Improving aerosol and spray process computation fluid dynamics models with machine learning approaches

利用机器学习方法改进气溶胶和喷雾过程计算流体动力学模型

• 批准号: 2881557
• 金额: --
• 依托单位: University of Leeds
• 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-2881557
• 关键词: Improving; aerosol; spray; process; computation

Computational fluid dynamic (CFD) models of aerosol and sprays are used across a wide range of fields; from understanding dispersion of exhaled aerosols, to designing and optimising spray drying process for production of food, pharmaceuticals and consumer goods. These CFD models often use particle scale models of drying kinetics to capture the dynamics and solidification behaviour as the droplets interact with the air flow.The drying behaviour of single droplets can be complex due to nature of the phase changes occurring and interaction between the drying behaviour and the solid structures formed on drying. Models which capture the complexity of the drying behaviour have been developed for single particles, however integrating these models into a CFD model with multiple particles present is typically limited by computational time. The drying models used in CFD models are therefore simple approximations which do not capture the complex physics involved in drying and solid formation.There is therefore a need to develop models which capture the more complex physics but that run faster and can therefore be used in CFD models. One recent innovation, that offers some potential to be able to do this, is the development of machine learning approaches in fluid mechanics. Amongst the numerous methods available are Physics Informed Neural Networks4 (PINNs), these are neural network models which preserve the physical constraints present in first principle models, but can be executed far more rapidly than direct solution of the differential equations involved in the model. This project will explore the potential of these methods. Initially PINN models of single particles will be developed and their accuracy and speed tested These models will then be incorporated into a CFD model of a spray drying tower and their performance evaluated.

气溶胶和喷雾的计算流体动力学（CFD）模型用于广泛的领域;从了解呼出气溶胶的分散，到设计和优化食品，药品和消费品生产的喷雾干燥过程。这些CFD模型通常使用干燥动力学的颗粒尺度模型来捕捉液滴与气流相互作用时的动力学和固化行为。由于发生相变的性质以及干燥行为与干燥过程中形成的固体结构之间的相互作用，单个液滴的干燥行为可能很复杂。已经针对单个颗粒开发了捕获干燥行为的复杂性的模型，但是将这些模型集成到存在多个颗粒的CFD模型中通常受到计算时间的限制。因此，CFD模型中使用的干燥模型是简单的近似值，无法捕捉干燥和固体形成中涉及的复杂物理过程。因此，需要开发能够捕捉更复杂物理过程但运行速度更快的模型，从而可以用于CFD模型。最近的一项创新，提供了一些能够做到这一点的潜力，是流体力学中机器学习方法的发展。在众多可用的方法中有物理信息神经网络4（PINN），这些神经网络模型保留了第一原理模型中存在的物理约束，但可以比模型中涉及的微分方程的直接解更快地执行。本项目将探索这些方法的潜力。首先将开发单个颗粒的PINN模型，并测试其精度和速度。然后将这些模型纳入喷雾干燥塔的CFD模型，并评估其性能。