Parallelizing the Simulation of Shipboard Power Systems

As a result of this research the Navy has a simulation approach for ship power systems that is computationally effective enough to permit efficient simulation. In addition to simulating the basic power system, significant progress has been made in the simulation of the control system.United States Office of Naval ResearchCenter for Electromechanic

[Research activities in applied mathematics, fluid mechanics, and computer science]

This report summarizes research conducted at the Institute for Computer Applications in Science and Engineering in applied mathematics, fluid mechanics, and computer science during the period April 1, 1995 through September 30, 1995

Novel sampling techniques for reservoir history matching optimisation and uncertainty quantification in flow prediction

Modern reservoir management has an increasing focus on accurately predicting the likely range of field recoveries. A variety of assisted history matching techniques has been developed across the research community concerned with this topic. These techniques are based on obtaining multiple models that closely reproduce the historical flow behaviour of a reservoir. The set of resulted history matched models is then used to quantify uncertainty in predicting the future performance of the reservoir and providing economic evaluations for different field development strategies. The key step in this workflow is to employ algorithms that sample the parameter space in an efficient but appropriate manner. The algorithm choice has an impact on how fast a model is obtained and how well the model fits the production data. The sampling techniques that have been developed to date include, among others, gradient based methods, evolutionary algorithms, and ensemble Kalman filter (EnKF). This thesis has investigated and further developed the following sampling and inference techniques: Particle Swarm Optimisation (PSO), Hamiltonian Monte Carlo, and Population Markov Chain Monte Carlo. The inspected techniques have the capability of navigating the parameter space and producing history matched models that can be used to quantify the uncertainty in the forecasts in a faster and more reliable way. The analysis of these techniques, compared with Neighbourhood Algorithm (NA), has shown how the different techniques affect the predicted recovery from petroleum systems and the benefits of the developed methods over the NA. The history matching problem is multi-objective in nature, with the production data possibly consisting of multiple types, coming from different wells, and collected at different times. Multiple objectives can be constructed from these data and explicitly be optimised in the multi-objective scheme. The thesis has extended the PSO to handle multi-objective history matching problems in which a number of possible conflicting objectives must be satisfied simultaneously. The benefits and efficiency of innovative multi-objective particle swarm scheme (MOPSO) are demonstrated for synthetic reservoirs. It is demonstrated that the MOPSO procedure can provide a substantial improvement in finding a diverse set of good fitting models with a fewer number of very costly forward simulations runs than the standard single objective case, depending on how the objectives are constructed. The thesis has also shown how to tackle a large number of unknown parameters through the coupling of high performance global optimisation algorithms, such as PSO, with model reduction techniques such as kernel principal component analysis (PCA), for parameterising spatially correlated random fields. The results of the PSO-PCA coupling applied to a recent SPE benchmark history matching problem have demonstrated that the approach is indeed applicable for practical problems. A comparison of PSO with the EnKF data assimilation method has been carried out and has concluded that both methods have obtained comparable results on the example case. This point reinforces the need for using a range of assisted history matching algorithms for more confidence in predictions

A Partitioning Approach for Parallel Simulation of AC-Radial Shipboard Power Systems

