The fluid-structure interaction of reacting materials requires computational models capable of resolving the wide range of scales present in both the condensed phase energetic materials and the turbulent reacting gas phase. This effort is focused on the development of a micro-scale structural model designed to simulate heterogeneous energetic materials used for solid propellants and explosives. These two applications require a model that can track moving surfaces as the material burns, handle spontaneous formation of discontinuities such as cracks, model viscoelastic and viscoplastic materials, include finite-rate kinetics, and resolve both micro-scale features and macro-scale trends. Although a large set of computational models is applied to energetic materials, none meet all of these criteria. The Micro-Scale Dynamical Model serves as the basis for this work. The model is extended to add the capabilities required for energetic materials. Heterogeneous solid propellant burning simulations match experimental burn rate data and descriptions of material surface. Simulations of realistic heterogeneous plastic-bound explosives undergoing impact predict the formation of regions of localized heating called hotspots which may lead to detonation in the material. The location and intensity of these hotspots is found to vary with the material properties of the energetic crystal and binder and with the impact velocity. A statistical model of the hotspot peak temperatures for two frequently used energetic crystals indicates a linear relationship between the hotspot intensity and the impact velocity. This statistical model may be used to generate hotspot fields in macro-scale simulations incapable of resolving the micro-scale heating that occurs in heterogeneous explosives.

What materials designers most envy is nature’s building design. It has long been a dream for scientists to mimic and further engineer the behaviors, interactions, and reactions of biomolecules beyond experimental limits. To interpret and facilitate novel materials’ design, a hierarchical approach is presented in this dissertation. With the advent of molecular modeling, many biomolecular interactions can be studied. Using both computational and experimental approaches, we investigated the self-assembly of fluorenylmethoxycarbonyl-conjugated dipeptides (which are called “biomimetic materials”) including Fmoc-dialanine (Fmoc-AA) and Fmoc-Alanine-Lactic acid (Fmoc-ALac) molecules. We simulated the assembly of Fmoc-dipeptides and compared with experiments. We illustrated not only the angstrom-scale self-assembled structures, but also a prevalent polyproline II conformation with β-sheet-like hydrogen bonding pattern among short peptides. Further, simulations to calculate the potential of mean force (PMF) and melting temperatures were performed to gain deeper insights into the inter-fibril interaction. An energetic preference for fibril-fibril surface contact was demonstrated for the first time, which arises from a fibril-level amphiphilicity. From our study, a hierarchical self-assembly process mediated by the balance between hydrophobicity and hydrophilicity of fibril structures was unveiled. The next major topic in this dissertation involves the development of a chemically accurate polarizable multipole-based molecular mechanics model with the investigation of a series of chloromethanes. The ability of molecular modeling to make prediction is determined by the accuracy of underlying physical model. The traditional fixed-charge based force field is severely limited when applied to highly charged systems, halogen, phosphate and sulfate compounds. Via a sophisticated electrostatic model, an accurate description of electrostatics in organochlorine compounds and halogen bonds were achieved. Our model demonstrated its advantages by reproducing the experimental density and heat of vaporization; besides, the calculated hydration free energy, solvent reaction fields, and interaction energies of several homo- and heterodimer of chloromethanes were all in good agreement with experimental and ab initio data. / text

Discrete-event simulation is a commonly used technique to model changes within a complex physical systems as a series of events that occur at discrete points of time. As the complexity of the physical system being modeled increases, the simulator can reach a point where it is no longer feasible for it to run efficiently on one computing resource. A common solution is to break the physical system into multiple logical processes. When breaking a simulation over multiple computing nodes, care must be taken to ensure the results obtained are the same as would be obtained from a non-distributed simulation. This is done by ensuring that the events processed in each individual logical process are processed in chronological order. The task is complicated by the fact that the computing nodes will be exchanging timestamped messages and will often be operating at different points of simulation time. Therefore, highly efficient synchronization methods must be used. It is also important that the logical processes have a capable means to transport messages among themselves or the benefits of parallelization will be lost. The objective of this dissertation is to design, develop, test, and evaluate tech- niques to improve the performance of large-scale discrete-event simulations. The techniques include improvements in messaging passing, state management, and time synchronization. Along with specific implementation improvements, we also examine techniques on how to effectively make use of resources such as shared memory and graphical processing units.

Abstract Stirred tanks are typically used in many reactions. The quality of mixing generated by the impellers can be determined using either experimental and simulation methods, or both methods. The experimental techniques have evolved from traditional approaches, such as the application of hot-wire anemometry, to more modern ones like laser Doppler velocimetry (LDV). Similarly, computational fluid dynamics (CFD) simulation techniques have attracted a lot of attention in recent years in the study of the hydrodynamics in stirred tanks, compared to the empirical modelling approach. Studies have shown that the LDV technique can provide very detailed information on the spatio-temporal variations in a tank, but the method is costly. For this reason, CFD simulation techniques may be employed to provide such data at a lower cost. In recent years, both integrated experimental and CFD approaches have been used to determine flow field and to design various systems. Both CFD and LDV data reveal the existence of flow maldistribution caused by system design features, and these in turn show that the configurations that have, over the years, been regarded as standard may not provide the optimal operating conditions with regards to the system homogeneity and power consumption. The current trends in CFD studies point towards an increasing application of more refined grids, such as in large eddy simulation, to capture turbulent structures at microscales. This trend will further improve the quality of the simulation results for processes such as precipitation, in which micromixing and reaction kinetics are important.

