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Study of full and truncated aerospike nozzles on performances at different working conditions

2018· article· en· W2891299362 on OpenAlexaboutno aff
Oana Dumitrescu, Bogdan Gherman, Valeriu Drăgan

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicRocket and propulsion systems research
Canadian institutionsnot available
Fundersnot available
KeywordsNozzlePropulsionAerospace engineeringSpark plugTruncation (statistics)AerodynamicsThrustMarine engineeringComputational fluid dynamicsMechanical engineeringEngineeringMechanicsEnvironmental scienceComputer sciencePhysics

Abstract

fetched live from OpenAlex

Aerospike nozzles have been a spinoff the plug nozzle alternative for propulsion systems that require adaptation for outside pressure variations. Their capacity to adapt their aerodynamics without the need of moving parts makes them very interesting for space launching vehicles. Conventional Laval nozzles have to trade-off performance as they cross the atmosphere from sea level to their maximum altitude. In this study, the flow simulation is carried out for full and truncated nozzle. Three cases for the truncated length are chosen: 40%, 50% and 60% plug in different working conditions. In over-expansion conditions, with the increase of plug truncation a loss of thrust is observed, compared with the under-expansion conditions, were the nozzle truncation has a negligible effect. CFD analysis shows which plug truncation is giving the optimum performances and how great is the influence of altitude and temperature on this type of propulsion system.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.260
Teacher spread0.225 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations5
Published2018
Admission routes1
Has abstractyes

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