Attachment of particles to one another due to action of certain inter-particle forces is called as particle agglomeration. It has applications ranging from efficient capture of ultra-fine particles generated in coal-burning boilers to effective discharge of aerosol sprays. Aerosol sprays have their application in asthma relievers, coatings, cleaning agents, air fresheners, personal care products and insecticides. There are several factors that cause particle agglomeration and based on the application, agglomeration or de-agglomeration is desired. These various factors associated with agglomeration include van derWaals forces, capillary forces, electrostatic double-layer forces, effects of turbulence, gravity and brownian motion. It is therefore essential to understand the underlying agglomeration mechanisms involved. It is difficult to perform experiments to quantify certain effects of the inter-particle forces and hence we turn to numerical simulations as an alternative. Simulations can be performed using the various numerical simulation techniques such as molecular dynamics, discrete element method, dissipative particle dynamics or other probabilistic simulation techniques. The main objective of this thesis is to study the geometric characteristics of particle agglomerates using dissipative particle dynamics. In this thesis, agglomeration is simulated using the features of dissipative particle dynamics as the simulation technique. Forces of attraction from the literature are used to modify the form of the conservative force. Agglomeration is simulated and the characteristics of the result ing agglomerates are quantified. Simulations were performed on a sizeable number of particles and we observe agglomeration behavior. A study of the agglomerates resulting from the different types of attractive forces is performed to characterize them methodically. Also as a part of this thesis, a novel, dynamic particle simulation technique was developed by interfacing MATLAB and our computational C program.

In this study, the nanocomposite of graphene and PMMA at the different volume fractions was investigated by molecular dynamics and dissipative particle dynamics simulations. The MD simulation can be performed to simulate the nanocomposite system at different weight fractions to obtain the different repulsive parameters. After obtaining the repulsive parameters, the DPD simulation can be utilized to study the equilibrium phase of graphene and PMMA nanocomposite. From our result, all equilibrium phases at different volume fractions are cluster. However, it is difficult to enhance the property for nanocomposite material due to the aggregated graphene (cluster). Hence, we change the interaction repulsive parameters to stand for the different degrees of functionalized graphene. When the interaction repulsive parameter is smaller than 80, the equilibrium phase is dispersion. In addition, the different number of functionalized garphene bead per graphene was studied, and results show that the equilibrium phase is dispersion when all graphene beads per graphene are functionalized.

An aqueous solution of electrolytes can be modelled simplistically as charged hard spheresdispersed in a dielectric continuum. We review various classical theories for hard sphere systems including the Percus-Yevick theory, the mean spherical approximation, the Debye-Hückel theory and the hyper-netted chain theory, and we compare the predictions of the theories with simulation results. The statistical associating fluid theory (SAFT) has proved to be accurate for neutral polymers. It is modified to cope with charged polyelectrolyte systems. A chain term for the charged reference fluid is introduced into the theory. Some well-established results are reproduced in this study and we also introduce new terms and discuss their effects. The results show that the SAFT is semi-quantitatively correct in predicting the phase behaviour of polyelectrolytes. The electrostatic attraction between unlike charged particles at low temperature is very strong. The short-range attractions between unlike pairs are treated via an association theory while the remaining interactions are handled by hypernetted chain theory. This method works quite well with multiple associating sites. The phase prediction for the size and charge symmetric restricted primitive model is quantitatively correct as compared with simulation results. Furthermore, it also gives semi-quantitatively correct predictions for the phase behaviour of size- and charge-asymmetric cases. Dissipative particle dynamics (DPD) is a powerful simulation technique for mesoscopic systems. Molecules with specific shapes (rods and spheres) are simulated using this technique.By tuning the density of the system, some liquid crystal phase transitions can be observed.The properties of spider silk fibroin are also modelled by DPD, indicating a possible route offorming spider silk.

