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Record W2886521791 · doi:10.11159/htff18.122

Large Eddy Simulation of Drying of a Potato Slice in Turbulent Flow

2018· article· en· W2886521791 on OpenAlexvenueno aff
L.C. Lara-Guzmán, E. Martínez, M. Salinas‐Vázquez, W. Vicente

Bibliographic record

VenueProceedings of the World Congress on Mechanical, Chemical, and Material Engineering · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Drying and Modeling
Canadian institutionsnot available
FundersDirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de MéxicoUniversidad Nacional Autónoma de MéxicoConsejo Nacional de Ciencia y Tecnología
KeywordsTurbulenceLarge eddy simulationFlow (mathematics)MechanicsEnvironmental scienceComputer sciencePetroleum engineeringPhysicsGeology

Abstract

fetched live from OpenAlex

In this paper, the drying of the potato parallelepiped slice by a turbulent air stream is analysed in a conjugate model with the Large Eddy Simulation (LES) technique. The heat and mass transport equations are solved inside of the potato and continuity, movement, energy and species transport equations are solved to the external flow. Transport equations are discretized in the space and time using the McCormack scheme and a refined mesh at boundary of solid is utilized. The diffusion coefficient is calculated through Arrhenius equation for potato and the numerical model is developed in a Fortran code. Two cases are analysed: air stream at 333K and air stream at 353 K. Numerical simulations agree with the behaviour of the flow around immersed body and diffusion mechanism inside of food match with Chandramohan results. Predictions show the diffusion coefficient inside of potato slice is strongly related to the air temperature. The moisture loss in the potato slice is better in a semi-elliptic temperature distribution because changes of temperature imply variations in density of moisture, where mass transfer is promoted.

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 score0.058
Threshold uncertainty score0.227

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 head0.010
GPT teacher head0.213
Teacher spread0.203 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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