Physics-Informed Neural Networks for In-Cylinder Pressure Prediction in Hydrogen/Diesel Dual-Fuel Engines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it