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Record W4206323221 · doi:10.1016/j.cscm.2022.e00873

Application of kinetic pyrolysis models to the analysis of flax chipboards under fire

2022· article· en· W4206323221 on OpenAlexaff
T.T. Tran, Amar Khennane, M. Khelifa, Pierre Girods, Marc Oudjène, Yann Rogaume

Bibliographic record

VenueCase Studies in Construction Materials · 2022
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsPyrolysisThermogravimetric analysisFinite element methodSubroutineKinetic energyMaterials scienceThermalNumerical analysisStructural engineeringComposite materialComputer scienceEngineeringMathematicsThermodynamicsWaste managementChemical engineeringPhysics

Abstract

fetched live from OpenAlex

This study presents a three-dimensional finite element model to describe the thermomechanical behaviour of flax chipboards under fire conditions. The model is based on kinetic models considering the thermal degradation during the pyrolysis phase and the evolution of the physico-mechanical properties as functions of temperature. The numerical model is integrated into Abaqus via user subroutines (Umat and Umatht) and applied to the analysis of the fire behaviour of panels made of flax chipboards. Thermogravimetric tests are performed on flax particles to serve for the identification of the kinetic parameters of the pyrolysis models. Once these kinetic parameters are determined, they are integrated into a complete numerical model to simulate the behaviour under fire of flax chipboards on small and large scales. The obtained trends in the predicted values indicate good agreements when compared to the measured values. The simulations show that the numerical model is capable of accurately modelling the thermomechanical transfers taking place within the material during exposure to fire.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.336
Threshold uncertainty score0.324

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.022
GPT teacher head0.260
Teacher spread0.238 · 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 designBench or experimental
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

Citations2
Published2022
Admission routes1
Has abstractyes

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