The development of new manufacturing techniques such as 3D printing have enabled the creation of previously infeasible chemical reactor designs. Systematically optimizing the highly parameterized geometries involved in these new classes of reactor is vital to ensure enhanced mixing characteristics and feasible manufacturability. Here we present a framework to rapidly solve this nonlinear, computationally expensive, and derivative-free problem, enabling the fast prototype of novel reactor parameterizations. We take advantage of Gaussian processes to adaptively learn a multi-fidelity model of reactor simulations across a number of different continuous mesh fidelities. The search space of reactor geometries is explored through an amalgam of different, potentially lower, fidelity simulations which are chosen for evaluation based on weighted acquisition function, trading off information gain with cost of simulation. Within our framework we derive a novel criteria for monitoring the progress and dictating the termination of multi-fidelity Bayesian optimization, ensuring a high fidelity solution is returned before experimental budget is exhausted. The class of reactor we investigate are helical-tube reactors under pulsed-flow conditions, which have demonstrated outstanding mixing characteristics, have the potential to be highly parameterized, and are easily manufactured using 3D printing. To validate our results, we 3D print and experimentally validate the optimal reactor geometry, confirming its mixing performance. In doing so we demonstrate our design framework to be extensible to a broad variety of expensive simulation-based optimization problems, supporting the design of the next generation of highly parameterized chemical reactors.

翻译：增材制造（如3D打印）等新型制造技术的发展，使得过去无法实现的化学反应器设计成为可能。系统优化这类新型反应器中高度参数化的几何结构，对于确保增强的混合特性及可行的可制造性至关重要。本文提出一种快速求解该非线性、高计算成本且无导数问题的框架，从而加速新型反应器参数化设计的原型开发。我们利用高斯过程自适应学习跨多级连续网格保真度的反应仿真多保真度模型。通过融合不同（可能更低）保真度的仿真结果，并基于加权采集函数（权衡信息增益与仿真成本）选择评估对象，实现对反应器几何结构的搜索空间探索。在该框架中，我们推导出一套监测进度并终止多保真度贝叶斯优化的新准则，确保在实验预算耗尽前返回高保真度解。本文研究的反应器类型为脉冲流动条件下的螺旋管反应器，该类反应器具有卓越的混合特性、高度参数化潜力，且易于通过3D打印制造。为验证结果，我们通过3D打印制备最优反应器几何结构，并实验验证其混合性能。通过这一过程，我们证明了所提出的设计框架可推广至各类高计算成本的仿真优化问题，为下一代高度参数化化学反应器的设计提供支持。