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Record W4246148043 · doi:10.5383/ijtee.08.01.002

Numerical Study of Heat and Mass Transfer during the Thermal Drying of Tropical Woods

2014· article· en· W4246148043 on OpenAlexvenueno aff
Merlin Simo Tagne

Bibliographic record

VenueInternational Journal of Thermal and Environmental Engineering · 2014
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsnot available
FundersUniversité de Lorraine
KeywordsWood dryingWork (physics)Mass transferHeat transferGreen woodThermalMaterials scienceEnvironmental scienceThermodynamicsPulp and paper industryComposite materialPhysicsEngineering

Abstract

fetched live from OpenAlex

A sector of utilization and transformation of wood give much money at the countries of the central Africa region. If we want to increase these advantages, it is important to do locally the first and second transformations of wood. Self-control of the wood drying is necessary to ameliorate a wood quality and to preserve an art work doing in wood. In this article, we are modeling a drying of one piece of bete wood (Mansonia altissima) with dimensions are 1m of length and thickness is no more than 25cm. We have used a literature to obtain a mass and heat equations and the thermophysical properties of the present wood. We have considered some thermophysical properties that we are unkempt in the precedent work [1]. We have experimentally obtained a relationship between a density of this wood with a water content. The others thermophysical properties come from the literature [1,2]. We have simulated a wood drying in the constant conditions and in the conditions of the bete drying table established by CIRAD Organization. A sensibility study is doing to validate a modeling. Then, the present modeling explains temperature, mass fraction of the vapor in air and water content evolutions during the industrial process of drying. This modeling can to be used for to overhaul a drying table of bete wood and the others of tropical woods. This work uses a gaseous pressure can be used to explain the drying with the high temperatures conditions.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.680
Threshold uncertainty score0.264

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 head0.004
GPT teacher head0.165
Teacher spread0.161 · 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 designObservational
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

Citations6
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Thermal and Environmental EngineeringSame topicWood Treatment and PropertiesFrench-language works237,207