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Record W7034101457

Shock reflection in internal axisymmetric flow

2023· dissertation· en· W7034101457 on OpenAlexafffund

Bibliographic record

VenueeScholarship@McGill (McGill) · 2023
Typedissertation
Languageen
FieldComputer Science
TopicNonlinear Dynamics and Pattern Formation
Canadian institutionsMcGill University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsFreestreamMach numberWedge (geometry)Inviscid flowMach reflectionShock (circulatory)Rotational symmetryFlow (mathematics)Leading edge
DOInot available

Abstract

fetched live from OpenAlex

A new model to predict internal axisymmetric shocks with Mach reflections at their centreline is developed.The model combines the method of characteristics and the quasi-one-dimensional flow equations.A method to extend the model for cases with extremely small Mach stems, generated by weak incident shocks, is developed and applied.Model predictions are compared with those from inviscid CFD, for a range of axisymmetric wedge geometries, and the effects of wedge length and shock angle at the wedge leading edge are studied.These various wedge geometries are found to generate flowfields with similar flow features, with Mach discs that vary greatly in size.This observation forms the basis of a method that uses the results from a CFD mesh convergence study, conducted for a single wedge geometry, to determine the mesh resolution requirements and uncertainty due to finite mesh resolution for all other wedge geometries.The model indicates that these geometries generate a flowfield that can be treated as two separate parts: one supported by the wedge surface and another supported by the sharp corner at the wedge trailing edge.The influence 5-3 Characteristic lines at a point, P ; η and ξ represent the local C + and C -directions, respectively, x 1 is the local flow direction, θ is the flow inclination angle relative to the x-axis, and µ is the Mach angle.120 xv 5-4 Schematic showing the situation where mass flow rate at b is computed by integration along the local C + characteristic.The mass flow rate at a is known and the mass flow rate through the segment a to b, d ṁ′ , is computed. . . . . . . . . . . . . . . . . . . . . . . . . . . . .124 5-5 MOC prediction of a Prandtl-Meyer expansion fan (planar twodimensional) generated at a 20 • sharp corner, with a freestream Mach number of 3.0, γ = 1.4.The pink marker indicates the node at which the MOC solution is taken. . . . . . . . . . . . . . . . . .126 5-6 Discretization error, DE, plotted against MOC grid spacing, ∆c. . . .126 5-7 Error in MOC predicted average shock angle, β, in comparison to the theoretical value of β t = 150 • . . . . . . . . . . .

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: none
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.264
Teacher spread0.244 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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