Prediction and multi‐objective optimization of tidal current turbines considering cavitation based on GA‐ANN methods-Reference-Cited by-同舟云学术

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Prediction and multi‐objective optimization of tidal current turbines considering cavitation based on GA‐ANN methods

Published:2019-07-16 Issue:5 Volume:7 Page:1896-1912
ISSN:2050-0505
Container-title:Energy Science & Engineering
language:en
Short-container-title:Energy Sci Eng

Author:

Sun Zhaocheng¹^ORCID,Li Zengliang¹,Fan Menghao¹,Wang Meng¹,Zhang Le¹

Affiliation:

1. College of Mechanical and Electronic Engineering China University of Petroleum (East China) Qingdao China

Funder

Ministry of Industry and Information Technology High-tech Ship Research of China

Publisher

Wiley

Subject

General Energy,Safety, Risk, Reliability and Quality

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ese3.399

Reference45 articles.

1. Developments in large marine current turbine technologies – A review

2. Hydrokinetic energy conversion systems: A technology status review

3. Multi objective optimization of horizontal axis tidal current turbines, using Meta heuristics algorithms

4. Turbine design and field development concepts for tidal, ocean, and river applications

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1. A Scoping Review on Simulation-Based Design Optimization in Marine Engineering: Trends, Best Practices, and Gaps;Archives of Computational Methods in Engineering;2024-05-15

2. Comprehensive Parametric Study of Blockage Effect on the Performance of Horizontal Axis Hydrokinetic Turbines;Energies;2022-04-01

3. Computational study on multi-objective optimization of the diffuser augmented horizontal axis tidal turbine;Journal of Marine Science and Technology;2021-04-04

4. Hydrodynamic optimization of high-performance blade sections for stall regulated hydrokinetic turbines using Differential Evolution Algorithm;Ocean Engineering;2021-01

5. Optimization of Marine Renewable Energy Systems;Reference Module in Earth Systems and Environmental Sciences;2021

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