A Parallel Method for Population Balance Equations Based on the Method of Characteristics

In this paper, we present a parallel scheme to solve the population balance equations based on the method of characteristics and the finite element discretization. The application of the method of characteristics transform the higher dimensional population balance equation into a series of lower dimensional convection-diffusion-reaction equations which can be solved in a parallel way.Some numerical results are presented to show the accuracy and efficiency.Comment: 10 pages, 0 figur

Power Management Strategies for Wired Communication Networks.

With the exponential traffic growth and the rapid expansion of communication infrastructures worldwide, energy expenditure of the Internet has become a major concern in IT-reliant society. This energy problem has motivated the urgent demands of new strategies to reduce the consumption of telecommunication networks, with a particular focus on IP networks. In addition to the development of a new generation of energy-efficient network equipment, a significant body of research has concentrated on incorporating power/energy-awareness into network control and management, which aims at reducing the network power/energy consumption by either dynamically scaling speeds of each active network component to make it capable of adapting to its current load or putting to sleep the lightly loaded network elements and reconfiguring the network. However, the fundamental challenge of greening the Internet is to achieve a balance between the power/energy saving and the demands of quality-of-service (QoS) performance, which is an issue that has received less attention but is becoming a major problem in future green network designs. In this dissertation, we study how energy consumption can be reduced through different power/energy- and QoS-aware strategies for wired communication networks. To sufficiently reduce energy consumption while meeting the desire QoS requirements, we introduce several different schemes combing power management techniques with different scheduling strategies, which can be classified into experimental power management (EPM) and algorithmic power management (APM). In these proposed schemes, the power management techniques that we focus on are speed scaling and sleep mode. When the network processor is active, its speed and supply voltage can be decreased to reduce the energy consumption (speed scaling), while when the processor is idle, it can be put in a low power mode to save the energy consumption (sleep mode). The resulting problem is to determine how and when to adjust speeds for the processors, and/or to put a device into sleep mode. In this dissertation, we first discuss three families of dynamic voltage/frequency scaling (DVFS) based, QoS-aware EPM schemes, which aim to reduce the energy consumption in network equipment by using different packet scheduling strategies, while adhering to QoS requirements of supported applications. Then, we explore the problem of energy minimization under QoS constraints through a mathematical programming model, which is a DVFS-based, delay-aware APM scheme combing the speed scaling technique with the existing rate monotonic scheduling policy. Among these speed scaling based schemes, up to 26.76% dynamic power saving of the total power consumption can be achieved. In addition to speed scaling approaches, we further propose a sleep-based, traffic-aware EPM scheme, which is used to reduce power consumption by greening routing light load and putting the related network equipment into sleep mode according to twelve flow traffic density changes in 24-hour of an arbitrarily selected day. Meanwhile, a speed scaling technique without violating network QoS performance is also considered in this scheme when the traffic is rerouted. Applying this sleep-based strategy can lead to power savings of up to 62.58% of the total power consumption

