FSI-based structural optimization of thin bladed composite propellers

Lamination of thin bladed composite marine propellers necessitates dropping layers following thickness variation to produce lighter propeller with good elastic damping. Composite blade is subjected to multiaxial loads resulted from nonuniformly distributed hydrodynamic pressure that cause the blade...

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Bibliographic Details
Main Authors: Mohamed Hussain, Yehia Abdel-Nasser, Adel Banawan, Yasser M. Ahmed
Format: Article
Language:English
Published: Elsevier 2020-10-01
Series:Alexandria Engineering Journal
Subjects:
CFD
Online Access:http://www.sciencedirect.com/science/article/pii/S1110016820302970
Description
Summary:Lamination of thin bladed composite marine propellers necessitates dropping layers following thickness variation to produce lighter propeller with good elastic damping. Composite blade is subjected to multiaxial loads resulted from nonuniformly distributed hydrodynamic pressure that cause the blade to undergo combined structural response of bent and twist therefore, strength evaluation of such tapered laminate is a highly nontrivial process that include significant fluid–structure interaction (FSI). This research aims to investigate the strength of composite propeller using unconstrained stacking-sequence optimization based on fully coupled CFD-FEA analysis. Different hub idealization techniques were studied to determine the most accurate treatment in predicting stress and deformation of the blade. Composite model configured for propeller VP1304 after modification on blade thickness and, initial analysis on balanced-stacking of [0, 90, 45,−45] were set as a benchmark for optimization process targeting to minimize failure index; calculated according to Puck failure theory considering fiber and matrix failure as well as delamination. Delamination is evidenced to be utmost critical mode of failure whereas, the optimization resulted in an optimum laminate with unbalanced nonsystematic stacking that succeeded to; reduce interlaminar stresses, avoid failure and, reduce maximum value of IRF (Inverse Reserve Factor) by 50% compared to the predefined balanced-stacking benchmark.
ISSN:1110-0168