Investigation into the Influence of Flow Variation on Estimated Available Riverine Hydrokinetic Energy by Modelling a Reach of the Rouge River, Quebec, Canada
Bibliographic record
Abstract
Background: Hydrokinetic energy extraction uses the velocity of naturally moving water to turn a turbine to generate renewable electricity. The amount of hydrokinetic energy available is proportional to the cube of the velocity of the water. Because the amount of hydrokinetic energy is heavily dependant on the velocity of the flow, the flow condition of the river is expected to greatly influence the amount of hydrokinetic energy available for extraction, although this has not been explicitly tested before. To characterize the influence of flow condition on the available energy, the International Electrotechnical Commission (IEC) recommended using a 15-point velocity duration curve (VDC) based on 10 years of available data or modelled data with less than 5% calibration error (IEC 2019). So far, no studies have met these standards, likely because the IEC standards were produced in response to the inconsistencies in the published methods at that time. For example, Nordino et al. (2016) generated a VDC from 5 years of discharge data, Holanda et al. (2017) considered maximum, minimum, and mean discharge conditions, and Kasman et al. (2019) used five years of discharge data to develop their simulation. Other studies, such as Petrie et al. (2014), Kalnacs et al. (2014), Filizola et al. (2015), and Montoya Ramírez, et al. (2016) did not consider seasonal variation of flow at all in their assessments of hydrokinetic energy potential. By not considering the complexities of hydrokinetic energy availability across the river reach, it may limit the meaningfulness and practicality of these studys’ findings. Objectives: This research aims to understand the influence of flow conditions on hydrokinetic energy potential in a river by considering the 2D velocity flow field, rather than a typical single per-reach average velocity value. The goal is to explore the impact that flow condition consideration and spatially dense bathymetry and velocity measurements can have on the findings of hydrokinetic energy assessments. This will be done by utilizing data collected on two different dates (November 5, 2020 and June 4, 2021) under two different flow conditions to model a reach of the Rouge River, QC under additional flow conditions. Novelty: These types of hydrokinetic assessments are on the rise in popularity, but methods have been inconsistent with some only considering average velocity, some taking cross-sectional velocity measurements, some utilizing spatially intense data. This is the first study to consider the 2D velocity flow field in a reach under multiple flow conditions to estimate and analyze the hydrokinetic energy potential. Additionally, because hydrokinetic energy assessments are relatively new, there is a gap in understanding of best practices for defining the feasibility of energy extraction for a reach and quantification of available energy. Bibliography: References have not been included for space considerations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".