Gas fluidization of nanoparticles

The primary objective of this study is to perform a systematic investigation on the gas fluidization of various nanoparticle agglomerates. Firstly, the gas fluidization characteristics and regime classifications without any additional external force fields are identified using both experimental measurements and modeling. Secondly, the effect of introducing certain external force fields on nanoparticle fluidization is experimentally investigated. Two external force fields were applied: sound waves from a loud speaker (acoustic assistance) and in-bed magnets that were excited by an external oscillating magnetic field (magnetic assistance). Thirdly, exploratory experimental research on the use of nanoparticle agglomerates as a granular filtration media for airborne fine particles is conducted. The last part of this dissertation is an exploratory modeling study to interpret the newly-found core-annulus-wall flow structure in gas fluidization. The experimental study on the gas fluidization of nanoparticles shows that most nanoparticles can be fluidized in the form of nanoparticle agglomerates. For those agglomerates (fluffy carbon black and very large agglomerates) that are difficult to fluidize, channeling always occurs. For those nanoparticle agglomerates that can be fluidized, the fluidization behaviors can be classified into two general categories, namely, agglomerate particulate fluidization (APF) and agglomerate bubbling fluidization (ABF). The classification appears to be depend mainly on the primary nanoparticle size and the bulk density. Nanoparticle agglomerates have a special structure with extremely high porosity. In this study, an analytical model is developed to calculate the flow partition through and around the porous agglomerates, as well as the drag force on an agglomerate of nanoparticles in a swarm of other similar agglomerates. Also, an analytical model based on the Richardson-Zaki equation has been developed to predict the fluidizing agglomerate size, the voidage around the agglomerates, and the minimum fluidization velocities of APF nanoparticles. The introduction of an external field such as sound excitation and magnetic excitation with in-bed magnets can significantly change the fluidization characteristics of nanoagglomerates, including a significant reduction in the minimum fluidization velocity and agglomerate size. The intensity and frequency of the external sound and magnetic fields will influence the fluidization quality of the nanoparticles. In this study, a series of exploratory experiments have been conducted to remove sub-micron particles (including solid particles and liquid droplets) generated by burning incense. The results show that nanoparticle agglomerates in a packed bed can be used successfully as a filter media for airborne submicron particulates. In addition, this study interprets the formation mechanism of the recently discovered core-annulus-wall structure in a circulating fluidized bed, which originates from the wall region mixing of a down flow of solids from the top section of a riser and the upward solids flow near the bottom of the riser, and the strong solid particle collisions in the dense phase suspension. A mathematic model of this phenomenon has been successfully developed and solved numerically

Learning Author’s Writing Pattern System By Automata

The purpose of the report is to document our project’s theory, implementation and test results. The project works on an automata-based learning system which models authors’ writing characters with automatons. Since there were pervious works done by Dr. T.Y. Lin and Ms. S.X. Zhang, we continue on ALERGIA algorithm analysis and initial common pattern study in this project. Although every author has his/her own writing style, such as sentence length and word frequency etc, there are always some similarities in writing style. We hypothesize that common strings fogged the expected test result, just like the noise in radio wave. This report gives the design and implementation of finding common pattern, as well as testing results. This report also describes the implementation of ALERGIA algorithm based on paper of Learning Stochastic Regular Grammars by Means of a State Merging Method by Rafael C. Carrasco and Jose Oncina [2]. The coding is done in Java 6 on Eclipse Helios version

Self-Assembled Chiral Photonic Crystals From Colloidal Helices Racemate

Chiral crystals consisting of micro-helices have many optical properties while presently available fabrication processes limit their large-scale applications in photonic devices. Here, by using a simplified simulation method, we investigate a bottom-up self-assembly route to build up helical crystals from the smectic monolayer of colloidal helices racemate. With increasing the density, the system undergoes an entropy-driven co-crystallization by forming crystals of various symmetries with different helical shapes. In particular, we identify two crystals of helices arranged in the binary honeycomb and square lattices, which are essentially composed by two sets of opposite-handed chiral crystal. Photonic calculations show that these chiral structures can have large complete photonic bandgaps. In addition, in the self-assembled chiral square crystal, we also find dual polarization bandgaps that selectively forbid the propagation of circularly polarized lights of a specific handedness along the helical axis direction. The self-assembly process in our proposed system is robust, suggesting possibilities of using chiral colloids to assemble photonic metamaterials.Comment: Accepted in ACS Nan

Human Capital and Risky Asset Allocation

Much research has been done to examine the relation between investors\u27 human capital and their financial asset allocation. While some showed that the value of human capital should be taken into consideration to make financial asset allocation decisions on the composition of investing portfolios, most argued not. In this paper, we selected the monthly return of 9 industrial ETFs from June of 2007 to July 2011, used the present value of total future income as estimate of human capital, and relied on the Mean-Variance Optimal Asset Allocation framework to reexamine if human capital will impact investors optimal financial portfolios. Based on our tests, we found significant connection between human capital and risky asset allocation, which resulted in significant change to weights allocated to the risk assets to create a Mean-Variance optimal portfolio