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Technology of computational analysis of the working process parameters of low-thrust rocket engines running on gaseous oxygen-hydrogen fuel with the use of ANSYS CFD

2019· article· en· W2953671007 on OpenAlexaboutno aff
V. V. Ryzhkov, И. И. Морозов

Bibliographic record

VenueVESTNIK of Samara University Aerospace and Mechanical Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicRocket and propulsion systems research
Canadian institutionsnot available
Fundersnot available
KeywordsRocket engineCombustion chamberRocket engine nozzleNozzleThrustAerospace engineeringComputational fluid dynamicsCombustionRocket (weapon)Mechanical engineeringAutomotive engineeringEngineeringNuclear engineeringChemistry

Abstract

fetched live from OpenAlex

The paper presents the description of a mathematical model of the working process of a low-thrust rocket engine operating on gaseous oxygen-hydrogen fuel and some fragments of the technology of computational analysis of distribution of gas-dynamic parameters in the engine duct. We present the results of calculating the stream line distribution, the distribution of total temperature profile along the flow path of the engine chamber and at its characteristic cross sections, the axial component of (total) speed of combustion products in the Laval nozzle output section. The results of calculating the temperature in the area of the rocket engines inner wall are presented. It is shown that the distribution of the combustion products stagnation temperature has a significant impact on the efficiency of fuel conversion in the engine chamber, its thermal state and makes it possible to identify the ways of improving the workflow of the low-thrust rocket engine.

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 categoriesnone
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 score0.333
Threshold uncertainty score0.418

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.015
GPT teacher head0.192
Teacher spread0.177 · 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.

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

Citations0
Published2019
Admission routes1
Has abstractyes

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