Recovery type a posteriori error estimation of an adaptive finite element method for Cahn--Hilliard equation

In this paper, we derive a novel recovery type a posteriori error estimation of the Crank-Nicolson finite element method for the Cahn--Hilliard equation. To achieve this, we employ both the elliptic reconstruction technique and a time reconstruction technique based on three time-level approximations, resulting in an optimal a posteriori error estimator. We propose a time-space adaptive algorithm that utilizes the derived a posteriori error estimator as error indicators. Numerical experiments are presented to validate the theoretical findings, including comparing with an adaptive finite element method based on a residual type a posteriori error estimator.Comment: 36 pages, 7 figure

An adaptive finite element method based on Superconvergent Cluster Recovery for the Cahn-Hilliard equation

In this study, we construct an error estimate for a fully discrete finite element scheme that satisfies the criteria of unconditional energy stability, as suggested in [1]. Our theoretical findings, in more detail, demonstrate that this system has second-order accuracy in both space and time. Additionally, we offer a powerful space and time adaptable approach for solving the Cahn-Hilliard problem numerically based on the posterior error estimation. The major goal of this technique is to successfully lower the calculated cost by controlling the mesh size using a Superconvergent Cluster Recovery (SCR) approach in accordance with the error estimation. To demonstrate the effectiveness and stability of the suggested SCR-based algorithm, numerical results are provided

Unconditionally energy stable IEQ-FEMs for the Cahn-Hilliard equation and Allen-Cahn equation

In this paper, we present several unconditionally energy-stable invariant energy quadratization (IEQ) finite element methods (FEMs) with linear, first- and second-order accuracy for solving both the Cahn-Hilliard equation and the Allen-Cahn equation. For time discretization, we compare three distinct IEQ-FEM schemes that position the intermediate function introduced by the IEQ approach in different function spaces: finite element space, continuous function space, or a combination of these spaces. Rigorous proofs establishing the existence and uniqueness of the numerical solution, along with analyses of energy dissipation for both equations and mass conservation for the Cahn-Hilliard equation, are provided. The proposed schemes' accuracy, efficiency, and solution properties are demonstrated through numerical experiments.Comment: 33 pages, 15 figure

Numerical phase-field model validation for dissolution of minerals

Modelling of a mineral dissolution front propagation is of interest in a wide range of scientific and engineering fields. The dissolution of minerals often involves complex physico-chemical processes at the solid–liquid interface (at nano-scale), which at the micro-to-meso-scale can be simplified to the problem of continuously moving boundaries. In this work, we studied the diffusion-controlled congruent dissolution of minerals from a meso-scale phase transition perspective. The dynamic evolution of the solid–liquid interface, during the dissolution process, is numerically simulated by employing the Finite Element Method (FEM) and using the phase–field (PF) approach, the latter implemented in the open-source Multiphysics Object Oriented Simulation Environment (MOOSE). The parameterization of the PF numerical approach is discussed in detail and validated against the experimental results for a congruent dissolution case of NaCl (taken from literature) as well as on analytical models for simple geometries. In addition, the effect of the shape of a dissolving mineral particle was analysed, thus demonstrating that the PF approach is suitable for simulating the mesoscopic morphological evolution of arbitrary geometries. Finally, the comparison of the PF method with experimental results demonstrated the importance of the dissolution rate mechanisms, which can be controlled by the interface reaction rate or by the diffusive transport mechanism.Fil: Yang, Sha. Universitat Technische Darmstadt; AlemaniaFil: Ukrainczyk, Neven. Universitat Technische Darmstadt; AlemaniaFil: Caggiano, Antonio. Universitat Technische Darmstadt; Alemania. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Tecnologías y Ciencias de la Ingeniería "Hilario Fernández Long". Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Tecnologías y Ciencias de la Ingeniería "Hilario Fernández Long"; ArgentinaFil: Koenders, Eddie. Universitat Technische Darmstadt; Alemani

Analysis of Coarsening of Complex Structures.

