Effect of rigid body motion in phase-field models of solid-state sintering

In the last two decades, many phase-field models for solid-state sintering have been published. Two groups of models have emerged, with and without the contribution of rigid body motion. This paper first describes the previously published phase-field model with an advection term driven by rigid body motion. The model is then used to investigate the differences between models with and without rigid body motion in new benchmark geometries exhibiting markedly different behavior. Sensitivity studies concerning the parameters of the rigid-body motion model are conducted and their effects on equilibrium and kinetic properties explored. In particular, it is shown by simulations that a shrinkage rate independent of system size requires the inclusion of an advection term. Finally, the reason behind this behavior is explored and implications for diffusion-only models are drawn

Modelling and simulation of the freeze casting process with the phase-field method

The freeze casting process is a novel manufacturing method for both near net-shape parts as well as directed porous structures as employed by filters and implants. Depending on the choice of liquid and processing conditions a very wide range of pore shapes and sizes can be achieved. In order to predict the resulting microstructure, a phase-field model is developed on the basis of the grand potential formalism. The model and its parametrization approximate the freeze-casting process of water by linking its thermodynamics with established theory. Directional solidification simulations with varying suspension concentrations, velocities and temperature gradients are carried out. From these, microstructural lengths are determined and linked with the processing parameters, so as to derive linkages between the microstructure and the processing conditions

Unravelling densification during sintering by multiscale modelling of grain motion

The resulting microstructure after the sintering process determines many materials properties of interest. In order to understand the microstructural evolution, simulations are often employed. One such simulation method is the phase-field method, which has garnered much interest in recent decades. However, the method lacks a complete model for sintering, as previous works could show unphysical effects and the inability to reach representative volume elements. Thus the present paper aims to close this gap by employing molecular dynamics and determining rules of motion which can be translated to a phase-field model. The key realization is that vacancy absorption induced motion of grains travels through a grain structure without resistance. Hence the total displacement field of a green body is simply the superposition of all grains reacting in isolation to local vacancy absorption events. The resulting phase-field model is shown to be representative starting from particle counts between 97 and 262 and contains the qualitative correct dependence of sintering rate on particle size

Unravelling densification during sintering by multiscale modelling of grain motion

The resulting microstructure after the sintering process determines many materials properties of interest. In order to understand the microstructural evolution, simulations are often employed. One such simulation method is the phase-field method, which has garnered much interest in recent decades. However, the method lacks a complete model for sintering, as previous works could show unphysical effects and the inability to reach representative volume elements. Thus the present paper aims to close this gap by employing molecular dynamics and determining rules of motion which can be translated to a phase-field model. The key realization is that vacancy absorption induced motion of grains travels through a grain structure without resistance. Hence the total displacement field of a green body is simply the superposition of all grains reacting in isolation to local vacancy absorption events. The resulting phase-field model is shown to be representative starting from particle counts between 97 and 262 and contains the qualitative correct dependence of sintering rate on particle size.Comment: 28 pages, 16 figures; with comments from reviewers incorporate

Simulation of dendritic-eutectic growth with the phase-field method

Solidification is an important process in many alloy processing routes. The solidified microstructure of alloys is usually made up of dendrites, eutectics or a combination of both. The evolving morphologies are largely determined by the solidification process and thus many materials properties are dependent on the processing conditions. While the growth of either type of microstructure is well-investigated, there is little information on the coupled growth of both microstructures. This work aims to close this gap by formulating a phase-field model capable of reproducing dendritic, eutectic as well as dendritic-eutectic growth. Following this, two-dimensional simulations are conducted which show all three types of microstructures depending on the composition and processing conditions. The effect of the dendritic-eutectic growth on the microstructural lengths, which determine materials properties, is investigated and the morphological hysteresis between eutectic growth and dendritic-eutectic growth is studied by employing solidification velocity jumps. Further, the influence of primary crystallization is investigated in large-scale two-dimensional simulations. Finally, qualitative three-dimensional simulations are conducted to test for morphological changes in the eutectic.Comment: 51 pages, 19 figure

Data workflow to incorporate thermodynamic energies from Calphad databases into grand-potential-based phase-field models

In order to approximate Gibbs energy functions, a semi-automated framework is introduced for binary and ternary material systems, using CALPHAD databases. To generate Gibbs energy formulations by means of second-order polynomials, the framework includes a precise approach. Furthermore, an optional extensional step enables the modeling of systems in which a direct generation leads to the unsatisfactory results in the representation of the thermodynamics. Furthermore, an optional extensional step enables the modeling of systems, in which a direct generation leads to the unsatisfactory results, when representing the thermodynamics. Within this extension, the commonly generated functions are modified to satisfy the equilibrium conditions in the observed material systems, leading to a better correlation with thermodynamic databases. The generated Gibbs energy formulations are verified by recalculating the equilibrium concentrations of the phases and rebuilding the phase diagrams in the considered concentration and temperature ranges, prior to the simulation studies. For all comparisons, a close match is achieved between the results and the CALPHAD databases. As practical examples of the method, phase-field simulation studies for the directional solidification of the binary Ni–35Mo and the ternary NiAl–10Mo eutectic systems are performed. Good agreements between the simulation results and the reported theoretical and experimental studies from literature are found, which indicates the applicability of the presented approaches

DNA methylation-based classification of central nervous system tumours.

Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging-with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology