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Record W4310604771 · doi:10.5194/wes-2022-100-rc1

Comment on wes-2022-100

2022· peer-review· en· W4310604771 on OpenAlexafffund
Patrick Connolly, Curran Crawford

Bibliographic record

Venuenot available
Typepeer-review
Languageen
FieldEnvironmental Science
TopicMaritime Transport Emissions and Efficiency
Canadian institutionsPacific Institute for Climate SolutionsUniversity of Victoria
FundersPacific Institute for Climate Solutions
KeywordsOffshore wind powerWind powerMarine engineeringRenewable energyResource (disambiguation)Computer scienceSubmarine pipelineFossil fuelEnvironmental scienceMarine energyGridEngineeringElectrical engineeringOceanographyGeologyWaste management

Abstract

fetched live from OpenAlex

<strong class="journal-contentHeaderColor">Abstract.</strong> As the need to transition from global reliance on fossil fuels grows, solutions for producing green alternative fuels are necessary. These fuels will be especially important for hard-to-decarbonize sectors such as shipping. Mobile offshore wind energy systems (MOWESs) have been proposed as one such solution. These systems aim to harness the far-offshore wind resource, which is abundant and yet untapped because of installation and grid-connection limitations. Two classes of MOWES have been proposed in the literature: unmoored floating offshore wind turbines (UFOWTs) and energy ships (ESs). Both systems operate as autonomous Power to X (PtX) plants, powered entirely by wind energy, and so can be used to produce synthetic green fuels such as hydrogen or ammonia, or for other energy intensive applications such as direct air carbon capture. The two technologies differ in form; UFOWTs are based on a conventional FOWT but include propellers in place of mooring lines for sea-/course-keeping, while ESs operate like a sailing ship and generate power via hydroturbines mounted on the underside of the hull. Though much research and development is necessary for these systems to be feasible, the promise of harnessing strong winds far offshore, as well as the potential to avoid siting regulatory challenges, are enticing. This paper develops models of each MOWES concept to compare their power production on a consistent basis. The performance of the technologies are examined at steady-state operating points across relative wind speeds and angles. An optimization scheme is used to determine the values of the control variables which define the operating point for each set of environmental conditions. Results for each model show good agreement with published results for both UFOWTs and ESs. Model results suggest that UFOWTs can generate more power than ESs under ideal environmental conditions, but are very sensitive to off-design operating conditions. In above-rated wind speeds, the UFOWT is able to produce as much power as a conventional, moored FOWT, whereas the ES cannot, since some power is always consumed to spin the Flettner rotors. The models developed here and their results may both be useful in future works that focus on the routing of UFOWTs, or holistically designing a mobile UFOWT. Although differences in the performance of the systems have been identified, more work is necessary to discern which is a more viable producer of green e-fuels.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient 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 score0.860
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.8640.004

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 head0.022
GPT teacher head0.273
Teacher spread0.251 · 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; both teacher heads 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
Published2022
Admission routes2
Has abstractyes

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