High-confidence modelling of particle resuspension

长期以来，气溶胶扩散模型忽略了颗粒从表面重新悬浮的现象，这主要是由于缺乏对其控制因素的了解[1]。该项目旨在开发一个现有的再悬浮模型，即由Reek和Hall创建并由Biasi等人实施的‘Rock n’Roll‘模型，使用模型开发的逐步方法和实验验证，文森特等人已经成功地证明了这一点。[1][2][3]。其目的是通过逐步引入已知的影响粒子再悬浮的因素，建立在“Rock n‘Roll”模型的强大物理基础上，但不包括在现有模型中。其中许多因素，特别是那些影响颗粒-表面相互作用的因素，还没有得到广泛的研究，需要在模型开发之前进行实验分析。这将通过一系列技术来完成；包括原子力显微镜，以评估颗粒与表面的粘附性，以及风洞实验，结合高帧频成像，观察再悬浮。该项目最初的重点将是颗粒形态和表面粗糙度。前者特别重要，因为它提供了实施布里斯托尔大学开发的一种新方法的机会，该方法用于干燥液滴，以形成受控和均匀的形态结构。通过纳入这些因素，并在时间允许的情况下，其他模型改进的目的是开发一个更复杂的模型，允许提高准确性和更大的灵活性，以适应一系列现实世界的情景。

The phenomenon of particles being resuspended from surfaces has long been neglected from aerosol dispersion models, mainly due to a lack of knowledge on its governing factors [1]. This project aims to develop an existing model for resuspension, the 'Rock n' Roll' model, created by Reeks and Hall and implemented by Biasi et al., using a stepwise method of model development alongside experimental validation that has been demonstrated successfully by Vincent et al. [1][2][3]. The intent is to build upon the strong physical foundations of the "Rock n' Roll" model by gradually introducing factors that are known to affect particle resuspension but are not incorporated into the existing model. Many of these factors, particularly those influencing particle-surface interactions, have not been extensively researched and will need to be experimentally analysed prior to model development. This will be completed with a range of techniques; including atomic force microscopy, to evaluate the adhesion of particles to a surface, and wind tunnel experiments, coupled with high frame rate imaging, to observe resuspension. The initial focus of the project will be on particle morphology and surface roughness. The former is particularly relevant as it offers the opportunity to implement a novel method developed at the University of Bristol for drying droplets to form structures of a controlled and uniform morphology. By incorporating these factors, and time permitting, other model improvements, the intention is to develop a model with increased complexity, allowing for improved accuracy and greater flexibility to a range of real-world scenarios.