Contact angle of sessile drops in Lennard-Jones systems

Molecular dynamics simulation is used for studying the contact angle of nanoscale sessile drops on a planar solid wall in a system interacting via the truncated and shifted Lennard-Jones potential. The entire range between total wetting and dewetting is investigated by varying the solid--fluid dispersive interaction energy. The temperature is varied between the triple point and the critical temperature. A correlation is obtained for the contact angle in dependence of the temperature and the dispersive interaction energy. Size effects are studied by varying the number of fluid particles at otherwise constant conditions, using up to 150 000 particles. For particle numbers below 10 000, a decrease of the contact angle is found. This is attributed to a dependence of the solid-liquid surface tension on the droplet size. A convergence to a constant contact angle is observed for larger system sizes. The influence of the wall model is studied by varying the density of the wall. The effective solid-fluid dispersive interaction energy at a contact angle of 90 degrees is found to be independent of temperature and to decrease linearly with the solid density. A correlation is developed which describes the contact angle as a function of the dispersive interaction, the temperature and the solid density. The density profile of the sessile drop and the surrounding vapor phase is described by a correlation combining a sigmoidal function and an oscillation term

Multiresolution Molecular Mechanics: Dynamics, Adaptivity, and Implementation

Full atomistic Molecular Dynamics (MD) simulations are very accurate but too costly; however, atomistic resolution is not actually required everywhere in many problems. For this reason, a concurrent atomistic/continuum coupling method called Multiresolution Molecular Mechanics (MMM) has been developed. The method employs atomistic resolution in the localized regions of interest and coarser continuum description elsewhere. A number of such multiscale methods have been developed but they fail to demonstrate consistency, accuracy, adaptivity, flexibility, and efficiency all in one. The goal of this research is thus to develop a multiscale method that possesses these properties to outperform the MD method by 1) formulating new dynamics equations under the MMM framework, 2) developing an adaptivity scheme, and 3) implementing efficient algorithms for the method. First, the derivation of the governing MMM equations from a Hamiltonian that approximates the energy of the original system is presented. Second, the adaptivity analysis of the MMM method is presented. Refinement and coarsening mechanisms of the adaptivity scheme are described in detail and the step-by-step procedures are outlined. Third, the implementation and efficiency of the MMM software is presented. The structure of the software along with the associated technologies is introduced. Many improvements that contribute to the efficiency of the MMM software are described and demonstrated through benchmark tests. The efficiency of the software is found to be as good as one of the best state-of-the-art MD codes, i.e., LAMMPS. The speed-up of the code in proportion to reduction in the rep atom ratio is demonstrated. The scalability of the software is demonstrated and competing effects of multiscale modeling and parallelization is discussed. The dynamics, adaptivity, and efficiency of the method are demonstrated by numerical examples including wave and crack propagation, dislocation glide, nanoindentation, and modal analysis in 1/2/3 dimensions. All results agree well with the true full atomistic solutions. Ultimately, the MMM method demonstrates an improvement of 6.3 – 8.3 times in efficiency over MD method by means of a combined reduction in simulation time and number of processors. In conclusion, this dissertation shows that the MMM method is consistent, accurate, flexible, and efficient

A framework for evaluating the impact of communication on performance in large-scale distributed urban simulations

A primary motivation for employing distributed simulation is to enable the execution of large-scale simulation workloads that cannot be handled by the resources of a single stand-alone computing node. To make execution possible, the workload is distributed among multiple computing nodes connected to one another via a communication network. The execution of a distributed simulation involves alternating phases of computation and communication to coordinate the co-operating nodes and ensure correctness of the resulting simulation outputs. Reliably estimating the execution performance of a distributed simulation can be difficult due to non-deterministic execution paths involved in alternating computation and communication operations. However, performance estimates are useful as a guide for the simulation time that can be expected when using a given set of computing resources. Performance estimates can support decisions to commit time and resources to running distributed simulations, especially where significant amounts of funds or computing resources are necessary. Various performance estimation approaches are employed in the distributed computing literature, including the influential Bulk Synchronous Parallel (BSP) and LogP models. Different approaches make various assumptions that render them more suitable for some applications than for others. Actual performance depends on characteristics inherent to each distributed simulation application. An important aspect of these individual characteristics is the dynamic relationship between the communication and computation phases of the distributed simulation application. This work develops a framework for estimating the performance of distributed simulation applications, focusing mainly on aspects relevant to the dynamic relationship between communication and computation during distributed simulation execution. The framework proposes a meta-simulation approach based on the Multi-Agent Simulation (MAS) paradigm. Using the approach proposed by the framework, meta-simulations can be developed to investigate the performance of specific distributed simulation applications. The proposed approach enables the ability to compare various what-if scenarios. This ability is useful for comparing the effects of various parameters and strategies such as the number of computing nodes, the communication strategy, and the workload-distribution strategy. The proposed meta-simulation approach can also aid a search for optimal parameters and strategies for specific distributed simulation applications. The framework is demonstrated by implementing a meta-simulation which is based on case studies from the Urban Simulation domain

A framework for evaluating the impact of communication on performance in large-scale distributed urban simulations

A primary motivation for employing distributed simulation is to enable the execution of large-scale simulation workloads that cannot be handled by the resources of a single stand-alone computing node. To make execution possible, the workload is distributed among multiple computing nodes connected to one another via a communication network. The execution of a distributed simulation involves alternating phases of computation and communication to coordinate the co-operating nodes and ensure correctness of the resulting simulation outputs. Reliably estimating the execution performance of a distributed simulation can be difficult due to non-deterministic execution paths involved in alternating computation and communication operations. However, performance estimates are useful as a guide for the simulation time that can be expected when using a given set of computing resources. Performance estimates can support decisions to commit time and resources to running distributed simulations, especially where significant amounts of funds or computing resources are necessary. Various performance estimation approaches are employed in the distributed computing literature, including the influential Bulk Synchronous Parallel (BSP) and LogP models. Different approaches make various assumptions that render them more suitable for some applications than for others. Actual performance depends on characteristics inherent to each distributed simulation application. An important aspect of these individual characteristics is the dynamic relationship between the communication and computation phases of the distributed simulation application. This work develops a framework for estimating the performance of distributed simulation applications, focusing mainly on aspects relevant to the dynamic relationship between communication and computation during distributed simulation execution. The framework proposes a meta-simulation approach based on the Multi-Agent Simulation (MAS) paradigm. Using the approach proposed by the framework, meta-simulations can be developed to investigate the performance of specific distributed simulation applications. The proposed approach enables the ability to compare various what-if scenarios. This ability is useful for comparing the effects of various parameters and strategies such as the number of computing nodes, the communication strategy, and the workload-distribution strategy. The proposed meta-simulation approach can also aid a search for optimal parameters and strategies for specific distributed simulation applications. The framework is demonstrated by implementing a meta-simulation which is based on case studies from the Urban Simulation domain