Browsing by Author "Yiannakopoulos, G"

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    Optimisation of numerical modelling for structures subjected to internal blast
    (International Symposium on Military Aspects of Blast and Shock (MABS), 2018-09-23) Saleh, M; Pickerd, V; Yiannakopoulos, G; Brincat, M; Bortolan Neto, L; Mathys, Z; Reid, W
    The design of modern military and naval platforms is often assisted by experiments and computational simulations, that provide relevant insights about material reliability, mechanical performance and design vulnerability to blast loading. An important design consideration for naval platforms is the damage response of structures from internal blast loading which is characterized by high strain rate loading and complex shock and blast wave interactions and reflections. To understand the damage response of structures under this loading condition, scaled experiments coupled with numerical simulations are used to identify (a) the temporal displacement fields using in-situ DIC measurements (b) onset of critical failure in various elements and (c) spatial distribution of internal pressure fields. A methodology for understanding the failure response of structures to internal blast loading is investigated using both scaled experiments and numerical modelling. Experimental data, including pressure, displacement, plastic strain and acceleration measurements, are compared with simulation results to determine modelling accuracy for both elastic and plastic deformation. The multi-scale modelling approach adopts a discretization technique for the structure by way of variations in the material property attributes of: weld material, Heat Affected Zone (HAZ) and parent material. The blast propagation and fluid structure interaction are achieved through an ALE simulation framework and provided insights into the deformation mechanisms exhibited in stiffened containers. Multiple structure configurations are simulated to explore this design space and results are compared with the experimentally observed loading and structural response behaviours. The simulation results, alongside the scaled experiments, provide a robust framework for the prediction of blast response of representative naval structures and allows for their optimization to improve both the subsystem and platform integrity.
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    Rapid mechanical evaluation of quadrangular steel plates subjected to localised blast loadings
    (Elsevier, 2020-03) Bortolan Neto, L; Saleh, M; Pickerd, V; Yiannakopoulos, G; Mathys, Z; Reid, W
    The design of modern military and naval platforms against weapon threats is often assisted by a combination of experimental, analytical and computational simulations. These tools provide relevant insights about material reliability, mechanical performance and platform design vulnerability to support the determination of safety critical aspects, such as response to blast and fragmentation loading. Analytical models are inherently simplified, limiting their ability to accurately model scenarios with complicated geometries and material properties, or highly non-linear loadings. Appropriate experimental and numerical modelling can overcome the limitations of analytical models but also require long lead times and high associated costs. These issues can be a point of concern for projects with strict development schedules, short time-to-solution, and limited resources. Machine learning techniques have proven viable in the development of fast-running models for highly non-linear problems. The present work explores four models based on the Multilayer Perceptron (MLP), a type of Artificial Neural Network (ANN), for assessing the mechanical response of mild steel plates subjected to localised blast loading. Experiments combined with validated Finite Element Analysis (FEA) models provide a hybrid dataset for training ANNs. The resultant dataset is a combination of sparsely populated experimental data with a denser dataset of validated FEA simulations. The final results demonstrate the potential of ANNs to incorporate high strain-rate material response behaviour, such as that from blast loading, into optimised models that can yield timely predictions of structural response. Crown Copyright © 2019 Published by Elsevier Ltd.
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    Rapid vulnerability assessment of naval structures subjected to localised blast
    (Curran Associates, 2017-10-04) Bortolan Neto, L; Saleh, M; Pickerd, V; Yiannakopoulos, G; Mathys, Z; Reid, W
    The development of modern naval vessels is driven by the optimum balance between operational performance, technology restrictions and the costs of ownership. These factors impose limitations on all features of surface ships, including weaponry, structural materials, radar systems, and propulsors. Strategies must be set to identify design features and materials that can enhance the vessels protection in the event of shock loadings e.g. air blast and underwater explosions. Assessment of design solutions is a complicated task due to the large number of unknowns involved. Appropriate computational models and experimental tests can give insights into the expected mechanical behaviour to support the design process. The authors are developing a framework for vulnerability assessment, which includes experimental tests and appropriate finite element (FE) models of representative structural parts subjected to blast loading. This combined approach provides a comprehensive analysis tool but its complexity prevents the quick assessment of the vessel structural vulnerability when various design features and a range of materials are to be considered. To overcome this hurdle, a machine learning model based on Artificial Neural Networks is proposed to identify patterns in numerical and experimental data, yielding timely conclusions about the structural response. © 2017 The Royal Institution of Naval Architects