In this study, an efficient coupling between Monte Carlo (MC) molecular simulation and Darcy-scale flow in porous media is presented. The cell-centered finite difference method with a non-uniform rectangular mesh were used to discretize the simulation domain and solve the governing equations. To speed up the MC simulations, we implemented a recently developed scheme that quickly generates MC Markov chains out of pre-computed ones, based on the reweighting and reconstruction algorithm. This method astonishingly reduces the required computational time by MC simulations from hours to seconds. In addition, the reweighting and reconstruction scheme, which was originally designed to work with the LJ potential model, is extended to work with a potential model that accounts for the molecular quadrupole moment of fluids with non-spherical molecules such as CO2. The potential model was used to simulate the thermodynamic equilibrium properties for single-phase and two-phase systems using the canonical ensemble and the Gibbs ensemble, respectively. Comparing the simulation results with the experimental data showed that the implemented model has an excellent fit outperforming the standard LJ model. To demonstrate the strength of the proposed coupling in terms of computational time efficiency and numerical accuracy in fluid properties, various numerical experiments covering different compressible single-phase flow scenarios were conducted. The novelty in the introduced scheme is in allowing an efficient coupling of the molecular scale and Darcy scale in reservoir simulators. This leads to an accurate description of the thermodynamic behavior of the simulated reservoir fluids; consequently enhancing the confidence in the flow predictions in porous media.

This dissertation focuses on the application of Monte Carlo (MC) molecular simulation and Molecular Dynamics (MD) in modeling thermodynamics and flow of subsurface reservoir fluids. At first, MC molecular simulation is proposed as a promising method to replace correlations and equations of state in subsurface flow simulators. In order to accelerate MC simulations, a set of early rejection schemes (conservative, hybrid, and non-conservative) in addition to extrapolation methods through reweighting and reconstruction of pre-generated MC Markov chains were developed. Furthermore, an extensive study was conducted to investigate sorption and transport processes of methane, carbon dioxide, water, and their mixtures in the inorganic part of shale using both MC and MD simulations. These simulations covered a wide range of thermodynamic conditions, pore sizes, and fluid compositions shedding light on several interesting findings. For example, the possibility to have more carbon dioxide adsorbed with more preadsorbed water concentrations at relatively large basal spaces. The dissertation is divided into four chapters. The first chapter corresponds to the introductory part where a brief background about molecular simulation and motivations are given. The second chapter is devoted to discuss the theoretical aspects and methodology of the proposed MC speeding up techniques in addition to the corresponding results leading to the successful multi-scale simulation of the compressible single-phase flow scenario. In chapter 3, the results regarding our extensive study on shale gas at laboratory conditions are reported. At the fourth and last chapter, we end the dissertation with few concluding remarks highlighting the key findings and summarizing the future directions.

We introduce an efficient thermodynamically consistent technique to extrapolate and interpolate normalized Canonical NVT ensemble averages like pressure and energy for Lennard-Jones (L-J) fluids. Preliminary results show promising applicability in oil and gas modeling, where accurate determination of thermodynamic properties in reservoirs is challenging. The thermodynamic interpolation and thermodynamic extrapolation schemes predict ensemble averages at different thermodynamic conditions from expensively simulated data points. The methods reweight and reconstruct previously generated database values of Markov chains at neighboring temperature and density conditions. To investigate the efficiency of these methods, two databases corresponding to different combinations of normalized density and temperature are generated. One contains 175 Markov chains with 10,000,000 MC cycles each and the other contains 3000 Markov chains with 61,000,000 MC cycles each. For such massive database creation, two algorithms to parallelize the computations have been investigated. The accuracy of the thermodynamic extrapolation scheme is investigated with respect to classical interpolation and extrapolation. Finally, thermodynamic interpolation benefiting from four neighboring Markov chains points is implemented and compared with previous schemes. The thermodynamic interpolation scheme using knowledge from the four neighboring points proves to be more accurate than the thermodynamic extrapolation from the closest point only, while both thermodynamic extrapolation and thermodynamic interpolation are more accurate than the classical interpolation and extrapolation. The investigated extrapolation scheme has great potential in oil and gas reservoir modeling.That is, such a scheme has the potential to speed up the MCMC thermodynamic computation to be comparable with conventional Equation of State approaches in efficiency. In particular, this makes it applicable to large-scale optimization of L-J model parameters for hydrocarbons and other important reservoir species. The efficiency of the thermodynamic dependent techniques is expected to make the Markov chains simulation an attractive alternative in compositional multiphase flow simulation.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第17828号 / 工博第3771号 / 新制||工||1576(附属図書館) / 30643 / 京都大学大学院工学研究科分子工学専攻 / (主査)教授 渡辺 宏, 教授 金谷 利治, 教授 山本 量一 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第21464号 / 工博第4539号 / 新制||工||1708(附属図書館) / 京都大学大学院工学研究科高分子化学専攻 / (主査)教授 古賀 毅, 教授 吉崎 武尚, 教授 竹中 幹人 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DGAM

