CFD in the Cloud [PODCAST]
This week, guest Kevin Smith, M-Star’s Director of Software Development, joins John to discuss computational fluid dynamics in the cloud.
July 29, 2021
A digital twin is a digital replica of a physical system. Designed to run real-time numerical experiments based on relevant mathematical models, the predictions from a digital twin should be identical to real experimental data—hence, twin.
Thanks to modern algorithms and advancements in graphics processing unit (GPU) hardware, digital twins are becoming an optimal method for engineers who are trying to optimize processes with complex fluid mechanics and design limitations that make other approaches inapplicable or too limited.
For a successful digital twin, four things need to be true:
For certain applications, developing a digital twin makes sense. Let’s take a look at an example to see why.
In the biopharmaceutical manufacturing process, it’s common to mix two miscible liquids with differences between fluid density and viscosity. But these differences make predicting mixing processes within agitated tanks complicated, causing significant processing and scale-up challenges.
To optimize pharmaceutical blending and mixing, manufacturers can hybridize the three traditional approaches—literature correlations, numerical modeling, and experiments—with a digital twin. This is because too few literature correlations are applicable to multi-fluid systems with large variations in density and viscosity, experiments are limited by costs and access to equipment, and the transport physics involved limit the applicability of numerical modeling approaches.
In this example, a digital twin that pairs Lattice Boltzmann–based transport algorithms with GPU resources allowed the pharmaceutical manufacturer to simulate minutes/hours of fluid mechanics within hours/days of computer wall time. The transient processing insights that the twins generated rivaled experimental data—but at a cost orders-of-magnitude lower.
For similar results, follow these four steps.
To develop a digital twin, you must first identify the requirements that your model must satisfy.
In the example above, there are three primary requirements based on the fluid properties of the mixing system:
For successful outcomes, practitioners need to understand influential operating parameters and the transport physics within the system you’re trying to model.
After you identify modeling and operating requirements, you can determine the numerical approach that satisfies those requirements.
In our miscible blending example, we identified three equations to solve:
These three equations together can be solved via the Lattice Boltzmann method.
To build the digital twin model—and solve the above equations—you need a tool that supports the Lattice Boltzmann method.
The Lattice Boltzmann method is high-resolution, which means direct numerical simulation and/or large-eddy simulation can be used to build a digital twin that can handle laminar, transitional, and turbulent flow regimes with no reconfiguration.
Compared to traditional finite element and finite difference approaches, the Lattice Boltzmann approach can model multiphase and multiphysics transport processes in fluid mechanical systems at much faster computational speeds. This speed is only further amplified when run in a GPU-based computing environment.
This is where computational fluid dynamics software (CFD) comes in.
Modern CFD software solves Lattice Boltzmann algorithms on GPUs, which can be used to build digital twins and quickly produce detailed, accurate process simulations.
But before you can apply the model, you have to validate it.
In our pharmaceutical blending example, the manufacturer validated the twin by first comparing the single fluid blend times and power numbers predicted from the twin to experimental data across a wide range of Reynolds numbers. From there, they used the twin to explore blending in two-fluid, density stratified systems. Then, they verified output against experimental data taken at multiple impeller speeds.
The takeaway: Before you can use the twin for process optimization and design on unmeasured systems, you must first measure and supply relevant fluid properties to the twin. Appropriate experimental data is crucial for validating output.
The initial development of a digital twin does not eliminate the need for experiment. However, once developed, it can be used to generate processing insights with a fidelity that rivals experimental measurement at a much lower cost.
When you’re dealing with complex fluid mechanics, the initial set up of a digital twin is worth it—especially when backed by modern CFD software.
Build advanced fluid models in minutes, predict real-time dynamics with precision, and solve more complex fluid flow problems faster with M-Star CFD—CFD software for the real world.