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Record W7008037152

Assessment of tidal power opportunities in Indonesian waters

2021· dissertation· en· W7008037152 on OpenAlexaboutno aff

Bibliographic record

VenueOxford University Research Archive (ORA) (University of Oxford) · 2021
Typedissertation
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsnot available
Fundersnot available
KeywordsTidal powerTurbineHydropowerBaseline (sea)Duration (music)Resource (disambiguation)Metric (unit)Tidal Model
DOInot available

Abstract

fetched live from OpenAlex

<p>As the world’s largest archipelagic country, Indonesia has enormous tidal resource potential. However, complexities that occur in this area complicate tidal resource assessment. The relative magnitude of different tidal components gives this area a variety of tidal types, from semidiurnal, as commonly found in the UK and Canada, to the diurnal type. There are not only daily and neap-spring cycle variations, but also in areas with significant diurnal components the power and thrust varies on an annual basis that follows the timing of the solstice-equinox cycle. These variations mean that an assessment in this region must be carefully planned in terms of the duration and the simulation start time. An assessment with a limited computational time should avoid the solstices and equinoxes in the simulation period.</p>\n\n<p>The interaction between the diurnal and semidiurnal components also creates an asymmetric tidal flow. As the tidal stream moves bi-directionally, the asymmetry leads to uneven power production for flow in different directions. Therefore, turbine developers should consider this phenomenon in their turbine design, as the turbine might have more thrust in one direction. This asymmetry also tends to create a low Capacity Factor (CF).</p>\n\n<p>Since the tidal stream varies daily, fortnightly and annually, it would not be practicable for a turbine developer to remove the maximum power from the flow. A capping strategy is necessary to address this issue. CF is a metric that is widely used to optimise turbine capacity. However, this thesis argues that CF might not be the only metric for decision making. The fraction of average power removed by the turbine and the thrust before and after the capping strategy is implemented are perhaps more important as decision-making tools in tidal energy exploitation. </p>\n\n<p>The assessment of tidal energy resources in Indonesia shows that this country has a great opportunity for tidal energy exploitation. Five potential sites with different characteristics in terms of socio-economic background and environmental features are selected for assessment: Lombok Strait, Larantuka Strait, Sunda Strait, Lingga Regency and Sula Regency. A total of 5 GW average electricity production from just these five selected locations could be produced.</p>\n\n<p>However, that number is based on analyses using a uniform blockage ratio. In reality, the deployment of turbines is limited by several factors, such as the use of straits for other purposes, the depth of the sites and environmental constraints. A study with more realistic turbines is essential for a proper assessment of tidal energy resources in an area. The assessments in this thesis also consider economic constraints. Based on realistic turbine deployment strategies, tidal energy exploitation in this area is economically viable. Apart from in Lingga Regency, the realistic assessments show that all other locations have a projected levelized cost of energy (LCOE) less than GBP 250/MW. </p>

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.286
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.027
GPT teacher head0.257
Teacher spread0.229 · 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 teacher head, not a consensus.

Study designObservational
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
Published2021
Admission routes1
Has abstractyes

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