An End-to-End Framework Coupling CFD and Active Machine Learning Optimizer (ActivO) for Rapid Simulation-driven Design of Turbulent Jet Mixers

Chemical process efficiency is a critical part of sustainability efforts in the chemical industry and often relies on effective mixing of multiple components. These types of mixing equipment (known as "mixers") can have complex geometries and internal features that pose optimization and scale-up challenges, particularly when no guiding principles or empirical correlations exist for designing a new type of mixer. In this context, computational fluid dynamics (CFD) can be leveraged to guide mixer design. However, it is difficult and time-consuming to perform design optimization using manual design-of-experiments type approaches or conventional evolutionary optimizers. In this work, an end-to-end simulation-AI framework is developed for optimizing the design of a gas-liquid turbulent jet mixer wherein a CFD model of the mixer is coupled with a novel active ensemble machine learning optimizer (ActivO) developed at Argonne National Laboratory. The workflow is structured as a loop of the following steps: (i) multi-parameter morphing of the mixer geometry with the CAD tool CAESES, (ii) automated grid and case definition and CFD simulation with open-source software OpenFOAM, (iii) multi-dimensional optimization guided by ActivO to modify the geometry towards the maximization of mixing performance and energy efficiency.