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Combining methods for end-to-end calculation of gas flow in a Laval nozzle

2021· article· en· W3133152594 on OpenAlexaboutno aff
Roman Dushkov, R. I. Dzerzhinsky

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicRadiative Heat Transfer Studies
Canadian institutionsnot available
Fundersnot available
KeywordsNozzleMechanicsInviscid flowDischarge coefficientRocket engine nozzleRotational symmetryFlow (mathematics)MathematicsComputer scienceMechanical engineeringPhysicsEngineering

Abstract

fetched live from OpenAlex

Abstract The paper solves the problem of selecting optimal methods and combining them when performing a complex calculation of all sections of the Laval nozzle. The problem is significant, since different flow modes are realized in different areas of the nozzle and it is impossible to use a single method for the full calculation. Solving the direct problem, parameters in the flow of gas are determined. Solving the reverse problem determine the profile of the Laval nozzle. Profiling is an important applied task for calculating the nozzles of rocket and aircraft engines, or designing experimental turbines. In this paper, we consider a two-dimensional plane axisymmetric flow of an inviscid perfect gas (on the example of dry air). The problem is solved starting with an accelerating subsonic flow via using methods of establishing and finite elements. For subsonic flow found an exact solution (Prandtl-Mayer flow). Solving the problem in a supersonic flow via using proximate methods (the method of characteristics and interpolation for the grid normalization). The solution is supplemented by a method that allows taking into account the features of axisymmetric flow in the nozzle to expand the applicability of the solution for real nozzles. The results obtained – the gas velocity field and the coordinates of nodes. Results allows estimation of the optimal size of the Laval nozzle for the given conditions, its shape, and obtain a flow profile depending on its initial characteristics defined in the previous section of the nozzle. The results of flow simulation can be applied to calculate the flow in circular sections, which are the most common in the current application. The results are confirmed by the solution using the finite element method, and the convergence of the solution in the diffuser is described in the current work. Theoretically, the solution can be applied to a mixture of gases, if peculiarities of physical and chemical transformations and the participation of combustion products are taken into consideration. In this case instead of parameters in the pre-chamber, the parameters of the combustion chamber and the products in it should be used as initial conditions. This calculation will be carried out in future studies of the authors.

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)0.001
metaresearch head score (Gemma)0.003
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.025
GPT teacher head0.282
Teacher spread0.256 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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