Development of a Robust Eulerian-Lagrangian Model for Process Intensification of Gas Fluidized and Spouted Beds
Bibliographic record
Abstract
RÉSUMÉ: L’existence des écoulements multiphasiques, en particulier les écoulements solide-fluides, dans plusieurs applications industrielles, a conduit les chercheurs à investir beaucoup de temps pour comprendre les phénomènes sous-jacents qui régissent le comportement physique de ces écoulements. En génie chimique, les processus les plus connus impliquant de tels écoulements sont les lits fluidisés et les lits à jets, qui restent prévalents dans l’industrie en raison de leurs propriétés améliorées de transfert de masse et de chaleur. Cependant, ces lits sont complexes à concevoir et à exploiter. Plusieurs études ont été menées pour permettre une conception améliorée et efficace de ces lits, dont beaucoup sont des expériences utilisant des techniques invasives ou non invasives. Néanmoins, les expériences présentent plusieurs limitations, telles que leur coût élevé et le temps nécessaire, ce qui les rend moins efficaces. C’est là que les méthodes numériques jouent un rôle en surmontant les limites des expériences, même si les expériences restent importantes pour la validation des modèles computationnels. Parmi ces méthodes, le modèle à deux fluides (TFM), qui est une approche eulérienne-eulérienne, est capable de modéliser les écoulements multiphasiques, mais sa précision est limitée puisqu’il traite les particules comme un continuum. Une autre approche, l’approche eulérienne-lagrangienne, implique l’application de la dynamique des fluides numérique (CFD) couplée à la méthode des éléments discrets (DEM). Cette méthode est de plus en plus utilisée comme elle simule les écoulements solide-fluide avec une meilleure précision. Néanmoins, elle présente plusieurs limitations, en particulier en termes de temps de calcul, ce qui rend la CFD-DEM inefficace pour la simulation de systèmes industriels de très grande taille. Dans cette thèse, un modèle CFD-DEM à éléments finis d’ordre élevé est développé pour modéliser la fluidisation de particules solides dans des lits fluidisés et des lits à jets. Le modèle permet de prédire tous les régimes, depuis les écoulements laminaires à faibles nombres de Reynolds jusqu’aux écoulements turbulents à nombres de Reynolds élevés. Il permet une meilleure compréhension du comportement de fluidisation des particules et aborde plusieurs des limitations de la CFD-DEM, notamment les schémas de projection des particules sur la grille de la CFD et la limite du ratio entre la taille des particules et la taille des cellules. ABSTRACT: The existence of multiphase flows specifically solid-fluid flows in countless industrial appli-cations such as fluid catalytic cracking units has led scientists and researchers to invest a lot of time trying to understand the underlying phenomena governing the physical behavior of such flows. In chemical engineering, the most known processes involving such flows are fluidized and spouted beds which remain prevalent in industries due to their improved mass and heat transfer properties. However, these beds are complex to design and operate. Various studies have been performed to allow for improved and efficient design of such beds, many of which are experiments using either invasive or non-invasive techniques. Nonetheless, experiments suffer from several limitations, such as being expensive and time consuming which render them less effective. This is where numerical methods play a role in overcoming the limitations of experiments, even though experiments remain important for the validation of the computational models. Among these methods, the two fluid model (TFM) which is a Eulerian-Eulerian approach is capable of modelling multiphase flows, but its accuracy is limited as it treats the particles as a continuum. Another approach, most commonly known as the Eulerian-Lagrangian approach involves the application of computational fluid dynamics (CFD) coupled with the discrete element method (DEM). This method is increasingly being used as it simulates solid-fluid flows with better accuracy. Nevertheless, it suffers from several limitations, specifically computational time, which renders CFD-DEM inefficient for the simulation of very large industrial systems. In this thesis, a high-order finite element CFD-DEM model is developed to model the fluidiza-tion of solid particles in gas fluidized and spouted beds. The model entails the prediction of all regimes from laminar flows at low Reynolds numbers to turbulent flows at high Reynolds numbers. It allows for a better understanding of the fluidization behavior of particles and tackles several of the limitations of CFD-DEM, notably the projection schemes of particles onto the CFD mesh and the particle to cell size ratio 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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".