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Record W4206320736 · doi:10.1115/1.4053618

Maximum Wall Stress on a Smooth Flat Plate Under Planar Jet Impingement

2022· article· en· W4206320736 on OpenAlexaff
Tie Wei, Yanxing Wang, Cat Vo Tu, David Wood

Bibliographic record

VenueJournal of Fluids Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer Mechanisms
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsReynolds numberMechanicsJet (fluid)Laminar flowShear stressNozzlePhysicsStagnation pressureMaterials scienceMach numberThermodynamicsTurbulence

Abstract

fetched live from OpenAlex

Abstract This paper investigates the maximum wall shear stress value τmax and its location xmax as measured on a smooth flat plate impinged upon by a normal planar jet. τmax and xmax are found to be closely related to the stagnation pressure Ps and the half-width of the mean wall pressure profile bpw. The measurements were made by two different techniques: a Stanton probe and oil film interferometry. The maximum wall shear stress location xmax is found to be independent of the jet Reynolds number. At a small nozzle-to-plate distance H≲6 Djet, xmax is related to the jet slot width as xmax≈1.1Djet. At a large nozzle-to-plate distance H≳6 Djet, the maximum wall shear stress location is related to the mean wall pressure half-width as xmax≈1.4 bpw. A new Reynolds number, referred to as the stagnation Reynolds number, is defined as Res=def2bpwPs/ρ/ν, where ρ is the fluid density and ν is the kinematic viscosity. The maximum wall shear stress is found to be strongly influenced by the stagnation Reynolds number, and the dependence as measured by Stanton probes is approximated by a power law of τmax/Ps≈0.38/Res0.38. The solution of the laminar flow equations in the Appendix gives an alternate relation for τmax, which is in better agreement with the oil film interferometry measurements. Dimensional analysis is performed to gain insight into the empirical findings.

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 categoriesMeta-epidemiology (narrow)
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 score0.331
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.012
GPT teacher head0.204
Teacher spread0.192 · 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.

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

Citations1
Published2022
Admission routes1
Has abstractyes

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