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Record W3052774193 · doi:10.2514/6.2020-3521

Book-keeping Investigations for BLI Aircraft

2020· article· en· W3052774193 on OpenAlexaff
Ye-Bonne Maldonado, Panagiotis Giannakakis, Benoit Rodriguez, Nicolas Tantot

Bibliographic record

VenueAIAA Propulsion and Energy 2020 Forum · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced Aircraft Design and Technologies
Canadian institutionsSafran Electronics (Canada)
Fundersnot available
KeywordsPropulsorAirframeAerodynamicsFuselageContext (archaeology)Computational fluid dynamicsAerospace engineeringEngineeringCoupling (piping)PropulsionBoundary layerComputer scienceMechanical engineeringGeology

Abstract

fetched live from OpenAlex

Due to the high coupling between airframe and engine aerodynamics, new metrics are required to analyse and design aircraft configurations with Boundary Layer Ingestion (BLI). The objective of this work is to evaluate different methods of analysing and quantifying the benefits of BLI and to investigate their applicability in a preliminary design phase. In this context, CFD calculations have been carried out for different propulsor operating points on a simple fuselage with an actuator disk on its trailing edge to model the propulsor. A comparison between different book-keeping methods was performed and focused on the physical analysis of the aerodynamic coupling between the propulsor and fuselage. The power balance approach allows an understanding of the phenomena related to boundary layer ingestion and can be used even at a preliminary design stage. Furthermore, it gives better insights and allows the quantification of the interaction effects between the propulsor and the airframe. The comparison between the isentropic expansion method and the rigorous power balance shows that the isentropic expansion approach gives reasonable results when the interaction effects are limited. The accuracy degrades if inappropriate averaging is applied.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.850
Threshold uncertainty score0.479

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 head0.015
GPT teacher head0.222
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2020
Admission routes1
Has abstractyes

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