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EFFECTS OF DISCRETE CONTROLLERS ON THE STABILIZATION OF NATURAL CONVECTION INDUCED BY INTERNAL HEAT GENERATION IN A SHALLOW CAVITY

2019· article· en· W2917264204 on OpenAlexaff
Abdellhakiim Mebrouki, Zineddine Alloui, P. Vasseur

Bibliographic record

VenueComputational Thermal Sciences An International Journal · 2019
Typearticle
Languageen
FieldEngineering
TopicHeat transfer and supercritical fluids
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsNatural convectionMechanicsRayleigh numberActuatorConvectionInternal heatingIsothermal processIntensity (physics)Materials scienceThermalHeat generationControl theory (sociology)PhysicsOpticsThermodynamicsEngineeringComputer science

Abstract

fetched live from OpenAlex

The stabilization of natural convection in a horizontal fluid layer with internal heat generation is studied numerically. The horizontal boundaries of the system are cooled isothermally. The system is stabilized using multiple sensors and discrete individually controlled actuators that modify the local intensity of the heating power. Discrete controllers of finite length and spacing are located on the horizontal boundaries of the system. The thermal sensors are positioned at a given vertical height of the fluid layer. Upon using a feedback proportional control, the heating power of the system is modulated in order to postpone the onset of motion or annihilate the intensity of convection. Two-dimensional numerical simulations of the full governing equations are carried out. The results are used to determine the influence of the governing parameters, such as the length and spacing of the actuators, positions of the thermal sensors, and control gain on the control of the system. A correlation equation is proposed to predict the critical length of the actuators, above which the no-motion state cannot be maintained in the layer, as a function of the Rayleigh number.

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 score0.530
Threshold uncertainty score0.282

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 head0.013
GPT teacher head0.262
Teacher spread0.249 · 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
Published2019
Admission routes1
Has abstractyes

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