Structural reliability assessment of complex offshore structures based on non-intrusive stochastic methods
| dc.contributor.advisor | Mehmanparast, Ali | |
| dc.contributor.advisor | Kolios, Athanasios | |
| dc.contributor.author | Shittu, Abdulhakim Adeoye | |
| dc.date.accessioned | 2024-10-10T10:13:13Z | |
| dc.date.available | 2024-10-10T10:13:13Z | |
| dc.date.freetoread | 2024-10-10 | |
| dc.date.issued | 2020-11 | |
| dc.description | Kolios, Athanasios - Associate Supervisor | |
| dc.description.abstract | Offshore Wind Turbines (OWTs) are deployed in harsh environments often characterised by stochastic loads and resistance properties. It becomes necessary to propose an accurate and efficient approach for the assessment of uncertainties in material properties and operating environments. Structural Reliability Assessment (SRA) as a form of uncertainty analysis is a useful tool in the design of structures because it can directly quantify how uncertainty about input parameters can affect structural performance. First, this thesis developed a novel non-intrusive SRA method for an OWT jacket structure which maps the response of the structure through a finite number of simulations to develop a response surface and then employ First Order Reliability Methods (FORM) to evaluate the reliability index. This method was validated against a commercial FEA package (DesignXplorer© from ANSYS) which employs direct simulations to predict the probability of failure. The method developed was used in performing stochastic sensitivity analysis of the variables imposed on the OWT support structure. The results from this study, reveals that the uncertainties in the design wind speed is a design driving factor and the hydrodynamic load effects are secondary to this, for the ultimate (ULS), and fatigue limit states (FLS), among others. Second, the SRA of the same structure subjected to pitting corrosion-fatigue was assessed using a damage tolerance modelling approach. The non-intrusive formulation in this study used an Artificial Neural Network (ANN) response surface modelling technique instead of the Multivariate (Quadratic) Polynomial Regression (MPR) method used previously apart from the FEA to represent the crack propagation regimes. The results reveal that for the inherent stochastic conditions, the structure becomes unsafe after the 18th year, before the attainment of the design life of 20 years, among others. The benefit of this approach is that it allows for high fidelity computational tools to be employed for the analysis, hence extending its applicability to various specialist engineering problems through the advanced modelling techniques. | |
| dc.description.coursename | PhD in Energy and Power | |
| dc.identifier.uri | https://dspace.lib.cranfield.ac.uk/handle/1826/23024 | |
| dc.language.iso | en | |
| dc.publisher | Cranfield University | |
| dc.publisher.department | SWEE | |
| dc.rights | © Cranfield University, 2020. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright holder. | |
| dc.subject | First order reliability method | |
| dc.subject | finite element analysis | |
| dc.subject | surrogate/ meta-modelling | |
| dc.subject | multivariate polynomial regression | |
| dc.subject | artificial neural networks | |
| dc.subject | time-variant phenomena | |
| dc.title | Structural reliability assessment of complex offshore structures based on non-intrusive stochastic methods | |
| dc.type | Thesis | |
| dc.type.qualificationlevel | Doctoral | |
| dc.type.qualificationname | PhD |