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Record W2321151015 · doi:10.11159/jffhmt.2016.006

Exposure the System of Polystyrene and the Steel to Various Flow Velocities and Finding its Equation of Motion

2016· article· en· W2321151015 on OpenAlexaffvenue
Hassan Hamed Alhachami

Bibliographic record

VenueJournal of Fluid Flow Heat and Mass Transfer · 2016
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsPolystyreneMotion (physics)Flow (mathematics)Materials scienceMechanicsClassical mechanicsComposite materialPhysicsPolymer

Abstract

fetched live from OpenAlex

The main purpose of this paper is to examine some practical aspects of a D-section that are related with flow induced vibration. In this work, the first considerable thing that will be covered is the effect of various flow velocities on geometrical shapes and specifically on the D-Section. Therefore, the geometrical properties of shape and its dimensions are very useful to find the value of susceptibility of material to withstand external stress. On the other hand, Young's modulus could have a big effect on the body excitation, the actual geometrical properties of body, which are consisted of one degree of freedom are m= 0.7438032 Kg, K= 1144.8641 N/m, the natural frequency 4.219 rad/sec, and = 0.00183. The model was developed for one degree of freedom aero dynamic galloping. This model is useful for analysing of elastic Dstructure, which was made from polystyrene (C8H8) n and the steel, exposed to various flow velocities where the D-section was put in various attack angles (0 , 45, 90, 135, 180, and then 0) 0 in front of the wind tunnel. Whereas the derivation of the equation of motion of the D-section is the other worthwhile thing because it might be used to simulate the system in the future. The experimental results of the D-section and discussions will be done on some response curves which will be simulated in different ways.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.921
Threshold uncertainty score0.179

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 head0.009
GPT teacher head0.188
Teacher spread0.179 · 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

Citations0
Published2016
Admission routes2
Has abstractyes

Explore more

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