Micro-Mixing Injector Manufacturing in Refractory Metal Using Laser Powder Bed Fusion Process
Bibliographic record
Abstract
Abstract The decarbonization and reduction of pollutant emissions are at the core of research in the field of gas turbine combustion. One of the hot topics in this regard is the integration of hydrogen, which comes with design challenges and combustor operability limits due to its higher flame temperatures and its propensity to form nozzle-attached flames. These characteristics lead to higher operating temperature requirements for hydrogen fuel injectors to extend their operability. Refractory metals are interesting because their melting temperatures are significantly higher than the combustor outlet temperatures, which opens up new design possibilities. However, multiple challenges, such as oxidation and manufacturing issues, arise with refractory metals. The laser powder bed fusion (LPBF) process is gaining momentum in the manufacturing of micro-mixing injectors because of its suitability to produce complex geometry parts. This additive manufacturing process can be applied to refractory metals, opening up new perspectives for micro-mixing injector designs. In this paper, a small-scale micro-mixing injector in molybdenum was manufactured using the LPBF process to demonstrate the printability of such parts in refractory metals. The geometric precision achieved was acceptable for micro-mixing combustion requirements. Small holes with a nominal size of 0.3 mm, representing injection nozzles, and 0.3 mm-thin walls, demonstrating the possibility to print complex geometries, were successfully produced. An environmental barrier coating (EBC) was developed in parallel to explore the challenges of protecting complex LPBF parts from high-temperature oxidizing environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".