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For years, I have seen the challenges faced by the product development teams in companies of various sizes across all industries.  Amongst those challenges, two in paticular stand out:  One is the ability to perform simulation earlier in the product development cycle and the other is focused on improving collaboration between the design and simulation teams. Addressing the latter of these has been difficult due to a "brick wall" that has always existed between the design and simulation teams due to a lack of interoperability between CAD and simulation software tools.  Although software vendors have, over time, proposed various solutions to this problem, until now none have offered a scalable solution set that could address these issues elegantly for the broad range of simulation problems faced in industry.

With the imminent arrival of bi-directional CAD Clients in STAR-CCM+ v8.06, all that is about to change forever.

CD-adapco is committed to the philosophy of “simulating systems”. This gives our users the opportunity to take into account all the factors likely to influence the performance of their product in its operational life when building a simulation. As part of that commitment, one of our ongoing development themes in STAR-CCM+ involves making multiphase flows more accessible to all of our users. For this, we introduce a feature that allows you to tackle a wider range of industrial applications (bridging the “gaps” described above) by further improving solver performance.

RSM turbulence is useful for swirling flows

Let's explore some of the great new multiphase flow features that you'll be able to enjoy in STAR-CCM+ v8.06...

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)

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Stephen Ferguson
Communications Manager
Dr Mesh
Meshing Guru
Brigid Blaschak
Communications Specialist
Sabine Goodwin
Senior Engineer, Technical Marketing
Jean-Claude Ercolanelli
Senior Vice President, Product Management at CD-adapco
Bob Ryan
President Red Cedar Technology
Joel Davison
Product Manager, STAR-CCM+
Deborah Eppel
Technical Marketing Engineer