Modélisation des interactions et du transport de particules dans une phase liquide : des comportements locaux aux effets à l’échelle des procédés
Bibliographic record
Abstract
Scientific and industrial challenges regarding non-metallic inclusions in processes involving liquid metal are core thematics of the research team “Process Metallurgy”, that I joined in 2013. To address the needs of such research activities, I have developped a software framework to simulate fundamental mechanisms ruling the transport and the interactions between particles in a liquid phase and to model their impacts at process scale. This piece of software couples a Lattice Boltzmann Method (LBM) to a Discrete Element Method (DEM) using an Immersed Boundary Method (IBM).This document first presents a description of the implementations of the three methods (LBM, DEM and IBM), followed by three PhD theses that were conducted at Institut Jean Lamour (Université de Lorraine) and at the University of Alberta. These projects all started at the same time, in spring 2016 and ended between the years 2019 and 2021. Matthieu Gisselbrecht (2019) and Manoj Joishi (2019) focused on topics related to the control of inclusion cleanliness in metallurgical processes, more specifically investigating the aggregation kinetics of inclusions and their capture by deposition at the walls of ladle reactors. Akash Saxena (2021) quantified restructuring and breakage of aggregates transported in a flow as well as the dependence of morphological evolution of aggregates on the cohesive and hydrodynamic interactions between the primary particles of which they are made of.Targeted applications fall in the scope of metallurgy, but the underlying physics and their mathematical modelling are transversal to multiple domains, such as colloids and aerosols.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".