Hybrid modelling of heteroagglomeration in gas-borne flows using CFD-DEM simulation and machine learning methods

使用 CFD-DEM 模拟和机器学习方法对气流中的异质团聚进行混合建模

• 批准号: 462426077
• 负责人: Professor Dr.-Ing. Carsten Schilde
• 金额: --
• 依托单位: Institut für Partikeltechnik
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/462426077?language=en
• 关键词: Hybrid; modelling; heteroagglomeration; gas; borne

The mechanistic understanding of particulate processes is the basis for creating tailor-made, particulate products with additionally integrated functions. One way to combine existing particle properties or to create new functions by combining them is heteroagglomeration via gas phase processes. Submicron particle systems in particular have a particularly high potential for heteroagglomeration processes, but can only be characterized with high measurement effort with regard to the complex interplay between process parameters, disperse particle properties and particle interactions. The use of coupled flow (CFD) and particle simulations (DEM) offers the possibility to systematically investigate the currently hardly investigated heteroagglomeration processes of submicron particles as a function of process parameters, disperse particle properties and the different particle interactions. Due to the increasing computing power, much larger data sets can be generated on the basis of CFD-DEM simulations, which allow the derivation of mechanistic relationships and thus a targeted and transferable process and product development. The use of realistic particle contact models and their sufficiently accurate parameterization is crucial for the correct simulative representation of the heteroagglomerate structures obtained in the experiment. For calibration and validation, defined spherical primary aggregates in the submicron size range are to be generated in a measurement setup by processing nanoparticulate suspensions in the microspray dryer. In the subsequent agglomeration zone, the dry particles will be agglomerated under defined conditions. Inline measurement technology and sampling will enable a time-resolved measurement of the agglomeration state. For 3D reconstruction of the resulting structures and measurement of particle interaction forces, suitable measuring methods (FIB-SEM, AFM) are established and applied. The use of the obtained CFD-DEM data sets for semi-mechanistic modelling requires the application of data-driven methods that are able to evaluate, further develop and combine existing mechanistic models. Here genetic programming is advantageously (Grey Box Modelling). It is the aim of the project proposal to reduce costly experimental measurements and to calibrate and validate CFD-DEM simulations in order to use them afterwards as a basis for a data-driven hybrid modelling based on intelligent data-driven methods.

对颗粒过程的机械理解是创建具有额外集成功能的量身定制的颗粒产品的基础。结合现有粒子特性或通过将新功能组合起来的一种方法是通过气相过程进行异质量。尤其是亚微米粒子系统在异质过程过程中具有特别高的潜力，但只能以高测量的努力来表征有关过程参数，分散粒子特性和粒子相互作用之间的复杂相互作用的表征。使用耦合流（CFD）和粒子模拟（DEM）的使用提供了系统研究亚微米颗粒的当前几乎没有研究的异质过程，这是过程参数，分散粒子特性和不同粒子相互作用的函数。由于计算能力的增加，可以根据CFD-DEM模拟生成更大的数据集，从而允许衍生机械关系，从而导致有针对性且可转移的过程和产品开发。使用逼真的粒子接触模型及其足够准确的参数化对于对实验中获得的异凝结结构的正确模拟表示至关重要。为了进行校准和验证，将在测量设置中通过处理微喷雾干燥机中的纳米悬浮液来在测量设置中生成定义的球形主要聚集体。在随后的聚集区中，在定义的条件下，干颗粒将被聚集。内联测量技术和抽样将实现对聚集状态的时间分辨测量。对于所得结构的3D重建和颗粒相互作用力的测量，合适的测量方法（FIB-SEM，AFM）已建立并应用。获得的CFD数据集用于半机械建模需要应用数据驱动的方法，这些方法能够评估，进一步开发和结合现有的机械模型。在这里，遗传编程是有利的（灰色框建模）。该项目建议的目的是减少昂贵的实验测量结果并校准和验证CFD-DEM模拟，以便之后将其用作基于智能数据驱动方法的数据驱动混合模型的基础。