The Experts below are selected from a list of 57 Experts worldwide ranked by ideXlab platform
J M Hallen - One of the best experts on this subject based on the ideXlab platform.
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probability distribution of Pitting corrosion Depth and rate in underground pipelines a monte carlo study
Corrosion Science, 2009Co-Authors: F Caleyo, J C Velazquez, A Valor, J M HallenAbstract:The probability distributions of external-corrosion pit Depth and pit growth rate were investigated in underground pipelines using Monte Carlo simulations. The study combines a predictive pit growth model developed by the authors with the observed distributions of the model variables in a range of soils. Depending on the pipeline age, any of the three maximal extreme value distributions, i.e. Weibull, Frechet or Gumbel, can arise as the best fit to the Pitting Depth and rate data. The Frechet distribution best fits the corrosion data for long exposure periods. This can be explained by considering the long-term stabilization of the diffusion-controlled pit growth. The findings of the study provide reliability analysts with accurate information regarding the stochastic characteristics of the Pitting damage in underground pipelines.
Jin Xu - One of the best experts on this subject based on the ideXlab platform.
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Effect of alternating current frequency on corrosion behavior of X80 pipeline steel in soil extract solution of Dagang
International Journal of Pressure Vessels and Piping, 2020Co-Authors: Jin Xu, Changkun YuAbstract:Abstract Effects of AC frequencies on corrosion behavior of X80 pipeline steel was investigated in soil-extract solution of Dagang using polarization curves, electrochemical impedance spectroscopy, weight-loss test and surface analysis techniques. Results show that the corrosion potential of X80 pipeline steel shift in the negative direction under the effect of AC. The maximum Pitting Depth and corrosion rate of the steel specimen decrease with the AC frequency increasing, and there is a function relationship, y = 45.63x−0.5, between the maximum Pitting Depth and the AC frequency. The alternating current just like an oxidant changes the component proportion of corrosion products rather than the types due to AC rectification.
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effects of d phenylalanine as a biocide enhancer of thps against the microbiologically influenced corrosion of c1018 carbon steel
Journal of Materials Science & Technology, 2019Co-Authors: Dongqing Yang, Jin Xu, Tingyue GuAbstract:Abstract Microbiologically influenced corrosion (MIC) is caused by biofilms such as those of sulfate reducing bacteria (SRB). To mitigate MIC, biocide treatment is often needed. Tetrakis (hydroxymethyl) phosphonium sulfate (THPS) is an environmentally friendly biocide that is often used in the oil and gas industry. However, its prolonged use leads to biocide resistance, leading to dosage escalation. A biocide enhancer can be used to slow down the trend. In recent years, d -amino acids have been investigated as an enhancer for THPS and other biocides. Published works used anaerobic vials and flow devices, which could not reveal the real-time changes of the biocide treatment on corrosion. In this work, it was proven that the biocide enhancement effects of d -Phenylalanine ( d -Phe) on THPS against the Desulfovibrio vulgaris biofilm on C1018 carbon steels could be assessed in real time using linear polarization resistance and electrochemical impedance spectroscopy to collaborate sessile cell count, weight loss and Pitting Depth data. The results showed that 500 ppm (w/w) d -Phe effectively enhanced 80 ppm THPS against MIC by the D. vulgaris (a corrosive SRB) biofilm. The sessile cell count and pit Depth were all reduced with the enhancement of d -Phe.
F Caleyo - One of the best experts on this subject based on the ideXlab platform.
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probability distribution of Pitting corrosion Depth and rate in underground pipelines a monte carlo study
Corrosion Science, 2009Co-Authors: F Caleyo, J C Velazquez, A Valor, J M HallenAbstract:The probability distributions of external-corrosion pit Depth and pit growth rate were investigated in underground pipelines using Monte Carlo simulations. The study combines a predictive pit growth model developed by the authors with the observed distributions of the model variables in a range of soils. Depending on the pipeline age, any of the three maximal extreme value distributions, i.e. Weibull, Frechet or Gumbel, can arise as the best fit to the Pitting Depth and rate data. The Frechet distribution best fits the corrosion data for long exposure periods. This can be explained by considering the long-term stabilization of the diffusion-controlled pit growth. The findings of the study provide reliability analysts with accurate information regarding the stochastic characteristics of the Pitting damage in underground pipelines.
A Valor - One of the best experts on this subject based on the ideXlab platform.
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probability distribution of Pitting corrosion Depth and rate in underground pipelines a monte carlo study
Corrosion Science, 2009Co-Authors: F Caleyo, J C Velazquez, A Valor, J M HallenAbstract:The probability distributions of external-corrosion pit Depth and pit growth rate were investigated in underground pipelines using Monte Carlo simulations. The study combines a predictive pit growth model developed by the authors with the observed distributions of the model variables in a range of soils. Depending on the pipeline age, any of the three maximal extreme value distributions, i.e. Weibull, Frechet or Gumbel, can arise as the best fit to the Pitting Depth and rate data. The Frechet distribution best fits the corrosion data for long exposure periods. This can be explained by considering the long-term stabilization of the diffusion-controlled pit growth. The findings of the study provide reliability analysts with accurate information regarding the stochastic characteristics of the Pitting damage in underground pipelines.
J C Velazquez - One of the best experts on this subject based on the ideXlab platform.
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probability distribution of Pitting corrosion Depth and rate in underground pipelines a monte carlo study
Corrosion Science, 2009Co-Authors: F Caleyo, J C Velazquez, A Valor, J M HallenAbstract:The probability distributions of external-corrosion pit Depth and pit growth rate were investigated in underground pipelines using Monte Carlo simulations. The study combines a predictive pit growth model developed by the authors with the observed distributions of the model variables in a range of soils. Depending on the pipeline age, any of the three maximal extreme value distributions, i.e. Weibull, Frechet or Gumbel, can arise as the best fit to the Pitting Depth and rate data. The Frechet distribution best fits the corrosion data for long exposure periods. This can be explained by considering the long-term stabilization of the diffusion-controlled pit growth. The findings of the study provide reliability analysts with accurate information regarding the stochastic characteristics of the Pitting damage in underground pipelines.