Heat transfer in a fixed bed of particles for energy storage: a multi-scale numerical study
Bibliographic record
Abstract
Electricity storage, like Advanced Adiabatic Compressed Air Energy Storage (AA-CAES), is a potential solution to address the problem of intermittency of renewable power sources. AA-CAES stores not only the compressed air, but also the heat released upon compression of the air, in a Thermal Energy Storage (TES) system which plays a prevailing role in the global efficiency of AA-CAES process. At IFP Energies nouvelles, we develop TES systems based on fixed bed reactors to store heat in particles. The objective of this study is to investigate the impact of different parameters (porosity, particulate Reynolds number, presence of walls) on heat transfers within the bed thanks to different numerical approaches at different scales: at the particle scale using Particle Resolved Simulation (Fictitious Domain / Discrete Element Methods (DEM) or body-fitted approach), at a small bed of particles scale (DEM-CFD model) or at the full bed scale (Continuous Porous Media model).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".