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Record W2047535764 · doi:10.1115/1.4002845

Effects of Combustion Chamber Geometry Deviations Upon Exit Temperature Profiles for Populations With Varied Service Limitations

2011· article· en· W2047535764 on OpenAlexaff
Clayton Kotzer, Marc LaViolette, W. Allan, Asad Asghar

Bibliographic record

VenueJournal of Engineering for Gas Turbines and Power · 2011
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsCombustion chamberCombustionSauter mean diameterMechanicsMach numberGeometric meanMaterials scienceGeometryAnalytical Chemistry (journal)MathematicsNozzleChemistryEngineeringPhysicsMechanical engineering

Abstract

fetched live from OpenAlex

The purpose of this continuing research was to investigate the effects of combustion chamber geometry on exit temperature fields using a validated ambient pressure test rig. Rig test conditions were set to simulate an engine operating condition of 463 km/h (250 kn) at 7620 m (25,000 ft) by matching Mach number, equivalence ratio, and Sauter mean diameter of the fuel spray. Using a thermocouple rake, high resolution temperature measurements were obtained in the combustion chamber exit plane. Following the previously published procedures, a three-dimensional laser scanning system was used to quantify geometric deviations from two populations of combustion chambers. These populations differed in that one had a significantly higher allowable engine operating temperature for continuous cruise condition. Geometric deviations of both populations were compared with the reference model. The relationship between combustion chamber exit temperature profile and geometric deviation of each population was then compared. The main conclusion of this research was that the temperature profile degradation of both populations due to geometric deviations followed similar trends. These results highlighted that the difference in operating limitations of these populations did not significantly affect component performance.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.399
Threshold uncertainty score0.538

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 head0.023
GPT teacher head0.232
Teacher spread0.209 · 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

Citations5
Published2011
Admission routes1
Has abstractyes

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