With the advent of high performance computing, the application areas of the phase-field method, traditionally used to numerically model the phase transformation in metals and alloys, have now spanned into geoscience. A systematic investigation of the two distinct scientific problems in consideration suggest a strong influence of interfacial energy on the natural and induced pattern formation in diffusion-controlled regime. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.

With the advent of high performance computing, the application areas of the phase-field method, traditionally used to numerically model the phase transformation in metals and alloys, have now spanned into geoscience. A systematic investigation of the two distinct scientific problems in consideration suggest a strong influence of interfacial energy on the natural and induced pattern formation in diffusion-controlled regime.

Most storage materials exhibit phase changes, which cause stresses and, thus, lead to damage of the electrode particles. In this work, a phase-field model for the cathode material NaxFePO4 of Na-ion batteries is studied to understand phase changes and stress evolution. Furthermore, we study the particle size and SOC dependent miscibility gap of the nanoscale insertion materials. Finally, we introduce the nonlocal species concentration theory, and show how the nonlocality influences the results.

The utilization of thermodynamic and mobility data plays a major role in phase-field modeling. This work discusses different formulations for the thermodynamic quantities of a grand potential model along with practices to determine parameters from datasets. The framework is used to study solidification of Al-Si-Mg for a variation of composition, diffusivities and surface energy anisotropies. To verify the simulations, they are compared with solidification theories.

Recent studies of deformation mechanisms of metals and alloys pioneer the better investigation of the friction and wear behavior of materials with well-defined initial microstructures. Within this scope, in this work, the effect of sub-surface deformations on the resulting friction and wear behavior has been searched by means of a systematic experimental study on Au-Ni metallic multilayer model alloy system.

During the production of fiber-reinforced thermosets, the resin material undergoes a reaction that can lead to damage. A two-stage polymerization reaction is modeled using molecular dynamics and evaluations of the system including a fiber surface are performed. In addition, a phase-field model for crack propagation in heterogeneous systems is derived. This model is able to predict crack growth where established models fail. Finally, the model is used to predict crack formation during curing.

This work presents a data mining framework applied to industrial heattreatment (bainitization and case hardening) aiming to optimize processes and reduce costs. The framework analyses factors such as material, production line, and quality assessment for preprocessing, feature extraction, and drift corrections. Machine learning is employed to devise robust prediction strategies for hardness. Its implementation in an industry pilot demonstrates the economic benefits of the framework. - This work presents a data mining framework applied to industrial heattreatment (bainitization and case hardening) aiming to optimize processes and reduce costs. The framework analyses factors such as material, production line, and quality assessment for preprocessing, feature extraction, and drift corrections. Machine learning is employed to devise robust prediction strategies for hardness. Its implementation in an industry pilot demonstrates the economic benefits of the framework.