Complex Lattice Structure-Based Heat Exchangers Through Additive Manufacturing: Opportunities and Challenges
Bibliographic record
Abstract
Abstract Cellular structures are a class of materials that offer greater stiffness, strength-to-weight ratio, good energy absorption capacity, and high heat transfer capability compared to solid parts. Metallic lattice structures have been applied in different industry sectors, such as in biomedical implants, lightweight components, energy absorbers, and catalytic reactors. With the development of advanced manufacturing techniques, especially additive manufacturing (AM), lattice structures with complicated designs can be produced. Lattice structure-based heat exchangers produced by AM techniques have recently gained significant attention due to their promising performance. Interconnected cavities in lattice structures provide flow of fluid and effective thermal conductivity, which is desirable in heat exchangers. AM methods provide the possibility to promote tortuosity and intricate flow patterns leading to improved performance of heat exchangers. Between different AM techniques, laser powder bed fusion (LPBF) proved to be a suitable method for the manufacture of heat exchangers. Using LPBF methods, the distribution and geometry of cavities in the structure can be controlled with an accuracy that is typically better than for other AM methods. Although LPBF-produced heat exchanger showed enhanced thermal conductance, there are limitations associated with LPBF fabrication, such as surface roughness and need for post processing. In order to bridge this gap, the effects of different process parameters and levels of structural complexity in LPBF processes need to be evaluated. In this context, the present contribution constitutes a position paper that contrasts the opportunities that LPBF may provide for the fabrication of heat exchangers with the challenges that need to be overcome to realize design solutions that meet industry demands.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.002 | 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 teacher head, 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".