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Record W2805559271 · doi:10.1115/1.2015-dec-6

Model-Based Engine Control

2015· article· en· W2805559271 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMechanical Engineering · 2015
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsComputational fluid dynamicsCombustionCrankCombustion chamberStirling engineExternal combustion engineComputer scienceHomogeneous charge compression ignitionProcess (computing)Mechanical engineeringAutomotive engineeringCylinderEngineeringAerospace engineeringChemistry

Abstract

fetched live from OpenAlex

Abstract This article focuses on control-oriented engine modeling and model-based engine control techniques. The engine modeling research is centered on the engine combustion process. Multi-zone, three dimensional computational fluid dynamics (CFD) models, with detailed chemical kinetics are able to precisely describe the thermodynamics, fluid and flow dynamics, heat transfer, and pollutant formation of the combustion process. The simplified one-dimensional combustion models have also been implemented into commercial codes such as GT-Power and Wave. However, these high fidelity models cannot be used for model-based control since they are too complicated to be used for real-time computing. Crank-resolved engine air handling system modeling is also important for describing the in-cylinder charge-mixing process. Therefore, for model-based control and real-time hardware-in-the-loop simulations, it is necessary to have a crank-resolved engine model with its complexity intermediate between the time-based mean-value and one-dimensional CFD models.

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.

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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.855
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.022
GPT teacher head0.229
Teacher spread0.207 · 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