Flow Resistance of Randomly Packed Beds of Crushed Rock and Ellipsoidal Particles using CFD
Bibliographic record
Abstract
Rock bed thermal energy storage is a cost-effective solution to store waste heat from a solarized Brayton cycle for use in a Rankine cycle after sunset.However, rock bed thermal energy storage systems for utility scale concentrated solar power are huge and require multiple air inlets and outlets.As a result, the flow inside the bed is fully three dimensional and deviates considerably from plug flow conditions usually encountered in chemical reactors.Designing a rock bed thermal energy storage system for the minimum capital cost and pumping power depend on reliable predictions of the fluid flow paths and temperature profiles in the bed.Particle size and shape have a significant influence on how the particles will pack down, which in turn influences the flow pattern in the bed, and hence the pressure drop and heat transfer characteristics of the bed.In this work, we discuss the characterization of crushed rock particles and concluded that there are benefits in approximating particles by mono-dispersed ellipsoids.We used discrete element modelling to generate packed beds of the ellipsoidal particles, and computational fluid dynamics to model the flow in the interstitial voids.This way, we successfully captured the directional effect of the flow resistance for ellipsoidal particles in terms of sphericity, , porosity , particle diameter Dve, and particle Reynolds number ReOur current model under-predicted the pressure drop across a packed bed of crushed rock particles.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".