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Record W7130430614 · doi:10.5006/m2025_00723

Unraveling Moisture-Driven Performance Degradation: the Role of Heat, Water, and Insulation Design in CUI and ESCC of Carbon Steel Piping

2025· article· W7130430614 on OpenAlexaboutno aff
Mark Ti Krajewski, John Williams

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicStructural Integrity and Reliability Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPipingCorrosionMoistureCrackingStress corrosion crackingCladding (metalworking)Petrochemical

Abstract

fetched live from OpenAlex

Abstract Corrosion under insulation (CUI) and external stress corrosion cracking (ESCC) represent two of the most persistent threats to the integrity of insulated carbon steel piping. Traditionally treated as separate phenomena, recent research indicates they are part of a continuum of moisture-driven degradation mechanisms—both highly influenced by insulation system design, hygrothermal performance, and operating conditions. This presentation draws on new experimental and computational work conducted by Aspen Aerogels and the University of Michigan, building on industry attention following the Alberta Energy Regulator’s Bulletin 2021-36, which identifies ESCC as an emerging threat outside conventional stress and temperature windows. Findings demonstrate that moisture ingress, transport, and retention in insulation materials can dramatically promote both CUI (in the form of generalized or localized corrosion) and ESCC (involving stress-driven crack propagation). Particularly relevant to Middle Eastern operators, this work emphasizes how arid ambient conditions do not preclude insulation-related corrosion risks, especially when process temperatures drive internal condensation or when insulation systems fail to manage water effectively. The presentation will explore how insulation geometry, material selection, and cladding design influence hygrothermal dynamics and subsequent corrosion behavior. Practical mitigation strategies will be presented for both new installations and retrofit scenarios, with a focus on predictive modeling, material advancements, and risk-informed insulation design aimed at reducing the likelihood of moisture-induced degradation mechanisms. These recommendations are designed to support improved reliability, reduced maintenance costs, and enhanced long-term asset integrity in high-temperature, insulated systems common across oil & gas and petrochemical sectors in the Gulf region.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.211
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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