Experimental methods in chemical engineering: Unresolved CFD‐DEM
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract CFD‐DEM combines computational fluid dynamics (CFD), which solves the equation of motion of gas or liquids, with the discrete element method (DEM), a simulation technique based on a Lagrangian description of particle motion that predicts the flow of granular matter and powders. Resolved CFD‐DEM solves the fluid motion with CFD at a scale smaller than the particle diameter ( d p ), assuming no‐slip on the particle surface to couple the phases. The fluid solver scale is coarser than d p in unresolved CFD‐DEM and virtual mass, drag, and other solid‐fluid forces couple the phases. Resolved CFD‐DEM is more accurate, but is orders of magnitude more computationally intensive. Unresolved CFD‐DEM predicts solid distribution, pressure loss, mass flow rate, and dense and dilute phase flow patterns when the solid to fluid and fluid to solid coupling between the fluid phase and the solid phase are non‐trivial. Researchers apply CFD‐DEM to predict gas‐fluid dynamics of fluidized beds, spouted beds, hoppers, cyclones, costal erosion, and rock slides. Open source codes, commercial software, and parallel computer architectures have accelerated its adoption in pharmaceutical, agro‐industrial, and reactor design. Current research targets improving the solid‐fluid coupling strategies and multiphysics problems including heat transfer, mass transfer, and chemical reactions within or at the surface of the particles. The field has grown to over 200 indexed articles per year (Web of Science) in 2018. This article is part of a special series dedicated to experimental methods in chemical engineering that reviews the most important concepts, applications, and limitations of each technique.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it