Inventory and assessment of tidal energy resources near northern communities part 2: numerical tide modelling
Bibliographic record
Abstract
Polar Knowledge Canada (POLAR) desired to investigate and assess the potentially exploitable tidal energy resources located near communities in northern Canada and make this information available to stakeholders through a software application/tool with functionality for geospatial analysis and interactive mapping. This second work package constituted the development of a numerical model to simulate spatial and temporal variation of tidal flows and hydrokinetic energy near the four northern communities selected in the first work package; Cape Dorset, Igloolik, Iqaluit and Kuujjuaq. The numerical model was developed using the methodology proposed in the International Electro Commission standard: Tidal Energy Resource Assessment and Characterization (62600-201:2015). This provides a standardized methodology that ensures consistency and accuracy in the estimation, measurement, characterization and analysis of the theoretical tidal currents. From the standard, the methodology from Stage 1 was used to develop and validate the model, and to calculate the annual energy density, velocity probability distribution and annual energy production. Other characteristics that impacts the tidal resource assessment, such as ice cover, ice debris, vertical velocity variation, sediments and mounting arrangement are also discussed in this report. Hotspots were identified near all four northern communities. Cape Dorset has a few hotspots with average speeds of 0.75 to 1.5 m/s, which tends to be on the low end for potential hydrokinetic energy sites. Most tidal energy turbines have a cut-in speed of 1.0 to 1.5 m/s, and thus would not generate energy for significant periods of time at Cape Dorset. The Labrador Narrows, 50 km from the Igloolik community, has average speed of 2.0 m/s and can attain maximum speed of 5 m/s (50 MWh/m²). Depth varies from 50 to 100 m with limited shallow spots, which can be quite challenging for turbine deployment, operation and maintenance. As for Iqaluit, there is limited amount of hydrokinetic energy near the community. However, there is a lot of hydrokinetic energy between the Frobisher Bay Islands, 50 to 100 km away from the community. The average speed is around 1.5 m/s and the maximum speed is around 3.5 m/s (20 MWh/m²) per year. Kuujjuaq is the most promising site with significant hydrokinetic energy (10-60 MWh/m² per year) along the Koksoak River: from the Kuujjuaq community to the mouth of the river (50 km away from the community). The average speed is around 1.75 m/s with maximum speed of 6.0 m/s. With depths varying from 5 to 30 m and ice covered 8 months of the year from mid-November to mid-July, a bottom mounted turbine would be feasible in terms of deployment, operation and maintenance.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".