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sts.components.contact.mr.placeholder Sebastian Webel
Mr. Sebastian Webel

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Pictures of the Future
The Magazine for Research and Innovation
 

Urban Mobility

Simulations: Putting Vehicle Safety in the Driver’s Seat

With a view to fostering innovation in industry, the Virtual Vehicle Research Center focuses on creating bonds between university research and industrial development.

Simulations enable vehicles to be tested faster, more precisely, and at lower cost. In Graz, Austria, Siemens operates one of the world’s largest development and production facilities for state-of-the-art railway bogies. It also works closely with Virtual Vehicle, an international research and development center, to study a wide variety of solutions designed to make vehicles safer, more comfortable, and more efficient.

Simulations enable vehicles to be tested faster, more precisely, and at lower cost. In Graz, Austria, Siemens operates one of the world’s largest development and production facilities for state-of-the-art railway bogies. It also works closely with Virtual Vehicle, an international research and development center, to study a wide variety of solutions designed to make vehicles safer, more comfortable, and more efficient.

On average, a railway bogie is fitted with around 20 sensors. During operation, those sensors continuously transmit data about the bogie’s condition to a diagnostic system for analysis and evaluation. Are the wheels running evenly? Is the wheel suspension system flawlessly transmitting data on steering, braking, and acceleration forces? Which vibrations are affecting the bogie? In short, when will repairs be needed?

Questions such as these are posed by researchers at the Virtual Vehicle Research Center in Graz, Austria, which develops vehicle concepts for road and rail use. Using the data delivered by bogie sensors, researchers make diagnostic forecasts based on actual bogie conditions. The advantage of such forecasts is that maintenance technicians don’t have to take any action unless it is objectively necessary — a major step forward compared to maintenance based on predetermined intervals, which can result in late detection of serious faults.  “In the long run, predictive maintenance optimizes maintenance times and ensures a high level of rail vehicle availability,” says Dr. Andreas Haigermoser, who is in charge of innovation management at Siemens’ Graz facility.

Developers from Virtual Vehicle Research Center discuss how best to optimize bogie driving characteristics.

The predictive maintenance project is one of seven research projects that Siemens is running in cooperation with the Virtual Vehicle Research Center. Siemens’ mobility plant in Graz is one of the world’s largest development and production facilities for state-of-the-art railway bogies. Around 950 people work at the facility developing and producing bogies for local and long-distance trains that are used worldwide. Approximately 3,000 bogies are delivered by the plant each year. “Because bogies play a key role with regard to safety and comfort and account for up to one fourth of a vehicle’s total costs, it is crucial that they be continuously refined. That’s why close cooperation with research partners such as the Virtual Vehicle Research Center is essential,” says Haigermoser. Siemens has held a 12 percent share of Virtual Vehicle since 2007.

Founded in 2002, the research center focuses completely on virtualization, which has the advantage of speed. Although one could theoretically acquire increasingly smart measurement technology, in practice this would be too expensive; as a consequence validated forecasts have to be made. Simulations make such forecasts possible. The known properties of bogie components serve as the basis for forecasting their future behavior. These predictions are then tested in simulations. ”Hypotheses can be verified very quickly today through the use of algorithms that are tested in simulation models,” says Dr. Martin Rosenberger, who manages rail vehicle research at Virtual Vehicle.

When a train encounters a curve, dynamic forces arise between the vehicle and the rails (red arrows). Simulations can be used to represent the effects of these forces — wear, for example — on the train.

Virtual Vehicle was jointly founded by Graz University of Technology, the drive system developer AVL List, the automotive supplier Magna, and JOANNEUM RESEARCH Forschungsgesellschaft mbH. Over the years, the research center developed into an internationally leading institute in its field and now has around 200 employees.

Simulations not only often proceed faster than reality, but can very specifically and automatically test innumerable critical scenarios.

Understanding, Forecasting and Minimizing the Causes of Wear and Tear

Siemens is the research center’s key rail vehicle partner. Together, the two organizations have jointly conducted around 15 research projects, including a series of projects for studying the causes of damaged tracks and train wheels. This research is essential because wear is dangerous as well as expensive. In fact, it costs Europe’s railway operators around €1 billion per year. That’s why researchers in Graz want to know how simulations can help to optimize the interaction between tracks and wheels so that wear and tear can be minimized. “We need to understand the complex physical mechanisms that cause damage,” says Haigermoser. The associated research project produced computer models that simulate these processes as well as surface production methods that better prevent material fatigue. The study analyzed rail vehicles of all kinds, from streetcars to high-speed trains.

Digital Optimization for Rail and Road Vehicles

Interest in the relationship between infrastructures and vehicles is not limited to rail vehicle manufacturers and operators; the automotive industry also sees opportunities in this area. Driver assistance systems and autonomous vehicles, for example, are of great interest to the Virtual Vehicle Research Center. Indeed, one of its most extensive research projects is designed to find out how automated driving functions can be reliably assessed. Once again, it would cost too much money and effort to conduct purely physical component and vehicle tests, so the organization is supplementing physical tests with virtual ones.  “In principle, you would have to test autonomous vehicles in every imaginable driving scenario in cities and rural areas,” says Rosenberger. “You’d have to drive hundreds of millions of kilometers. That’s why we use simulations to make validated forecasts about the behavior of autonomous vehicles. Simulations not only often proceed faster than reality, but can very specifically and automatically test innumerable critical scenarios. That saves lots of time and money,” he adds. 

Gitta Rohling
Picture credits: from top: 1. and 3. Picture: VIRTUAL VEHICLE research center