MétaCan
Menu
Back to cohort
Record W6999869857

Detonation Physics-Based Modelling & Design of a Rotating Detonation Engine

2019· dissertation· en· W6999869857 on OpenAlexfundaboutno aff

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2019
Typedissertation
Languageen
FieldEngineering
TopicCombustion and Detonation Processes
Canadian institutionsnot available
FundersMcGill University
KeywordsDetonationPropulsionCombustionIsentropic processConstant (computer programming)Flow (mathematics)Limit (mathematics)Mass flow
DOInot available

Abstract

fetched live from OpenAlex

A rotating detonation engine (RDE) is a new, more thermodynamically efficient, propulsion concept that replaces the traditional constant pressure combustion mechanism found in all currently used rockets and power generation devices. The constant pressure combustion is replaced by a detonation wave: a coupled shock-flame complex propagating at speeds of up to 2-3 km/s and generating combustion products at pressures 5-10 times the initial reactant pressure. This pressure gain through the combustion process leads to more compact, simpler devices that no longer require (or depend less upon) initial reactant precompression. Detonation-based cycles also have the added advantage of being theoretically more thermodynamically efficient than their constant pressure combustion counterparts. As such, RDEs have become increasingly popular in the propulsion research community, although there is still a lack of understanding in the underlying physics which govern their operability, though the existence of a minimum mass flow rate limit for stable operation has been observed. To help engineers and researchers design an RDE, a model was developed which combines geometric properties, 1D isentropic flow, and detonation physics to predict the stable operating bounds of an RDE. An engine testing facility was also constructed in collaboration with McGill University to test RDEs and confirm the performance of the prediction model developed.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.650
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.044
GPT teacher head0.270
Teacher spread0.226 · 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.

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
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueSpectrum Research Repository (Concordia University)Same topicCombustion and Detonation ProcessesFrench-language works237,207