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Record W2925689611 · doi:10.1177/0306419019831393

Revival of water table experiments in fluid mechanics courses, part I

2019· article· en· W2925689611 on OpenAlexaboutno aff
Oleg Goushcha

Bibliographic record

VenueInternational Journal of Mechanical Engineering Education · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicExperimental and Theoretical Physics Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAnalogyFluid mechanicsMach numberCompressible flowCompressibilityFluid dynamicsNozzleMechanicsDivision (mathematics)Computer scienceEngineeringMathematicsMechanical engineeringPhysicsEpistemologyArithmetic

Abstract

fetched live from OpenAlex

In a classroom environment, after a comprehensive theoretical discussion of compressible flows, it is beneficial to conduct a visual experiment in which students can observe these flows and some of the features associated with them. Experimental study of compressible fluid dynamics is associated with high equipment costs; therefore, conducting an experiment is not feasible for some colleges. This article describes an experiment implemented at Manhattan College upper division and graduate fluid dynamics courses at a relatively low cost. In the experiment, a water table hydraulic analogy was used. Theoretical considerations of this analogy are explained in this article. An area–velocity relation was used to study the Mach number at the exit of a Laval nozzle. The theory and measurement came within 10% of each other, which is sufficient for a teaching demonstration. This exercise can be conducted in two class sessions: (1) discussing the theoretical considerations and (2) performing the experiment and analyzing data. The overall experience is a good way to help students understand some of the compressible flow features, and further promote their interest in fluid mechanics.

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)0.006
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0080.002

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 head0.259
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 designBench or experimental
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

Citations5
Published2019
Admission routes1
Has abstractyes

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