Discoveries and advances in metallurgy—the science and technology of metals—have enabled the development of our modern society from the Bronze Age to the present. Today, in addition to their traditional roles, metals are important to worldwide clean energy efforts. That’s because many proposed clean energy solutions hinge upon our ability to understand and predict how metallic structural components behave over time when subjected to extreme and complex environments, such as harsh temperature or irradiation. As a metal is subjected to evolving mechanical and thermal loads, ensuring subsequent deformation will not result in unfavorable—or even catastrophic—conditions is essential. Being able to predict the response of the material can help mitigate future problems without stifling deployment.
This requires relating how the state of internal stresses, microstructure, and key elements of the material’s composition will affect the likely response of a structure. Risk can vary even within seemingly identical components; small changes in fabrication parameters can result in unseen changes in microstructure that can have large impacts on component reliability over decades. The challenge is to relate these micrometer-scale states to performance of large-scale structures.
Advanced mechanistic and microstructure sensitive models provide a pathway to simulate the expected performance of a structural metallic component. Given the pressing needs for rapid technology deployment, models that give designers, regulators, and the public confidence in clean-energy technologies are worth the investment.
Researchers at Los Alamos National Laboratory developed Los Alamos Reduced Order Model for advanced non-linear equations (LAROMance, pronounced la romance) to meet these challenges. The suite of data-driven models predicts the mechanical response of structural engineering metals subjected to extreme environments. These constitutive models are fully integrated into finite element solvers and allow simulations of the response of engineering structures as a function of the state of the microstructure of the base metal.
Usually, the expected lifetime of a material system is established by performing a series of tests on the structure to assess its performance and safety. LAROMance goes one step further by being able to extrapolate beyond the data available. LAROMance models are derived from mining a database of responses of the metals. This database stores the result of advanced simulations using complex scientific constitutive models that are sensitive to the state of the microstructure. 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 predictiveness stems from the fact that the LAROMance models are fit against a large database of expected mechanical response of microstructure changes generated via use of a high-fidelity mechanistic polycrystal model.
LAROMance opens new avenues to incorporate microstructural information into real-world decision making on engineering problems. In particular, the ability to analyze nuclear reactor or gas turbine components, which are expected to perform flawlessly while subjected to truly extreme conditions, provides a solid pathway to accelerate technology deployment. LAROMance introduces highly predictive materials tools as a turnkey solution to any user irrespective of their expertise in materials science. ★