Data-driven modelling of blast waves for safer cities

数据驱动的冲击波建模，让城市更安全

• 批准号: 2132491
• 金额: --
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2132491
• 关键词: Data; driven; modelling; blast; waves

Currently, blast protection engineers are not equipped with adequate or appropriate tools to study holistic blast behaviour in complex urban environments. As such, blast protection solutions are implemented at a local level only, i.e. "what happens to this particular structural element if a bomb of this size is placed in this exact location". This project aims to develop a new analysis method that can rapidly model the propagation of a blast wave in a cityscape or urban environment, in order for blast protection engineers and city planners to build blast protection solutions into the fabric of cities and public spaces, as opposed to being optional 'bolt-ons'. To do this, we will take advantage of state-of-the-art Machine Learning methodologies and develop a data-driven blast propagation emulator, based on approximations to physical behaviour learnt through interrogation of both advanced numerical modelling and detailed experimental data gathered at the world-leading University of Sheffield Blast and Impact Laboratory. High-fidelity physics-based solvers can provide accurate answers to very specific situations. These approaches, however, become unwieldy and unsuitable even when considering only a small number of potential situations. On the other hand, approximate design tools can evaluate hundreds of thousands of permutations within seconds, but currently the available approaches are lacking in sophistication and cannot accurately model blast behaviour in complex environments, when a blast wave may coalesce, diffract, and reflect. Data-driven modelling based on Machine Learning (ML) offers the potential to combine the best of both worlds with respect to the state-of-the art in blast load quantification: quick-running solvers that can simulate events with many, many potential permutations, but with physically valid and verified assumptions underpinning the analyses. Research in data-driven modelling under high uncertainty will initially be investigated, followed by physics-constraint supervised learning in ML modelling structures.Machine learning has shown enormous potential for pattern recognition in large data sets, and has recently been used to forecast ocean wave conditions [James et al. (2017) A Machine Learning Framework to Forecast Wave Conditions. Journal of Coastal Engineering]. Here, the developed ML model was able to predict ocean waves in 1/1000th the computation time of a physics-based model. Such an approach extended to the prediction of blast wave propagation will provide a more robust and accurate blast load predictor with accuracy commensurate with FEA.ML requires large data input. Initially, the modelling structure will be trained using geometric 3D point clouds measured from the Civil and Structural Engineering Urban Flows Laboratory, with the intention of subsequently being able to rapidly recreate any cityscape and therefore blast effects within that cityscape. Select numerical analyses will provide training data (a grid of points in 3D space provides 4 dimensions of training data for each run) based on the imported geometries, with the intention of developing a more generalised predictive approach.Finally, the developed model will be validated against a series of well-controlled experimental tests conducted at the world-leading University of Sheffield Blast and Impact Laboratory. Here, blast pressures in complex geometries will be measured using state-of-the-art high magnitude pressure gauges sampled at ~5 MHz to ensure a rich suite of data is gathered.

目前，爆炸防护工程师没有足够或适当的工具来研究复杂城市环境中的整体爆炸行为。因此，防爆解决方案仅在地方一级实施，即“如果将这种大小的炸弹放置在这个确切的位置，这个特定的结构元素会发生什么”。该项目旨在开发一种新的分析方法，可以快速模拟爆炸波在城市景观或城市环境中的传播，以便爆炸防护工程师和城市规划者将爆炸防护解决方案构建到城市和公共空间的结构中，而不是作为可选的“螺栓”。为此，我们将利用最先进的机器学习方法，开发一种数据驱动的爆炸传播模拟器，该模拟器基于通过询问世界领先的谢菲尔德大学爆炸和冲击实验室收集的高级数值模型和详细的实验数据而获得的物理行为的近似。高保真的基于物理的解算器可以为非常特定的情况提供准确的答案。然而，即使只考虑一小部分潜在情况，这些方法也变得笨拙和不合适。另一方面，近似设计工具可以在几秒钟内评估数十万种排列，但目前可用的方法缺乏复杂性，不能准确地模拟复杂环境中的爆炸行为，因为爆炸波可能会合并、绕射和反射。基于机器学习(ML)的数据驱动建模提供了在爆炸负载量化方面将两个领域的最佳结合在一起的可能性：快速运行的求解器可以模拟具有许多许多潜在排列的事件，但具有物理上有效和经过验证的假设作为分析的基础。首先将研究高不确定性下的数据驱动建模，然后是ML建模结构中的物理约束监督学习。机器学习在大数据集中的模式识别方面显示出巨大的潜力，最近已被用于预测海浪条件[James等人]。(2017)预报波浪条件的机器学习框架。海岸工程杂志]。在这里，开发的ML模型能够预测海浪，其计算时间是基于物理的模型的1/1000。将这种方法推广到爆炸波传播的预测中，将提供一个更稳健、更准确的爆炸载荷预测器，其精度与FEA相当。ML需要大量的数据输入。最初，将使用从土木工程和结构工程城市流动实验室测量的几何3D点云来训练建模结构，目的是随后能够快速重建任何城市景观，从而在该城市景观内产生爆炸效果。精选的数值分析将基于导入的几何图形提供训练数据(3D空间中的点网格为每次运行提供4维训练数据)，目的是开发更通用的预测方法。最后，开发的模型将通过在世界领先的谢菲尔德大学爆炸和冲击实验室进行的一系列良好控制的实验测试进行验证。在这里，复杂几何形状的爆炸压力将使用最先进的高量级压力计在~5 MHz采样来测量，以确保收集到丰富的数据套件。