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Record W2388902598

Design and Analysis of Steam Turbine Blades using FEM

2012· article· en· W2388902598 on OpenAlexvenueno aff
A.K. Matta, R.B. Pothula, Ravi Rao

Bibliographic record

VenueMechanical Engineering Research · 2012
Typearticle
Languageen
FieldEngineering
TopicHigh Temperature Alloys and Creep
Canadian institutionsnot available
Fundersnot available
KeywordsInconelMaterials scienceTurbine bladeFinite element methodSteam turbineStress (linguistics)CreepBlade (archaeology)Gas turbinesStructural engineeringCombined cycleTurbineMechanical engineeringMetallurgyEngineeringAlloy
DOInot available

Abstract

fetched live from OpenAlex

The analysis of Stress values that are produced while the turbine is running are the key factors of study while designing the steam turbines. Hot section blades typically fail because of creep, oxidation, low cycle fatigue and High cycle fatigue. Therefore this report has primarily focused on heat transfer characteristics, centrifugal and thermal stresses in the blade. The maximum stresses obtained from different analysis by using innovative high heat resistant material N155 are found to be within the yield strength of the material. Results are obtained interms of thermal stresses, Vonmisses stress are compared with high heat resistant material INCONEL 718.Inspite of all operating conditions the blade with material INCONEL 718 is expected to perform well .The object is to improve the life and efficiency of future generation of engines.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.058
GPT teacher head0.308
Teacher spread0.251 · 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 designSimulation or modeling
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

Citations2
Published2012
Admission routes1
Has abstractyes

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