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Record W4312236118 · doi:10.1115/gt2022-83091

Numerical Simulation and Experimental Study on Ejector of Lubricating Oil System of Gas Turbine Engine

2022· article· en· W4312236118 on OpenAlexaboutno aff
Shuo Zhang, Ruishi Feng, Wenjun Gao, Pengfei Zhu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsInjectorNozzleWorking fluidMixing (physics)MechanicsTurbineMaterials scienceMechanical engineeringFluid dynamicsComputational fluid dynamicsRefrigerationEnvironmental scienceEngineeringPhysics

Abstract

fetched live from OpenAlex

Abstract The ejector is a device that uses a high-speed, high-energy working fluid to eject another low-speed, low-energy fluid. The working fluid enters the mixing chamber after being accelerated by the nozzle and forms a low-pressure area in the mixing chamber. Through the mixing and entrainment of the two-fluid boundaries, the ejected fluid mixes with the working fluid and obtains kinetic energy. At the exit of the mixing chamber, the flow tends to be uniform. An expansion pipe is usually connected behind the outlet of the mixing chamber to reduce the flow rate and increase the static pressure. The ejector has a simple structure without moving parts or electrical equipment, and is widely used in wind tunnel facilities, ventilation equipment, refrigeration equipment and other fields. In recent years, ejectors have also been gradually used in aero-engine lubricating oil systems for the supply and discharge of oil and oil-gas mixtures. Although the ejector has a simple structure, many factors affect its ejection efficiency, including but not limited to the shape of the working fluid nozzle and the volume of the mixing chamber. The parameter that measures the efficiency of the ejector is the ejection coefficient, that is, the ratio of the volume flow of the ejected fluid to the working fluid. How to improve the ejector coefficient of ejector under different working conditions is an important subject of ejector research. This research is mainly aimed at a kind of ejector used in an oil-gas mixture of gas turbine engine lubricating oil system. In this study, a single-phase numerical simulation of the internal flow field of the ejector was carried out, and the numerical calculation results were verified experimentally. Under the premise of maintaining the original structure of the ejector, the relative position of the low-pressure zone and the ejected fluid in the mixing chamber was changed to explore the influence of this distance on the ejection efficiency. Under the same inlet and outlet boundary conditions, the design of the ejector working fluid nozzle was changed to explore the influence of the working fluid nozzle shape on the ejection efficiency. These structures include sudden shrinking nozzles, Laval nozzles and convergent nozzles. Numerical calculation results show that the relative position of the low-pressure zone in the mixing chamber and the ejected fluid has a greater impact on the ejection efficiency: 1. If the distance is too small or too large, the ejection efficiency will decrease, and the effect of too large distance is more obvious. 2. When the ejected fluid enters the mixing chamber, the ejection efficiency is maximum when the angle between the streamline direction and the working fluid flow direction is about 75°. The working fluid nozzle has a decisive influence on the ejection efficiency: 1. The sudden shrinking nozzle has a large local loss, and the ejection effect is not obvious. 2. The Laval nozzle ejector has higher requirements on the flow state of the working fluid. When the flow state of the working fluid does not match the geometric design, the ejector efficiency is low. When the two are matched, the Laval nozzle ejector has a higher ejection efficiency; 3. The convergent nozzle ejector has the problem of flow congestion, but it is suitable for working fluids in a variety of flow conditions and has low requirements for geometric design. Compared with the Laval nozzle ejector, this configuration has low efficiency. The results of this research are helpful to determine the design scheme and installation location of the oil-gas mixture ejector of the lubricating oil system and provide reference ideas for the design and optimization of the external pipeline layout of the gas turbine 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 score0.124
Threshold uncertainty score0.225

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 head0.015
GPT teacher head0.251
Teacher spread0.237 · 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
Published2022
Admission routes1
Has abstractyes

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