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Record W7161963526 · doi:10.82308/14650

Multi-scale analysis of freezing process in mining applications: From equilibrium to non-equilibrium

2024· dissertation· en· W7161963526 on OpenAlexaboutno aff
Minghan Xu

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicPhase Change Materials Research
Canadian institutionsnot available
Fundersnot available
KeywordsPermafrostProcess (computing)Climate changeGlobal warmingThermalFossil fuelMathematical modelPhase changePhase (matter)

Abstract

fetched live from OpenAlex

The mining sector is crucial to Canada's economy but faces challenges in transitioning to sustainable practices amid increasing global energy demands and climate change. Dependency on fossil fuels contributes to carbon emissions, while rising temperatures in northern regions accelerate permafrost degradation, threatening critical infrastructure and mining operations. Addressing these challenges requires innovative solutions and a deeper understanding of physical phenomena in cold climates that are unique to Canada, such as solid-liquid phase changes, to ensure the industry's long-term sustainability.This dissertation focuses on advancing our fundamental knowledge of solidification, including both equilibrium and non-equilibrium processes, at multiple temporal and spatial scales through theoretical and experimental analyses. It begins with a state-of-the-art review of freezing processes, outlining recent progress from fundamental, methodological, and applied perspectives. The thesis then addresses macro-scale equilibrium solidification by developing novel analytical solutions to two-phase Stefan problems via singular perturbation and asymptotic analysis, which are both accurate and computationally efficient. These solutions facilitate the thermal estimation of phase change materials (PCMs) for cold thermal energy storage, as well as artificial ground freezing (AGF) for stabilizing ore deposits and protecting permafrost.The thesis advances to the study of multi-stage and multi-scale non-equilibrium solidification, characterized by innovative laboratory experiments and unified mathematical models. Specifically, it examines the five-stage solidification of pure substances and mixtures, including stochastic heterogeneous nucleation, non-linear crystal growth, and coupled heat and mass transport with freeze-point depression. The multi-scale analysis captures temporal and spatial phenomena using novel experimental and mathematical frameworks. Non-equilibrium solidification significantly enhances the development of PCMs for cold thermal energy storage, as well as spray freezing (SF) for heating, cooling, and decontaminating wastewater in mines.The combination of equilibrium and non-equilibrium solidification, investigated through theoretical and experimental frameworks, contributes to our fundamental understanding of complex phase-change processes. The developed analytical solution to two-phase Stefan problems significantly reduces the computational time for equilibrium processes compared to numerical methods. The novel multi-stage and multi-scale frameworks for both pure substances and mixtures accurately characterize non-equilibrium behaviors (e.g., heterogeneous nucleation and non-linear crystal growth) that are otherwise difficult to obtain via conventional approaches.From an applied perspective, the freezing process (both at equilibrium and non-equilibrium) is a key influential factor in the thermal estimation and design of AGF, PCMs, and SF. The developed frameworks delineate practical parameters such as total freezing time, interface movement, thermal storage capacity, freeze concentration, ice quality, and ice production. Accurate analysis of freezing phenomena is of great importance in the innovation, development, and integration of these cutting-edge clean technology solutions, tailored specifically to the unique landscapes and environments of Canadian mines in today's global energy transition and climate crisis

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.445
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
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 head0.029
GPT teacher head0.339
Teacher spread0.311 · 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
Published2024
Admission routes1
Has abstractyes

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