Performance, Manufacturability and Mechanical Properties of Near-Net Shaped Pyramidal Fin Arrays for Compact Heat Exchangers Produced Using Cold Spray as an Additive Manufacturing Technique
Bibliographic record
Abstract
Significant efforts have been made in the last decades to decrease the world’s dependency to fossil fuels. One of the fronts which has shown major improvement is gas turbine efficiency. To this end, components such as recuperators have been developed to recover heat that is usually trapped and wasted in the exhaust gases of combustion processes. Brayton Energy Canada has recently developed a promising compact heat exchanger that could be used as a recuperator in gas turbines. Nevertheless, this novel type of wire mesh heat exchanger still has room for improvement, especially regarding the way that its fin arrays are manufactured due to the fact that the technique presently used is time consuming and consequently costly. The present research aims to manufacture near-net shaped pin fin arrays using cold gas dynamic spray as an additive manufacturing technique by selectively covering the substrate by the means of a mask. Moreover, this research work studies the feasibility of using CGDS as an additive manufacturing technique to produce pin fin arrays, the thermal and hydrodynamic performances of this new type of pin fin created, the effect of geometric parameters such as fin density and height on the performances, the viability of the sprayed pin fins in a real environment by means of finding mechanical properties such as adhesion strength, the possibility of producing a streamwise material anisotropic fin arrays, and finally the different adhesion mechanisms by means of numerical modeling of the relevant impact physics.
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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".