CAE tools advance for engine design

  • 06-Nov-2016 09:44 EST
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System models such as GT SUITE are convenient early in the development process, and embedding engine models in the machines they power is vital. (Gamma Technologies)

Gamma Technologies, maker of the well-known GT POWER modeling tool for engines and its GT SUITE for powertrain and vehicle-level simulations, has seen the growing complexity in off-highway engines over time.

“In anticipation of these trends, GT-SUITE has been created specifically as an integrated vehicle system tool capable of responding to these demands,” explained Dr. Thomas Morel, president and founder of the company. “Integration of the whole vehicle system enables evaluations of subsystem alternatives, even individual component alternatives and their effect on fuel economy, emissions and NVH.”

The tool also combines modeling methods, using so-called 0D and 1D system models integrated with 3D models. The company notes that this makes GT-SUITE a particularly useful tool for model-based systems engineering, or MBSE, which enables the progressive development of an engine or powertrain from an initial concept to the final product. It does this by first using 0D and 1D system modeling tools then switching to high-fidelity 3D models that examine system performance, efficiency, dynamics, structural stresses and temperatures and other parameters.

Morel emphasized that GT-SUITE models the whole vehicle. “From fuel combustion to the wheels, including the effects of fluids, hydraulics, all mechanical systems, thermal analysis, chemistry, tribology, friction, hardware-in-the-loop and controls,” he said.

Another key point that Morel emphasized—and is echoed by many other tool suppliers—is the growing need to interface and incorporate simulation tools from other suppliers. For example, the company offers an engine combustion tool from Convergent Science and a CAD-to-model tool as well as a virtual analysis tool for complete vehicles, including GT-SUITE and the predictive 3D chassis models, from Adams, the dynamics tool from MSC Software.

Toward that end, the company recently released its own unique technology for co-simulation employing xLink. To simulate a system, it accepts any tool that matches the Functional Mockup Interface (FMI) standard, Simulink, or executables compiled as a Dynamic-Link Library (DLL). It can also implement a user-defined code through the xLink interface.

(For further information on Functional Mockup Interface, see “CAE’s next leap forward,” http://articles.sae.org/14859/.)

“Access to the right computing power will not be a limiting factor.” That is the view of Vivian Page, engineering systems team leader, Cat Industrial Engines. “The challenge will be in how we manage and process the vast amounts of data and information that are generated by these simulation tools to understand the complex multi-physics environment within a diesel engine.”

Siemens PLM Software is responding to comments like Page's, looking at a grander view of engineering. “There is a movement across all industries, including off-highway, that is looking to incorporate a new set of technologies,” explained Ravi Shankar, director of marketing, simulation and test solutions, Siemens PLM Software. These include use of advanced lightweight materials, additive manufacturing, mass customization and wide deployment of inexpensive sensors.

Through acquisitions and investment, Siemens PLM is looking to help companies incorporate these new technologies in a new way. It has created a comprehensive portfolio of simulation and data management tools in its newly announced Simcenter portfolio, combining advanced simulation and the potential to access real-world data through the Industrial Internet of Things (IIoT).

“Data can be fed back to engineering teams to provide more useful predictive results and react with more updates to the customers,” explained Shankar. “We think of that as opening a new front with [Big Data] analytics combined with physics-based simulations that we are now calling Predictive Engineering Analytics.”

This means constraints for optimization runs can be further honed from actual data on, say, bulldozers or agricultural equipment. Actual operator preferences can be accounted for, and actual operating cycles can be used for setting engine parameters for best fuel economy in engines.

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