Publications

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This document summarizes a three year Laboratory Directed Research and Development (LDRD) program effort to improve our understanding of algal flocculation with a key to overcoming harvesting as a techno-economic barrier to algal biofuels. Flocculation is limited by the concentrations of deprotonated functional groups on the algal cell surface. Favorable charged groups on the surfaces of precipitates that form in solution and the interaction of both with ions in the water can favor flocculation. Measurements of algae cell-surface functional groups are reported and related to the quantity of flocculant required. Deprotonation of surface groups and complexation of surface groups with ions from the growth media are predicted in the context of PHREEQC. The understanding of surface chemistry is linked to boundaries of effective flocculation. We show that the phase-space of effective flocculation can be expanded by more frequent alga-alga or floc-floc collisions. The collision frequency is dependent on the floc structure, described in the fractal sense. The fractal floc structure is shown to depend on the rate of shear mixing. We present both experimental measurements of the floc structure variation and simulations using LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator). Both show a densification of the flocs with increasing shear. The LAMMPS results show a combined change in the fractal dimension and a change in the coordination number leading to stronger flocs.

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We present results of molecular dynamics simulations of the flocculation of model algae particles under shear. We study the evolution of the cluster size distribution as well as the steady-state distribution as a function of shear rates and algae interaction parameters. Algal interactions are modeled through a DLVO-type potential, a combination of a HS colloid potential (Everaers) and a yukawa/colloid electrostatic potential. The effect of hydrodynamic interactions on aggregation is explored. Cluster strucuture is determined from the algae-algae radial distribution function as well as the structure factor. DLVO parameters including size, salt concentration, surface potential, initial volume fraction, etc. are varied to model different species of algae under a variety of environmental conditions.

In this report a model for simulating aerosol cluster impact with rigid walls is presented. The model is based on JKR adhesion theory and is implemented as an enhancement to the granular (DEM) package within the LAMMPS code. The theory behind the model is outlined and preliminary results are shown. Modeling the interactions of small particles is relevant to a number of applications (e.g., soils, powders, colloidal suspensions, etc.). Modeling the behavior of aerosol particles during agglomeration and cluster dynamics upon impact with a wall is of particular interest. In this report we describe preliminary efforts to develop and implement physical models for aerosol particle interactions. Future work will consist of deploying these models to simulate aerosol cluster behavior upon impact with a rigid wall for the purpose of developing relationships for impact speed and probability of stick/bounce/break-up as well as to assess the distribution of cluster sizes if break-up occurs. These relationships will be developed consistent with the need for inputs into system-level codes. Section 2 gives background and details on the physical model as well as implementations issues. Section 3 presents some preliminary results which lead to discussion in Section 4 of future plans.

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The most feasible way to disperse particles in a bulk material or control their packing at a substrate is through fluidization in a carrier that can be processed with well-known techniques such as spin, drip and spray coating, fiber drawing, and casting. The next stage in the processing is often solidification involving drying by solvent evaporation. While there has been significant progress in the past few years in developing discrete element numerical methods to model dense nanoparticle dispersion/suspension rheology which properly treat the hydrodynamic interactions of the solvent, these methods cannot at present account for the volume reduction of the suspension due to solvent evaporation. As part of LDRD project FY-101285 we have developed and implemented methods in the current suite of discrete element methods to remove solvent particles and volume, and hence solvent mass from the liquid/vapor interface of a suspension to account for volume reduction (solvent drying) effects. To validate the methods large scale molecular dynamics simulations have been carried out to follow the evaporation process at the microscopic scale.

