Digital Prototyping a Mercedes

By John Russell

July 14, 2015

ISC 2015’s emphasis on HPC use in industry was reflected in the choice of Monday’s opening keynote speaker, Jürgen Kohler, senior manager, NVH (noise, vibration, and harshness) CAE & Vehicle Concepts, Mercedes-Benz Cars Development. Kohler presented a fascinating overview of the evolution of the auto industry’s use of HPC-based modeling and simulation. (Did you know simulating road noise on American roads is one of the toughest challenges? The surfaces are rougher than elsewhere, said Kohler.)

“I’m not an HPC guy, not an expert who deals all day with exascale or new chip architectures. I’m an engineer developing fascinating cars with the help of modern HPC-based CAE tools. Our goal is that these cars are as safe and as comfortable and as efficient as possible,” Kohler told the ISC audience.

In the rarified air of HPC it’s sometimes forgotten that technical computing has a concrete role to play in industry. The auto industry has long been a poster child for its effective use of modeling and simulation to improve performance, increase safety, and achieve cost savings and remarkable manufacturing efficiencies.

Begun in the 1970s, early modeling and simulation of the Mercedes fleet was relatively crude (hundreds to a few thousands of elements). The results were taken as rough guides and physical testing regimes were remained the gold standard relied upon. Today the situation is nearly reversed. Structural integrity, airflow, in-car acoustics, crash dynamics, passenger safety are just a few of the many variables simulated prior to manufacturing.

In his talk, Kohler loosely summarized the development of M&S at Daimler and reviewed a few examples of how it is used.

Daimler’s pursuit of a digital prototype program started about 15 years ago he said and has since become standard operating procedure. Today there are more than 30 digital prototype projects underway, and the computational requirements necessary for effective simulation have grown steadily with the sophistication of the models. Besides assisting in the design and manufacture of beautiful cars, the increased use of M&S has dragged along familiar HPC headaches (bandwidth problems, IO and latency roadblocks, data management and storage challenges, etc.).

“Beside expanding our product line (new models), we are facing many new technologies like dealing with electric drives, dealing with hybrids, and still improving traditional combustion engines. Maintain sustained mobility through networks [is] another – you can now know if your son or daughter is driving the car when they shouldn’t be. Consider the fascinating field of autonomous driving. We already have [that] available in the new S Class or E Class with autonomous driving in a traffic jam up to a speed of 30 km/h,” said Kohler.

Without digital modeling and simulation it would be virtually impossible to design and efficiently manufacture modern cars and trucks. Moreover investments in required plants and manufacturing equipment are typically made two years ahead of market launch and are based on the digital prototype.

“These results have to be absolutely reliable. It’s very expensive to have to change expensive tooling [after the fact],” he said. “We need competence in software and hardware interacting together modeling especially in transferring our ideas and measure into the product. We have usually local clusters with specific applications that run hundreds of jobs every day.”

While not revealing much detail about the Daimler’s specific HPC infrastructure, Kohler presented a handful of M&S examples including collision safety modeling, passenger safety, and ride quality. He also showed a short video:

“In [about] 1970 when the film was made and we had started working on these methods and it took quite a long time before method got established. This is one of our first [crash] simulation models for stiffness with 1119 elements,” he said. Simulating crashes and NVH in the new S Class uses models with millions of elements and some aerodynamics applications with 80 million cells.”

Kohler then showed a video of modern simulation of a crash between an S Class car and Smart Car (made by Daimler). “Our goal is that both cars are very safe. The S Class is a big car, weighing more than 2000kg and has a long crumple zone in the front. The Smart Car weighs about half as much and has a very stiff cell, which protects the passengers, along with an elaborate restraint systems. The simulation is of 50km/h collision run on 490 cpus for about 30 hours (8 million elements.) The mesh size is critical.”

Today, Daimler simulates about 70,000 crashes year in addition to conducting 700 physical crashes per year. “You see that’s a lot of work. Turnaround time began at five days and today it’s a half–day or one day for bigger problems, said Kohler.

These simulations generate an avalanche of data. “If you do 70,000 crash simulations a year and you store all the data which is computed, it would be about 40 exabytes. We don’t. Instead we temporarily store about 6 petabytes and reduce that down and store only 400 terabytes a year. I hear a lot about big data and it’s an important topic but not as important for us in simulation. There are some big data projects in our company, but they are quality and sales,” he said.

Not surprisingly passenger safety is an area of emphasis and an area where simulation has distinct advantages. “A traditional dummy is [essentially] an instrument for measuring defined forces and simulates a crash. The problem is the bones are made of steel in order to measure forces. If you take a human arm or leg it is so different and so much lighter.

“We use human models. It’s very important to have valid human kinematics to evaluate injury or risk. We are able to make models of ten different body shapes, about 400k elements in the model, and the total cpu time varies from 1 hour up to 25 hours,” said Kohler.

NVH is another important measure as it directly affects comfort in the car. Kohler said these models can get quite large and bog down processing time. Engine excitation, cabin vibration, motor housing vibrations, and stiffness of rubber bearings are just a few of the aspects measured. “You can have a very small excitation of the chassis and you wouldn’t see it without simulations,” said Kohler. Airflow, of course, is important.

“At higher frequencies you have fluctuating turbulence, and street noise. [Simulating] an S Class with a mesh size here with 150 million cells on 500 cores takes two weeks. There’s still potential for improvement,” Kohler said.

The ballooning of model sizes has been challenging. It was necessary to adopt parallelization techniques to get runtimes down. Adoption of HPC software, such as Automated MultiLevel Substructuring simulation and optimizing the system for it has helped cut processing times.

“Today we are able to simulate very detailed models. As an example, the current model of the S Class with 25 million degrees of freedom running on 6000 nodes would take 200 hours for computer; with AMLS solver it takes less than two hours,” said Kohler.

Clearly HPC technology and techniques, said Kohler, have made a major impact. “HPC gives us a deeper understanding of a system and helps reduce the need for prototypes and tests, and shortens development. He is quick to add M&S alone isn’t enough. Physical testing is required and indeed Daimler has a wind tunnel with a 28m2 nozzle.

Most of us take our cars for granted but the truth is they are in many ways technological marvels and remarkably reliable given the wide range of conditions (weather, roads, collisions, temperature swings) in which they operate and the years of service we expect from them.

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