RELIABILITY OF DETERIORATED MARINE STRUCTURES BASED ON MEASURED DATA

2021 ◽  
Vol 158 (A4) ◽  
Author(s):  
Y Garbatov ◽  
C Guedes Soares

Reliability assessment of a corroded deck of a tanker ship subjected to non-linear general corrosion wastage is performed, accounting for an initial period without corrosion due to the presence of a corrosion protection system, and a non-linear increase in wastage up to a steady state value. The reliability model is based on the analysis of corrosion depth data. Two types of uncertainties are accounted for. The first one is related to the corrosion degradation trend as a function of time, which is identified by a sequence independent data analysis. The second uncertainty is related to the variation of the corrosion degradation around its trend, which is identified as a stochastic process, and is defined based on the time series analysis. The time series determines the autocorrelation and spectral density functions of the stochastic process applying the Fast Fourier transform. The reliability estimates with respect to a corroded deck of cargo tank of a tanker ship is analysed by a time variant formulation and the effect of inspections is also incorporated employing the Bayesian updating formulation.

1982 ◽  
Vol 19 (2) ◽  
pp. 463-468 ◽  
Author(s):  
Ed Mckenzie

A non-linear stationary stochastic process {Xt} is derived and shown to have the property that both the processes {Xt} and {log Xt} have the same correlation structure, viz. the Markov or first-order autoregressive correlation structure. The generation of such processes is discussed briefly and a characterization of the gamma distribution is obtained.


2020 ◽  
Vol 20 (14) ◽  
pp. 8709-8725 ◽  
Author(s):  
Frauke Fritsch ◽  
Hella Garny ◽  
Andreas Engel ◽  
Harald Bönisch ◽  
Roland Eichinger

Abstract. Mean age of air (AoA) is a diagnostic of transport along the stratospheric Brewer–Dobson circulation. While models consistently show negative trends, long-term time series (1975–2016) of AoA derived from observations show non-significant positive trends in mean AoA in the Northern Hemisphere. This discrepancy between observed and modelled mean AoA trends is still not resolved. There are uncertainties and assumptions required when deriving AoA from trace gas observations. At the same time, AoA from climate models is subject to uncertainties, too. In this paper, we focus on the uncertainties due to the parameter selection in the method that is used to derive mean AoA from SF6 measurements in Engel et al. (2009, 2017). To correct for the non-linear increase in SF6 concentrations, a quadratic fit to the time series at the reference location, i.e. the tropical surface, is used. For this derivation, the width of the AoA distribution (age spectrum) has to be assumed. In addition, to choose the number of years the quadratic fit is performed for, the fraction of the age spectrum to be considered has to be assumed. Even though the uncertainty range due to all different aspects has already been taken into account for the total errors in the AoA values, the systematic influence of the parameter selection on AoA trends is described for the first time in the present study. For this, we use the EMAC (ECHAM MESSy Atmospheric Chemistry) climate model as a test bed, where AoA derived from a linear tracer is available as a reference and modelled age spectra exist to diagnose the actual spatial age spectra widths. The comparison of mean AoA from the linear tracer with mean AoA from a SF6 tracer shows systematic deviations specifically in the trends due to the selection of the parameters. However, for an appropriate parameter selection, good agreement for both mean AoA and its trend can be found, with deviations of about 1 % in mean AoA and 12 % in AoA trend. In addition, a method to derive mean AoA is evaluated that applies a convolution to the reference time series. The resulting mean AoA and its trend only depend on an assumption about the ratio of moments. Also in that case, it is found that the larger the ratio of moments, the more the AoA trend gravitates towards the negative. The linear tracer and SF6 AoA are found to agree within 0.3 % in the mean and 6 % in the trend. The different methods and parameter selections were then applied to the balloon-borne SF6 and CO2 observations. We found the same systematic changes in mean AoA trend dependent on the specific selection. When applying a parameter choice that is suggested by the model results, the AoA trend is reduced from 0.15 to 0.07 years per decade. It illustrates that correctly constraining those parameters is crucial for correct mean AoA and trend estimates and still remains a challenge in the real atmosphere.