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This research investigates through computational methods whether the physical properties of DNA contribute to its harmonic signature, the uniqueness of that signature if present, and motion of the DNA molecule in water. When DNA is solvated in water at normal 'room temperature', it experiences a natural vibration due to the Brownian motion of the particles in the water colliding with the DNA. The null hypothesis is that there is no evidence to suggest a relationship between DNA's motion and strand length, while the alternative hypothesis is that there is evidence to suggest a relationship between DNA's vibrational motion and strand length. In a similar vein to the first hypothesis, a second hypothesis posits that DNA's vibrational motion may be dependent on strand content. The nature of this relationship, whether linear, exponential, logarithmic or non-continuous is not hypothesized by this research but will be discovered by testing if there is evidence to suggest a relationship between DNA's motion and strand length. The research also aims to discover whether the motion of DNA, when it varies by strand length and/or content, is sufficiently unique to allow that DNA to be identified in the absence of foreknowledge of the type of DNA that is present in a manner similar to a signature. If there is evidence to suggest that there is a uniqueness in DNA's vibrational motion under varying DNA strand content or length, then additional experimentation will be needed to determine whether these variances are unique across small changes as well as large changes, or large changes only. Finally, the question of whether it might be possible to identify a strand of unique DNA by base pair configuration solely from its vibrational signature, or if not, whether it might be possible to identify changes existing inside of a known DNA strand (such as a corruption, transposition or mutational error) is explored. Given the computational approach to this research, the NAMD simulation package (released by the Theoretical and Computational Biophysics Group at the University of Illinois at Urbana-Champaign) with the CHARMM force field would be the most appropriate set of tools for this investigation (Phillips et al., 2005), and will therefore be the toolset used in this research. For visualization and manipulation of model data, the VMD (Visual Molecular Dynamics) package will be employed. Further, these tools may be optimized and/or be aware of nucleic acid structures, and are free. These tools appear to be sufficient for this task, with validated fidelity of the simulation to provide vibrational and pressure profile data that could be analyzed; sufficient capabilities to do what is being asked of it; speed, so that runs can be done in a reasonable period of time (weeks versus months); and parallelizability, so that the tool could be run over a clustered network of computers dedicated to the task to increase the speed and capacity of the simulations. The computer cluster enabled analysis of 30,000 to 40,000 atom systems spending more than 410,000 CPU computational hours of hundreds of nano second duration, experimental runs each sampled 500,000 times with two-femtosecond “frames.” Using Fourier transforms of run pressure readings into frequencies, the simulation investigation could not reject the null hypotheses that the frequencies observed in the system runs are independent on the DNA strand length or content being studied. To be clear, frequency variations were present in the in silicon replications of the DNA in ionized solutions, but we were unable to conclude that those variations were not due to other system factors. There were several tests employed to determine alternative factors that caused these variations. Chief among the factors is the possibility that the water box itself is the source of a large amount of vibrational noise that makes it difficult or impossible with the tools that we had at our disposal to isolate any signals emitted by the DNA strands. Assuming the water-box itself was a source of large amounts of vibrational noise, an emergent hypothesis was generated and additional post-hoc testing was undertaken to attempt to isolate and then filter the water box noise from the rest of the system frequencies. With conclusive results we found that the water box is responsible for the majority of the signals being recorded, resulting in very low signal amplitudes from the DNA molecules themselves. Using these low signal amplitudes being emitted by the DNA, we could not be conclusively uniquely associate either DNA length or content with the remaining observed frequencies. A brief look at a future possible isolation technique, wavelet analysis, was conducted. Finally, because these results are dependent on the tools at our disposal and hence by no means conclusive, suggestions for future research to expand on and further test these hypothesis are made in the final chapter.

Yes / The use of peptides to nucleate, grow, and stabilize nanoparticles in aqueous media via non-covalent interactions offers new possibilities for creating functional, water-dispersed inorganic/organic hybrid materials, particularly for Au nanoparticles. Numerous previous studies have identified peptide sequences that both possess a strong binding affinity for Au surfaces and are capable of supporting nanoparticle growth in water. However, recent studies have shown that not all such peptide sequences can produce stable dispersions of these nanoparticles. Here, via integrated experiments and molecular modeling, we provide new insights into the many factors that influence Au nanoparticle growth and stabilization in aqueous media. We define colloidal stability by the absence of visible precipitation after at least 24 hours post-synthesis. We use binding affinity measurements, nanoparticle synthesis, characterization and stabilization assays, and molecular modeling, to investigate a set of sequences based on two known peptides with strong affinity for Au. This set of biomolecules is designed to probe specific sequence and context effects using both point mutations and global reorganization of the peptides. Our data confirm, for a broader range of sequences, that Au nanoparticle/peptide binding affinity alone is not predictive of peptide-mediated colloidal stability. By comparing nanoparticle stabilization assay outcomes with molecular simulations, we establish a correlation between the colloidal stability of the Au nanoparticles and the degree of conformational diversity in the surface-adsorbed peptides. Our findings suggest future routes to engineer peptide sequences for bio-based growth and dispersion of functional nanoparticles in aqueous media. / Air Office of Scientific Research, grant number FA9550-12-1-0226.

