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：新型制造技术（如三维打印）的发展使得以往难以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所涉及的高度参数化几何结构，对于确保其增强的混合特性及可行的可制造性至关重要。本文提出了一种框架，可快速求解此类非线性、计算成本高且无导数的优化问题，从而加速新型反应器参数化设计的原型开发。我们利用高斯过程在多个不同连续网格保真度下自适应学习反应器仿真的多保真度模型。通过融合不同（可能较低）保真度的仿真数据探索反应器几何空间，并基于加权采集函数权衡信息增益与仿真成本来选定评估方案。在该框架中，我们推导出新的准则用于监控多保真度贝叶斯优化的进程并判定终止条件，确保在实验预算耗尽前返回高保真度解。本研究考察的反应器类别为脉冲流条件下的螺旋管式反应器，该类反应器已展现出卓越的混合特性，具备高度参数化潜力，且易于通过三维打印制造。为验证结果，我们采用三维打印制造最优反应器几何构型并进行实验验证，确认其混合性能。由此证明，我们的设计框架可扩展至各类计算成本高昂的仿真优化问题，为下一代高度参数化化学反应器的设计提供支持。