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Record W1213426171

Numerical simulation of cantilevered ramp injector flow fields for hypervelocity fuel/air mixing enhancement

2000· dissertation· en· W1213426171 on OpenAlexfundno aff
J. Schumacher

Bibliographic record

VenueTSpace (University of Toronto) · 2000
Typedissertation
Languageen
FieldEngineering
TopicRocket and propulsion systems research
Canadian institutionsnot available
FundersUniversity of Toronto
KeywordsHypervelocityInjectorAerospace engineeringMixing (physics)CantileverComputer simulationMechanicsEngineeringAutomotive engineeringFlow (mathematics)PhysicsMechanical engineering
DOInot available

Abstract

fetched live from OpenAlex

Increasing demand for affordable access to space and high speed terrestrial transport has spawned research interest into various air-breathing hypersonic propulsion systems. Propulsion concepts such as the supersonic combustion ramjet (scramjet) and the shock-induced combustion ramjet (shcramjet) utilize oxygen freely available in the atmosphere and thereby substantially reduce the weight penalty of on-board oxidizer tankage used in rocket based systems. Of key importance to the ultimate success of an air-breathing concept is the ability to efficiently mix the fuel with atmospheric air. In the case of a hypersonic air-breather the challenge is accentuated due to the requirement of supersonic combustion. Flow velocities through the combustor on the order of thousands of meters per second provide the fuel and air with only a brief time to adequately combine. Contemporary mixing augmentation methods to address this issue have focused on fuel injection devices which promote axial vortices to enhance the mixing process. Much research effort has been expended on investigation of ramp injectors for this purpose. The present study introduces a new ramp injector design, based on the conventional ramp injector, dubbed the cantilevered ramp injector. A two-pronged numerical approach was employed to investigate the mixing performance and characteristics of the cantilevered injector consisting of, (1) comparison with conventional designs and (2) a parametric study of various cantilevered injector geometries. A laminar, three-dimensional, multispecies flowsolver was developed in generalized coordinates to solve the Navier-Stokes equations for the flow fields of injected H2 into high-enthalpy air. The scheme consists of an upwind TVD scheme for discretization of the convective fluxes coupled with a semi-implicit LU-SGS scheme for temporal discretization. Through analysis of the numerical solutions, it has been shown that the cantilevered ramp injector is a viable fuel injection system facilitating enhanced mixing of fuel and air. Comparison with conventional designs have revealed a competitive and, in most cases, superior design in the context of mixing performance. A strong counter-rotating vortex pair generated under the cantilevered injector was shown to be the distinguishing characteristic of this design and largely accounted for improved mixing performance. Results also elucidated the importance of a coupled design approach between the fuel injector and propulsive duct to optimize mixing performance.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.020
GPT teacher head0.273
Teacher spread0.253 · 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 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

Citations15
Published2000
Admission routes1
Has abstractyes

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