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Record W4292582824 · doi:10.3389/fpace.2022.951987

Design and test a converging and de Laval nozzle using additive manufacturing

2022· article· en· W4292582824 on OpenAlexaboutno aff
Mingtai Chen, Ruksana Baby, Seth Dillard, Yi Tsung Lee, Srinath V. Ekkad

Bibliographic record

VenueFrontiers in Aerospace Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsnot available
FundersDivision of Electrical, Communications and Cyber SystemsNorth Carolina State UniversityNational Science Foundation
KeywordsNozzleSurface roughnessMechanical engineeringAerospaceFused filament fabricationSurface finishFluentCompressibilityIsentropic processComputer scienceEngineering3D printingMaterials scienceAerospace engineeringComputational fluid dynamicsMechanicsPhysicsComposite material

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.238
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.165
Teacher spread0.161 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2022
Admission routes1
Has abstractyes

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