Data-informed modelling of aerosol resuspension under aerodynamic loads

空气动力载荷下气溶胶再悬浮的数据知情建模

• 批准号: 2885860
• 金额: --
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2885860
• 关键词: Data; informed; modelling; aerosol; resuspension

Experimental data for test particles and surfaces will be built in a structured and systematic manner, eventually extending to complex surfaces and particles relevant to real suspension problems. A broad range of sizes will be considered up to environmental particles, the effects of deposit surface topology and time-varying flow effects will be explored. Key parameters influencing the resuspension process and their correlation to other variables will be identified from the carefully controlled experiments and the data will be used to cross-validate with a selected set of numerical simulations. Subsequently, the numerical studies can be used to elucidate the complete resuspension process and moreover be extended to scenarios not easily replicable by the experiments. Machine learning techniques will be employed to learn from the data and develop simplified models, based on the experimental and computational data obtained.

测试颗粒和表面的实验数据将以结构化和系统的方式建立，最终扩展到与实际悬浮问题相关的复杂表面和颗粒。将考虑环境颗粒的广泛尺寸，将探索沉积物表面拓扑的影响和随时间变化的流动效应。影响重悬浮过程的关键参数及其与其他变量的相关性将从仔细控制的实验中确定，并且数据将用于与一组选定的数值模拟进行交叉验证。随后，数值研究可用于阐明完整的重悬浮过程，并可扩展到实验不易复制的场景。将采用机器学习技术从数据中学习并根据获得的实验和计算数据开发简化模型。