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Enregistrement W2769257727

Numerical and experimental modelling of an oscillating wave surge converter in partially standing wave systems

2017· dissertation· en· W2769257727 sur OpenAlexaboutno aff
Bryce Bocking

Notice bibliographique

RevueUVic’s Research and Learning Repository (University of Victoria) · 2017
Typedissertation
Langueen
DomaineEngineering
ThématiqueWave and Wind Energy Systems
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésSurgePhysicsMechanicsAcousticsElectrical engineeringEngineering
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

In the field of ocean wave energy converters (WECs), active areas of research are on a priori or in situ methods for power production estimates and on control system design. Linear potential flow theory modelling techniques often underpin these studies; however, such models rely upon small wave and body motion amplitude assumptions and therefore cannot be applied to all wave conditions. Nonlinear extensions can be applied to the fluid loads upon the structure to extend the range of wave conditions for which these models can provide accurate predictions. However, careful consideration of the thresholds of wave height and periods to which these models can be applied is still required. Experimental modelling in wave tank facilities can be used for this purpose by comparing experimental observations to numerical predictions using the experimental wave field as an input. This study establishes a recommended time domain numerical modeling approach for power production assessments of oscillating wave surge converters (OWSCs), a class of WEC designed to operate in shallow and intermediate water depths. Three candidate models were developed based on nonlinear numerical modelling techniques in literature, each with varying levels of complexity. Numerical predictions provided by each model were found to be very similar for small wave amplitudes, but divergence between the models was observed as wave height increased. Experimental data collected with a scale model OWSC for a variety of wave conditions was used to evaluate the accuracy of the candidate models. These experiments were conducted in a small-scale wave flume at the University of Victoria. A challenge with this experimental work was managing wave reflections from the boundaries of the tank, which were significant and impacted the dynamics of the scale model OWSC. To resolve this challenge, a modified reflection algorithm based upon the Mansard and Funke method was created to identify the incident and reflected wave amplitudes while the OWSC model is in the tank. Both incident and reflected wave amplitudes are then input to the candidate models to compare numerical predictions with experimental observations. The candidate models agreed reasonably well with the experimental data, and demonstrated the utility of the modified wave reflection algorithm for future experiments. However, the maximum wave height generated in the wave tank was found to be limited by the stroke length of the wavemaker. As a result, no significant divergence of the candidate model predictions from the experimental data could be observed for the limited range of wave conditions, and therefore a recommended model could not be selected based solely on the experimental/numerical model comparisons. Preliminary assessments of the annual power production (APP) for the OWSC were obtained for a potential deployment site on the west coast of Vancouver Island. Optimal power take-off (PTO) settings for the candidate models were identified using a least-squares optimization to maximize power production for a given set of wave conditions. The power production of the OWSC at full scale was then simulated for each bin of a wave histogram representing one year of sea states at the deployment site. Of the three candidate models, APP estimates were only obtained for Model 1, which has the lowest computational requirements, and Model 3, which implements the most accurate algorithm for computing the fluid loads upon the OWSC device. Model 2 was not considered as it provides neither advantages of Models 1 and 3. The APP estimates from Models 1 and 3 were 337 and 361 MWh per year. For future power production assessments, Model 3 is recommended due to its more accurate model of the fluid loads upon the OWSC. However, if the high computational requirements of Model 3 are problematic, then Model 1 can be used to obtain a slightly conservative estimate of APP with a much lower computational effort.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,005
Score d'incertitude au seuil0,009

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,001
Communication savante0,0010,000
Science ouverte0,0010,000
Intégrité de la recherche0,0010,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,045
Tête enseignante GPT0,262
Écart entre enseignants0,217 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2017
Routes d'admission1
Résumé présentoui

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