Near-field blast loading and transient target response: A collaboration between Sheffield and Cape Town

Rigby, S.E.; Tyas, A.; Curry, R.J.; Langdon, G.S.

doi:10.1201/9780429426506-130

Cited by 1 publication

(1 citation statement)

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“…However, experimental data remains a fundamental requirement for validation of numerical modelling schemes. Computational fluid dynamics (CFD) and finite element (FE) approaches have been shown generally to provide good agreement with experimental data for near-field blast loading where it is available (Shin et al, 2014a; Rigby et al, 2018; Whittaker et al, 2019; Pannell et al, 2021). In spite of research into near-field blast loading currently being limited by a lack of well-controlled experimental validation data (Tyas, 2019), FE/CFD analyses can provide data at considerably higher spatial and temporal resolution than experimental studies and are therefore suitable tools with which to develop a refined predictive approach.…”

Section: Introductionmentioning

confidence: 97%

Application of transfer learning for the prediction of blast impulse

Pannell

Rigby

Panoutsos

2022

International Journal of Protective Structures

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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.

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Section: Introductionmentioning

confidence: 97%