Design and test a converging and de Laval nozzle using additive manufacturing
Bibliographic record
Abstract
The advent of additive manufacturing technology has facilitated the design and fabrication of parts and models in both academia and aerospace industry. Compressible flow in the nozzles is not a new research topic; however, the accuracy of the experimental results obtained from the nozzles using additive manufacturing has not been assessed comprehensively. Surface roughness and strength of 3D-printed nozzles are two major concerns when they are applied to compressible flows. In this paper, a converging and a de Laval nozzle fabricated by additive manufacturing using ABS filament are designed and tested. Surface roughness inside the converging nozzle is quantified using a nondestructive method. In general, the experimental results compare well with the analytical solutions from isentropic equations for the converging nozzle and the numerical simulations conducted in ANASYS Fluent for the de Laval nozzle. 3D-printed nozzles can be employed to quickly demonstrate and verify novel ideas and concepts in the pedagogy and research at large Reynolds numbers.
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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) | 0.000 | 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".