Topology optimization for the design of additively-manufactured cooling channels
Bibliographic record
Abstract
The design of cooling channels has a crucial impact on the efficiency of heat exchangers and other devices employed in the thermal management of industrial processes. The complexity and the multidisciplinary nature of the physics involved in these processes make the optimal design a challenging task. Furthermore, the limitations of traditional manufacturing techniques reduce the domain of possible designs. The recent advent of additive manufacturing has opened the door to new design paradigms guided by optimality considerations. We propose a framework for the development of a topology optimization methodology targeted at the optimal design of cooling channels in die casting molds. We consider two optimization approaches, one diffuse and one sharp, arising from the way the interface between the cooling fluid and the solid body of the mold is represented. These representations result in different thermo-fluid models which are both implemented in our in-house software and validated against a conformal solver. An adjoint-based methodology is adopted to compute the gradient of the cost function, taken as the average temperature in the domain for instance, with respect to the design parameters, e.g. the solid fraction value at discrete points in the domain. For an initial solid fraction distribution, the values of the cost function and its gradient are provided to an optimization module, which produces a new solid fraction field intended to minimize the cost function. The sequential application of these steps results in the evolution of the initial cooling channel design to an optimal design, which efficiently minimizes the average temperature. An example of such an optimization is presented for laminar flows, followed by a discussion of the results.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".