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.

对颗粒工艺的机械理解是创建具有额外集成功能的定制颗粒产品的基础。联合收割机组合现有颗粒特性或通过组合它们产生新功能的一种方法是通过气相方法的异相附聚。亚微米颗粒系统特别具有特别高的异质附聚过程的潜力，但是只能通过关于过程参数、分散颗粒性质和颗粒相互作用之间的复杂相互作用的高测量工作来表征。使用耦合流（CFD）和颗粒模拟（DEM）提供了可能性，系统地研究目前几乎没有调查的亚微米颗粒的异附聚过程作为工艺参数的函数，分散颗粒的性能和不同的颗粒之间的相互作用。由于计算能力的提高，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，从而实现有针对性的可转移过程和产品开发。使用现实的颗粒接触模型和它们足够精确的参数化是至关重要的正确的模拟表示在实验中获得的异质团聚体结构。对于校准和验证，通过在微喷雾干燥器中处理纳米颗粒悬浮液，在测量装置中生成亚微米尺寸范围内的规定球形初级聚集体。在随后的附聚区中，干燥颗粒将在限定的条件下附聚。在线测量技术和取样将能够对凝聚状态进行时间分辨测量。对于所得结构的3D重建和颗粒相互作用力的测量，建立并应用合适的测量方法（FIB-SEM，AFM）。使用获得的CFD-DEM数据集半机械建模需要应用数据驱动的方法，能够评估，进一步开发和联合收割机现有的机械模型。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，校准和验证CFD-DEM模拟，以便以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。