Extreme response prediction for fixed offshore structures by Monte Carlo time simulation technique
For an offshore structure, wind, wave, current, tide, ice and gravitational forces are all important sources of loading which exhibit a high degree of statistical uncertainty. The capability to predict the probability distribution of the response extreme values during the service life of the structu...
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| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Published: |
American Society of Mechanical Engineers (ASME)
2016
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/73646/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-84996569829&doi=10.1115%2fOMAE2016-54200&partnerID=40&md5=ca836c31f424cd9d5d3b4d656fbf26b6 |
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| Summary: | For an offshore structure, wind, wave, current, tide, ice and gravitational forces are all important sources of loading which exhibit a high degree of statistical uncertainty. The capability to predict the probability distribution of the response extreme values during the service life of the structure is essential for safe and economical design of these structures. Many different techniques have been introduced for evaluation of statistical properties of response. In each case, sea-states are characterised by an appropriate water surface elevation spectrum, covering a wide range of frequencies. In reality, the most versatile and reliable technique for predicting the statistical properties of the response of an offshore structure to random wave loading is the time domain simulation technique. To this end, conventional time simulation (CTS) procedure or commonly called Monte Carlo time simulation method is the best known technique for predicting the short-term and long-term statistical properties of the response of an offshore structure to random wave loading due to its capability of accounting for various nonlinearities. However, this technique requires very long simulations in order to reduce the sampling variability to acceptable levels. In this paper, the effect of sampling variability of a Monte Carlo technique is investigated. |
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