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Record W2012995233 · doi:10.15376/biores.9.2.3304-3322

High-frequency Heating Behavior of Veneer-based Composites: Modelling and Validation

2014· article· en· W2012995233 on OpenAlexaff
Peixing Wei, Brad Jianhe Wang, Chunping Dai, Siwei Huang, Xin Rao, Wending Li

Bibliographic record

VenueBioResources · 2014
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsFPInnovations
Fundersnot available
KeywordsVeneerMaterials scienceComposite materialLaminated veneer lumberThermal conductivityMoistureHeat transferMass transferMechanics

Abstract

fetched live from OpenAlex

A one-dimensional theoretical heat and mass transfer model was developed for high-frequency (HF) heating of veneer-based composites, such as laminated veneer lumber (LVL) and plywood. This model was based on the basic principles of energy and mass conservation, momentum conservation of gas flow, and gas thermodynamic relations. The response variables, including temperature, gas pressure, and moisture content (MC), were linked to basic material properties, such as veneer density, thermal conductivity, permeability, and dielectric properties. Initial and boundary conditions for solving the governing equations were also considered. The model was further validated by experiments with veneer HF heating and LVL HF heating. The model predictions agreed well with the experimental results. During veneer HF heating, the inner veneer core layers had lower MC than the outer surface layers. Compared to conventional hot platen heating, HF heating was proven to be an efficient and robust method for manufacturing veneer-based composites.

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 score0.490
Threshold uncertainty score0.293

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 head0.017
GPT teacher head0.194
Teacher spread0.177 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

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