Angle of Incidence Effects on Far-Field Positive and Negative Phase Blast Parameters

Rigby, S.E.; Fay, Stephen; Tyas, A.; Warren, James A.; Clarke, Steve

doi:10.1260/2041-4196.6.1.23

Cited by 49 publications

(44 citation statements)

References 13 publications

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“…9a. The angle of incidence, θ , at a point on the surface (denoted by the letter A in this figure) is defined as the angle between the outward normal of the surface and the direct vector from the explosive charge to that point [43]. Accordingly, tan(θ ) = x/y, where x is the distance along the target to the point of impingement, and y is the distance from the centre of the explosive to the point of impingement (termed stand-off distance).…”

Section: Reflected Pressure Calculations From Fireball Velocitymentioning

confidence: 99%

“…Localised increases in reflected pressure can be seen in the HSV predictions for the 80 mm tests at a distance from the plate centre, x, of approximately 65 mm (θ = 40 • ) as a result of the Mach stem. The pressure increase 3 The apparent decrease in pressure at the centre of the plate in the 380 mm tests is as a result of averaging over a limited dataset (only 1 Hopkinson pressure bar per test, giving a total of 3 data points) rather than being a genuine physical feature of the loading distribution caused by the Mach Stem persists only for a short duration before being followed by a marked temporal decrease in pressure and rapid return to regular reflection conditions thereafter [43]. The disagreement between HPB and HSV pressures at 75 mm from the plate centre in Fig.…”

Section: Comparison To Directly Measured Peak Pressuresmentioning

confidence: 99%

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Reflected Near-field Blast Pressure Measurements Using High Speed Video

et al. 2020

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Background: The design and analysis of protective systems requires a detailed understanding of, and the ability to accurately predict, the distribution of pressure loads acting on an obstacle following an explosive detonation. In particular, there is a pressing need for accurate characterisation of blast loads in the region very close to a detonation, where even small improvised devices can produce serious structural or material damage. Objective: Accurate experimental measurement of these near-field blast events, using intrusive methods, is demanding owing to the high magnitudes (> 100 MPa) and short durations (< 1 ms) of loading. The objective of this article is to develop a non-intrusive method for measuring reflected blast pressure distributions using image analysis. Methods: This article presents results from high speed video analysis of nearfield spherical PE4 explosive blasts. The Canny edge detection algorithm is used to track the outer surface of the explosive fireball, with the results used to derive a velocity-radius relationship. Reflected pressure distributions are calculated using this velocity-radius relationship in conjunction with the Rankine-Hugoniot jump conditions. Results: The indirectly measured pressure distributions from high speed video are compared with directly measured pressure distributions and are shown to be in good qualitative agreement with respect to distribution of reflected pressures, and in good quantitative agreement with peak reflected pressures (within 10% of the maximum recorded value). Conclusions: The results indicate that it is possible to accurately measure blast loads in the order of 100s MPa using techniques which do not require sensitive recording equipment to be located close to the source of the explosion.

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Section: Reflected Pressure Calculations From Fireball Velocitymentioning

confidence: 99%

Section: Comparison To Directly Measured Peak Pressuresmentioning

confidence: 99%

Reflected Near-field Blast Pressure Measurements Using High Speed Video

et al. 2020

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“…Results from the mesh sensitivity analysis are shown in Figure 4, each sub-figure shows: peak overpressure; peak specific impulse and total analysis time (termed 'wall time'), all plotted against the ratio of stand-off distance, S , to ultimate cell length. The solid black line shows the average experimental value from Rigby et al (2015) and the dashed line shows a 10% variation from the experimental value. More information on the experiments is provided below in Experimental validation.…”

Section: Validation Of Apollo Blastsimulatormentioning

confidence: 99%

“…Experimental validation. Rigby et al (2015) present a series of experimental trials where pressure gauges, embedded flush with the surface of a large, reinforced concrete bunker wall, were used to record reflected pressure histories from 0.18 to 0.35 kg PE4 hemispheres located 2 to 10 m from the bunker wall. The experimental set-up is shown in Figure 5.…”

Section: Validation Of Apollo Blastsimulatormentioning

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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“…With this fitting, the key parameters of the positive phase can be extracted. See Chiquito et al (2019) and Rigby et al (2015) for more details. Finally, the TNT equivalent is calculated and the value obtained was 0,855.…”

Section: Field Blast Testing Programmentioning

confidence: 99%

Application of grid convergence index to shock wave validated with LS-DYNA and ProsAir

et al. 2020

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The discretization error is not always calculated, even though it is essential for the studies of computational solid mechanics. However, it is well known that an error committed by the mesh used can be as large as the measured variable, which greatly invalidates the results obtained. The grid convergence index (GCI) method makes possible to determine on a solid basis, the order of convergence and the asymptotic solution. This method seems to be a suitable estimator despite further research is needed in the context of blast situations and finite element (FE) calculations. For this purpose, field trials were performed consisting in the detonation of a spherical hanging load of homemade explosive. The pressure generated by the shock wave was measured in different positions at two distances. With these data, a TNT equivalent has been obtained and used to calculate the shock propagation with the solvers LS-DYNA and ProsAir. This work aims to verify the GCI method by comparing its results with field data along with the simulations carried out. The comparison also seeks to validate the methodology used to obtain the TNT equivalent.This research shows that the GCI gives good results for both solvers despite the complexity of the physical problem. Besides, LS-DYNA displays better correlation with the experimental data than the ProsAir results, with an error of less than 10% in all values. RESUMENLa estimación del error de discretización no siempre se calcula, aunque es algo fundamental para el estudio de la mecánica computacional de los sólidos. Sin embargo, es bien sabido que el error cometido por la malla utilizada puede ser del mismo orden que la variable medida, lo que llega a invalidar los resultados obtenidos. El método del ındice de convergencia de la malla (GCI) permite determinar sobre una base sólida el orden de convergencia y la solución asintótica, por lo que parece ser un buen estimador, a pesar de que es necesario seguir investigando en el contexto de las situaciones de ondas de choque (explosivos) y de los cálculos de elementos finitos (FE). Para este fin, se realizaron pruebas de campo consistentes en la detonación de una carga esférica colgada de explosivo casero. La presión generada por la onda de choque se midió en diferentes posiciones a dos distancias. Con estos datos, se obtuvo un equivalente de TNT que se utilizó para calcular la propagación del choque con los programas LS-DYNA y ProsAir. Este trabajo pretende verificar el método GCI comparando sus resultados con los datos de campo junto con las simulaciones realizadas. También, la comparación busca validar la metodologıa empleada para la obtención del equivalente TNT.La investigación muestra que el GCI da buenos resultados para ambos programas a pesar de la complejidad del problema fısico. Además, el LS-DYNA produce una mejor correlación con los datos experimentales que los aportados por el ProsAir, con todos los valores por debajo del 10 % de error.Palabras clave: ındice de convergencia de malla (GCI), equivalente de TNT, LS-DYNA, ProsAir.How to ...

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Angle of Incidence Effects on Far-Field Positive and Negative Phase Blast Parameters

Abstract: The oblique results show that peak overpressure, impulse and duration are highly dependent on angle of incidence for the positive phase, and are invariant of angle of incidence for the negative phase.

Cited by 49 publications

References 13 publications

Reflected Near-field Blast Pressure Measurements Using High Speed Video

Reflected Near-field Blast Pressure Measurements Using High Speed Video

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

Application of grid convergence index to shock wave validated with LS-DYNA and ProsAir

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