Frequency and water immersion aging effect in fatigue performance of fiber reinforced composites / 海水老化與外力頻率於玻璃纖維複合材料疲勞特性之探討

碩士 / 國立臺灣大學 / 工程科學及海洋工程學研究所 / 106 / With the development of marine energy technology, Taiwan, with the Kuroshio resources, has also started in the related technology research and development. However, Taiwan''s marine energy technology has just started and not fully developed. In particular, there are no precedent of operating floating turbine generators in deepwater environments. Issues related to flow fields and structures remain uncertain. Therefore, it is necessary to establish appropriate simulation and experiment, as well as to predict the status and fatigue life of the floating Kuroshio turbine generators.
In this research, fatigue characteristics of composite blades of the Kuroshio turbine generator designed by the Taiwan University team is discussed experimentally. First, we used Seemann Composite Resin Infused Molding Process(SCRIMP) to fabricated the designed composite coupon of the turbine blade, and simulated the aging conditions of the blades of the Kuroshio turbine under seawater operation for 20 years through hot water bath. Three-point bending quasi-static test, carried out by MTS 810, to obtain the ultimate stress that the composite under this laminate can withstand,then Fatigue tests were performed according to the percentage of ultimate stress. In the fatigue test, aging, external force frequency and temperature effects on the fatigue life of the composite material are discussed. According to the results of this study, the damage form of the test composite coupon after aging will be changed. The increase of the frequency will cause the temperature rise, then resulting in a significant decrease in the fatigue life. However, after effective cooling, excluding the temperature effect caused by the high frequency on the material, the fatigue life of the composite tends to increase with the increase of the external force frequency. This paper also compares S-N curve with stiffness loss model, both of them can provide good predictions.

Identiferoai:union.ndltd.org:TW/106NTU05345010
Date January 2018
CreatorsPai-Yu Liu, 劉百育
ContributorsHsin-Haou Huang, 黃心豪
Source SetsNational Digital Library of Theses and Dissertations in Taiwan
Languagezh-TW
Detected LanguageEnglish
Type學位論文 ; thesis
Format65

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