MétaCan
Menu
Back to cohort
Record W4389129346 · doi:10.1139/tcsme-2023-0100

Comparison of flow and loss aspects in the rotors of a counter-rotating turbine

2023· article· en· W4389129346 on OpenAlexvenueno aff
Rayapati Subbarao

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsMechanicsStreamlines, streaklines, and pathlinesTurbineTurbulenceRotor (electric)Flow (mathematics)Turbulence kinetic energyWakeInletSecondary flowWells turbineAxial compressorPhysicsTurbine bladeGas compressorEngineeringAerospace engineeringMechanical engineering

Abstract

fetched live from OpenAlex

A counter-rotating turbine (CRT) is considered as an alternative way of obtaining more work without the use of another guide vane in a multi-stage turbine. In such a scenario, the present study discusses the flow transmission that takes place in rotors, which are rotating in the reverse direction to each other. The CRT stage with nozzles and rotors is modeled using ICEMCFD 14.5. Total pressure is specified at the inlet of the turbine stage, and flow rate is specified at the second rotor outlet. Contours of total pressure and turbulence kinetic energy provide the flow pattern in terms of steadiness, wake formation, incidence, flow circulation, and flow turbulence. Velocity vectors and streamlines offer clarity about flow separation, vortex formation, and wake detection. The deviation of flow characteristics from inlet to outlet of the CRT stage is also presented. For further understanding of the flow, transverse planes at different locations of the rotors are taken. Entropy and secondary velocity vectors are used to identify the loss aspect at each section of the rotors. From the blade-to-blade contours, the effect of the absence of a second guide vane is clear. Clearly, flow through rotor 1 is advantageous, and flow through rotor 2 is chaotic.

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 score0.103
Threshold uncertainty score0.470

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 head0.012
GPT teacher head0.230
Teacher spread0.218 · 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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicTurbomachinery Performance and OptimizationFrench-language works237,207