Additive manufacturing has enabled the fabrication of advanced reactor geometries, permitting larger, more complex design spaces. Identifying promising configurations within such spaces presents a significant challenge for current approaches. Furthermore, existing parameterisations of reactor geometries are low-dimensional with expensive optimisation limiting more complex solutions. To address this challenge, we establish a machine learning-assisted approach for the design of the next-generation of chemical reactors, combining the application of high-dimensional parameterisations, computational fluid dynamics, and multi-fidelity Bayesian optimisation. We associate the development of mixing-enhancing vortical flow structures in novel coiled reactors with performance, and use our approach to identify key characteristics of optimal designs. By appealing to fluid mechanical principles, we rationalise the selection of novel design features that lead to experimental performance improvements of ~60% over conventional designs. Our results demonstrate that coupling advanced manufacturing techniques with `augmented-intelligence' approaches can lead to superior design performance and, consequently, emissions-reduction and sustainability.

翻译：增材制造使先进反应器几何结构的制造成为可能，从而允许更大、更复杂的设计空间。在当前方法中，识别此类空间中的有前景构型仍是一项重大挑战。此外，现有反应器几何参数化方法维度低，且伴随昂贵的优化过程，限制了更复杂解决方案的探索。为应对这一挑战，我们建立了一种机器学习辅助方法用于设计下一代化学反应器，结合了高维参数化、计算流体动力学和多保真度贝叶斯优化。我们将新型螺旋反应器中增强混合的涡流结构与性能相关联，并利用该方法识别最优设计的关键特征。通过借鉴流体力学原理，我们合理解释了新型设计特征的选择，这些特征使实验性能相比传统设计提升约60%。我们的结果表明，将先进制造技术与"增强智能"方法相结合，可实现卓越的设计性能，进而促进减排与可持续发展。