Isoporous membranes manufactured from diblock copolymer are successfully produced at laboratory scale under controlled conditions. Because of the complex phenomena involved, membrane preparation requires trial and error methodologies to find the optimal conditions, leading to a considerable demand of resources. Experimental insights demonstrate that the self-assembly of the block copolymers in solution has an effect on the final membrane structure. Nevertheless, the complete understanding of these multi-scale phenomena is elusive. Herein we use the coarse-grained method Dissipative Particle Dynamics to study the self-assembly of block copolymers that are used for the preparation of the membranes. To simulate representative time and length scales, we introduce a framework for model reduction of polymer chain representations for dissipative particle dynamics, which preserves the properties governing the phase equilibria. We reduce the number of degrees of freedom by accounting for the correlation between beads in fine-grained models via power laws and the consistent scaling of the simulation parameters. The coarse-graining models are consistent with the experimental evidence, showing a morphological transition of the aggregates as the polymer concentration and solvent affinity change. We show that hexagonal packing of the micelles can occur in solution within different windows of polymer concentration depending on the solvent affinity. However, the shape and size dispersion of the micelles determine the characteristic arrangement. We describe the order of crew-cut micelles using a rigid-sphere approximation and propose different phase parameters that characterize the emergence of monodisperse-spherical micelles in solution. Additionally, we investigate the effect of blending asymmetric diblock copolymers (AB/AC) over the properties of the membranes. We observe that the co-assembly mechanism localizes the AC molecules at the interface of A and B domains, and induces the swelling of the B-rich domains. The B-C interactions control the curvature of the assemblies in these blends. Finally, we study the self-assembly triblock copolymers used for membranes fabrication. We show that the polymer concentration, the block-copolymer composition, and the swelling of the micelle are responsible for the formation of elongated micelles in the casting solution. The formation of nanoporous membranes arises from the network-like packing of those micelles.

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This thesis addresses the sampling problem in a high-dimensional space, i.e., the computation of averages with respect to a defined probability density that is a function of many variables. Such sampling problems arise in many application areas, including molecular dynamics, multiscale models, and Bayesian sampling techniques used in emerging machine learning applications. Of particular interest are thermostat techniques, in the setting of a stochastic-dynamical system, that preserve the canonical Gibbs ensemble defined by an exponentiated energy function. In this thesis we explore theory, algorithms, and numerous applications in this setting. We begin by comparing numerical methods for particle-based models. The class of methods considered includes dissipative particle dynamics (DPD) as well as a newly proposed stochastic pairwise Nosé-Hoover-Langevin (PNHL) method. Splitting methods are developed and studied in terms of their thermodynamic accuracy, two-point correlation functions, and convergence. When computational efficiency is measured by the ratio of thermodynamic accuracy to CPU time, we report significant advantages in simulation for the PNHL method compared to popular alternative schemes in the low-friction regime, without degradation of convergence rate. We propose a pairwise adaptive Langevin (PAdL) thermostat that fully captures the dynamics of DPD and thus can be directly applied in the setting of momentum-conserving simulation. These methods are potentially valuable for nonequilibrium simulation of physical systems. We again report substantial improvements in both equilibrium and nonequilibrium simulations compared to popular schemes in the literature. We also discuss the proper treatment of the Lees-Edwards boundary conditions, an essential part of modelling shear flow. We also study numerical methods for sampling probability measures in high dimension where the underlying model is only approximately identified with a gradient system. These methods are important in multiscale modelling and in the design of new machine learning algorithms for inference and parameterization for large datasets, challenges which are increasingly important in "big data" applications. In addition to providing a more comprehensive discussion of the foundations of these methods, we propose a new numerical method for the adaptive Langevin/stochastic gradient Nosé-Hoover thermostat that achieves a dramatic improvement in numerical efficiency over the most popular stochastic gradient methods reported in the literature. We demonstrate that the newly established method inherits a superconvergence property (fourth order convergence to the invariant measure for configurational quantities) recently demonstrated in the setting of Langevin dynamics. Furthermore, we propose a covariance-controlled adaptive Langevin (CCAdL) thermostat that can effectively dissipate parameter-dependent noise while maintaining a desired target distribution. The proposed method achieves a substantial speedup over popular alternative schemes for large-scale machine learning applications.

