Prediction of structural response of naval vessels based on available structural health monitoring data. (1st October 2016)

• Record Type: Journal Article
• Authors: Mondoro, Alysson; Soliman, Mohamed; Frangopol, Dan M.
• Is Part Of: Ocean engineering. Volume 125(2016)
• Page Start: 295
• Page End: 307
• Publication Date: 2016-10-01
• Subjects: Fatigue -- Aluminum vessels -- Structural health monitoring -- Ocean wave spectra -- Power spectral density; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2016.08.012
• Languages: English
• ISSNs: 0029-8018

Abstract

Abstract: Structural health monitoring (SHM) can be beneficial in reducing epistemic uncertainties associated with fatigue life prediction. For naval ships, available SHM data can be discretized into operational cells, each referring to a certain navigation speed, heading angle, and sea condition. Cell-based approaches for predicting future fatigue life can be applied if monitoring information is known for all cells. However, available SHM data may populate some, but not all, potential cells. Moreover, since SHM data is only available for a given set of operating conditions, potential changes in climate or operational profiles cannot be accounted for. Accordingly, there is a need for an approach to predict structural responses in unmonitored cells as a function of limited available monitoring data. This paper proposes a methodology to predict the responses of naval vessels in unobserved cells by incorporating data from the limited number of observed cells. The power spectral density (PSD) of the SHM data is fit using generalized functions, based on sea wave spectra, and integrated into the prediction of the PSD for unobserved cells. The proposed methodology enables both spectral and time-domain fatigue methods. The methodology is illustrated on the SHM data from a high speed aluminum catamaran. Highlights: Structural health monitoring data used to predict performance of naval vessels. Spectral-based functions fit response observed under certain operational conditions. The approach predicts structural response in unmonitored operational conditions. Low and high frequency contributions to stress response adequately represented. Fatigue life investigated based on frequency and/or time domain analysis.