Quantification of heat transfer associated with water cooling in dies for low pressure die casting
Bibliographic record
Abstract
The low-pressure die casting (LPDC) process is widely used to manufacture aluminum alloy wheels and other rotationally symmetric components. Effective thermal management of the die is essential to control solidification and avoid casting defects. Water-cooled channels embedded in the die play a key role in heat extraction, yet their design is often based on trial and error knowledge due to limited fundamental understanding of boiling behavior under transient cooling conditions. This research investigates the evolution of boiling heat transfer regimes in water-cooled steel channels under LPDC-relevant conditions. A lab-scale experimental system was developed using a heated H13 steel block with internal water channels and subsurface thermocouples to measure transient temperature histories. The recorded data were analyzed using an inverse heat conduction (IHC) algorithm to determine surface heat transfer coefficients (HTCs) as a function of time and temperature. The resulting boiling curves reveal four distinct regimes: an initial unsteady boiling region, a partial subcooled boiling regime dominated by nucleate boiling, a transition boiling region, and a single-phase convection regime. The partial boiling region was identified as the most effective for heat extraction, with its behavior strongly influenced by flow rate, initial die temperature, coolant temperature, and flow duration. Empirical correlations were developed for each heat transfer regime, including a modified Dittus–Boelter equation for the single-phase region. These correlations were implemented in a forward thermal model to assess their predictive capability. The model demonstrated strong agreement with experimental measurements across a range of cooling scenarios, including steady-state and interrupted flow conditions.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".