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Record W2010084123 · doi:10.4271/2015-01-0822

Model Predictive Control for Combustion Timing and Load Control in HCCI Engines

2015· article· en· W2010084123 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

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2015
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHomogeneous charge compression ignitionModel predictive controlControl theory (sociology)CombustionIgnition systemController (irrigation)ActuatorComputer scienceAutomotive engineeringValve timingEngineeringControl (management)Internal combustion engineCombustion chamber

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">A Model Predictive Control (MPC) strategy for Homogeneous Charge Compression Ignition (HCCI) combustion timing and output work control that takes into account actuator constraints is designed. The MPC is based on the linearized version of a nonlinear Control Oriented Model (COM). The COM for the HCCI engine has combustion timing and engine load as outputs and valve timing and fueling rate as the inputs. The COM model is developed and validated and found to be accurate enough for control purposes and can be implemented in real-time. A Detailed Physical Model (DPM) is used to test the controller using the valve timing and fueling rate as constrained actuators. Constraints on combustion timing and output work are also considered to prevent ringing or misfire. The simulation results show that the developed controller works over a range of load conditions and can maintain HCCI combustion timing and load to their desired values.</div></div>

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)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.806
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.019
GPT teacher head0.253
Teacher spread0.234 · 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