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  • 2018-04-13 17:05:00
  • Article ID: 692834

Argonne's Next Top Model

  • Credit: (Image by Shutterstock / Photo_works.)

    Machine learning techniques can help organizations reduce design time from months to days and slash development costs.

  • Credit: (Image by Shutterstock / Aumm graphixphoto.)

    Argonne’s scientists and engineers are augmenting high-fidelity modeling with machine learning to dramatically accelerate manufacturing processes.

  • Credit: (Image by Shutterstock / BeeBright.)

    Machine learning trains computers to discover hidden patterns in data to make novel predictions without being explicitly programmed. Argonne scientists and engineers are applying the technique to manufacturing to quickly find the best design for a product or the most efficient production process.

  • Credit: (Image courtesy of U.S. Department of Energy.)

    Argonne researchers look to machine learning to uncover insights in virtually all fields of science and engineering – from engine combustion design to materials science to cosmology. With architectural features and tools that support data-centric workloads, Theta, the ALCF’s new Intel-Cray supercomputer, is particularly well suited for research involving data science and machine learning methods.

Designing and manufacturing a new part or product, such as a car engine or wind turbine, can be time-consuming and costly. To combat limitations on these processes, scientists and engineers at the U.S. Department of Energy’s (DOE) Argonne National Laboratory are using cutting-edge machine learning techniques to help organizations reduce design time from months to days and slash development costs.

Machine learning is a type of artificial intelligence that trains computers to discover hidden patterns in data to make novel predictions without being explicitly programmed. This technology can be applied to manufacturing to quickly find the best design for a product or the most efficient production process.

"Machine learning ... is similar to how biologists study fruit flies instead of humans. The flies share significant characteristics with humans, but they can generate and evolve much faster.” — Janardhan Kodavasal, mechanical engineer in Argonne’s Energy Systems division

The traditional approach to optimizing the design of a new product involves much experimental testing and evaluation of many prototypes. As the volume and complexity of data derived from these tests increase, industry relies more and more on high-fidelity computer models that virtually represent real-world devices and processes.

These models take in certain values corresponding to controlled aspects of the manufacturing process, for example, fuel pressure, when the fuel is injected into the case of an engine. By using data drawn from physical experiments, the model can determine how well the set of inputs would achieve the desired outputs, such as efficiency and cost-effectiveness.

While they are an improvement over costly investments in physical development and testing, high-fidelity models take a long time to run due to their computationally intensive nature.

Argonne’s solution is to augment high-fidelity modeling with machine learning to dramatically accelerate the process, while maintaining the reliability of the data. A job that might take hours to run using high-fidelity modeling takes milliseconds when augmented by machine learning.

“Machine learning converts the very complex physical processes represented by the virtual model into a compact computational process that can be run in much less time. It’s similar to how biologists study fruit flies instead of humans. The flies share significant characteristics with humans, but they can generate and evolve much faster,” said Janardhan Kodavasal, a mechanical engineer in Argonne’s Energy Systems division, who heads the initiative.

To start the process, the scientists run several thousand simulations of a high-fidelity model on Argonne’s supercomputer Mira, at the same time and with different inputs. This step generates virtual data that train the machine learning model to find the best input combination. (Mira is located at the Argonne Leadership Computing Facility, a DOE Office of Science User Facility.)

The scientists then use an evolutionary approach to find an optimal design by prioritizing the product’s desired outputs. For example, a manufacturer might want a product to exhibit both low emissions and high cost-effectiveness, but could instruct the machine learning model to favor one slightly over the other.

With the ideal outputs specified, the model runs a set of designs and chooses the best ones from that generation. Those designs exchange some of their input features, like children taking genes from their parents, and the model is run again. The process repeats until the merit of the design can’t be enhanced any further. Once the machine learning model spits out the optimal inputs, the scientists run the original high-fidelity model with those inputs to verify that it is the ideal set.

In one recent case, Argonne worked with a global petroleum and natural gas company to optimize an engine to run on a new fuel that the company is developing. The company was previously using a high-fidelity model of the engine, but development was taking months. By tapping into Argonne’s machine learning expertise, development time was reduced to days.

But it’s not just engines to which machine learning can be applied. The technique can aid in the development of parts and products for virtually any industry, including materials, transportation, construction and utilities.

Machine learning models can even maximize the efficiencies of processes, from ventilation systems in a building to the production of a car dashboard, or even 3-D printing.

“We work with collaborators at different levels of research experience and capabilities,” said Kodavasal. “We can help companies at any step of the process, whether that is developing the initial high-fidelity model of the system, or just implementing the machine learning aspect.”

As demand for machine learning continues to increase, Argonne scientists will continue to expand its competencies. Techniques such as active learning will allow machine learning models to interact with high-fidelity models to improve accuracy and efficiency as the models provide data, and real-time optimization that will help guide manufacturing processes as they happen.

The wide variety of research taking place at Argonne fosters the exchange of ideas between different fields that could also improve machine learning capabilities. Kodavasal is optimistic about continued advancements in machine learning and its potential to handle industry’s growing needs.

For more information, contact Janardhan Kodavasal at 630-252-3794 for technical inquiries, or John Harvey at 630-252-3566 for business inquiries.

Argonne National Laboratory seeks solutions to pressing national problems in science and technology. The nation's first national laboratory, Argonne conducts leading-edge basic and applied scientific research in virtually every scientific discipline. Argonne researchers work closely with researchers from hundreds of companies, universities, and federal, state and municipal agencies to help them solve their specific problems, advance America's scientific leadership and prepare the nation for a better future. With employees from more than 60 nations, Argonne is managed by UChicago Argonne, LLC for the U.S. Department of Energy's Office of Science.

The U.S. Department of Energy's Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of the most pressing challenges of our time. For more information, visit the Office of Science website.

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