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

The optimum slaughter weight for different ewe mature sizes

Lamb producers have the option to market lambs at a range of slaughter weights. However, there are limited price premiums for heavier carcasses on a per kilogram basis. Any economic advantage of heavy lambs is realised by extra weight and not price. Both genetic and on-farm factors contribute to extra weight gain. Firstly, lamb weight and growth is correlated to its mature size and lambs from larger parents grow faster and reach heavier weights, but also have greater feed requirements. Secondly, stocking and reproductive rate account for the majority of variation in whole-farm profit, but increasing these also increases feed requirements. The production of heavy lambs is therefore a trade-off with maximising stocking and reproductive rate within the pool of available feed resources. We hypothesise that slaughter weight does not increase with mature size, due to the priority to increase stocking and reproductive rate for profit maximisation

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

Towards a Decision Support System for Economic Evaluation of Agricultural Research

Estimation of on-farm benefits is highlighted as a critical but complex issue in research evaluation. We identify many different potential impacts of research, many of which• do not fit into the standard supply-shift framework for research evaluation. Given the difficulty and complexity of benefit estimation, we see a renewed role for farm-level economic models (such as whole-farm linear programming model) in this area. The benefits of undertaking a more sophisticated and detailed analysis to estimate research benefits include not just greater accuracy but also greater credibility with researchers and greater relevance through representing factors which they perceive to be important. We discuss how, if such respect is engendered, a formal research evaluation this can yield additional benefits by improving the design and conduct of research

Different mature ewe sizes require different stocking rates and lamb slaughter weights to maximise whole-farm profit

To understand the tradeoff between maintaining a larger ewe and the higher income received from producing larger and faster-growing lambs, we used bio-economic simulation modelling to explore the relationship between ewe mature size, lamb slaughter weight and stocking rate. For the majority of factors tested, ewe feed costs did not reduce gross margin, with the exception of the 80 kg ewe at 14 ewes/ha. Conversely, the 50 kg ewe had higher lamb finishing costs and lower lamb income due to the reduce lamb growth potential, which counteracted the lower ewe feeding costs. Unless enterprises are near the upper limits of stocking rate and mature size tested here, the selection for growth rate in Merinos should continue. To maximise gross margin at each level of mature size, management factors (stocking rate and lamb slaughter weight) were different for each mature size, which influenced income and expense sources differently. When setting breeding objectives and formulating selection indexes the complex interactions between genetic and management factors should be considered

What Can Science, Technology, and Innovation Offer in the Achievement of Sustainable Development Goals?

This chapter discusses the many ways that science, technology, and innovation (STI) can bolster the global agenda of the United Nations (UN) toward meeting the sustainable development goals (SDGs). It shows how STI applications can make multiple contributions to the achievement of SDGs. It is particularly important for developing countries to harness STI, while managing resulting trade-offs, to deliver sustainable development effectively. The SDGs simultaneously touch upon environmental, social and economic aspects of development but integrating these aspects into the implementation of the SDGs is challenging for both policymakers and researchers. To meet its SDG targets, the global community must mobilize STI across multiple sectors, support new investments in innovation, and contribute to policy design that addresses a range of barriers

Conclusions and Future Policies for Meeting the Sustainable Development Goals

The final chapter reviews contributions from throughout this book, drawing out common themes, differences, and key lessons. Previous studies indicate the potential role of science, technology, and innovation (STI) in tackling global challenges, yet in many developing countries, little attention is paid to harnessing STI in addressing these problems. The global development agenda, including the millennium development goals (MDGs), often underemphasized the potential for STI contributions, resulting in impacts that fell short of their potential. The chapter and other evidence presented in this book illustrate how a failure to provide the institutions and resources needed to build STI capacity, and a failure of key actors to engage synergistically, can be serious impediments to development. To conclude, the chapter sets out recommendations based on the insights provided in the earlier chapters