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It occurred to me just the other day, that I've now been a simulation engineer for over 20 years (despite my youthful good looks, I really am that old). Back in the early 90s, when I fired up pro-STAR up for the first time, there were no 40 year old simulation engineers (or at least none that had survived to tell the tale).

Slide Rule

To celebrate 20 years in the game, I thought that I'd write my top seven tips for a long (if not successful) career in engineering simulation.

How do you ensure that methods developed in one part of your organization are deployed by engineers working elsewhere?

Introducing the Simulation Assistant

To answer this question, CD-adapco have developed the Simulation Assistant, an interactive user interface that allows you to capture best practices and deploy them across your whole organization, ensuring repeatability of process and guaranteeing consistency of results.

I am Dr. Mesh and designing is not my cup of tea. Yet somehow, I was roped into, ahem… asked to do a design job. As luck would have it, most of my colleagues were on holiday when my boss ordered, ahem… suggested that I create a more efficient system.

Efficiency is my middle name, but I had to start from a Part that one of my colleagues created earlier. Of course this was one of the aforementioned colleagues, now sunning himself on a beach in Papua New Guinea. I was stuck. I had to know the exact coordinates of a set of faces to continue the project.

It always happens at the last minute. My simulations have been run and converged and are ready to be presented for tomorrow morning's board meeting. Then it happens... I try to create a plot of the temperature variation along a line in my STAR-CCM+ simulation 5 minutes before the end of my day and I find my data is scattered all over the place! There's no way I could possibly present this result! Argh!!!

How is it that honeybees (average brain size 1g) manage to outmesh those CFD engineers (average brain size 1250g) who still religiously rely on tetrahedral meshing?

Honeycomb: Bees are better meshers than many engineers

The answer is obviously not that bees are more intelligent than engineers (although there are a few notable exceptions). Whereas CFD and associated meshing technology has been around for just 40 years, bees benefit from several billion years of evolution.

I wanted to take a moment and shine the spotlight on our coupled density-based solver. I must admit, I am perhaps a bit of a biased writer because after all, I have spent most of my career in industry analyzing and optimizing aircraft performance in flight regimes where the need for accurate shock capturing was part of daily life. Coupled solvers with a density-based approach have a proven track record for delivering robust solutions for these types of applications so it should come as no surprise that these numerical methods continue to spark my interest.

Does it matter which optimization technology an engineering team chooses?
We think that the answer is unequivocally: yes!

All optimization algorithms are not created equal. Many work well only on certain types of problems, and some are very inefficient at finding optimal solutions. The difference between a robust, efficient algorithm and an inferior one can be substantial in terms of real measures such as product cost, mass, and performance.

Structural Crash Optimization Using HEEDS

This is paticularly important when optimizing CAE solutions such as CFD and Crash, for which the cost of individual function evaluations is computationally expensive.

Driven by the twin demands of evolving customer expectations and increasing emissions regulation, the global automotive industry is in a race to deliver a sustainable compliment (if not replacement) to the Internal Combustion Engine. For now, propulsion systems based partially, or entirely, around electricity seem like the most credible prospect for providing the greatest reduction in CO2 emissions, within a reasonable timescale.

However, compared to gasoline engines, the cost of electrified power trains remains high, mainly due to the high cost of the batteries required to store and deliver the electrical power needed to drive such vehicles. Both Automotive OEMs and battery manufacturers are investing heavily in battery technology, with the aim of extending battery life, achieving higher energy densities and faster charging times, while improving both safety and reliability. A lot of this investment focuses on the efficient thermal control of battery cells.

Temperature distribution analysis of a module of 84 cells: 42 cells connected in series, and each row is connected in parallel. Liquid cooled plate are lateraly postionned on those rows (Image courtesy of ASCS, Stuttgart and Behr)

When Dave Brailsford announced the formation of Team Sky in 2010, he did so with the explicit ambition of propelling a British rider to the top step of the Tour de France podium by 2015. To cycling experts, it seemed like a brave and almost foolhardy prediction. In the 97 editions of the Tour de France that preceded Brailsford's announcement, no British rider had finished in the top 3 of the world's most important cycle race, let alone threatened to win it. Therefore, it seemed unlikely that Brailsford - a newcomer to the world of professional cycling would be able to reverse that lack of fortune in such a short period of time.

PinelloThe experts were wrong and spectacularly so. This Sunday as the Tour wrapped up its 100 year anniversary (two years ahead of Brailsford schedule) Team Sky rider, Christopher Froome rode into Paris wearing the coveted yellow jersey on his shoulders with a comfortable 5 minute margin over the second place rider. In doing so, he claimed not the first, but the second consecutive victory for a British Team Sky rider at the Tour de France, following in the footsteps of last year's winner Sir Bradley Wiggins.

So how did Team Sky manage to beat their own prediction and deliver a double British victory two years ahead of their plan?

How Do You Consider Surface Tension Effects Between Particles When Using DEM?

In STAR-CCM+, we can model the effect of the presence of the liquid film on the surface of DEM particles in the approximation of liquid bridge model. 

The capillary force resulting from the surface tension and the pressure difference inside the liquid bridge has known dependence on the wetting angle, liquid surface tension, particle size, etc.

If one assumes particular shape of liquid bridge, the solution of Laplace-Young equation provides the solution for hydrostatic pressure within liquid bridge. This gives the analytical solution for the maximum force needed to separate two particles connected with liquid bridge (lots of literature is available on this, including using liquid bridge model with DEM). Now, you just need to equate the liquid bridge force with STAR-CCM+ expression for linear cohesion force and voilà! - obtain the value of STAR-CCM+ cohesion parameter. Using cohesion model this way should account for the surface tension effect on the bulk flow of wet grains.Liquid Bridge

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Brigid Blaschak
Communications Specialist
Dr Mesh
Meshing Guru
Stephen Ferguson
Communications Manager
Tammy de Boer
Global Academic Program Manager
Sabine Goodwin
Senior Engineer, Technical Marketing
Joel Davison
Product Manager, STAR-CCM+
Matthew Godo
STAR-CCM+ Product Manager
Prashanth Shankara
Technical Marketing Engineer