Posts by John Thomas

will cfd ever replace experiment

Will CFD Ever Replace Experiment? [PODCAST]

This week, guest Aaron Sarafinas, Principal of Sarafinas Process & Mixing Consulting LLC, joins John to discuss one loaded question: “Will CFD or some CFD parallels ever replace experiment, and in an increasingly digitized world, what’s the role of experiment?”

Do You Need a PhD to Run CFD? [PODCAST]

This week, guest Eric Janz, Faculty of Practice at University of Dayton Innovation Center, joins John to discuss his experience with CFD and address a frequently asked question: Do you need a Ph.D. to run information from a CFD simulation?

Let’s Stop Using RANS [PODCAST]

This week, guest Johannes Wutz from M-Star Center Europe GmbH joins John to discuss turbulence modeling.

cloud computing for cfd

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. 

What’s the Big Deal About GPU Computing? [PODAST]

This week, guest Brian DeVincentis, M-Star’s Lead Algorithm Developer, joins John to discuss the question, “What’s the big deal about GPU computing?”

What’s Wrong with CFD Software Today? [VIDEO]

M-Star President John Thomas talks about the problems with traditional CFD software – and what’s possible today with modern tools.

Scaling Up Bioreactors

Scaling Up Bioreactors with CFD Software:
Three Steps

Scaling up bioreactors is often one of the most complex steps of a biologic production process. Here’s how you can overcome this.

cfd digital twin

Developing a CFD Digital Twin:
Four Steps for Successful Outcomes

When other experimental approaches are too limited to effectively predict a fluid process, engineers can create a CFD digital twin. Here are four steps to get started.

computational fluid dynamics software

What is Computational Fluid Dynamics Software?
Three Essential Features for Engineers

In this blog, we discuss the benefits, evolution and key features of CFD software.

How To: Predicting Blend Time in Agitated Tanks

Here we present a step-by-step tutorial for predicting fluid blend times in agitated tanks.

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