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Record W2991684706 · doi:10.15866/irea.v4i6.11065

Simulation and FPGA Implementation of Thermal Convection Equation for Complex System Design

2016· article· en· W2991684706 on OpenAlexaff
Aziz Oukaira, Naresh Pal, Ouafaa Ettahri, Emmanuel Kengne, Ahmed Lakhssassi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsUniversité du Québec en Outaouais
Fundersnot available
KeywordsNatural convectionForced convectionConvectionMechanicsConvective heat transferThermal conductionCombined forced and natural convectionComputer scienceMechanical engineeringPhysicsEngineeringThermodynamics

Abstract

fetched live from OpenAlex

The objective of this work is to make a thermal study beginning with the simulation and implementation of the equation of thermal convection in a complex system design through numerical simulation. This simulation is based on the code of the numerical calculation by finite element module CFD that will allow us to model a variety of physical phenomena characterizing a real problem as the heat exchange by convection. The proposed governing equations are based on the Newton law of conduction and heat convection. The temperature profile in the convection is obtained from the simulation analysis using the COMSOL tool to ensure a uniform temperature distribution in both natural and forced convection. In general, in order to obtain the temperature differences between the two types of convection and also to demonstrate the capability of a type with respect to the other, it is necessary to validate this equation of convection which consequently helps verify the dissipated power of 0.6 W for a surface of 4.68 mm×5.97 mm. The power is uniformly divided in the volume of the ASIC. The DBC (Dirichlet Boundary conditions) method is applied around the ASIC at 25°C. Through these simulations, the relationship between the powers dissipated by ASIC and the difference of temperature in both forced and natural convection will be validated to implementation on FPGA using VHDL code to monitor and verify the power dissipated 0.6 W a surface ASIC circuit.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.954
Threshold uncertainty score0.150

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 head0.065
GPT teacher head0.289
Teacher spread0.224 · 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

Citations7
Published2016
Admission routes1
Has abstractyes

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