Combustion Characterization and Heat Release Rate Modeling of a Heavy-Duty Hydrogen-Diesel Dual-Fuel Engine
Bibliographic record
Abstract
Introducing hydrogen (H2) into the intake air of diesel engines provides a near-term approach to reducing tailpipe CO2 emissions from heavy-duty commercial vehicles. The premixed hydrogen results in a complex H2-Diesel dual fuel (H2DF) combustion process, where H2 can both participate in the non-premixed diesel combustion and result in a propagating H2/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 H2 introduced as a function of the total fuel energy (H2 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 H2DF 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 H2DF 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 H2 introduction significantly impacts combustion characteristics and emissions, primarily influenced by the H2 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 H2 equivalence ratios than 0.1, an H2/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 H2 utilization strategies tailored for the retrofit of heavy-duty H2DF truck applications.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".