Periodic Reporting for period 1 - CINEMA (Creating an Infrastructure for the Numerical Exploration of Metallurgical Alloys)
Reporting period: 2019-04-01 to 2021-03-31
Manufacturing has considerable economical, technological, and environmental importance at the global scale, impacting fields such as transportation, energy, safety, and healthcare, among others. In this sector, due to the complex links existing between materials processing, microstructure, properties, and their performance, the discovery of new materials has, for centuries, relied on costly and ineffective trial-and-error experiments. However, with the advent of high-performance computing, this paradigm is ready to be guided and made substantially more efficient (in terms of both time and resources) by the use of “computational experiments”, provided that the appropriate physics and scales are appropriately integrated. Within this context, the key objective of this project is to build a computational infrastructure for computational exploration of alloys, by adapting, combining, and linking together different materials models, at different scales, and with different purposes – e.g. models linking processing to microstructures to other models linking microstructures to properties. With a strong focus on metallic materials produced by solidification processing (e.g. casting, welding, or additive manufacturing), this project shapes the foundations for computational discovery of structural materials and the optimization of metallurgical process.
In this project, we build links between material models, in order to close the loop between processing, microstructure, and properties in solidification processing of metallic alloys. More specifically, we combined: (1) the CalPhaD method, used to calculate the phase diagrams and thermophysical properties of complex multicomponent technological alloys (e.g. Ni-based superalloys); (2) the phase field (PF) method, used to simulate dendritic growth at the scale of the individual dendrite, and thus to obtain morphological details and the so-called “tip selection parameter” critical to link the dendrite morphology to its growth velocity; (3) the multiscale dendritic needle network (DNN) approach, which allows upscaling PF simulations to full dendritic arrays, in a typical solidification regime for concentrated alloys for which the PF method becomes computationally unfeasible; and (4) a FFT-based crystal plasticity framework that predicts the mechanical behavior of heterogeneous microstructures, thereby allowing the full linking from processing to microstructure to properties. In addition to this particular computational framework, we also explored alternative pathways for linking microstructure evolution to micromechanics (e.g. coupling a meshless crystal plasticity framework to the phase-field method) and the extension of our multiscale solidification model to more realistic processing conditions (e.g. incorporating gravity-driven fluid flow in the liquid phase). During its execution, the project has led to the publication of four peer-reviewed articles (with two more full-length articles in preparation at the date of closing of the project), a dozen talks at international conferences (e.g. ICASP, EUROMAT, MCWASP), industry workshops (e.g. Airbus ICME workshop 2019), and invited seminars at international institutions (e.g. Institut Jean Lamour in France, and Argonne National laboratory in the USA), as well as a half-dozen of communication and outreach events aimed at broad audiences of all ages and backgrounds (e.g. Madrid Science & Innovation Fair 2019 and the European Researcher’s Night 2020).
Microstructures being the pivotal link between materials processing and performance, the ability to predict microstructures – and further on the properties – of a material from a computational approach has the potential accelerate the discovery and deployment of new materials and processes. Yet, outstanding challenges in physical metallurgy – i.e. in closing the loop between processing, microstructure, properties, and performance – stem from its multidisciplinary aspect, at the crossroads of physics, mechanical engineering, and computer science, and from the wide range of length and time scales at play. In this project, we have proposed and explored several pathways to link material models together in a single framework. In particular, we proposed a computational framework combining computational thermodynamics (CalPhaD), phase-field modeling (at the individual dendrite scale), dendritic needle network (at the scale of full dendritic arrays), and crystal plasticity theory (to calculate the mechanical response of heterogeneous microstructures). By demonstrating the feasibility of such linking strategies, the project has provided key insight into the challenges and most promising pathways to couple models together, thereby bringing manufacturing industries one step closer to a fully integrated computational approach to metallurgical innovation.