sea, spending the majority of her time at speeds between 10 and 15 knots, and demonstrating a strong tendency to utilize a gas turbine for slower speeds where the propulsion diesel engine (PDE) would have been most efficient. The study shows that if the ship assumed the most efficient, available drive mode, she could have saved 10% of total fuel without compromising mission capability. These results suggest that over a 15-year timeframe, enough fuel could be saved to send the entire fleet to sea for two years. This analysis highlights the criticality of the ship’s PDE, due to its fuel economy when compared to the more powerful, but less efficient gas turbines. The reliability and maintenance shortfalls of the PDE may prevent achievable fuel savings unless the PDE’s performance can be improved for more frequent use, especially at lower speeds. \n\n The analysis also defines the baseline electrical energy use patterns of the VAN, which used an average of 961 kW per hour at sea, and 620 kW per hour in harbor. The ship used a quarter of its total energy to supply costly onboard electrical power, to feed the high energy demands of key systems, including chilled water, fireman, air compressors, and machinery space ventilation. \n \nThis analysis shows that significant energy savings are possible through the implementation of efficient machinery configurations, improved system maintenance, and the isolation of redundant equipment. However in some cases, these savings would require additional investment for more efficient system performance. The information in this study can be used to support additional, detailed energy assessments of individual systems to identify attractive areas for saving energy and costs, with coincidental benefits to capability, and environmental and reputational performance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 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".