Analysis of heat transfer in spray cooling systems using numerical simulations
Bibliographic record
Abstract
The impingement of sprays onto dry and wet walls and the associated heat transfer occurs in many engineering applications. These applications include internal combustion engines, gas turbines, spray drying, spray coating and spray cooling. The fluid dynamics and heat transfer characteristics of liquid films created by spray impingement are very complex and determining the underlying physics requires fundamental studies. In this study, an efficient and practical approach is devised for tackling many aspects of the spray cooling process. The computational fluid dynamics (CFD) methodology used here includes numerous droplets and it is designed to predict the spray-wall impact outcome based on reliable correlations. Even though it is not an exact representation of the interaction between the spray and the liquid layer due to computational considerations, it provides an acceptable picture of the transport phenomena. The STAR-CCM+ CFD code has been used to solve continuity, momentum, and energy equations coupled with a Lagrangian-Eulerian solver capable of simulating droplets as well as thin fluid film. The model is validated against relevant experimental data available in the literature and good agreement is observed for heat transfer coefficient (HTC) values for cases involving spray impact and fluid film formation over a flat solid surface. The effect of mass flux and spray Reynolds number on the spray behaviour has been studied. The model is extended to predict the cooling performance of sealed cans containing hot liquids when the cans are cooled by the impingement of spray formed from a cold liquid. The CFD results are compared with field data obtained at Heinz Canada, Leamington, ON. The effect of the can rotational speed on the cool-down behaviour is investigated. The results show that there is an optimum rotational speed beyond which the heat transfer enhancement will not be as significant. This research is the first study which solves the transport phenomena of fluid and heat outside, through and inside a sealed solid can containing a hot liquid while being cooled by the spray of a cold liquid.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".