Prediction of BLEVE blast loading using CFD and artificial neural network

Spherical equivalence of cylindrical explosives: Effect of charge shape on deflection of blast-loaded plates

Accurate quantification of the blast load arising from detonation of a high explosive has applications in transport security, infrastructure assessment and defence. In order to design efficient and...

https://journals.sagepub.com/doi/pdf/10.1177/2041419621993492

Predicting specific impulse distributions for spherical explosives in the extreme near-field using a Gaussian function

Explosive loading in a confined internal environment is highly complex and is driven by nonlinear physical processes associated with reflection and coalescence of multiple shock fronts. Prediction ...

https://journals.sagepub.com/doi/pdf/10.1177/2041419620970570

Prediction of blast loading in an internal environment using artificial neural networks

Machine learning offers the potential to enable probabilistic-based approaches to engineering design and risk mitigation. Application of such approaches in the field of blast protection engineering would allow for holistic and efficient strategies to protect people and structures subjected to the effects of an explosion. To achieve this, fast-running engineering models that provide accurate predictions of blast loading are required. This paper presents a novel application of a physics-guided regularisation procedure that enhances the generalisation ability of a neural network (PGNN) by implementing monotonic loss constraints to the objective function due to specialist prior knowledge of the problem domain. The PGNN is developed for prediction of specific impulse loading distributions on a rigid target following close-in detonation of a spherical mass of high explosive. The results are compared to those from a traditional neural network (NN) architecture and stress-tested through various data holdout approaches to evaluate its generalisation ability. In total the results show five statistically significant performance premiums, with four of these being achieved by the PGNN. This indicates that the proposed methodology can be used to improve the accuracy and physical consistency of machine learning approaches for blast load prediction.

https://journals.sagepub.com/doi/pdf/10.1177/20414196211073501

Physics-informed regularisation procedure in neural networks: An application in blast protection engineering

Transfer learning offers the potential to increase the utility of obtained data and improve predictive model performance in a new domain, particularly useful in an environment where data is expensive to obtain such as in a blast engineering context. A successful application in this respect will improve existing surrogate modelling approaches to allow for holistic and efficient strategies to protect people and structures subjected to the effects of an explosion. This paper presents a novel application of transfer learning for the prediction of peak specific impulse where we demonstrate that previous knowledge learned when modelling spherical charges can be transferred to provide a performance benefit when modelling cylindrical charges. To evaluate the influence of transfer learning, two artificial neural network architectures were stress tested for three levels of random data removal: the first model (NN) did not implement transfer learning whilst the second model (TNN) did by including a bolt-on network to a previously published NN model trained on the spherical dataset. It is shown the TNN consistently outperforms the NN, with this out-performance increasing as the proportion of data removed increases and showing statistically significant results for the low and high threshold with less variability in all cases. This paper indicates transfer learning applications can be used successfully with considerable benefit with respect to surrogate modelling in a blast engineering context.

Jordan J Pannell

Papers

Predicting specific impulse distributions for spherical explosives in the extreme near-field using a Gaussian function

Prediction of blast loading in an internal environment using artificial neural networks

Physics-informed regularisation procedure in neural networks: An application in blast protection engineering

Application of transfer learning for the prediction of blast impulse

Predicting near-field specific impulse distributions using machine learning