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Enregistrement W7062344753

Synergistic Effect of Different Testing Environments on Mechanical Characteristics and Failure of X70 Pipeline Steel

2023· dissertation· en· W7062344753 sur OpenAlexfundno aff

Notice bibliographique

RevueUniversity Library (University of Saskatchewan) · 2023
Typedissertation
Langueen
DomaineEngineering
ThématiqueAdvanced Power Generation Technologies
Établissements canadiensnon disponible
Organismes subventionnairesMitacsGovernment of Jiangsu Province
Mots-clésCharpy impact testHydrogen embrittlementUltimate tensile strengthHydrogenEmbrittlementMicrostructureScanning electron microscopeHigh-strength low-alloy steelTensile testing
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Recently, with the depletion of high-quality oil and gas, there has been significant increase in the presence of H2S in transported resources. In the presence of H2S (sour conditions), high strength low alloy (HSLA) steel used in the pipelines is often susceptible to corrosion, hydrogen degradation, embrittlement, and cracking. The ingress of hydrogen results in deterioration of mechanical properties such as ductility, strength, and toughness. In this work, the effect of different hydrogen environments on mechanical behavior of X70 pipeline steels having the same chemical composition but different thermomechanical controlled processing (TMCP) parameters was evaluated. A vast number of mechanical tests have been conducted in the laboratory simulating, to some extent, the environment in which these steels operate. Experimental results of tensile and Charpy tests conducted in different hydrogen environments are discussed and analyzed based on recorded microstructural features, to propose the mechanism of hydrogen embrittlement. In addition, corrosion and crack susceptibility were also evaluated in the sour media. Firstly, optical microscope, scanning electron microscope (SEM), electron backscattered diffraction (EBSD), X-ray diffraction (XRD) were used to investigate the microstructures and crystallographic texture. Afterwards, tensile tests were performed in different hydrogen environments (in air, ex-situ test, in corrosive media, in-situ test) in order to understand the effect of severity of service environments on hydrogen embrittlement (HE) susceptibility. Additionally, low temperature embrittlement effects were assessed through Charpy impact tests before and after hydrogen charging. Furthermore, fracture surface examination of tensile and Charpy impact test samples was done to explain the role of hydrogen environments on embrittlement mechanisms. Also, electrochemical hydrogen charging was used to induce blisters and cracks in X70 steel plates and subsequently, blistering and hydrogen induce cracking (HIC) susceptibility was examined. In addition, electrochemical corrosion analysis was performed on selected steels in hydrogen charging (acidic) and non-hydrogen charging (saline) media. Slow strain rate tensile test (SSRT) results indicated that synergistic action of tensile stress and hydrogen leads to the largest increase of susceptibility to HE in the in-situ charged sample. SSRT results also revealed that yield strength increased for all steel samples tested in different hydrogen environments; however, the amount of increment was the highest in the in-situ charged samples. On contrary, the ultimate tensile strength did not reveal any particular trend of changes. Low temperature Charpy impact test results indicated that ductile to brittle transition temperature (DBTT) increased after hydrogen charging in both X70 steel plates. The correlation established between microstructural features and HE susceptibility revealed that steel with the higher volume fraction ratio of Ƴ-fiber /{100}<011>, exhibited higher HE susceptibility. It was also found that low HE susceptible steel had higher fraction of low temperature transformed microstructures such as granular bainite (GB), and martensitic/austenitic islands (M/A). Furthermore, low temperature Charpy tests analysis showed that steels with the higher fraction of {100} and {113}<110> orientation exhibited lower DBTT. Fractographic analysis from the SSRT showed that the fracture type changed from ductile in the ex-situ charged steel to cleavage in the in-situ charged steel, while the SSRT in the corrosive medium exhibited mixed fracture types (ductile and brittle)/quasi cleavage. Likewise, the fracture morphology of the Charpy sample revealed that below -60 °C, the dominant mode of failure in both steel plates is ductile. However, the fracture morphology undergoes a drastic change near -100 °C, where the fracture surface largely transforms to cleavage types. Furthermore, analyses of blistering and HIC on both X70 steel plates revealed that X70-1 is more susceptible to blistering as well as to HIC in the sour environment than X70-2. Although cracking occurred predominantly at inclusions consisting of Al, Ca, Si, O, S, and Ti, blistering occurred both with and without inclusions. Detailed analysis using the hydrogen permeation test demonstrated that X70-2 has a higher hydrogen diffusivity compared to X70-1. In addition, corrosion analysis indicated that both steels have lower corrosion rate in the acidic media than in saline media due to passivation. Moreover, steel with higher fraction of {111} oriented grains, lower misorientations and deformed grains, exhibited higher corrosion resistance in both media. Overall, synergistic action of tensile stress and hydrogen during in-situ hydrogen charging seems to be the most detrimental failure environment for pipeline steel. Additionally, synergy between low temperatures and hydrogen further enhances the tendency of hydrogen embrittlement in steels.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,122
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,005
Tête enseignante GPT0,162
Écart entre enseignants0,156 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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