Ignition delay prediction for fuels with diverse molecular structures using transfer learning-based neural networks
In this study, a transfer learning-based neural network approach to predict ignition delays for a variety of fuels is proposed to meet the demand for accurate combustion analysis. A comprehensive dataset of ignition delays was generated using a random sampling technique across different temperatures...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Energy and AI |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666546824001332 |
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| author | Mo Yang Dezhi Zhou |
| author_facet | Mo Yang Dezhi Zhou |
| author_sort | Mo Yang |
| collection | DOAJ |
| description | In this study, a transfer learning-based neural network approach to predict ignition delays for a variety of fuels is proposed to meet the demand for accurate combustion analysis. A comprehensive dataset of ignition delays was generated using a random sampling technique across different temperatures and pressures, focusing on hydrocarbon fuels with 1–4 carbon atoms. Two machine learning models, an artificial neural network and a graph convolutional network, are trained on this dataset, and their prediction performance was evaluated. A transfer learning framework was subsequently developed, enabling the models trained on smaller molecules (1–3 carbon atoms) to predict ignition delays for larger molecules (4 carbon atoms) with minimal additional data. The proposed framework demonstrated reliable and high prediction accuracy, achieving a high level of reliability for fuels with limited experimental measurements. This approach offers significant potential to streamline the prediction of ignition delays for novel fuels, reducing the dependence on resource-intensive experiments and complex simulations while contributing to the advancement of clean and efficient energy technologies. |
| format | Article |
| id | doaj-art-92547cd1726b412685c7d3d006bed5b6 |
| institution | Kabale University |
| issn | 2666-5468 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy and AI |
| spelling | doaj-art-92547cd1726b412685c7d3d006bed5b62025-01-27T04:22:22ZengElsevierEnergy and AI2666-54682025-01-0119100467Ignition delay prediction for fuels with diverse molecular structures using transfer learning-based neural networksMo Yang0Dezhi Zhou1UM-SJTU Joint Institute, Shanghai Jiao Tong University, Shanghai, 200240, ChinaCorresponding author.; UM-SJTU Joint Institute, Shanghai Jiao Tong University, Shanghai, 200240, ChinaIn this study, a transfer learning-based neural network approach to predict ignition delays for a variety of fuels is proposed to meet the demand for accurate combustion analysis. A comprehensive dataset of ignition delays was generated using a random sampling technique across different temperatures and pressures, focusing on hydrocarbon fuels with 1–4 carbon atoms. Two machine learning models, an artificial neural network and a graph convolutional network, are trained on this dataset, and their prediction performance was evaluated. A transfer learning framework was subsequently developed, enabling the models trained on smaller molecules (1–3 carbon atoms) to predict ignition delays for larger molecules (4 carbon atoms) with minimal additional data. The proposed framework demonstrated reliable and high prediction accuracy, achieving a high level of reliability for fuels with limited experimental measurements. This approach offers significant potential to streamline the prediction of ignition delays for novel fuels, reducing the dependence on resource-intensive experiments and complex simulations while contributing to the advancement of clean and efficient energy technologies.http://www.sciencedirect.com/science/article/pii/S2666546824001332Ignition delayFuel combustion propertiesTransfer learningArtificial neural networkGraph convolutional network |
| spellingShingle | Mo Yang Dezhi Zhou Ignition delay prediction for fuels with diverse molecular structures using transfer learning-based neural networks Energy and AI Ignition delay Fuel combustion properties Transfer learning Artificial neural network Graph convolutional network |
| title | Ignition delay prediction for fuels with diverse molecular structures using transfer learning-based neural networks |
| title_full | Ignition delay prediction for fuels with diverse molecular structures using transfer learning-based neural networks |
| title_fullStr | Ignition delay prediction for fuels with diverse molecular structures using transfer learning-based neural networks |
| title_full_unstemmed | Ignition delay prediction for fuels with diverse molecular structures using transfer learning-based neural networks |
| title_short | Ignition delay prediction for fuels with diverse molecular structures using transfer learning-based neural networks |
| title_sort | ignition delay prediction for fuels with diverse molecular structures using transfer learning based neural networks |
| topic | Ignition delay Fuel combustion properties Transfer learning Artificial neural network Graph convolutional network |
| url | http://www.sciencedirect.com/science/article/pii/S2666546824001332 |
| work_keys_str_mv | AT moyang ignitiondelaypredictionforfuelswithdiversemolecularstructuresusingtransferlearningbasedneuralnetworks AT dezhizhou ignitiondelaypredictionforfuelswithdiversemolecularstructuresusingtransferlearningbasedneuralnetworks |