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Record W4406861003 · doi:10.1016/j.powtec.2025.120677

A critical review of the coupled CFD–DEM method for the simulation of two-phase liquid–solid systems

2025· review· en· W4406861003 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePowder Technology · 2025
Typereview
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCFD-DEMComputational fluid dynamicsPhase (matter)MechanicsEnvironmental scienceMaterials sciencePhysics

Abstract

fetched live from OpenAlex

Two-phase liquid–solid systems, such as mixing units, fluidized beds , hydraulic conveyors, and separation systems, are commonly found in various industries. The coupled CFD–DEM method has become a widely accepted approach for analyzing these systems due to its accuracy and versatility. However, it is common in the literature to apply the assumptions used in simulating gas–solid systems to liquid–solid contexts. This critical distinction is often overlooked: the forces of interaction between the fluid and particles, as well as the dynamics between particles, differ significantly in liquid–solid systems compared to gas–solid systems. Neglecting these differences can result in inaccurate simulations. This study emphasizes the key differences between using CFD–DEM for simulating liquid–solid systems versus gas–solid systems. The study also examines the capabilities of the CFD–DEM method in simulating various liquid–solid unit operations. Lastly, it identifies gaps in current CFD–DEM models and suggests areas for model improvement.

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 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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.815
Threshold uncertainty score0.810

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.036
GPT teacher head0.397
Teacher spread0.361 · 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