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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=753724
• 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，旨在通过开发创新的设备和技术，在中试规模上实现全面的过程性能，从而显著提高产量和成本效益。对于大多数装置的操作，这是通过极大地强化传质和传热来实现的。必须缩短强化工艺的上市时间。实验方法昂贵，并且不能提供对速度、浓度和温度分布的足够深入的了解，从而增加PI的应用。使用多物理计算流体力学模型的模拟已被研究人员准确地确定为需要利用的关键技术，以释放单元操作的PI潜力。高阶计算流体力学(CFD)是一种相对较新的模拟方法，它不仅可以通过改变网格来控制模型的精度，还可以通过增加格式的阶数来控制模型的精度。高阶CFD有能力提供与传统方法相同的精度，但计算时间要短得多(10倍)。由于其数值耗散小，适合于用大涡模拟(LES)方法预报强化过程中的湍流和过渡流。然而，到目前为止，高阶CFD的使用仅限于航空业。这是因为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和大涡模拟的结合将使人们有可能为化工过程中发生的反应性和多相流开发出更准确和更稳健的模型。这一研究计划的总体长期目标是开发开源、高阶、高性能的多物理和多尺度CFD模型，用于单相和固液两相流动。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)生成高阶未分辨的CFD-DEM(离散单元法)模型来模拟任意几何形状的固液接触器中的湍流流动2)开发大涡和直接的数值模拟模型来模拟催化反应并预测反应器中的物种和温度分布3)详细的多尺度建模策略利用深度学习来连接颗粒/孔和过程尺度这项工作的结果将使PI成为可能的单一和固液过程如反应器。PI可以显著缩小工厂规模(&gt；100倍)，提供20%至80%的能源节约，并将试剂库存减少高达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来设计新的高效单元操作或优化现有操作。