Engineers use response surface methodology (RSM) to understand bioreactor performance.
It’s amazing to think of all the products created in bioreactors. The medications we take, the beer we drink and the yogurt we eat are all made in bioreactors optimized for the manufacturing of these products.
Unfortunately, optimizations take a lot of work and data. The first step is to perform a design of experiments (DoE).
The DoE requires that numerous simulations be performed to map out the design space in the form of response surface methodology (RSM).
The computational resources to perform all of these simulations can be burdensome — even prohibitive. Fortunately, engineers can use cloud-based high-performance computing (HPC) to speed up these simulations in a cost-effective manner.
Reduce the Computational Time of Simulation Projects with Cloud Computing
Comparison of solution speed scale-up with different mesh densities
Cloud-based HPC offers the computing power of numerous cores to a simulation project. This will speed up the DoE, as each simulation’s computational time is reduced.
UberCloud offers cloud-based HPC as a software-as-a-service (SaaS) that is tailored for computer- aided engineering (CAE) software like ANSYS simulation products.
This cloud computing resource is available in over 50 data centres around the world and can help:
- Simplify software portability through browser-based access.
- Offer instant use of engineering workflows and computational hardware through CAE- application software containers.
- Maintain computational scalability across multiple compute nodes.
For this reason, we partnered with UberCloud to show how HPC can be used in bioreactor design.
How to Simulate a Bioreactor in ANSYS Fluent
Iso-surface of gas volume fraction colored with bubble diameter
Engineers can simulate the water and air present in the bioreactor using Fluent’s Eulerian multiphase model.
A population balance model with quadrature method of moments (QMOM) can be used to simulate how bubbles coalesce and break up.
This methodology predicts the overall gas distribution and the distribution of bubble size throughout the tank.
These predictions are key to determining the bioreactor’s mass transfer rate.
These simulations were run using the UberCloud platform in the Microsoft Azure cloud data centre in Singapore. Fluent showed linear scalability on UberCloud throughout the study of the bioreactor.
Scalability study based on a 688K polyhedral mesh
Each simulation — running on 168 cores in the cloud — took less than an hour (versus a week on a typical workstation). As a result, mapping out the design space took less than 12 hours.
RSM outlining the average mass transfer coefficient versus gas flow rate and agitation speed
The next step employed ANSYS DesignXplorer to generate the RSM. In this example, the RSM outlines how the mass transfer coefficient is affected by the agitation speed and gas flow rate within the bioreactor.
The results show that combining UberCloud with Fluent and DesignXplorer streamlined the DOE. This setup also reduced simulation time without investing the entire development budget on HPC.
To learn more about how UberCloud can speed up ANSYS products, click here.
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