Study on the prediction and inverse prediction of detonation properties based on deep learning
The accurate and efficient prediction of explosive detonation properties has important engineering significance for weapon design. Traditional methods for predicting detonation performance include empirical formulas, equations of state, and quantum chemical calculation methods. In recent years, with...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2023-06-01
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| Series: | Defence Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214914722002495 |
| _version_ | 1797796486161891328 |
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| author | Zi-hang Yang Ji-li Rong Zi-tong Zhao |
| author_facet | Zi-hang Yang Ji-li Rong Zi-tong Zhao |
| author_sort | Zi-hang Yang |
| collection | DOAJ |
| description | The accurate and efficient prediction of explosive detonation properties has important engineering significance for weapon design. Traditional methods for predicting detonation performance include empirical formulas, equations of state, and quantum chemical calculation methods. In recent years, with the development of computer performance and deep learning methods, researchers have begun to apply deep learning methods to the prediction of explosive detonation performance. The deep learning method has the advantage of simple and rapid prediction of explosive detonation properties. However, some problems remain in the study of detonation properties based on deep learning. For example, there are few studies on the prediction of mixed explosives, on the prediction of the parameters of the equation of state of explosives, and on the application of explosive properties to predict the formulation of explosives. Based on an artificial neural network model and a one-dimensional convolutional neural network model, three improved deep learning models were established in this work with the aim of solving these problems. The training data for these models, called the detonation parameters prediction model, JWL equation of state (EOS) prediction model, and inverse prediction model, was obtained through the KHT thermochemical code. After training, the model was tested for overfitting using the validation-set test. Through the model-accuracy test, the prediction accuracy of the model for real explosive formulations was tested by comparing the predicted value with the reference value. The results show that the model errors were within 10% and 3% for the prediction of detonation pressure and detonation velocity, respectively. The accuracy refers to the prediction of tested explosive formulations which consist of TNT, RDX and HMX. For the prediction of the equation of state for explosives, the correlation coefficient between the prediction and the reference curves was above 0.99. For the prediction of the inverse prediction model, the prediction error of the explosive equation was within 9%. This indicates that the models have utility in engineering. |
| first_indexed | 2024-03-13T03:33:49Z |
| format | Article |
| id | doaj.art-d85e7da93e054034a2ddc34f775ac004 |
| institution | Directory Open Access Journal |
| issn | 2214-9147 |
| language | English |
| last_indexed | 2024-03-13T03:33:49Z |
| publishDate | 2023-06-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Defence Technology |
| spelling | doaj.art-d85e7da93e054034a2ddc34f775ac0042023-06-24T05:16:42ZengKeAi Communications Co., Ltd.Defence Technology2214-91472023-06-01241830Study on the prediction and inverse prediction of detonation properties based on deep learningZi-hang Yang0Ji-li Rong1Zi-tong Zhao2Department of Mechanics, School of Aerospace Engineering, Beijing Institute of Technology, Beijing, 100081, ChinaCorresponding author.; Department of Mechanics, School of Aerospace Engineering, Beijing Institute of Technology, Beijing, 100081, ChinaDepartment of Mechanics, School of Aerospace Engineering, Beijing Institute of Technology, Beijing, 100081, ChinaThe accurate and efficient prediction of explosive detonation properties has important engineering significance for weapon design. Traditional methods for predicting detonation performance include empirical formulas, equations of state, and quantum chemical calculation methods. In recent years, with the development of computer performance and deep learning methods, researchers have begun to apply deep learning methods to the prediction of explosive detonation performance. The deep learning method has the advantage of simple and rapid prediction of explosive detonation properties. However, some problems remain in the study of detonation properties based on deep learning. For example, there are few studies on the prediction of mixed explosives, on the prediction of the parameters of the equation of state of explosives, and on the application of explosive properties to predict the formulation of explosives. Based on an artificial neural network model and a one-dimensional convolutional neural network model, three improved deep learning models were established in this work with the aim of solving these problems. The training data for these models, called the detonation parameters prediction model, JWL equation of state (EOS) prediction model, and inverse prediction model, was obtained through the KHT thermochemical code. After training, the model was tested for overfitting using the validation-set test. Through the model-accuracy test, the prediction accuracy of the model for real explosive formulations was tested by comparing the predicted value with the reference value. The results show that the model errors were within 10% and 3% for the prediction of detonation pressure and detonation velocity, respectively. The accuracy refers to the prediction of tested explosive formulations which consist of TNT, RDX and HMX. For the prediction of the equation of state for explosives, the correlation coefficient between the prediction and the reference curves was above 0.99. For the prediction of the inverse prediction model, the prediction error of the explosive equation was within 9%. This indicates that the models have utility in engineering.http://www.sciencedirect.com/science/article/pii/S2214914722002495Deep learningDetonation propertiesKHT thermochemical CodeJWL equation of statesArtificial neural networkOne-dimensional convolutional neural network |
| spellingShingle | Zi-hang Yang Ji-li Rong Zi-tong Zhao Study on the prediction and inverse prediction of detonation properties based on deep learning Defence Technology Deep learning Detonation properties KHT thermochemical Code JWL equation of states Artificial neural network One-dimensional convolutional neural network |
| title | Study on the prediction and inverse prediction of detonation properties based on deep learning |
| title_full | Study on the prediction and inverse prediction of detonation properties based on deep learning |
| title_fullStr | Study on the prediction and inverse prediction of detonation properties based on deep learning |
| title_full_unstemmed | Study on the prediction and inverse prediction of detonation properties based on deep learning |
| title_short | Study on the prediction and inverse prediction of detonation properties based on deep learning |
| title_sort | study on the prediction and inverse prediction of detonation properties based on deep learning |
| topic | Deep learning Detonation properties KHT thermochemical Code JWL equation of states Artificial neural network One-dimensional convolutional neural network |
| url | http://www.sciencedirect.com/science/article/pii/S2214914722002495 |
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