2019 ◽  
Author(s):  
Frauke Fritsch ◽  
Hella Garny ◽  
Andreas Engel ◽  
Harald Bönisch ◽  
Roland Eichinger

Abstract. Mean age of air (AoA) is a diagnostic of transport along the stratospheric Brewer-Dobson circulation. While models consistently show negative trends, long-term time series (1975–2016) of AoA derived from observations show non-significant positive trends in mean AoA in the northern hemisphere. This discrepancy between observed and modeled mean AoA trends is still not resolved. There are uncertainties and assumptions required when deriving AoA from trace gas observations. At the same time, AoA from climate models is subject to uncertainties, too. In this paper, we focus on the uncertainties due to the parameter selection in the method that is used to derive mean AoA from SF6 measurements in Engel et al. (2009) and Engel et al. (2017). To correct for the non-linear increase in SF6 concentrations, a quadratic fit to the time-series at the reference location, i.e. the tropical surface, is used. For this derivation, the width of the AoA distribution (age spectrum) has to be assumed. In addition, to choose the number of years the quadratic fit is performed for, the fraction of the age spectrum to be considered has to be assumed. Even though the uncertainty range due to all different aspects has already been taken into account for the total errors on the AoA values, the systematic influence of the parameter selection on AoA trends is described for the first time in the present study. In addition, a method to derive mean AoA is evaluated that applies a convolution to the reference time series. The resulting mean AoA and its trend only depend on an assumption about the ratio of moments. Also in that case, it is found that the larger the ratio of moments, the more the AoA trend gravitates towards the negative. The linear tracer and SF6 AoA is found to agree within 0.3 % in the mean and 6 % in the trend. The different methods and parameter selections were then applied to the balloon borne SF6 and CO2 observations. We found the same systematic changes in mean AoA trend dependent on the specific selection. When applying a parameter choice that is suggested by the model results, the AoA trend is reduced from 0.15 years/decade to 0.07 years/decade. It illustrates that correctly constraining those parameters is crucial for correct mean AoA and trend estimates and still remains a challenge in the real atmosphere. Engel, A., Möbius, T., Bönisch, H., Schmidt, U., Heinz, R., Levin, I., Atlas, E., Aoki, S., Nakazawa, T., Sugawara, S., et al.: Age of stratospheric air unchanged within uncertainties over the past 30 years, Nature Geoscience, 2, 28, 2009. Engel, A., Bönisch, H., Ullrich, M., Sitals, R., Membrive, O., Danis, F., and Crevoisier, C.: Mean age of stratospheric air derived from AirCore observations, Atmospheric Chemistry and Physics, 17, 6825–6838, https://doi.org/10.5194/acp-17-6825-2017, 2017.


1982 ◽  
Vol 19 (02) ◽  
pp. 463-468 ◽  
Author(s):  
Ed Mckenzie

A non-linear stationary stochastic process {Xt } is derived and shown to have the property that both the processes {Xt } and {log Xt } have the same correlation structure, viz. the Markov or first-order autoregressive correlation structure. The generation of such processes is discussed briefly and a characterization of the gamma distribution is obtained.


Author(s):  
Ray Huffaker ◽  
Marco Bittelli ◽  
Rodolfo Rosa

In the process of data analysis, the investigator is often facing highly-volatile and random-appearing observed data. A vast body of literature shows that the assumption of underlying stochastic processes was not necessarily representing the nature of the processes under investigation and, when other tools were used, deterministic features emerged. Non Linear Time Series Analysis (NLTS) allows researchers to test whether observed volatility conceals systematic non linear behavior, and to rigorously characterize governing dynamics. Behavioral patterns detected by non linear time series analysis, along with scientific principles and other expert information, guide the specification of mechanistic models that serve to explain real-world behavior rather than merely reproducing it. Often there is a misconception regarding the complexity of the level of mathematics needed to understand and utilize the tools of NLTS (for instance Chaos theory). However, mathematics used in NLTS is much simpler than many other subjects of science, such as mathematical topology, relativity or particle physics. For this reason, the tools of NLTS have been confined and utilized mostly in the fields of mathematics and physics. However, many natural phenomena investigated I many fields have been revealing deterministic non linear structures. In this book we aim at presenting the theory and the empirical of NLTS to a broader audience, to make this very powerful area of science available to many scientific areas. This book targets students and professionals in physics, engineering, biology, agriculture, economy and social sciences as a textbook in Nonlinear Time Series Analysis (NLTS) using the R computer language.


2020 ◽  
Author(s):  
E. Priyadarshini ◽  
G. Raj Gayathri ◽  
M. Vidhya ◽  
A. Govindarajan ◽  
Samuel Chakkravarthi

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