Risk EvaLuatIon fAst iNtelligent Tool (RELIANT) for COVID19

该项目汇集了计算和实验流体动力学、模型简化和人工智能方面的独特专业知识，以确定当前流行病和封锁后人员和空间管理的解决方案。提出了一种新的交互式工具，用于评估呼吸、说话、咳嗽和打喷嚏时产生的飞沫和气溶胶在室内环境中的感染风险。随着冬季的临近，这种能力将变得更加重要，出于舒适性考虑，需要限制建筑物通风。考虑了液滴和气溶胶的流体动力学行为、使用口罩的影响以及其他参数，如房间体积、通风和居住者人数。将建立一个数据中心，能够存储、整理和管理项目内外来源的异构数据。将采取一种协同的实验和数值方法。这些将补充EPSRC资助的其他项目的现有文献和数据，为所有相关特征提供具有足够时间和空间分辨率的合适数据集。为了支持实验和数值模拟，将使用能够对数据库中收集的情景进行内插和外推的降阶模型。这将允许以低计算成本、近实时地估计未知情景中的液滴和气溶胶浓度和分布。一个最先进的基于人工智能的框架，结合了描述性、预测性和规范性技术，将从数据中提取知识，推动决策过程，并近乎实时地提供风险水平评估。

This project brings together unique expertise in Computational and Experimental Fluid Dynamics, Model Reduction and Artificial Intelligence, to identify solutions for the management of people and spaces in the current pandemic and post lockdown.A new interactive tool is proposed that evaluates the risk of infection in the indoor environment from droplets and aerosols generated when breathing, talking, coughing and sneezing. This capability will become more critical as winter approaches and building ventilation will need to be limited for comfort considerations. The fluid dynamic behaviour of droplets and aerosols, the effect of using face masks as well as other parameters such as room volume, ventilation and number of occupants are considered. A datahub capable of storing, curating and managing heterogeneous data from sources internal and external to the project will be created. A synergetic experimental and numerical approach will be undertaken. These will complement the existing literature and data from other EPSRC-funded projects providing suitable datasets with adequate resolution in time and space for all the relevant features. To support experiments and numerical simulations, reduced order models capable of interpolating and extrapolating the scenarios collected in the database will be used. This will permit the estimation of droplet and aerosol concentrations and distributions in unknown scenarios at low-computational cost, in near real-time. A state-of-the-art AI-based framework, incorporating descriptive, predictive and prescriptive techniques will extract the knowledge from the data and drive the decision-making process and provide in near real-time the assessment of risk levels.