Stress-assisted corrosion mechanism of 3Ni steel by using gradient boosting decision tree machining learning method

Traditional 3Ni weathering steel cannot completely meet the requirements for offshore engineering development,resulting in the design of novel 3Ni steel with the addition of microalloy elements such as Mn or Nb for strength enhancement becoming a trend.The stress-assisted corrosion behavior of a novel designed high-strength 3Ni steel was investigated in the current study using the corrosion big data method.The information on the corrosion process was recorded using the galvanic corrosion current monitoring method.The gradi-ent boosting decision tree(GBDT)machine learning method was used to mine the corrosion mechanism,and the importance of the struc-ture factor was investigated.Field exposure tests were conducted to verify the calculated results using the GBDT method.Results indic-ated that the GBDT method can be effectively used to study the influence of structural factors on the corrosion process of 3Ni steel.Dif-ferent mechanisms for the addition of Mn and Cu to the stress-assisted corrosion of 3Ni steel suggested that Mn and Cu have no obvious effect on the corrosion rate of non-stressed 3Ni steel during the early stage of corrosion.When the corrosion reached a stable state,the in-crease in Mn element content increased the corrosion rate of 3Ni steel,while Cu reduced this rate.In the presence of stress,the increase in Mn element content and Cu addition can inhibit the corrosion process.The corrosion law of outdoor-exposed 3Ni steel is consistent with the law based on corrosion big data technology,verifying the reliability of the big data evaluation method and data prediction model selection.

Data-mining and atmospheric corrosion resistance evaluation of Sn-and Sb-additional low alloy steel based on big data technology

Machine-learning and big data are among the latest approaches in corrosion research.The biggest challenge in corrosion research is to accurately predict how materials will degrade in a given environment.Corrosion big data is the application of mathematical methods to huge amounts of data to find correlations and infer probabilities.It is possible to use corrosion big data method to distinguish the influence of the minimal changes of alloying elements and small differences in microstructure on corrosion resistance of low alloy steels.In this research,corrosion big data evaluation methods and machine learning were used to study the effect of Sb and Sn,as well as environmental factors on the corrosion behavior of low alloy steels.Results depict corrosion big data method can accurately identify the influence of various factors on corrosion resistance of low alloy and is an effective and promising way in corrosion research.

Stress corrosion cracking failure of X80 carbon steel U-b end cause d by Desulfovibrio vulgaris biocorrosion

Sulfate reducing bacteria(SRB)are widely present in oil and gas industry,causing pitting corrosion on pipeline steel.Stress corrosion cracking(SCC)often occurs in the presence of mechanical stress before pit-ting perforation failure,leading to economic losses and even catastrophic accidents.In this study,stress distribution simulation using the finite element method(FEM),corrosion analysis techniques and elec-trochemical corrosion measurements were employed to investigate the SCC mechanism of X80 pipeline steel caused by Desulfovibrio vulgaris,which is a common SRB strain used in microbiologically influenced corrosion(MIC)studies.It was found that D.vulgaris MIC caused sharp microcracks on an X80 U-bend coupon after only 2 weeks of immersion at 37℃in the deoxygenated ATCC 1249 culture medium inocu-lated with D.vulgaris.The X80 U-bend coupon’s weight loss-based uniform corrosion rate for the 12 cm^(2)surface was 60%of that for the unstressed flat square coupon(2.3 mg cm^(−2)vs.3.8 mg cm^(−2)).This was likely because the square coupon had wide MIC pits,providing a larger effective surface area for more sessile cells(4.2×10^(8)cells cm^(−2)on square coupon vs.2.4×10^(8)cells cm^(−2)on U-bend coupon)to attach and harvest more electrons.An SCC failure occurred on an X80 U-bend pre-cracked at the outer bottom after a 6-week immersion in the D.vulgaris broth.Apart from MIC damage,this could also be because D.vulgaris metabolism increased the availability hydrogen atoms on the steel surface,and promoted the diffusion of hydrogen atoms into the metal lattice,thus increasing the brittleness of the steel.

Bimetal-organic-framework derived CoTiO_3 mesoporous micro-prisms anode for superior stable power sodium ion batteries

Durability, rate capability, capacity and tap density are paramount performance metrics for promising anode materials, especially for sodium ion batteries. Herein, a carbon free mesoporous CoTiO3 micro-prism with a high tap density （1.8 gcm^-3） is newly developed by using a novel Co-Ti- bimetal organic framework （BMOF） as precursor. It is also interesting to find that the Co-Ti-BMOF derived carbon-free mesoporous CoTiO3 micro-prisms deliver a superior stable and more powerful Na^＋ storage than other similar reported titania, titanate and their carbon composites. Its achieved ca- pacity retention ratio for 2,000 cycles is up to 90.1% at 5 A g^-1.