In a manufacturing setting, the traditional approach to design optimization of a new product involves a lot of experimental testing, evaluating prototypes, and going through multiple design iterations.
As the volume and complexity of data increases, it is extremely challenging for engineers to make sense of all the multi-dimensional information and make sound decisions in a timely manner. This uncertainty increases the number of costly experimental test campaigns, lengthens development timescales, and raises the cost of development.
In an effort to combat these limitations, industry increasingly relies on high-fidelity computer models as virtual representations of real-world devices. High-fidelity modeling represents an improvement over costly physical development and testing, but, importantly, it remains time-consuming.
Argonne National Laboratory is augmenting high-fidelity modeling with machine learning to dramatically accelerate the design optimization process while maintaining the reliability of the data.
A job that might take hours using high-fidelity modeling alone takes milliseconds when the modeling is augmented by machine learning.
Argonne recently worked with a global petroleum and natural gas company to optimize a diesel engine to run on a gasoline-like fuel. Using high-fidelity modeling, the company’s development time took months. By tapping into Argonne’s supercomputing resources and machine learning expertise, the company was able to reduce the development time to days, with the same quality of result.