An approach to parallelize the simulation of AC-Radial Shipboard Power Systems (SPSs) using multicore computers is presented. Time domain simulations of SPSs are notoriously slow, due principally to the number of components, and the time-variance of the component models. A common approach to reduce the simulation run-time of power systems is to formulate the electrical network equations using modified nodal analysis, use Bergeron's travelling-wave transmission line model to create subsystems, and to parallelize the simulation using a distributed computer. In this work, an SPS was formulated using loop analysis, defining the subsystems using a diakoptics-based approach, and the simulation parallelized using a multicore computer. A program was developed in C# to conduct multithreaded parallel-sequential simulations of an SPS. The program first represents an SPS as a graph, and then partitions the graph. Each graph partition represents a SPS subsystem and is computationally balanced using iterative refinement heuristics. Once balanced subsystems are obtained, each SPS subsystem's electrical network equations are formulated using loop analysis. Each SPS subsystem is solved using a unique thread, and each thread is manually assigned to a core of a multicore computer. To validate the partitioning approach, performance metrics were created to assess the speed gain and accuracy of the partitioned SPS simulations. The simulation parameters swept for the performance metrics were the number of partitions, the number of cores used, and the time step increment. The results of the performance metrics showed adequate speed gains with negligible error. An increasing simulation speed gain was observed when the number of partitions and cores were augmented, obtaining maximum speed gains of <30x when using a quadcore computer. Results show that the speed gain is more sensitive to the number partitions than is to the number of cores. While multicore computers are suitable for parallel-sequential SPS simulations, increasing the number of cores does not contribute to the gain in speed as much as does partitioning. The simulation error increased with the simulation time step but did not influence the partitioned simulation results. The number of operations caused by protective devices was used to determine whether the simulation error introduced by partitioning SPS simulations produced a inconsistent system behavior. It is shown, for the time step sizes uses, that protective devices did not operate inadvertently, which indicates that the errors did not alter RMS measurement and, hence, were non-influential

Time-domain Modeling of Light Matter Interactions in Active Plasmonic Metamaterials

Metamaterials are artificially engineered to obtain unprecedented electromagnetic control leading to new and exciting applications. In order to further the understanding of fundamental optical phenomena and explore the effects of dynamically changing media on light propagation, numerous modeling methods have been developed. Among them, due to the nature of transient, nonlinear, and impulsive behavior, the time domain modeling approach is viewed as the most viable method. In this work, we develop a time-domain model (method of finite-difference time-domain (FDTD)) of light matter interactions in active plasmonic metamaterials. In order to model the dispersion of plasmonic nanostructure in the time-domain, we introduce a generalized dispersive material model built on Padé approximants. The developed 3D FDTD solver is then applied to study several plasmonic nanostructures and metamaterials, such as metal-dielectric composite films, random nano-nets for transparent conducting electrodes, and a graphene photodetector enhanced by a fractal plasmonic metasurface. In addition to this we also developed a multi-physics time-domain model to investigate the properties of a silver nanohole array coated with Rhodamine-101 dye. With accurate modeling of the retrieved kinetic parameters, the simulated emission intensity shows clear lasing, which is in good agreement with our experimental measurements. By tracing the population inversion and polarization dynamics, the amplification and lasing regimes inside the nanohole cavity can be clearly distinguished. With the help of our systematic approach, we further the understanding of time-resolved physics in active plasmonic nanostructures with gain

Semiannual progress report no. 1, 16 November 1964 - 30 June 1965

Summary reports of research in bioelectronics, electron streams and interactions, plasmas, quantum and optical electronics, radiation and propagation, and solid-state electronic

