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Record W3211902531 · doi:10.1115/imece2001/aes-23645

Exergy Analysis of a Turbojet Engine Over a Complete Flight Cycle

2001· article· en· W3211902531 on OpenAlexaff
Marc A. Rosen

Bibliographic record

VenueAdvanced Energy Systems · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced Aircraft Design and Technologies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsCruiseTurbojetExergyEnvironmental scienceWork (physics)Sensitivity (control systems)Descent (aeronautics)ClimbComputer scienceMarine engineeringAutomotive engineeringEngineeringAerospace engineeringMechanical engineeringProcess engineering

Abstract

fetched live from OpenAlex

Abstract Exergy analysis is used to evaluate the efficiency of a turbojet engine over an entire flight (including climb, cruise and descent), and to assess the sensitivity of these results to the selection of the reference environment. This study is an extension of previous work where the accuracy of an exergy analysis of a turbojet engine is evaluated for different reference environments on an instantaneous basis. The present work evaluates cumulative engine efficiencies. The results show that the use of a constant reference environment set at cruise altitude conditions yields cumulative exergy efficiencies that are within 0.01% of those found using a variable reference environment (equal to the operating environment conditions at all times) over a 3,500 km flight. This result is in contrast to the use of a constant sea-level reference environment where such are 3.7%. This significant cumulative efficiency difference with the choice of reference environment is not observed when using an instantaneous exergy analysis. Care must be exercised when using a constant reference environment, as for both constant sea-level and cruise-altitude reference environments cumulative rational efficiencies increase over the majority of the flight, whereas for a continuously varying reference environment these efficiencies decrease.

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 score0.585
Threshold uncertainty score0.788

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
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 head0.008
GPT teacher head0.213
Teacher spread0.206 · 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
Published2001
Admission routes1
Has abstractyes

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