Multiphysics and multiscale modeling for the intensification of chemical engineering processes

用于强化化学工程过程的多物理场和多尺度建模

• 批准号: RGPIN-2020-04510
• 负责人: Blais, Bruno
• 金额: $ 2.4万
• 依托单位: École Polytechnique de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=717077
• 关键词: Multiphysics; multiscale; modeling; intensification; chemical

Process intensification (PI) is a strategy aimed at making striking improvements in yield and cost effectiveness by developing innovative equipments and technologies to reach full-scale process performance at the pilot scale. For most unit operations, this is achieved by greatly enhancing mass and heat transfer. The time-to-market of intensified processes must be reduced. Experimental approaches are expensive and do not provide enough insights into velocity, concentration, and temperature profiles to increase the application of PI. Simulations that use multiphysics computational fluid dynamics models have been pinpointed by researchers as the key technology that needs to be leveraged to unlock the potential of PI of unit operations. High-order computational fluid dynamics (CFD) is a relatively new simulation approach in which the accuracy of the models can be controlled not only by altering the mesh but also by increasing the order of the scheme. High-order CFD has the capacity to provide the same accuracy as traditional methods with, however, a much shorter computational time (10x). With its low numerical dissipation, it is suited for the prediction of the turbulent and transitional flows that occur in intensified processes by large eddy simulation (LES). However, the use of high-order CFD has thus far been confined to the aeronautics industry. This is due to a lack of available commercial or accessible open source software to leverage the capacity of high-order schemes to simulate the incompressible flows encountered in chemical engineering. The combination of high-order CFD and LES would make it possible to develop more accurate and robust models for the reactive and multiphase flows that occur in chemical processes. The overall long-term objective of this research program is to develop open source high-order high-performance multiphysics and multiscale CFD models for single phase and solid-fluid flows. With these models, researchers, industry and, other stakeholders will be able to use process intensification. Our short-term objectives (next 5 years) are: 1) To generate high-order unresolved CFD-DEM (Discrete Element Method) models to simulate turbulent flows in solid-fluid contactors of arbitrary geometry 2) To develop large-eddy and direct numerical simulation models to simulate catalyzed reactions and to predict species and temperature profiles in reactors 3) To elaborate multiscale modeling strategies that use deep learning to bridge the particle/pore and process scales The results of this work will make PI possible for single and solid-fluid processes such as reactors. PI can reduce plant size dramatically (>100x), provide energy savings ranging from 20% to 80%, and decrease reagent inventories up to 10- to 1000-fold. Achieving our objectives will provide chemical engineers with validated models and computational tools that will enable them to use PI to design new highly efficient unit operations or to optimize existing ones.

过程强化（PI）是一种战略，旨在通过开发创新设备和技术，以达到中试规模的全面工艺性能，从而显着提高产量和成本效益。对于大多数单元操作，这是通过极大地增强质量和热传递来实现的。必须缩短强化工艺的上市时间。实验方法是昂贵的，并没有提供足够的洞察速度，浓度和温度分布，以增加PI的应用。使用多物理场计算流体动力学模型的模拟已被研究人员确定为需要利用的关键技术，以释放单元操作的PI潜力。 高阶计算流体动力学（CFD）是一种相对较新的模拟方法，其中模型的精度不仅可以通过改变网格来控制，还可以通过增加方案的阶数来控制。高阶CFD能够提供与传统方法相同的精度，但计算时间要短得多（10倍）。由于其低数值耗散，它适用于通过大涡模拟（LES）预测在强化过程中发生的湍流和过渡流。然而，高阶计算流体动力学的使用迄今仅限于航空工业。这是由于缺乏可用的商业或可访问的开源软件来利用高阶格式的能力来模拟化学工程中遇到的不可压缩流动。高阶CFD和LES的结合将使人们有可能为化学过程中发生的反应和多相流开发更准确和鲁棒的模型。 该研究计划的总体长期目标是开发单相和固液流的开源高阶高性能多物理场和多尺度CFD模型。有了这些模型，研究人员，行业和其他利益相关者将能够使用过程强化。我们的短期目标（未来5年）是： 1)生成高阶不可分辨CFD-DEM（离散元法）模型，模拟任意几何形状固-流接触器中的湍流流动 2)发展大涡和直接数值模拟模式，以模拟催化反应，并预测反应器中的物种和温度分布 3)详细说明使用深度学习桥接颗粒/孔隙和过程尺度的多尺度建模策略 这项工作的结果将使PI的单一和固-流过程，如反应器。PI可以显著缩小工厂规模（> 100倍），节省20%至80%的能源，并将试剂库存减少10至1000倍。实现我们的目标将为化学工程师提供经过验证的模型和计算工具，使他们能够使用PI设计新的高效单元操作或优化现有操作。