MétaCan
Menu
Back to cohort
Record W4391838525 · doi:10.5194/wes-2023-171-rc2

Comment on wes-2023-171

2024· peer-review· en· W4391838525 on OpenAlexafffund
Sebastiano Stipa, Mehtab Ahmed Khan, Dries Allaerts, Joshua Brinkerhoff

Bibliographic record

Venuenot available
Typepeer-review
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsUniversity of British Columbia, Okanagan Campus
FundersNatural Sciences and Engineering Research Council of CanadaAlliance de recherche numérique du Canada
KeywordsEnvironmental scienceComputer science

Abstract

fetched live from OpenAlex

Abstract. The interaction of large wind farm clusters with the thermally-stratified atmosphere has emerged as an important physical process that impacts the productivity of wind farms. Under stable conditions, this interaction triggers the creation of atmospheric gravity waves (AGWs) due to the vertical displacement of the boundary layer by the wind farm. AGWs induce horizontal pressure gradients within the boundary layer that alter the wind speed distribution within the farm, influencing both wind farm power generation and wake development. Additional factors, such as the growth of an internal boundary layer originating from the wind farm entrance and increased turbulence due to the wind turbines, further contribute to wake evolution. Recent studies have highlighted the considerable computational cost associated with simulating gravity wave effects within large eddy simulations (LES), as a significant portion of the free atmosphere must be resolved due to the large vertical spatial scales involved. Additionally, specialized boundary conditions are required to prevent wave reflections from contaminating the solution. In this study, we introduce a novel methodology to model the effects of AGWs without extending the LES computational domain into the free atmosphere. The proposed approach addresses the wave reflection problem inherently, eliminating the need for these specialized boundary conditions. We utilize the recently developed multi-scale coupled (MSC) model of Stipa et al. (2023b) to estimate the vertical boundary layer displacement triggered by the wind farm, and apply the deformation to the domain of an LES that extends only to the inversion layer. We validate our AGW modeling technique for two distinct free atmosphere stability conditions, comparing it to the traditional approach in which AGWs are fully resolved using a domain extending several kilometers into the free atmosphere. The proposed approach accurately captures AGW effects on the row-averaged thrust and power distribution of wind farms while demanding less than 15 % of the computational resources compared to traditional methods. This can be further reduced in cases of conventionally neutral boundary layers, since there is no longer a need for solving the potential temperature equation. The developed approach is used to compare global blockage and pressure disturbances obtained from the simulated cases against a solution characterized by zero boundary layer displacement, which represents a limiting case of very strong free atmosphere stratification. Finally, we discuss the implications of making such "rigid-lid" approximation, instead of considering the full gravity wave solution, when predicting wind farm power.

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)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.649
Threshold uncertainty score0.926

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0120.007
Insufficient payload (model declined to judge)0.3510.240

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 head0.032
GPT teacher head0.292
Teacher spread0.260 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2024
Admission routes2
Has abstractyes

Explore more

Same topicWind Energy Research and DevelopmentFrench-language works237,207