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The ACM-Artificial Consumer Market is part of the integrated simulation endeavor named the "Ar-tificial Economy". Complementing and extending the concepts developed in the SIMSEG simulation environment of Working Paper No. 60 this report proceeds in two steps. (1) it outlines the basic con-structs and consumer behavior phenomena implemented in the ACM in a nontechnical manner. (2) it elaborates the formal structure and relationships in full detail. The ACM was never headed for mimicking any real consumer market. However, it is ambitious enough to capture a number of behavioral mechanisms that are deemed crucial for exposing the Artificial Firms' analytical and strategic agents to a challenging artificial marketplace. (author's abstract) / Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

Simulating hydrodynamics can require extensive calculations which becomes a problem when doing interactive simulations. This thesis investigates an efficient method for hydrodynamic simulations with the effects of buoyancy, drag, lift and added mass that is implementedand tested with the help of AgX Dynamics using triangulated meshes. For buoyancy, drag and lift a method of numerical integration over triangles was used to calculate the forces and torques acting on each triangle of a mesh. For added mass a large part of the calculations could be done before the simulation starts using a Boundary Element Method (BEM). The final value for the added mass was calculated each time step based on how the object was submerged. The method of triangle integration produced results that were close to the analytical values with a certain mesh dependence. The results had an increasing accuracy when the mesh had a more exact representation of the object. The drag and lift coefficients could however be better adjusted. The added mass results also had a mesh dependence, but with accuracy increasing with number of triangles even for shapes that already had an exact representation, e.g. a cube. For a fully submerged sphere with 4900 triangles the maximum error for the added mass was 0.65%. The time required for precalculations using BEM had a rapid growth with increasing number of triangles due to the factorization of a dense matrix that has a complexity of O(n3). For the hydrodynamic calculations done each time step the time requirement increased linearly with number of triangles.

The fundamental errors in the numerical modelling of the turbulent component of fluid flow are one of the main reasons why computational fluid dynamics techniques have not yet been fully accepted by the wind engineering community. This thesis is the result of extensive research that was undertaken to assess the various methods available for numerical simulation of turbulent fluid flow. The research was undertaken with a view to developing improved turbulence models for computational wind engineering. Investigations have concentrated on analysing the accuracy and numerical stability of a number of different turbulence models including both the widely available models and state of the art techniques. These investigations suggest that a turbulence model, suitable for wind engineering applications, should be able to model the anisotropy of turbulent flow as in the differential stress model whilst maintaining the ease of use and computational stability of the two equation k-e models. Therefore, non-linear expansions of the Boussinesq hypotheses, the quadratic and cubic non-linear k-e models, have been tested in an attempt to account for anisotropic turbulence and curvature related strain effects. Furthermore, large eddy simulations using the standard Smagorinsky sub-grid scale model have been completed, in order to account for the four dimensional nature of turbulent flow. This technique, which relies less heavily on the need to model turbulence by utilising advances in computer technology and processing power to directly resolve more of the flow field, is now becoming increasingly popular in the engineering community. The author has detailed and tested all of the above mentioned techniques and given recommendations for both the short and longer term future of turbulence modelling in computational wind engineering. Improved turbulence models that will more accurately predict bluff body flow fields and that are numerically stable for complex geometries are of paramount importance if the use of CFD techniques are to gain wide acceptance by the wind engineering community.

Mazanec, in Baier and Mazanec (1999), suggests a simulation environment for studying joint segmentation/positioning strategies for brands competing in a product class. The simulation operates on three-way data: consumers rate the set of brands on a set of dimensions, compare their perceptual brand profiles to their preferential profile, and make a choice. In the present paper a modeling framework for generation of such market data is suggested. First models of consumers perceptual/preferential positions are discussed. Second a model linking brand perceptions to consumers is suggested where the degree of perceptual competition between brands is explicitly modeled. Third a model linking consumers perceptions and preferences completes the data model from which a simulation can depart. (author's abstract) / Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

The current study focused on dyadic discrepancy, the difference between two individuals. A Monte Carlo simulation was used to compare three dyadic discrepancy estimation methods across a variety of potential research conditions, including variations on intraclass correlation, cluster number, reliability, effect size, and effect size variance. The methods compared were: raw score difference (RSD); empirical Bayes estimate of slope in multilevel modeling (EBD); and structural equation modeling estimate (SEM). Accuracy and reliability of the discrepancy estimate and the accuracy of prediction when using the discrepancy to predict an outcome were examined. The results indicated that RSD and SEM, despite having poor reliability, performed better than EBD when predicting an outcome. The results of this research provide methodological guidance to researchers interested in dyadic discrepancies.