New data-driven model could help the world meet clean-energy demands

‘LAROMance’ predicts metals’ response to extreme conditions

March 28, 2022

LAROMance is a data-driven model that predicts the mechanical response of structural engineering metals subjected to extreme environments, such as those in nuclear power plants, gas turbines, and heat exchangers.

Metallurgy has advanced society for millennia.

The earliest metal used by humans was copper. Fast forward 10,000 years, and now we’re using metals to meet essential global-energy demands. This is because many of the proposed clean-energy solutions hinge on the ability to understand and anticipate how metallic structural components behave over time when subjected to extreme and complex environments, such as high temperatures, stresses or even irradiation.

The challenge is to anticipate how a metal will respond over its extensive lifetime. As a metal is subjected to mechanical and thermal loads, or stresses, researchers must ensure subsequent deformation will not result in unfavorable or even catastrophic conditions. Being able to predict the response of the material through theoretical deduction — rather than direct observation — can help mitigate problems without stifling deployment.

Models simulate expected performance

Everything has internal microstructures visible only with a microscope. Engineered structures, from heat exchangers to gas turbines, inevitably have small changes in fabrication parameters that can result in unseen changes in microstructure. Individual components might look identical, but risk varies when it comes to the microstructures, and that risk might not be realized until much later.

Given the pressing needs for rapid technology deployment, tools are needed that give designers, regulators and the public confidence in clean-energy technologies. Fortunately, advanced mechanistic and microstructure-sensitive models provide a pathway to simulate the expected performance of a structural metallic component.

LAROMance predicts response

LAROMance, which stands for Los Alamos Reduced Order Model for advanced non-linear equations, was developed to relate these micrometer-scale states to the performance of large-scale structures, and thus offer much-needed peace of mind. The suite of data-driven models predicts the mechanical response of structural engineering metals subjected to extreme environments, such as those encountered in power generation applications. These models fully integrate into finite element solvers to help researchers simulate the response of engineered structures down to the microstructure of the base metal.

Usually, a series of laboratory tests are performed to establish the expected lifetime of a material system. However, the testing can take more than a decade, and the actual conditions experienced by a material in use might differ from those in the simulation. An ideal model should be able to extrapolate beyond the data available. LAROMance models draw from a database of advanced simulations that are sensitive to the microstructure of metals. It can be applied to any arbitrary quasi-static loading condition, such as stress relaxation, creep, tensile tests and cyclic tests. LAROMance captures the entire spectrum of behaviors of the metal in a unified fashion.

This development opens new avenues to incorporate microstructural information into real-world decision making. For example, the ability to analyze provides a solid pathway to accelerate deployment of nuclear reactor components or of hydrogen gas turbines, which must perform flawlessly while subjected to truly extreme conditions. With tools like these, we can reduce costs, increase safety and expedite deployment.

This work was funded under the extremeMAT and Nuclear Energy Advanced Modeling and Simulation (NEAMS) projects, which are respectively supported by the Department of Energy’s Fossil Energy and Carbon Management and Nuclear Energy offices.