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Record W2889451077 · doi:10.1115/gt2018-77168

On the Efficacy of Integrating Structural Struts With Lobed Mixers in Turbofan Engine Exhaust Systems

2018· article· en· W2889451077 on OpenAlexaff
Alexander Wright, Ali Mahallati, Martin J. Conlon, Julio Militzer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadiative Heat Transfer Studies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsTurbofanAtmospheric ductOffset (computer science)NozzleAutomotive engineeringThrustComputational fluid dynamicsPropulsive efficiencyEngineeringFlow (mathematics)Computer scienceAerospace engineeringMechanical engineeringMechanicsPhysics

Abstract

fetched live from OpenAlex

The efficacy of integrating the lobed mixer with the core flow deswirling struts to create a single component for reducing the exhaust system length, beyond that attainable through mixer optimization alone, has been investigated. This investigation has been conducted via CFD simulations of a medium-bypass turbofan exhaust system at engine cruise representative conditions. Comparative analysis shows that integration augmented thrust output by about 0.02% while total pressure loss was increased by 3.6%. The aim of the study, to show that this new integrated design would have either minimal impact on or improve exhaust system performance, was confirmed. Comparisons of the flow fields and characteristic quantities downstream of the mixer also showed minimal impact on flow through the nozzle. The deswirling strut was offset by 0.65 Dh axially when integrated with the mixer, therefore it can be concluded that the exhaust system ducting could be reduced in length by this same measure — saving engine weight in the process.

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 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: Empirical
Teacher disagreement score0.332
Threshold uncertainty score0.374

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)0.0000.000

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 head0.214
Teacher spread0.204 · 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.

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

Citations0
Published2018
Admission routes1
Has abstractyes

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