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Assessment and Nonlinear Modeling of Wave, Tidal and Wind Energy Converters and Turbines



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Autore: Karimirad Madjid Visualizza persona
Titolo: Assessment and Nonlinear Modeling of Wave, Tidal and Wind Energy Converters and Turbines Visualizza cluster
Pubblicazione: Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020
Descrizione fisica: 1 electronic resource (290 p.)
Soggetto topico: History of engineering & technology
Soggetto geografico: Daguragu / Kalkaringi / Wave Hill (Central NT SE52-08)
Soggetto non controllato: off-shore wind farms (OSWFs)
wake model
wind turbine (WT)
Extreme Learning Machine (ELM)
wind power (WP)
large-eddy simulation (LES)
point-absorbing
wave energy converter (WEC)
maximum power point tracking (MPPT)
flower pollination algorithm (FPA)
power take-off (PTO)
hill-climbing method
Kirsten–Boeing
vertical axis turbine
optimization
neural nets
Tensorflow
ANSYS CFX
metamodeling
FOWT
multi-segmented mooring line
inclined columns
semi-submersible
AFWT
floating offshore wind turbine (FOWT)
pitch-to-stall
blade back twist
tower fore–aft moments
negative damping
blade flapwise moment
tower axial fatigue life
wave energy
oscillating water column
tank testing
valves
air compressibility
air turbine
wave-to-wire model
energy harnessing
energy converter
flow-induced oscillations
vortex-induced vibration
flow–structure interaction
hydrodynamics
vortex shedding
cylinder wake
tidal energy
site assessment
wave-current interaction
turbulence
integral length scales
wave-turbulence decomposition
OWC
wave power converting system
parametric study
caisson breakwater application
floating offshore wind turbines
frequency domain model
semisubmersible platform
10 MW wind turbines
large floating platform
platform optimization
wind energy
floating offshore wind turbine
dynamic analysis
fatigue life assessment
flexible power cables
Persona (resp. second.): ColluMaurizio
KarimiradMadjid
Sommario/riassunto: The Special Issue “Assessment and Nonlinear Modeling of Wave, Tidal, and Wind Energy Converters and Turbines” contributes original research to stimulate the continuing progress of the offshore renewable energy (ORE) field, with a focus on state-of-the-art numerical approaches developed for the design and analysis of ORE devices. Particularly, this collection provides new methodologies, analytical/numerical tools, and theoretical methods that deal with engineering problems in the ORE field of wave, wind, and current structures. This Special Issue covers a wide range of multidisciplinary aspects, such as the 1) study of generalized interaction wake model systems with elm variation for offshore wind farms; 2) a flower pollination method based on global maximum power point tracking strategy for point-absorbing type wave energy converters; 3) performance optimization of a Kirsten–Boeing turbine using a metamodel based on neural networks coupled with CFD; 4) proposal of a novel semi-submersible floating wind turbine platform composed of inclined columns and multi-segmented mooring lines; 5) reduction of tower fatigue through blade back twist and active pitch-to-stall control strategy for a semi-submersible floating offshore wind turbine; 6) assessment of primary energy conversion of a closed-circuit OWC wave energy converter; 7) development and validation of a wave-to-wire model for two types of OWC wave energy converters; 8) assessment of a hydrokinetic energy converter based on vortex-induced angular oscillations of a cylinder; 9) application of wave-turbulence decomposition methods on a tidal energy site assessment; 10) parametric study for an oscillating water column wave energy conversion system installed on a breakwater; 11) optimal dimensions of a semisubmersible floating platform for a 10 MW wind turbine; 12) fatigue life assessment for power cables floating in offshore wind turbines.
Titolo autorizzato: Assessment and Nonlinear Modeling of Wave, Tidal and Wind Energy Converters and Turbines  Visualizza cluster
Formato: Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione: Inglese
Record Nr.: 9910557555603321
Lo trovi qui: Univ. Federico II
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