Semiconductor nanocrystals, also known as quantum dots (QDs), are an important class of materials that are being extensively studied for a wide variety of potential applications, such as medical diagnostics, photovoltaics, and solid-state lighting. The optical and electronic properties of these nanocrystals are different from their bulk properties and depend on the size of the QDs. Therefore an important requirement in their synthesis is a proper control on the final nanoparticle size. Recently, a technique has been developed for synthesizing zinc selenide (ZnSe) QDs using microemulsion droplets as templates. In these systems, a fixed amount of a reactant is dissolved in each droplet and a second reactant is supplied by diffusion through the interface. Spontaneous reaction between the two reactants at the droplet interface forms ZnSe nuclei, whose subsequent diffusion and coalescence into clusters ultimately leads to the formation of a single particle in each droplet. The size of the final particle can be adjusted by changing the initial concentration of the reactant that is dissolved in the dispersed phase of the microemulsion. In this thesis we use a modeling and simulation approach to study the phenomena underlying the formation of QDs in the droplets of a microemulsion. A Lattice Monte-Carlo model was developed to describe Brownian diffusion of a Zn-containing precursor (reactant) inside a droplet, formation of ZnSe nuclei via an irreversible reaction with a Se-containing precursor at the droplet interface, Brownian diffusion and coalescence of nuclei into clusters ultimately leading to the formation of a single nanoparticle inside the droplet. The time required for forming a single particle was found to initially increase as the final particle size was increased by increasing the initial concentration of the reactant in the droplet, but it quickly passed through a maximum, and subsequently decreased. The simulations revealed that this seemingly anomalous result can be explained by studying the intermediate cluster populations that show the formation of a large intermediate "sweeper" cluster. This sweeper cluster is a more effective collision partner to smaller ones and accelerates the coalescence process that eventually leads to the formation of a single particle. A generalized dimensionless equation was obtained that relates the formation time of the final particle to its size for various droplet sizes and diffusivities of the reactant and clusters in the droplet. A parametric study revealed that the final particle formation time is more sensitive to changes in the cluster coalescence probability than in the probability of nucleation. We subsequently compared these results with those obtained by simulating the coalescence of nuclei that are assumed to be formed spontaneously inside a droplet and to be initially uniformly dispersed in it. Comparison of the time required for forming a single final particle for the two cases revealed that for ZnSe particles with diameter smaller than 3.5 nm the predicted formation times were approximately the same. Surprisingly, for particles larger than 3.5 nm, the scenario that required diffusion of a reactant to the interface and formation of nuclei via a reaction at the interface led to the formation of a single particle faster than the scenario that started with nuclei uniformly dispersed in the droplet. Analysis of intermediate cluster populations indicates that the "sweeper" clusters are more effective in accelerating cluster coalescence when the nuclei are supplied gradually, as in the first scenario, compared to spontaneous nucleation throughout the domain. Generalized equations were obtained that describe the evolution of the number of different cluster sizes during coalescence starting from an initially monodispersed population of nuclei thus extending the classical theory of coalescence of monodisperse aerosols in an infinite domain to include coalescence in finite spherical domains with reflective boundaries. Finally, a generalized phenomenological model describing an energy balance during coalescence of two nanoparticles was developed. The reduction in the surface area of the coalescing system was modeled to be the source of thermal energy released due to the formation of additional bonds in the bulk of the coalesced particles. The temperature rise of the coalescing system was predicted for adiabatic coalescence and for coalescence with energy dissipation to a surrounding medium. Generalized equations were developed by scaling the temperature rise with its maximum value that corresponds to adiabatic conditions and the time with a characteristic time for coalescence obtained from the literature that depends on the mechanism (e.g., viscous flow, bulk diffusion, or surface diffusion). As a case study, the effects of the size of coalescing ZnSe nanoparticles on the temperature evolution of the coalescing system were studied by assuming that surface diffusion is the predominant mechanism for coalescence in this system. This modeling and simulation study of nanoparticle nucleation and coalescence presented in this thesis has revealed new phenomena and led to generalized models that can be used for studying such systems. Our work extended the classical theory for coalescence in an infinite domain to include finite spherical domains with reflective boundaries and provided a generalized approach for the analysis of transient thermal effects occurring during coalescence of two nanoparticles.