Combustion Characterization and Heat Release Rate Modeling of a Heavy-Duty Hydrogen-Diesel Dual-Fuel Engine
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Introducing hydrogen (H<sub>2</sub>) into the intake air of diesel engines provides a near-term approach to reducing tailpipe CO<sub>2</sub> emissions from heavy-duty commercial vehicles. The premixed hydrogen results in a complex H<sub>2</sub>-Diesel dual fuel (H<sub>2</sub>DF) combustion process, where H<sub>2</sub> can both participate in the non-premixed diesel combustion and result in a propagating H<sub>2</sub>/air combustion. These interactions influence engine combustion characteristics, including in-cylinder pressure and heat release rate (HRR), as well as emissions. The nature and extent of the impact depends on the amount of H<sub>2</sub> introduced as a function of the total fuel energy (H<sub>2</sub> energy share ratio - HES), the trapped air mass, and engine operating conditions. To optimize the HES ratio under different conditions, it is crucial to understand how H<sub>2</sub>DF combustion differs from diesel combustion and how this limits engine operation and impacts emissions. To investigate these effects, a heavy-duty class 8 truck fitted with an H<sub>2</sub>DF system developed by Hydra Energy Corp. was tested on a chassis dynamometer. The engine was fitted with a suite of instrumentation, including in-cylinder pressure, air system pressure and temperature, exhaust flow rate, and emissions measurement equipment. Tests were conducted over three loads and speeds at fixed HES ratios, and detailed HES ratio studies were conducted at low- and mid-load cases at 1200 RPM. The results show that H<sub>2</sub> introduction significantly impacts combustion characteristics and emissions, primarily influenced by the H<sub>2</sub> equivalence ratio, with the engine control unit’s adjustments to boost pressure and diesel injection timing playing a critical role in combustion characteristics and engine-out emissions. At higher H<sub>2</sub> equivalence ratios than 0.1, an H<sub>2</sub>/air premixed flame forms, advancing combustion phasing, which increases the maximum rate of pressure rise and reduces PM while raising NOx emissions. The Pcyl and HRR data are used to develop a semi-predictive combustion model imposing the net HRR profile using a multi-Wiebe function. A four-curve Wiebe function model can accurately capture the HRR and combustion characteristics across engine operating points, providing a reliable predictive tool at a given speed/load for various HES ratios. The developed understanding and combustion model provides valuable insight and techniques for future studies to further improve H<sub>2</sub> utilization strategies tailored for the retrofit of heavy-duty H<sub>2</sub>DF truck applications.</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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".