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Record W128065947 · doi:10.1139/tcsme-2009-0021

NUMERICAL SIMULATIONS OF INVISCID AIRFLOWS IN RAMJET INLETS

2009· article· en· W128065947 on OpenAlexvenueno aff
Mohsen Akbarzadeh, M.J. Kermani

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2009
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsnot available
Fundersnot available
KeywordsRamjetInviscid flowScramjetInletMechanicsCombustorAerospace engineeringFlux limiterSupersonic speedComputational fluid dynamicsMeteorologyPhysicsEngineeringMechanical engineeringCombustionThermodynamics

Abstract

fetched live from OpenAlex

The performances of three different ramjet inlets and an entire ramjet are numerically studied in this paper. The fluid is assumed to be inviscid. Inlet 1 is a SCRAMJET inlet and is chosen from the literature. Inlets 2 and 3 are instead designed based on the Oswatitsch principle. Inlets 2 and 3 produce a series of oblique shocks merging at the engine cowl lip followed by a terminating normal shock just downstream of the inlet throat. In ramjet, the combustion is modeled using a non-uniform volumetric heat source distributed in the combustor area. The position of the terminating normal shock in Inlets 2 and 3 is controlled via the inlet’s back pressure. Instead, in ramjet it is bounded by the amount of heat rate added in combustor and the exhaust nozzle throat area. For the numerical simulations, the Roe (1981) and MacCormack (1969) schemes are used. To prevent the spurious numerical oscillations in high resolution computations by Roe scheme the van Albada flux limiter (1982) is used, while in MacCormack scheme artificial viscosity terms are added to damp the oscillations. To double check the accuracy of the computations, the Fluent software package has also been used. Comparisons show very good agreement.

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: none
Teacher disagreement score0.845
Threshold uncertainty score0.407

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 head0.006
GPT teacher head0.196
Teacher spread0.190 · 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

Citations4
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicComputational Fluid Dynamics and AerodynamicsFrench-language works237,207