Control-Oriented Data-Driven and Physics-Based Modeling of Maximum Pressure Rise Rate in Reactivity Controlled Compression Ignition Engines
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.
Bibliographic record
Abstract
<div>Reactivity controlled compression ignition (RCCI) is a viable low-temperature combustion (LTC) regime that can provide high indicated thermal efficiency and very low nitrogen oxides (NOx) and particulate matter (PM) emissions compared to the traditional diesel compression ignition (CI) mode [<span>1</span>]. The burn duration in RCCI engines is generally shorter compared to the burn duration for CI and spark-ignition (SI) combustion modes [<span>2</span>, <span>3</span>]. This leads to a high pressure rise rate (PRR) and limits their operational range. It is important to predict the maximum pressure rise rate (MPRR) in RCCI engines and avoid excessive MPRRs to enable safe RCCI operation over a wide range of engine conditions. In this article, two control-oriented models are presented to predict the MPRR in an RCCI engine. The first approach includes a combined physical and empirical model that uses the first principle of thermodynamics to estimate the PRR inside the cylinder, and the second approach estimates MPRR through a machine learning method based on kernelized canonical correlation analysis (KCCA) and linear parameter-varying (LPV) methods. The KCCA-LPV approach proved to have higher prediction accuracy compared to physics-based modeling while requiring less amount of calibration. The KCCA-LPV approach could estimate MPRR with an average error of 47 kPa/CAD while the physics-based approach’s average estimation error was 87 kPa/CAD.</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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it