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Record W7014245304

Pattern formation in 2D continuum and discrete chemically reactive media

2018· dissertation· en· W7014245304 on OpenAlexfundno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2018
Typedissertation
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsnot available
FundersCanadian Space Agency
KeywordsLewis numberCombustionIgnition systemWork (physics)Limit (mathematics)Pattern formationThermal
DOInot available

Abstract

fetched live from OpenAlex

The spatio-temporal instabilities of a solid-gas combustion front and its resulting pattern formation are examined in the framework of thermal-mass diffusional instabilities, in the case of homogenous and heterogeneous media. In our numerical simulations, by employing a novel adaptive mesh refinement algorithm, we achieve experimentally relevant system sizes. To our knowledge, this is the first work where these methods are applied to a new class of solid-gas combustion models.We introduce a unified mathematical model called “master model” to describe the combustion of metal fuel particles in an oxidizer gas, based on the physical parameters of the problem (i.e. order of the chemical reaction (n = 0, 1), the ignition temperature Tign, and the Lewis number Le, defined as the ratio of thermal to mass diffusivity), and for different limits of oxidizer mass diffusivity, with continuous and random reactant fuel distributions.Our numerical findings for a combustion front in the continuum limit show that a front develops a cellular structure for a specific ignition temperature Tign, and below a critical Lewis number Lec. The linear regime of these morphologies is investigated numerically and found to agree well with the dispersion relation predicted analytically by Brailovsky et al.. The effect of system size and Lewis number on the linear regime were also addressed. For Lewis numbers Le close to the critical value Lec, the transition from linear to non-linear (i.e. late time) is prolonged. This regime is characterized by the appearance of shallow-cell structures, and those morphologies can be described qualitatively based on the growth modes available in the linear regime. For this reason, this regime is called “quasi-linear” regime. By lowering the Lewis number values below Le~0.4, the morphology of the front changes significantly and becomes complex, featuring non-symmetric deep cells and overhangs. The dendritic patterns simulated in this work are similar to those observed in experiments of flame propagation over a bed of nano-aluminum powder burning with a counter-flowing oxidizer conducted by Malchi et al.We further studied the linear regime of cellular combustion fronts under heat dissipation conditions. Our numerical results show that the growth rate of high k modes, in the presence of heat dissipation, can effectively be described by the same modes as in the adiabatic condition, but with their amplification rate increased. The dynamics of a combustion front propagating in random media, assuming zero-order kinetics n=0 and for a small Lewis number Le=0.3, were also studied numerically. These numerical findings suggest that the destabilizing effect of the random medium, is qualitatively analogous to lowering the Lewis number in the continuum limit. It leads to an increased stability range of modes and an increased growth rate. The late-time dynamics of cellular pattern formation in random media is also investigated. The results indicate that for both uniform and non-uniform random particle distributions, modifying the area fraction of the medium occupied by metal particles (denoted by s) alters the cell depth (i.e. max-to-min distance of cells). On the other hand, changing the number density Ns reshapes the morphology. Our numerical analysis for the effect of the Lewis number on the late-time dynamics show that even in the long run, the effect of a random distribution of particles is analogous to low-Lewis numbers in the continuum limit.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.769
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.007
GPT teacher head0.206
Teacher spread0.199 · 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.

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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