Coarsening is an ubiquitous phenomenon that alters the microstructure of the material and its properties. While coarsening of spherical particles has been extensively studied over the last half century, the understanding of coarsening of complex microstructures is still at an early stage. The complex morphology and topology pose difficulty in establishing a theory of coarsening of such microstructures. In an effort to elucidate the dynamics of coarsening, we examine the morphological evolution of bicontinuous structures simulated using the phase-field method. To improve the accuracy of the calculation of interfacial characteristics of the simulated structures, we develop a smoothing algorithm termed ``level-set smoothing.'' We employ statistical analyses to uncover correlations between interfacial characteristics and their rate of changes. As the framework for the coarsening theory development, we propose to consider the evolution as a consequence of (i) the interfacial velocity induced by diffusion and (ii) the resulting evolution of the interfacial curvatures. As a first step, we examine the evolution of a bicontinuous structure simulated via nonconserved dynamics, in which the interfacial velocity is proportional to the local mean curvature, in order to focus on the second aspect of the evolution (ii). We find that, while the interfacial velocity is locally determined, the evolution of mean curvature is nonlocal and depends on the curvatures of the nearby interfaces. As a second step, we examine the evolution of bicontinuous structures simulated via conserved dynamics to investigate both aspects of the evolution, (i) and (ii). Here, we find that the interfacial velocity is correlated with both the mean curvature and the surface Laplacian of mean curvature. Based on these correlations, we employ a semi-analytical approach to predict the average rate of change of mean curvature, which is found to be consistent with the simulation results. Lastly, in an effort to develop a theory of coarsening of complex microstructures, we derive a general continuity equation of interfacial area to predict the evolution of the overall morphology of a microstructure undergoing coarsening. Simulation of rods undergoing pinching is also conducted to provide insights into the source term arising from topological singularity.PhDMaterials Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133292/1/challan_1.pd

Numerical Phase-Field Model Validation for Dissolution of Minerals

Modelling of a mineral dissolution front propagation is of interest in a wide range of scientific and engineering fields. The dissolution of minerals often involves complex physico-chemical processes at the solid–liquid interface (at nano-scale), which at the micro-to-meso-scale can be simplified to the problem of continuously moving boundaries. In this work, we studied the diffusion-controlled congruent dissolution of minerals from a meso-scale phase transition perspective. The dynamic evolution of the solid–liquid interface, during the dissolution process, is numerically simulated by employing the Finite Element Method (FEM) and using the phase–field (PF) approach, the latter implemented in the open-source Multiphysics Object Oriented Simulation Environment (MOOSE). The parameterization of the PF numerical approach is discussed in detail and validated against the experimental results for a congruent dissolution case of NaCl (taken from literature) as well as on analytical models for simple geometries. In addition, the effect of the shape of a dissolving mineral particle was analysed, thus demonstrating that the PF approach is suitable for simulating the mesoscopic morphological evolution of arbitrary geometries. Finally, the comparison of the PF method with experimental results demonstrated the importance of the dissolution rate mechanisms, which can be controlled by the interface reaction rate or by the diffusive transport mechanism

Analytical and Numerical Methods for Differential Equations and Applications

The book is a printed version of the Special issue Analytical and Numerical Methods for Differential Equations and Applications, published in Frontiers in Applied Mathematics and Statistic

Data-Driven Exploration of Coarse-Grained Equations: Harnessing Machine Learning

In scientific research, understanding and modeling physical systems often involves working with complex equations called Partial Differential Equations (PDEs). These equations are essential for describing the relationships between variables and their derivatives, allowing us to analyze a wide range of phenomena, from fluid dynamics to quantum mechanics. Traditionally, the discovery of PDEs relied on mathematical derivations and expert knowledge. However, the advent of data-driven approaches and machine learning (ML) techniques has transformed this process. By harnessing ML techniques and data analysis methods, data-driven approaches have revolutionized the task of uncovering complex equations that describe physical systems. The primary goal in this thesis is to develop methodologies that can automatically extract simplified equations by training models using available data. ML algorithms have the ability to learn underlying patterns and relationships within the data, making it possible to extract simplified equations that capture the essential behavior of the system. This study considers three distinct learning categories: black-box, gray-box, and white-box learning. The initial phase of the research focuses on black-box learning, where no prior information about the equations is available. Three different neural network architectures are explored: multi-layer perceptron (MLP), convolutional neural network (CNN), and a hybrid architecture combining CNN and long short-term memory (CNN-LSTM). These neural networks are applied to uncover the non-linear equations of motion associated with phase-field models, which include both non-conserved and conserved order parameters. The second architecture explored in this study addresses explicit equation discovery in gray-box learning scenarios, where a portion of the equation is unknown. The framework employs eXtended Physics-Informed Neural Networks (X-PINNs) and incorporates domain decomposition in space to uncover a segment of the widely-known Allen-Cahn equation. Specifically, the Laplacian part of the equation is assumed to be known, while the objective is to discover the non-linear component of the equation. Moreover, symbolic regression techniques are applied to deduce the precise mathematical expression for the unknown segment of the equation. Furthermore, the final part of the thesis focuses on white-box learning, aiming to uncover equations that offer a detailed understanding of the studied system. Specifically, a coarse parametric ordinary differential equation (ODE) is introduced to accurately capture the spreading radius behavior of Calcium-magnesium-aluminosilicate (CMAS) droplets. Through the utilization of the Physics-Informed Neural Network (PINN) framework, the parameters of this ODE are determined, facilitating precise estimation. The architecture is employed to discover the unknown parameters of the equation, assuming that all terms of the ODE are known. This approach significantly improves our comprehension of the spreading dynamics associated with CMAS droplets