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造具有额外集成功能的定制颗粒产品的基础。结合现有粒子性质或通过结合它们来创建新功能的一种方法是通过气相过程进行异质凝聚。特别是亚微米粒子系统具有特别高的异质团聚过程的潜力，但只能通过高测量工作量来表征过程参数、分散粒子特性和粒子相互作用之间的复杂相互作用。耦合流(CFD)和粒子模拟(DEM)的应用为系统地研究亚微米粒子的异相团聚过程提供了可能，这些过程是工艺参数、分散粒子性质和不同粒子相互作用的函数。由于计算能力的不断增强，可以在CFD-DEM模拟的基础上生成更大的数据集，从而可以推导出机械关系，从而实现有针对性和可转移的过程和产品开发。对于实验中获得的异质团聚结构的正确模拟表示，使用真实的粒子接触模型及其足够精确的参数化是至关重要的。为了进行校准和验证，将在测量装置中通过在微喷雾干燥器中处理纳米颗粒悬浮液来产生亚微米尺寸范围内的定义的球形初级聚集体。在随后的凝聚区，干颗粒将在规定的条件下进行团聚。在线测量技术和采样将使团聚状态的时间分辨测量成为可能。对于所得到的结构的三维重建和粒子相互作用力的测量，建立并应用了合适的测量方法(FIB-SEM、AFM)。将获得的CFD-DEM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、进一步开发和组合现有的机械模型。在这里，遗传编程是有利的(灰箱建模)。该项目提案的目的是减少昂贵的实验测量，并校准和验证CFD-DEM模拟，以便随后将其用作基于智能数据驱动方法的数据驱动混合建模的基础。