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

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

Predicting the response of plates subjected to near-field explosions using an energy equivalent impulse

Recent experimental work by the current authors has provided highly spatially and temporally resolved measurements of the loading imparted to, and the subsequent dynamic response of, structures subjected to near-field explosive loading [1]. In this article we validate finite element models of plates subjected to near-field blast loads and perform a parametric study into the relationship between imparted load and peak and residual plate deformation. The energy equivalent impulse is derived, based on the theory of upper bound kinetic energy uptake introduced herein, which accounts for the additional energy imparted to a structure from a spatially non-uniform blast load. Whilst plate deflection is weakly correlated to total impulse, there is shown to be a strong positive correlation between deflection and energy equivalent impulse. The strength of this correlation is insensitive to loading distribution and mode of response. The method developed in this article has clear applications for the generation of fast-running engineering tools for the prediction of structural response to near-field explosions

Experimental studies of the effect of rapid afterburn on shock development of near-field explosions

Many conventional high explosives do not contain sufficient internal oxygen to fully combust the gaseous products which result from detonation of the explosive material. Because of this, under-oxygenated explosives continue to burn after detonation. This process, called afterburn, is known to influence the late-time pressure and energy released by the explosive, which has particular significance for confined explosives. Recent experimental work at the University of Sheffield, along with a small number of previous studies, has shown that some afterburn occurs at timescales commensurate with the development of the shock wave. This article presents the results from a series of tests measuring the reflected pressure acting on a rigid target following the detonation of small explosive charges. High-speed video is used to capture the emerging structure of the detonation products and air shock, while the spatial and temporal distributions of the reflected pressure are recorded using an array of 17 Hopkinson pressure bars set flush with an effectively rigid target. Tests are conducted in inert atmospheres and oxygen-rich atmospheres in order to assess the contribution of rapid afterburn on the development of the shock front and interaction with a rigid target situated close to the explosive charge. The results show that early-stage afterburn has a significant influence on the reflected shock parameters in the near-field

A branching algorithm to reduce computational time of batch models: application for blast analyses

Numerical analysis is increasingly used for batch modelling runs, with each individual model possessing a unique combination of input parameters sampled from a range of potential values. Whilst such an approach can help to develop a comprehensive understanding of the inherent unpredictability and variability of explosive events, or populate training/validation data sets for machine learning approaches, the associated computational expense is relatively high. Furthermore, any given model may share a number of common solution steps with other models in the batch, and simulating all models from birth to termination may result in large amounts of repetition. This paper presents a new branching algorithm that ensures calculation steps are only computed once by identifying when the parameter fields of each model in the batch becomes unique. This enables informed data mapping to take place, leading to a reduction in the required computation time. The branching algorithm is explained using a conceptual walk-through for a batch of 9 models, featuring a blast load acting on a structural panel in 2D. By eliminating repeat steps, approximately 50% of the run time can be saved. This is followed by the development and use of the algorithm in 3D for a practical application involving 20 complex containment structure models. In this instance, a ∼20% reduction in computational costs is achieved

Predicting specific impulse distributions for spherical explosives in the extreme near-field using a Gaussian function

Accurate quantification of the blast load arising from detonation of a high explosive has applications in transport security, infrastructure assessment and defence. In order to design efficient and safe protective systems in such aggressive environments, it is of critical importance to understand the magnitude and distribution of loading on a structural component located close to an explosive charge. In particular, peak specific impulse is the primary parameter that governs structural deformation under short-duration loading. Within this so-called extreme near-field region, existing semi-empirical methods are known to be inaccurate, and high-fidelity numerical schemes are generally hampered by a lack of available experimental validation data. As such, the blast protection community is not currently equipped with a satisfactory fast-running tool for load prediction in the near-field. In this article, a validated computational model is used to develop a suite of numerical near-field blast load distributions, which are shown to follow a similar normalised shape. This forms the basis of the data-driven predictive model developed herein: a Gaussian function is fit to the normalised loading distributions, and a power law is used to calculate the magnitude of the curve according to established scaling laws. The predictive method is rigorously assessed against the existing numerical dataset, and is validated against new test models and available experimental data. High levels of agreement are demonstrated throughout, with typical variations of <5% between experiment/model and prediction. The new approach presented in this article allows the analyst to rapidly compute the distribution of specific impulse across the loaded face of a wide range of target sizes and near-field scaled distances and provides a benchmark for data-driven modelling approaches to capture blast loading phenomena in more complex scenarios

Finite element simulation of plates under non-uniform blast loads using a point-load method: Buried explosives

