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Record W7000339165

Evaluating the accuracy of RANS wind flow modeling and its impact on capacity factor for moderately complex forested terrain

2017· other· en· W7000339165 on OpenAlexaboutno aff

Bibliographic record

VenueEspace École de technologie supérieure (École de technologie supérieure) · 2017
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
FundersConsejo Nacional de Ciencia y Tecnología
KeywordsTerrainWork (physics)Wind speedFlow (mathematics)Nowcasting
DOInot available

Abstract

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The growth of onshore wind energy into the second largest renewable energy source has depended on overcoming many technological challenges. One of the main research priorities has been minimizing the uncertainty associated with wind energy yield calculations. The success of these calculations strongly depends on accurate wind resource assessment mainly done with wind speed measurements. Notwithstanding, a lack of measurement data justifies the use of computational modeling with promising results. But significant modeling challenges remain when analyzing turbulence over forested complex sites. These challenges are considered in this work with the main objective of evaluating the uncertainty in the wind flow predictions over moderately complex forested terrain and its impact on capacity factor, using the Reynolds-Averaged Navier-Stokes (RANS) equations coupled with a modified k-ε turbulence closure in the open-source software OpenFOAM v.2.4.0. \n \nWith the effects of complex topography implicitly captured in the RANS equations, the effects of the forest are explicitly calculated with two models: a displacement height model, and a canopy model that estimates the pressure loss due to the forest through analogy with porous media. To properly simulate the atmospheric boundary layer (ABL), the specific boundary conditions that rely on the law of the wall are implemented based on the recommendations of Richards and Hoxey, and Hargreaves and Wright. To validate the canopy model, the case of a fully-developed wind flow within and above a horizontally homogeneous black spruce forest is reproduced. Furthermore, two practical limitations are considered: 1) the physical foliage parameters may not be accessible for all type of forests; therefore, a generic leaf area density (α) distribution that is in agreement with the published results is tested; and 2) the published case limits its use to cyclic boundary conditions which are not practical for real site cases. Therefore, for cases without cyclic boundary conditions, two sensitivity analyses on the friction velocity u∗ and roughness length at the inlet z0inlet are tested. Different values of either of them give no significant difference in the vicinity of the forest, but they do at higher altitudes approaching the top boundary. This highlights the importance of imposing a proper fully-developed flow at the inlet condition. \n \nFour model cases are calculated for a site located in Quebec, Canada: A) terrain only, B) displacement height, C) canopy model with a uniform forest, and D) canopy model with the real forest distribution. The results are compared in terms of speed-up factors S (normalized velocities) with two years of measurement data from EDF-EN. Overall, the canopy model provides a better agreement with the mean statistical results than the other models. And where the terrain is densely forested, the assumption of a constant forest height delivers promising results. Finally, it is shown that the uncertainty in the energy calculation in terms of capacity factor CF is a non-linear function of the uncertainty in S. In this case, the 2.76% uncertainty in speed-up factor associated with the real forest distribution model leads to an uncertainty in the energy calculation of just 5.76%.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.127
GPT teacher head0.395
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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