Probabilistic modelling of forces of hail

Shihara Perera (), Nelson Lam (), Mahil Pathirana (), Lihai Zhang (), Dong Ruan () and Emad Gad ()
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Shihara Perera: The University of Melbourne
Nelson Lam: The University of Melbourne
Mahil Pathirana: The University of Melbourne
Lihai Zhang: The University of Melbourne
Dong Ruan: Swinburne University of Technology
Emad Gad: Swinburne University of Technology

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2018, vol. 91, issue 1, No 7, 133-153

Abstract: Abstract Numerical modelling techniques enable forcing function at the point of contact in a hail impact to be predicted, but many models available in commercial software have not been verified by experiments. A recently developed deterministic model is only able to simulate the impact action of an idealised spherical hailstone. Results recorded from the impact testing of non-spherical simulated hail ice specimens show wide scatters and are different to spherical specimens. A stochastic model has been developed in this study to incorporate the variability of the modelling parameters representing the behaviour of non-spherical ice specimens and to generate time histories of contact force for given impact scenarios. A probabilistic model which was developed previously to provide predictions of the distribution of the size of hailstones in a severe category hailstorm has also been further developed to provide predictions of the maximum contact force values in probabilistic terms. For any given pre-defined forcing function denting into the surface of a steel plate can be estimated by finite element analysis or experimentally on a test machine. The new knowledge base introduced in this paper can be used by manufacturers and designers to re-assess existing products and in making improvements to future installations taking into account cost–benefit considerations.

Keywords: Hailstone; Contact force; Forcing function; Probabilistic; Deterministic (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s11069-017-3117-7

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