Experimental study of blast loading behind a building corner

Gajewski, Tomasz; Sielicki, Piotr W.

doi:10.1007/s00193-020-00936-1

Cited by 20 publications

(51 citation statements)

References 27 publications

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“…For the 500 g charge, the "explosion" (rapid release of energy) appeared to occur between 2.3 and The pressure-time histories from all tests were measured at 1 and 2 m from the explosive simultaneously. Two ICP ® blast pressure pencil probes were used simultaneously to acquire the data-these are the same probes that were used in [21]. The maximum pressure limit for these sensors is 345 kPa.…”

Section: Field Test Measurementsmentioning

confidence: 99%

Identification of Aluminium Powder Properties for Modelling Free Air Explosions

Sielicki

Clutter²,

Sumelka

et al. 2022

Materials

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Aluminium is a component in many energetic formulations. Therefore, its combustion is one of the main thermochemical processes that govern the output from the energetics. Modelling aluminium combustion is a challenging task because the process is highly complex and difficult to measure. Here, tests of aluminium powder were conducted in an effort to isolate the burning of the aluminium and to determine an adequate representation of this process. Charges of 100 g and 500 g were tested, and the size of the Al/air cloud and the ratio of components in the Al/air mixture were determined, which has not been published previously. This information was used to assess the validity of the assumption that the detonation of the mixture was representative of the event. Parameters for the Jones–Wilkins–Lee equation of state for the explosive mixture and detonation products were defined. Simulations of the tests were performed, and the results were consistent with the field test data, indicating that detonation occurred when there was a mixture of 70–75% Al and 25–30% air by mass.

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Section: Field Test Measurementsmentioning

confidence: 99%

Identification of Aluminium Powder Properties for Modelling Free Air Explosions

Sielicki

Clutter²,

Sumelka

et al. 2022

Materials

Self Cite

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“…The height of the gauges was set to be 1 m above ground level, that is, in line with the centre of the charge. Sampling data at a fixed height above the ground is a common technique for reducing complexity of the data harvesting process for studies using numerical analyses or practical experiments (Alterman et al, 2019;Gajewski and Sielicki, 2020). Additionally, this decision does not significantly alter the architecture and predictive ability of the ANN but considerably reduces the time required for training and validation.…”

Section: Generation Of Datasetmentioning

confidence: 99%

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

Dennis

Pannell

Smyl

et al. 2020

International Journal of Protective Structures

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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 of this loading is not currently feasible using simple tools, and instead specialist computational software or practical testing is required, which are impractical for situations with a wide range of input variables. There is a need to develop a tool which balances the accuracy of experiments or physics-based numerical schemes with the simplicity and low computational cost of an engineering-level predictive approach. Artificial neural networks (ANNs) are formed of a collection of neurons that process information via a series of connections. When fully trained, ANNs are capable of replicating and generalising multi-parameter, high-complexity problems and are able to generate new predictions for unseen problems (within the bounds of the training variables). This article presents the development and rigorous testing of an ANN to predict blast loading in a confined internal environment. The ANN was trained using validated numerical modelling data, and key parameters relating to formulation of the training data and network structure were critically analysed in order to maximise the predictive capability of the network. The developed network was generally able to predict specific impulses to within 10% of the numerical data: 90% of specific impulses in the unseen testing data, and between 81% and 87% of specific impulses for data from four additional unseen test models, were predicted to this accuracy. The network was highly capable of generalising in areas adjacent to reflecting surfaces and as those close to ambient outflow boundaries. It is shown that ANNs are highly suited to modelling blast loading in a confined internal environment, with significant improvements in accuracy achievable if a robust, well distributed training dataset is used with a network structure that is tailored to the problem being solved.

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“…Various research on blast wave propagation was conducted by Stoller in [5,6] and resistance of structures against such loads was researched in [7][8][9]. A field test that focused on overpressure measurements of various types of explosives was described in [10][11][12][13]. Prediction software for blast wave properties and numerical approaches were explained in [14,15].…”

Section: Introductionmentioning

confidence: 99%

Dependency of the Blast Wave Pressure on the Amount of Used Booster

2021

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Most of the damage caused by an explosion is caused by a pressure effect. The magnitude of the pressure generated by the explosion is influenced by the external characteristics of the environment (surrounding objects, their arrangement, geometry, etc.) and internal characteristics (type of explosive, type of charge, booster and others). An effective combination of internal factors creates a symmetry that results in the highest possible value of pressure generated by the charge explosion. The paper focuses on the influence of the booster reaction on this symmetry. The scope of the paper is to understand the dependency of the blast wave pressure on the amount of used blaster to increase the efficacy of explosions on the environment and structures to increase the protection of affected structures. The open-air field tests were conducted using different types of explosives: trinitrotoluene and three different types of industrially made ANFO explosives (pure ammonium nitrate and fuel oil, ammonium nitrate and fuel oil plus aluminum powder, ammonium nitrate and fuel oil mixed with trinitrotoluene). The obtained data were compared with the analytical approach for setting the generated maximal pressure on the front of the blast wave.

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Experimental study of blast loading behind a building corner

Cited by 20 publications

References 27 publications

Identification of Aluminium Powder Properties for Modelling Free Air Explosions

Identification of Aluminium Powder Properties for Modelling Free Air Explosions

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

Dependency of the Blast Wave Pressure on the Amount of Used Booster

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