Development of a Digital Twin to Support the Calibration of a Highly Efficient Spark Ignition Engine
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">The role of numerical simulations in the development of innovative and sustainable powertrains is constantly growing thanks to their capabilities to significantly reduce the calibration efforts and to point out potential synergies among different technologies. In such a framework, this paper describes the development of a fully physical 1D-CFD engine model to support the calibration of the highly efficient spark ignition engine of the PHOENICE (PHev towards zerO EmissioNs &amp; ultimate ICE efficiency) EU H2020 project. The availability of a reliable simulation platform is essential to effectively exploit the combination of the several features introduced to achieve the project target of 47% peak gross indicated efficiency, such as Swumble<sup>TM</sup> in-cylinder charge motion, Miller cycle combined with high Compression Ratio (CR), lean mixture exploiting cooled low pressure Exhaust Gas Recirculation (EGR) and electrified turbocharging. Particular attention was paid to the definition of a combustion model capable of predicting engine burn rates in highly diluted conditions as well as the likelihood of abnormal combustion phenomena such as knock. A set of preliminary experimental measurements carried out on the first engine prototype was used to assess the reliability of the developed digital twin. Afterwards, the 1D-CFD model was used to identify, under steady state conditions, the optimal setting of calibration parameters in terms of intake valves actuation, throughout the whole engine operating map. Findings demonstrated that the lean and diluted combustion process combined with the high CR of 13.6 and aggressive EIVC strategy enabling unthrottled operation made it possible to achieve the target of 47% peak gross indicated efficiency at part load. When operating at full load, the use of cooled low pressure EGR significantly reduced knock likelihood and permitted to avoid any mixture enrichment, allowing for the achievement of performance targets without incurring in fuel consumption penalties.</div></div>
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".