Using Digital Twins to Optimize Engineering Processes

In many agitated tanks and fluid handling systems, yield and performance is governed by complex turbulent fluid mechanics. These turbulent motions, which can inform both bulk transport and reaction kinetics, occur over a large range of length and time-scales. From a modeling perspective, direct numerical simulation of this entire turbulence spectrum is required to obtain complete insights into system performance. Due to time and resource constraints, however, such detailed simulations have not historically been practical within most industrial settings.

In this work, we show how graphical processing units (GPUs) can make large eddy simulations (LES) direct numerical simulation (DNS) of industrial mixing systems practical and timely. Although GPUs have historically been used for image rendering, in recent years they have emerged and as powerful computational rivals to traditional CPUs. We will begin the presentation by introducing the concepts governing fluid modeling on GPUs. We then show how, given identical algorithms, a single scientific GPU can execute simulations two to three orders-of-magnitude faster than a single CPU. We then present various criteria for monitoring the convergence from large-eddy simulation (LES) to direct numerical simulation, within the context of the turbulent fluid motion. We then apply these criteria to study blending, energy dissipation, and reaction rates in a bioreactor.

Beyond improvements in physical fidelity, GPU-based computational approaches enable real-time, virtual reality rendering of bioreactors and fluid handling equipment. Thus, in addition to providing basic numerical data, visual output can be piped real-time to a virtual reality headset for immersive insights into the transport processes ongoing inside a bioreactor.