Investigation of charged aerosol transport and deposition in human airway models

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Several theoretical and experimental studies of charged aerosol deposition in human airways have been reported. These studies suggest that higher charge values on particles lead to improve deposition efficiency in the human lung, especially in the alveolar region. Most of the previous numerical studies in realistic 3D geometrical models have been investigated only for uncharged particles. Hence, this research was aimed at numerically investigating aerosol transport and deposition by including the effect of electrostatic forces (both space and image charge forces). The numerical models that have been developed and presented in this thesis, treat the aerodynamics and electrodynamics as a coupled problem and successfully integrate both mechanisms. The physical model of the human lung used for this research consists of three sub-models: a 3D bifurcation airway model, a 3D reconstructed airway model representing the tracheobronchial region, and a 2D alveolar airway model representing the alveolar region. The airflow dynamics in these geometrical models were carried out using a Computational Fluid Dynamic software (CFD) with given boundary conditions related to corresponding breathing conditions. The space charge force was calculated using the Particle Mesh (PM) method, and the image charge force was computed using the mesh configuration. Both airflow dynamics and electrodynamics are integrated in the newly developed software, and the particle trajectories are then calculated. The numerical study of electrostatic forces is primarily focused on the submicron particle. The numerical study in the 3D tubular airway model gives a better understanding of parameters affecting the predicted deposition efficiency. The numerical study in the 3D tubular airway model focuses on the transport and deposition of particles near the branching regions between the parent and daughter tubes, where airflow profile is significantly altered, and secondary airflow also arises. Many charged particles are deposited near the carinae by the strong skewed axial velocity and image charge force. The space charge will influence the deposition efficiency if the number concentration of particles is high. Similarly, the charged particles in the 3D reconstructed airway model tends to have the deposition pattern near the branching regions, depending on the local airflow and charge value. In the 2D alveolar model, the image charge force can improve deposition efficiency. The outcome of this research clearly shows how the electrostatic forces play an important role in aerosol transport and deposition in human airways. The integrated numerical model provides a valuable tool for respiratory clinicians and the pharmaceutical industry to study the complex mechanism of drug aerosol deposition in human airways. Although this model is adequate for the intended purpose, it can be further improved by extending this work to develop a complete 3D model of entire human airways incorporating the full breathing cycle. Such a model would require extensive computing facilities, nevertheless it would be an enormous benefit to develop a better treatment for respiratory diseases.Financial support obtained from the Overseas Research Students Awards Scheme (ORS)and System Engineering department

Emerging Nanophotonic Applications Explored with Advanced Scientific Parallel Computing

The domain of nanoscale optical science and technology is a combination of the classical world of electromagnetics and the quantum mechanical regime of atoms and molecules. Recent advancements in fabrication technology allows the optical structures to be scaled down to nanoscale size or even to the atomic level, which are far smaller than the wavelength they are designed for. These nanostructures can have unique, controllable, and tunable optical properties and their interactions with quantum materials can have important near-field and far-field optical response. Undoubtedly, these optical properties can have many important applications, ranging from the efficient and tunable light sources, detectors, filters, modulators, high-speed all-optical switches; to the next-generation classical and quantum computation, and biophotonic medical sensors. This emerging research of nanoscience, known as nanophotonics, is a highly interdisciplinary field requiring expertise in materials science, physics, electrical engineering, and scientific computing, modeling and simulation. It has also become an important research field for investigating the science and engineering of light-matter interactions that take place on wavelength and subwavelength scales where the nature of the nanostructured matter controls the interactions. In addition, the fast advancements in the computing capabilities, such as parallel computing, also become as a critical element for investigating advanced nanophotonic devices. This role has taken on even greater urgency with the scale-down of device dimensions, and the design for these devices require extensive memory and extremely long core hours. Thus distributed computing platforms associated with parallel computing are required for faster designs processes. Scientific parallel computing constructs mathematical models and quantitative analysis techniques, and uses the computing machines to analyze and solve otherwise intractable scientific challenges. In particular, parallel computing are forms of computation operating on the principle that large problems can often be divided into smaller ones, which are then solved concurrently. In this dissertation, we report a series of new nanophotonic developments using the advanced parallel computing techniques. The applications include the structure optimizations at the nanoscale to control both the electromagnetic response of materials, and to manipulate nanoscale structures for enhanced field concentration, which enable breakthroughs in imaging, sensing systems (chapter 3 and 4) and improve the spatial-temporal resolutions of spectroscopies (chapter 5). We also report the investigations on the confinement study of optical-matter interactions at the quantum mechanical regime, where the size-dependent novel properties enhanced a wide range of technologies from the tunable and efficient light sources, detectors, to other nanophotonic elements with enhanced functionality (chapter 6 and 7)

Emerging Nanophotonic Applications Explored with Advanced Scientific Parallel Computing

