Marine risers are critical components of any offshore oil and gas production operations linking subsea field developments with production and drilling equipment atop fixed or floating facilities. These systems operate under significant loads from waves, currents, internal overpressure or depressurisation, accidental impacts from vessels or ice, and in harsh environments often in contact with corrosive fluids. They therefore often suffer from various types of damage including fatigue cracking, external and internal corrosion, wear, erosion, sheath collapse and ovalisation, armour unlocking, wire rupture or birdcaging, and damage to clamps and hold-downs. Utilizing global structural vibrations resulting from ambient excitations can be efficiently used for detecting damage, since vibrational responses are affected by changes in stiffness, mass or energy dissipation of the system, which are altered by damage. However, their potential for riser monitoring has been poorly explored so far, and this project will address the gap in knowledge, with the practical aim of reducing inspection and maintenance costs while increasing reliability and integrity of risers. The main objectives of this study are to develop, explore and validate new methods that use dynamic signals, such as accelerations and strains, recorded on risers by a monitoring system to detect damage early and assess structural condition and health. The study will start with numerical simulations of damaged riser response to evaluate and find the most promising data analysis concepts. It will then move to experimental work utilising hydrodynamic laboratory equipment. Alongside vibration signal analysis, advanced, multiscale computer models (‘digital twins’) will be formulated, and calibrated using experimental data and then used to predict damage.
This highly industry-relevant research will be supervised, amongst others, by academics associated with the University of Aberdeen Lloyd’s Register Foundation Centre for Safety and Reliability Engineering and will pave the way for industry uptake of the automated damage monitoring technologies.
The successful candidate should have (or expect to achieve) a minimum of a UK Honours degree at 2.1 or above (or equivalent) in Mechanical, Civil (Structural), Aerospace Engineering, Applied Physics, Mathematical Physics, Applied Mathematics.
Knowledge: Candidates must have a strong academic background in engineering, applied physics or applied mathematics. Enthusiasm, can-do attitude and strong skills in structural mechanics, materials mechanics, dynamics and mathematical and computer modelling (or strong motivation and clear potential to learn these), as well as willingness to engage in experimental work are all must-haves. Preference will be given to applicants who can demonstrate both a clear potential for research excellence and their suitability for the research project described below.
Formal applications can be completed online: http://www.abdn.ac.uk/postgraduate/apply
. You should apply for Degree of Doctor of Philosophy in Engineering, to ensure that your application is passed to the correct person for processing.
NOTE CLEARLY THE NAME OF THE SUPERVISOR AND EXACT PROJECT TITLE YOU WISH TO BE CONSIDERED FOR ON THE APPLICATION FORM.
Informal inquiries can be made to Dr P Omenzetter ([email protected]
) with a copy of your curriculum vitae and cover letter. All general enquiries should be directed to the Postgraduate Research School ([email protected]
MSC Kenny 2010 State of the art report on flexible riser integrity, Report 2-4-5-013/SR01
MSC Kenny 2010 Guidance note on monitoring methods and integrity assurance for unbonded flexible pipes, Report 2-4-5-013/SR02
Hoell, S., Omenzetter, P. 2016 Optimal selection of autoregressive model coefficients for early damage detectability with an application to wind turbine blades, Mechanical Systems and Signal Processing 70-71:557–577
Pavlovskaia, E., Keber, M., Postnikov, A., Reddington, K., Wiercigroch, M. 2016 Multi-modes approach to modelling of vortex-induced vibration, International Journal of Nonlinear Mechanics (in press)
Hara, S., Kawahara, Y., Washio, T., von Bünau, P., Tokunaga, T.,
Yumoto, K. 2015 Separation of stationary and non-stationary sources with a generalized eigenvalue problem, Neural Networks 33:7-20
Farrar, C. et al. 2007 Nonlinear System Identification for Damage Detection, Report LA-14353, Los Alamos National Laboratory