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Record W4400379456 · doi:10.2474/trol.19.287

Impact of Material on the Sealing Behaviour of Radial Segmented Seals

2024· article· en· W4400379456 on OpenAlexaff
Mohamed Andasmas, Henri Gajan, Lassad Amami, Erwan Fourt, Mihaï Arghir, Ibrahim Diallo, Karl Delbé

Bibliographic record

VenueTribology online · 2024
Typearticle
Languageen
FieldMaterials Science
TopicSilicone and Siloxane Chemistry
Canadian institutionsSafran Electronics (Canada)
Fundersnot available
KeywordsMaterials scienceComposite materialForensic engineeringMechanical engineeringMechanicsEngineering

Abstract

fetched live from OpenAlex

Segmented radial seals are modern technologies used in aeronautic engines and space turbopumps. They consist of several circumferential segments assembled as an annular ring freely mounted on the rotor. A circumferential (garter) spring tightens the segments around the shaft. The segments can move radially relative to each other, sliding against a casing and following the dynamic displacements of the rotor. Two similar segmented seals made of different materials were tested. The results showed different leakage flow rates and different correlations between leakage and wear rate. For example, the leakage rate decreases with wear for one seal and increases for the other. For explaining these results, wear was correlated with the microstructure of the materials of the two seals. Profilometric analyses were made for the two seals, highlighting that wear was not localised in the same areas. Raman spectra maps were taken on the pads of the segment in contact with the rotor and microstructural changes caused by friction were enlightened. The evolution of residual stresses was measured for new and worn segments. These findings strengthen the results obtained from profilometric wear analyses. They also allow a more precise understanding of tribological degradation scenarios depending on the operating conditions and the properties of the material.

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 categoriesInsufficient 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 score0.005
Threshold uncertainty score0.999

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 head0.028
GPT teacher head0.331
Teacher spread0.304 · 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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