Modeling of Conduction and Natural Convection in Ice-Water Systems Containing Porous Metal Foams
Bibliographic record
Abstract
Mathematical models and numerical predictions of heat conduction and laminar natural convection in ice-water systems containing porous metal foams are presented, in the context of computationally convenient two-dimensional steady-state problems with rectangular calculation domains. The Darcy-Brinkman-Forchheimer equations were used to model momentum transfer in the liquid-water-metal-foam region. For modeling the heat transfer, volume-averaged equations governing two intrinsic-phase-average temperature fields were used: one for the metal foam and the other for the water (solid or liquid). The following improvements are proposed: novel expressions for the interfacial (metal-water) heat transfer coefficient in both the convection and conduction regimes; and effective thermal conductivity correlations that provide consistency between the formulations of one-temperature and two-temperature models in the limit of local thermal equilibrium. A well-established fixed-grid, co-located, finite volume method (FVM) was adapted and used for the numerical solutions. The proposed models and FVM were used to solve the test and demonstration problems involving conduction and laminar natural convection in ice-water-aluminum-foam systems contained in rectangular enclosures. The findings and results of these investigations are presented and discussed in this paper.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".