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Record W4407412473 · doi:10.2514/6.2025-2461

Parametric Study on Accelerative Starting of Busemann Air Intakes

2025· article· en· W4407412473 on OpenAlexaff
Swadesh Suman, Evgeny Timofeev

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Fluid Dynamics Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsParametric statisticsComputer scienceEnvironmental scienceStatisticsMathematics

Abstract

fetched live from OpenAlex

This paper presents a numerical study on the accelerative starting of Busemann air intakes based on the axisymmetrical Euler (inviscid, non-heat-conducting) equations with an accelerative source term. The flow of an ideal gas with constant specific heats is considered. An in-house adaptive unstructured TVD finite volume flow solver is used to conduct intake starting experiments. A systematic parametric study covering the entire dual solution domain between the Kantrowitz and isentropic limits is undertaken. Non-dimensional critical accelerations (the minimum accelerations required to start an intake) are obtained for a wide range of design Mach numbers and indexes of startability (IoS). It is found that the critical acceleration exhibits a power-law growth with decreasing IoS. For a given IoS, the critical acceleration changes non-monotonically, achieving a maximum at a certain Mach number. Curve fits for the numerical data are provided. An order of magnitude estimate of critical acceleration is obtained by comparing the acceleration time and the characteristic time required for a disturbance to propagate through the intake. Within its area of validity, the estimate is in good qualitative correlation with the numerical results and allows to explain how the critical acceleration is influenced by design Mach number, IoS, and intake length.

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 score0.071
Threshold uncertainty score0.364

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.016
GPT teacher head0.293
Teacher spread0.277 · 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
Published2025
Admission routes1
Has abstractyes

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