The domain of nanoscale optical science and technology is a combination of the classical world of electromagnetics and the quantum mechanical regime of atoms and molecules. Recent advancements in fabrication technology allows the optical structures to be scaled down to nanoscale size or even to the atomic level, which are far smaller than the wavelength they are designed for. These nanostructures can have unique, controllable, and tunable optical properties and their interactions with quantum materials can have important near-field and far-field optical response. Undoubtedly, these optical properties can have many important applications, ranging from the efficient and tunable light sources, detectors, filters, modulators, high-speed all-optical switches; to the next-generation classical and quantum computation, and biophotonic medical sensors. This emerging research of nanoscience, known as nanophotonics, is a highly interdisciplinary field requiring expertise in materials science, physics, electrical engineering, and scientific computing, modeling and simulation. It has also become an important research field for investigating the science and engineering of light-matter interactions that take place on wavelength and subwavelength scales where the nature of the nanostructured matter controls the interactions. In addition, the fast advancements in the computing capabilities, such as parallel computing, also become as a critical element for investigating advanced nanophotonic devices. This role has taken on even greater urgency with the scale-down of device dimensions, and the design for these devices require extensive memory and extremely long core hours. Thus distributed computing platforms associated with parallel computing are required for faster designs processes. Scientific parallel computing constructs mathematical models and quantitative analysis techniques, and uses the computing machines to analyze and solve otherwise intractable scientific challenges. In particular, parallel computing are forms of computation operating on the principle that large problems can often be divided into smaller ones, which are then solved concurrently. In this dissertation, we report a series of new nanophotonic developments using the advanced parallel computing techniques. The applications include the structure optimizations at the nanoscale to control both the electromagnetic response of materials, and to manipulate nanoscale structures for enhanced field concentration, which enable breakthroughs in imaging, sensing systems (chapter 3 and 4) and improve the spatial-temporal resolutions of spectroscopies (chapter 5). We also report the investigations on the confinement study of optical-matter interactions at the quantum mechanical regime, where the size-dependent novel properties enhanced a wide range of technologies from the tunable and efficient light sources, detectors, to other nanophotonic elements with enhanced functionality (chapter 6 and 7)

Systematic Data Extraction in High-Frequency Electromagnetic Fields

The focus of this work is on the investigation of billiards with its statistical eigenvalue properties. Specifically, superconducting microwave resonators with chaotic characteristics are simulated and the eigenfrequencies that are needed for the statistical analysis are computed. The eigenfrequency analysis requires many (in order of thousands) eigenfrequencies to be calculated and the accurate determination of the eigenfrequencies has a crucial significance. Consequently, the research interests cover all aspects from accurate numerical calculation of many eigenvalues and eigenvectors up to application development in order to get good performance out of the programs for distributed-memory and shared-memory multiprocessors. Furthermore, this thesis provides an overview and detailed evaluation of the used numerical approaches for large-scale eigenvalue calculations with respect to the accuracy, the computational time, and the memory consumption. The first approach for an accurate eigenfrequency extraction takes into consideration the evaluated electric field computations in Time Domain (TD) of a superconducting resonant structure. Upon excitation of the cavity, the electric field intensity is recorded at different detection probes inside the cavity. Thereafter, Fourier analysis of the recorded signals is performed and by means of signal-processing and fitting techniques, the requested eigenfrequencies are extracted by finding the optimal model parameters in the least squares sense. The second numerical approach is based on a numerical computation of electromagnetic fields in Frequency Domain (FD) and further employs the Lanczos method for the eigenvalue determination. Namely, when utilizing the Finite Integration Technique (FIT) to solve an electromagnetic problem for a superconducting cavity, which enclosures excited electromagnetic fields, the numerical solution of a standard large-scale eigenvalue problem is considered. Accordingly, if the numerical solution of the same problem is treated by the Finite Element Method (FEM) based on curvilinear tetrahedrons, it yields to the generalized large-scale eigenvalue problem. Afterward, the desired eigenvalues are calculated with the direct solution of the large (generalized) eigenvalue formulations. For this purpose, the implemented Lanczos solvers combine two major ingredients: the Lanczos algorithm with polynomial filtering on the one hand and its parallelization on the other