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Record W4401946752 · doi:10.3390/ma17174265

Improvement of Hydrogen-Resistant Gas Turbine Engine Blades: Single-Crystal Superalloy Manufacturing Technology

2024· article· en· W4401946752 on OpenAlexaff
Alexander Balitskii, Yulia H. Kvasnytska, Ljubomyr M. Ivaskevych, Katrine H. Kvasnytska, О.A. Balitskii, Radosław Miśkiewicz, V.O. Noha, Z.V. Parkhomchuk, В.И. Вейс, Jakub Dowejko

Bibliographic record

VenueMaterials · 2024
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsUniversity of Waterloo
FundersNarodowa Agencja Wymiany AkademickiejMinistry of Education and Science of Ukraine
KeywordsMaterials scienceCrystallizationCeramicSuperalloyAlloyMetallurgyHydrogenHydrogen embrittlementUltimate tensile strengthComposite materialChemical engineeringCorrosionChemistry

Abstract

fetched live from OpenAlex

This paper presents the results of an analysis of resistance to hydrogen embrittlement and offers solutions and technologies for manufacturing castings of components for critical applications, such as blades for gas turbine engines (GTEs). The values of the technological parameters for directional crystallization (DC) are determined, allowing the production of castings with a regular dendritic structure of the crystallization front in the range of 10 to 12 mm/min and a temperature gradient at the crystallization front in the range of 165-175 °C/cm. The technological process of making GTE blades has been improved by using a scheme for obtaining disposable models of complex profile castings with the use of 3D printing for the manufacture of ceramic molds. The ceramic mold is obtained through an environmentally friendly technology using water-based binders. Short-term tensile testing of the samples in gaseous hydrogen revealed high hydrogen resistance of the CM-88 alloy produced by directed crystallization technology: the relative elongation in hydrogen at a pressure of 30 MPa increased from 2% for the commercial alloy to 8% for the experimental single-crystal alloy.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.198
Teacher spread0.189 · 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 teacher head, not a consensus.

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

Citations1
Published2024
Admission routes1
Has abstractyes

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