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Understanding the interaction of aerosol particle clusters/flocs with surfaces is an area of interest for a number of processes in chemical, pharmaceutical, and powder manufacturing as well as in steam-tube rupture in nuclear power plants. Developing predictive capabilities for these applications involves coupled phenomena on multiple length and timescales from the process macroscopic scale ({approx}1m) to the multi-cluster interaction scale (1mm-0.1m) to the single cluster scale ({approx}1000 - 10000 particles) to the particle scale (10nm-10{micro}m) interactions, and on down to the sub-particle, atomic scale interactions. The focus of this report is on the single cluster scale; although work directed toward developing better models of particle-particle interactions by considering sub-particle scale interactions and phenomena is also described. In particular, results of mesoscale (i.e., particle to single cluster scale) discrete element method (DEM) simulations for aerosol cluster impact with rigid walls are presented. The particle-particle interaction model is based on JKR adhesion theory and is implemented as an enhancement to the granular package in the LAMMPS code. The theory behind the model is outlined and preliminary results are shown. Additionally, as mentioned, results from atomistic classical molecular dynamics simulations are also described as a means of developing higher fidelity models of particle-particle interactions. Ultimately, the results from these and other studies at various scales must be collated to provide systems level models with accurate 'sub-grid' information for design, analysis and control of the underlying systems processes.

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In this presentation we examine the accuracy and performance of a suite of discrete-element-modeling approaches to predicting equilibrium and dynamic rheological properties of polystyrene suspensions. What distinguishes each approach presented is the methodology of handling the solvent hydrodynamics. Specifically, we compare stochastic rotation dynamics (SRD), fast lubrication dynamics (FLD) and dissipative particle dynamics (DPD). Method-to-method comparisons are made as well as comparisons with experimental data. Quantities examined are equilibrium structure properties (e.g. pair-distribution function), equilibrium dynamic properties (e.g. short- and long-time diffusivities), and dynamic response (e.g. steady shear viscosity). In all approaches we deploy the DLVO potential for colloid-colloid interactions. Comparisons are made over a range of volume fractions and salt concentrations. Our results reveal the utility of such methods for long-time diffusivity prediction can be dubious in certain ranges of volume fraction, and other discoveries regarding the best formulation to use in predicting rheological response.

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A method is developed for modeling fluid transport in domains that do not conform to the finite element mesh. One or more level set functions are used to describe the fluid domain. A background, non-conformal mesh is decomposed into elements that conform to the level set interfaces. Enrichment takes place by adding nodes that lie on the interfaces. Unlike other enriched finite element methods, the proposed technique requires no changes to the underlying element assembly, element interpolation, or element quadrature. The complexity is entirely contained within the element decomposition routines. It is argued that the accuracy of the method is no less than that for eXtended Finite Element Methods (XFEM) with Heaviside enrichment. The accuracy is demonstrated using multiple numerical tests. In all cases, optimal rates of convergence are obtained for both volume and surface quantities. Jacobi preconditioning is shown to remove the ill-conditioning that may result from the nearly degenerate conformal elements.

Nanoparticles are now more than ever being used to tailor materials function and performance in differentiating technologies because of their profound effect on thermo-physical, mechanical and optical properties. The most feasible way to disperse particles in a bulk material or control their packing at a substrate is through fluidization in a carrier, followed by solidification through solvent evaporation/drying/curing/sintering. Unfortunately processing particles as concentrated, fluidized suspensions into useful products remains an art largely because the effect of particle shape and volume fraction on fluidic properties and suspension stability remains unexplored in a regime where particle-particle interaction mechanics is prevalent. To achieve a stronger scientific understanding of the factors that control nanoparticle dispersion and rheology we have developed a multiscale modeling approach to bridge scales between atomistic and molecular-level forces active in dense nanoparticle suspensions. At the largest length scale, two 'coarse-grained' numerical techniques have been developed and implemented to provide for high-fidelity numerical simulations of the rheological response and dispersion characteristics typical in a processing flow. The first is a coupled Navier-Stokes/discrete element method in which the background solvent is treated by finite element methods. The second is a particle based method known as stochastic rotational dynamics. These two methods provide a new capability representing a 'bridge' between the molecular scale and the engineering scale, allowing the study of fluid-nanoparticle systems over a wide range of length and timescales as well as particle concentrations. To validate these new methodologies, multi-million atoms simulations explicitly including the solvent have been carried out. These simulations have been vital in establishing the necessary 'subgrid' models for accurate prediction at a larger scale and refining the two coarse-grained methodologies.

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