Reliability of arctic pipelines taking into account the global change of temperatures: wind loads case study

RELIABILITY OF ARCTIC PIPELINES TAKING INTO ACCOUNT THE GLOBAL CHANGE OF TEMPERATURES: WIND LOADS CASE STUDY

Opeyemi D.A.

Institute for Risk and Uncertainty, University of Liverpool, Liverpool, UK, David.Opeyemi[AT]liverpool.ac.uk

Patelli E.

Institute for Risk and Uncertainty, University of Liverpool, Liverpool, UK, Edoardo.patelli[AT]liverpool.ac.uk

Beer M.

Institute for Risk and Uncertainty, University of Liverpool, Liverpool, UK, M.beer[AT]liverpoolac.uk

Timashev S.A.

Ural Federal University, Ekaterinburg

Science and Engineering Center «Reliability and Safety of Large Systems and Machines» of the Ural Branch of the Russian Academy of Sciences, Ekaterinburg, timashevs[AT]gmail.com This paper describes reliability analysis of arctic pipelines taking into account the global change of temperatures using wind loads as case study. Wind speed is usually caused by air moving from high pressure to low pressure, due to changes in temperature; and this affects weather forecasting, arctic pipelines, aircraft and maritime operations, construction projects, etc. [1]. Surface wind speeds are strongly influenced by weather conditions and surrounding topography. Several studies on global warming impacts indicate that rising temperatures might decrease surface wind speed [2]. It is mandatory to consider and analyse the static and dynamic effects of high winds on above ground pipelines, because high winds can be very dangerous and destructive. Wind loads are randomly applied dynamic loads; the velocity of wind varies at various distances from ground, and increases with structural heights. Wind speed is most uncertain (i.e. unpredictable) when it is closer to the ground, this makes accurate wind load calculations difficult.

Some of the pipelines widely used in engineering structures for the transportation of fluids (such as oil and gas) from one place to another are placed above ground. These pipelines are required to resist a combination of loads such as dead load (weight of the pipe with insulation and fluid being pumped), operating pressure, wind load, and kinematic influence in the form of uneven vertical displacement of adjacent/closest vertical supports of the pipeline due to the frost upheaval/melting of the permafrost soil in case of an above ground arctic pipelines [3].

Therefore, for reliability assessment and construction of the permissible region in the load space for above ground arctic oil pipeline with surface corrosion type defects, subjected to a combination (simultaneous action) of loads. The dead load of the pipeline structure and the transported fluid operating pressure (OP) could be considered as deterministic values. The influence of the extra stresses due to development of corrosion defects and kinematic vertical settlement of pipe supports considered as random variables. While the wind loads which depends on climate change is considered as very uncertain with imprecise values.

Extreme value distributions are widely used in reliability analysis to model a variety of phenomena such as extreme winds, extremes in a changing climate, failures under stress, temperature extremes, flood data, etc. [4, 5], this is employed to analyze wind speed events of a 25 year meteorological data which reflects climate change in an arctic region. Set models of wind loads were created, by analyzing the distribution of the maxima annual values of the wind speed. The maxima measured wind speeds over a given period of 25 years were taken (Fig. 1), i.e. 25 data points were taken over 25 years. Comparison is made on probability plots (Normal, Lognormal, Exponential, Weibull, Rayleigh and Extreme value distributions). Likewise, comparison on Cumulative Density Functions (CDF) and other error measures for goodness of fit; this is to allow assessment of the degree to which a set of data fits a particular distribution. A lower and upper probability for the wind speed is obtained by constructing a p-box to characterize uncertainty in wind parameter (Fig. 2); this is to cater for incertitude and variability. Hence, the wind pressure is calculated using Equation 1, and the design wind load from Equation 2. In this contribution, the turbulence intensity, gust-effect factor, and integral length scale of turbulence are modeled [6] using Equations 3, 4 and 5.

Figure 1: Maximum measured wind speed over a period of 25 years

-Lower bound ■Upper bound

Wind speed, V (m/s)

Figure 2: Probability box for wind speed

qz = 0.613 KzKztKdV I

F = qzGC fAf

L ' = U()f Ru (^dT = О- S ' (0)

u v 'J a u' 4a u' u v 1

where qz = velocity pressure evaluated at height z; G = gust-effect factor; Cf = force coefficients; Af = projected area normal to the wind; Kz = velocity pressure exposure coefficient; Kzt = topographic factor; Kd = wind directional factor; V = wind speed (m/s); I = importance factor. U {t) = the temporal trend of wind speed; t1 3600 s; Ru(t) = the autocorrelation function of ii(t), and Su, = its Fourier transform; E[ ] = the expected value over the time interval T; &u,T represents the standard deviation of the fluctuating wind speed over the time interval T.

References

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