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Record W4391301706 · doi:10.2514/6.2024-0867

Semi-Analytical Model of Radiative Heat Transfer and Chemical Reactions in a Boundary Layer

2024· article· en· W4391301706 on OpenAlexaff
Samita Rimal, Kevin Pope, G.F. Naterer, Kelly Hawboldt

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadiative Heat Transfer Studies
Canadian institutionsUniversity of Prince Edward IslandMemorial University of Newfoundland
Fundersnot available
KeywordsRadiative transferHeat transferBoundary layerThermal radiationChemical reactionLayer (electronics)Materials scienceThermodynamicsMechanicsChemistryPhysicsOpticsComposite material

Abstract

fetched live from OpenAlex

This study examines heat transfer in a chemically reacting boundary layer flow with a hydrolysis reaction. A semi-analytical model is introduced for examining the impact of thermal radiation on the laminar boundary layer in the context of a hydrolysis reaction. Through a similarity transformation, the governing set of Partial Differential Equations in the system are transformed to Ordinary Differential Equations. The model accounts for changes during the process including variations in the specific heat constant and chemical reaction constant. Initially, the model is validated through a formerly established benchmark problem and further expanded to analyze the impacts of radiation terms, variations in specific heat, and modifications in the reaction rate constant. It is reported that during the hydrolysis reaction, it is crucial to account for temperature dependency of the chemical reaction rate constant and effects of specific heat for the operating temperature range. It is also recognized that the thermal radiation term has a significant role in the boundary layer flow. The thickness of the thermal boundary layer was observed to increase with higher radiation parameters. The result of this study provides useful data and trends to better understand the effects of radiation and chemical reactions on the boundary layer characteristics.

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 score0.647
Threshold uncertainty score0.434

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 head0.026
GPT teacher head0.256
Teacher spread0.230 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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