Mode Switching Control for Diesel Low Temperature Combustion with Fast Feedback Algorithms
Bibliographic record
Abstract
Low temperature combustion (LTC) in diesel engines can be enabled using a multitude of fuel injection strategies, coupled with the elevated use of exhaust gas recirculation and intake boost. The common modes of LTC include the single-injection LTC with heavy EGR and the homogeneous charge compression ignition (HCCI), implemented with multiple early-injections during the compression stroke. Previous research indicates that the single-injection LTC is more suitable at low engine loads while the HCCI combustion can be targeted towards mid-load operation. To extend the load range of the LTC cycles, there is an urgent need to enable switching on-the-fly between the two combustion modes. The mode-switching is complicated by the fact that the challenges of enabling and ensuring stable engine operation under these two LTC modes are notably different. Moreover, the LTC cycles are inherently more sensitive to small changes in the operating variables such as the combustion phasing and the intake dilution, and therefore, the combustion control system must be able to adequately respond to such disturbances on a cycle-by-cycle basis. In this work, cylinder pressure measurement-based computation of combustion phasing and indicated mean effective pressure (IMEP), demonstrated in the authors' previous work for single-injection diesel LTC, has been used to enable and stabilize the LTC modes by precise control of single/multi-injection events. The IMEP estimation technique has been extended to modulate multiple fuel-injection events for dynamic load and stability control of HCCI combustion. A mode-switching algorithm is then proposed and demonstrated with engine tests, for enabling seamless transition between the two modes of LTC, by executing a pre-defined sequence triggered by an IMEP threshold, while pressure feedback-based control over individual injection events ensures the stability of the combustion. Representative results with controller gain modification indicate the possibility of controller tuning for improving the mode-switching time and transient performance. Modifications in the proposed algorithm are suggested to optimize the performance and enhance the robustness of the mode-switching process.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".