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[pt] Suspensões correspondem a uma classe de materiais amplamente utilizada em uma grande variedade de aplicações e indústrias. Devido à sua extrema versatilidade, elas têm sido foco de inúmeros estudos nas últimas décadas. Suspensões também são muito flexíveis e podem apresentar diferentes propriedades reológicas e respostas macroscópicas dependendo da escolha dos parâmetros usados como entrada no sistema. Mais especificamente, a resposta reológica de suspensões está intimamente associada ao arranjo microestrutural das partículas que compõem o meio e a fatores externos, como o quão confinadas elas se encontram e a rigidez das partículas. No presente estudo, o efeito da rigidez, confinamento e vazão na microestrutura de suspensões altamente concentradas é avaliado usando Dinâmica Dissipativa de Partículas com Núcleo Modificado. Precedento este estudo principal, foram necessárias outras duas etapas para garantir um sistema de simulação confiável e representativo, que consistiu, essencialmente, na realização de estudos paramétricos para compreender e estimar os valores adequados para os parâmetros de interacção parede-partícula. O presente trabalho aborda estudos paramétricos realizados para auxiliar na escolha dos parâmetros de entrada para evitar a penetração de partículas em um sistema delimitado por paredes. Inicialmente um sistema mais simples, composto por solvente e paredes é construído e os parâmetros de interação e densidades de parede foram ajustados. Em seguida as interações são definidas para suspensões. Neste último caso, vários parâmetros desempenham um papel na penetração e a maneira tradicional de investigar esses efeitos seria exaustiva e demorada. Por isso, optamos por usar uma abordagem de Machine Learning para realizar este estudo. Uma vez ajustados os parâmetros, o estudo de confinamento pôde ser realizado. O objetivo principal deste estudo foi entender como a microestrutura de suspensões concentradas é afetada pela vazão, rigidez das partículas e confinamento. Verificou-se que partículas muito flexíveis sempre formam um aglomerado gigante independente da razão de confinamento; a diferença está em quão compactadas são as partículas. No caso de partículas rígidas, um confinamento mais forte leva à formação de aglomerados maiores. O estudo final aborda um estudo de aprendizado de máquina realizado para prever a reologia de suspensões não confinadas. Com este trabalho foi possível entender e ajustar parâmetros de simulação e desenvolver um domínio computacional que permite estudar sistematicamente efeitos do confinamento em suspensões. / [en] Suspensions correspond to a class of materials vastly used in a large set of applications and industries. Due to its extreme versatility, they have been the focus of numerous studies over the past decades. Suspensions are also very flexible and can display different rheological properties and macroscopic responses depending on the choice of parameters used as input in the system. More specifically, the rheological response of suspensions is intimately associated to the microstructural arrangement of the particles composing the medium and external factors, such as how strongly they are confined and particle rigidity. In the present study, the effect of particle rigidity, confinement and flow rate on the microstructure of highly concentrated suspensions is studied using CoreModified Dissipative Particle Dynamics. Preceding this main study, two other steps were necessary to guarantee a reliable and realistic simulation system, which consisted, essentially, on performing parametric studies to understand and estimate the appropriate values for wall-particle interaction parameters. The present work address parametric studies performed to assist the input parameters choice to prevent particle penetration in a wall-bounded system. Initially a simpler system, composed of solvent and walls, is built and the interaction parameters and wall densities were adjusted. Following, the interactions are set for suspensions. In the latter case multiple parameters play a role in penetration and the traditional way to investigate these effects would be exhaustive and time consuming. Hence, we choose to use a Machine Learning approach to perform this study. Once the parameters were adjusted, the study of confinement could be carried out. The main goal of this study was to understand how the microstructure of concentrated suspensions is affected by flow rate, particle rigidity and confinement. It was found that very soft particles always form a giant cluster regardless the confinement ratio; the difference being on how packed the particles are. In the rigid case, a stronger confinement leads the formation of larger clusters. The final study addresses a machine learning study carried out to predict the rheology of unconfined suspensions. The main contribution of this work is that it was possible to understand and adjust simulation parameters and develop a computational domain that enables to systematically study confinement effects on suspensions.