There are two primary challenges associated with assessing the adequacy of a protective structure to resist explosive events: firstly the spatial variation of load acting on a target must be predicted to a sufficient level of accuracy; secondly, the response of the target to this load must also be quantified. When a high explosive is shallowly buried in soil, the added confinement given by the geotechnical material results in a blast which is predominantly directed vertically. This imparts an extremely high magnitude, spatially non-uniform load on the target structure. A recently commissioned experimental rig designed by the authors has enabled direct measurements of the blast load resulting from buried explosive events. These direct measurements have been processed using an in-house interpolation routine which evaluates the load acting over a regular grid of points. These loads can then be applied as the nodal-point loads in a finite element model. This paper presents results from a series of experiments where a free-flying plate was suspended above a shallow buried explosive. Dynamic and residual deformations are compared with finite element simulations of plates using the experimentally recorded, and interpolated, nodal point-loads. The results show very good agreement and highlight the use of this method for evaluating the efficacy of targets subjected to non-uniform blast loads

Far-field positive phase blast parameter characterisation of RDX and PETN based explosives

A significant amount of scientific effort has been dedicated to measuring and understanding the effects of explosions, leading to the development of semi-empirical methods for rapid prediction of blast load parameters. The most well-known of these, termed the Kingery and Bulmash method, makes use of polylogarithmic curves derived from a compilation of medium to large scale experimental tests performed over many decades. However, there is still no general consensus on the accuracy and validity of this approach, despite some researchers reporting consistently high levels of agreement. Further, it is still not known whether blast loading can be considered deterministic, or whether it is intrinsically variable, the extent of this variability, and the range and scales over which these variations are observed. This article critically reviews historic and contemporary blast experiments, including newly generated arena tests with RDX and PETN-based explosives, with a view to demonstrating the accuracy with which blast load parameters can be predicted using semi-empirical approaches

The position of graptolites within Lower Palaeozoic planktic ecosystems.

An integrated approach has been used to assess the palaeoecology of graptolites both as a discrete group and also as a part of the biota present within Ordovician and Silurian planktic realms. Study of the functional morphology of graptolites and comparisons with recent ecological analogues demonstrates that graptolites most probably filled a variety of niches as primary consumers, with modes of life related to the colony morphotype. Graptolite coloniality was extremely ordered, lacking any close morphological analogues in Recent faunas. To obtain maximum functional efficiency, graptolites would have needed varying degrees of coordinated automobility. A change in lifestyle related to ontogenetic changes was prevalent within many graptolite groups. Differing lifestyle was reflected by differing reproductive strategies, with synrhabdosomes most likely being a method for rapid asexual reproduction. Direct evidence in the form of graptolithophage 'coprolitic' bodies, as well as indirect evidence in the form of probable defensive adaptations, indicate that graptolites comprised a food item for a variety of predators. Graptolites were also hosts to a variety of parasitic organisms and provided an important nutrient source for scavenging organisms

CHIMPS: the 13CO/C18O (J = 3 to 2) Heterodyne Inner Milky Way Plane Survey

We present the 13CO/C18O (J = 3 → 2) Heterodyne Inner Milky Way Plane Survey (CHIMPS) which has been carried out using the Heterodyne Array Receiver Program on the 15 m James Clerk Maxwell Telescope (JCMT) in Hawaii. The high-resolution spectral survey currently covers |b| ≤ 0.5° and 28° ≲ l ≲ 46°, with an angular resolution of 15 arcsec in 0.5 km s-1 velocity channels. The spectra have a median rms of ˜0.6 K at this resolution, and for optically thin gas at an excitation temperature of 10 K, this sensitivity corresponds to column densities of NH2 ˜ 3 × 1020 cm-2 and NH2 ˜ 4 × 1021 cm-2 for 13CO and C18O, respectively. The molecular gas that CHIMPS traces is at higher column densities and is also more optically thin than in other publicly available CO surveys due to its rarer isotopologues, and thus more representative of the three-dimensional structure of the clouds. The critical density of the J = 3 → 2 transition of CO is ≳104 cm-3 at temperatures of ≤20 K, and so the higher density gas associated with star formation is well traced. These data complement other existing Galactic plane surveys, especially the JCMT Galactic Plane Survey which has similar spatial resolution and column density sensitivity, and the Herschel infrared Galactic Plane Survey. In this paper, we discuss the observations, data reduction and characteristics of the survey, presenting integrated-emission maps for the region covered. Position-velocity diagrams allow comparison with Galactic structure models of the Milky Way, and while we find good agreement with a particular four-arm model, there are some significant deviations.Peer reviewedFinal Accepted Versio