Physics-Informed Neural Networks for In-Cylinder Pressure Prediction in Hydrogen/Diesel Dual-Fuel Engines
Bibliographic record
Abstract
A heavy-duty diesel engine converted to a hydrogen/diesel dual-fuel (HDDF) engine can reduce fossil fuel usage and harmful emissions. To maximize the hydrogen energy share compared to diesel, it is important to monitor and control the combustion process to maintain engine durability. Predicting the combustion process in these retrofitted engines at different operating points using simple combustion models such as the Wiebe function often leads to significant mismatches. These simple models are insufficient for real-time diagnostics, which is essential at high hydrogen replacement ratios. One promising way to improve the accuracy of combustion models is using machine learning (ML) methods, which can potentially enhance the computational speed and predictive accuracy. Combining physics knowledge with ML methods like deep neural networks (DNN) or Kolmogorov-Arnold networks (KAN) is a useful hybrid method called a physics-informed neural network (PINN). This study compares the ML methods and PINN networks for predicting the in-cylinder pressure of the HDDF engine. The most accurate model tested is an integrated KAN and DNN model that incorporates the underlying physics of the system to predict in-cylinder pressure for unseen data. All the models tested utilize crank-angle data and injection timings as the inputs. The results showed that while the ML models have a high prediction error on the unseen data, adding a physics loss function, which penalizes deviations from physical laws, increases the generalization capability, making them a good choice for diagnostic models. The root mean square error for the novel PINN-KAN-DNN network on unseen cylinder pressure data is 15.1 bar, which represents a decrease of 14.8%, 50.3%, and 16.3% compared to DNN, KAN, and KAN-DNN, respectively.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 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".