  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.
