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Record W4413216832 · doi:10.1115/gt2025-153405

Development of a Low-Density Superalloy Optimized for Components in Large Gas Turbines

2025· article· en· W4413216832 on OpenAlexaff
T. Depka, Birgit Grüger, Oliver Lüsebrink, Christian Kontermann, Yan Wang, Matthias Oechsner

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHigh Temperature Alloys and Creep
Canadian institutionsSiemens (Canada)
FundersTechnische Universität DarmstadtAustralian Government
KeywordsCastabilityMaterials scienceSuperalloyCreepAlloyInvestment castingStructural materialAerospaceSpecific strengthMetallurgyCastingComponent (thermodynamics)Gas turbinesMechanical engineeringComposite materialEngineeringThermodynamics

Abstract

fetched live from OpenAlex

Abstract In many aspects, the development of cast Ni-based superalloys is driven by demands from aerospace industry. Considering specific requirements for aero engine parts, alloy design often focuses on maximizing the creep strength at high temperatures up to 1100°C. Advances were mostly achieved by alloying concepts that contain higher concentrations of heavier elements, e.g. W, Ta, Re, Ru. In modern stationary turbomachinery, weight of parts is a key factor for mechanical loading of components and interfaces. For these components, the specific, which means density-compensated creep strength at elevated temperatures, i.e. between 700 and 850°C, is considered more important than high temperature creep strength alone and alloy density plays a major role in structural design to reduce stresses related to the weight of the component. In this case, adapted alloying concepts rather focus on reducing the alloy density while sustaining a sufficient level of mechanical strength and creep resistance. Thus, within this paper an alloy development will be introduced, which focuses on a significant reduction of density while keeping mechanical properties on a sufficient level for components operating in the above-mentioned temperature range. Targeting a density below 8 g/cm3, candidate alloy compositions have been selected using a CALPHAD-assisted selection process. As a major achievement, one selected alloy candidate revealed good castability and has been successfully cast into a prototype component geometry as used in modern stationary gas turbines using an industrial investment casting process. The parts showed no major defects or difference in casting quality in comparison to established alloys. A heat treatment cycle was defined to avoid incipient melting and secure sufficient gamma prime solutioning. In metallographic analysis, microstructure comparable to established Ni-base alloys was observed. The successfully cast alloy was then selected for testing of mechanical properties. First results suggest a performance potential competitive to industrially established Ni-based superalloys for the targeted application regime. Room temperature density was determined to be close to 7.9 g/cm3. In the targeted temperature range, the specific strength of the alloy lies on a competitive level with established Ni-based alloys for stationary gas turbine components. First creep tests reveal high creep elongations and thus indicate that long-term embrittlement is of minor relevance. Based on the promising results of the present study, application of the alloy for components in large gas turbines becomes considerable and further mechanical properties will be investigated. The overall objective is to extend the portfolio of Ni-base superalloys for gas turbine applications with an innovative new low-density 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 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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.217
Teacher spread0.207 · 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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