This thesis work studied the behavioral modeling, estimation of parameters, model performance and linearization of power amplifier (PA) using Digital Pre Distortion (DPD) technique. PAs are one of the fundamental block in communication systems and also one of the main sources of nonlinearities in the system, as these devices are frequently subjected to signals characterized by considerable bandwidths and non-constant envelopes due to use of modern modulation technique. Moreover, PAs have high efficiency level at its nonlinear region. So, to operate the PA at its high efficiency region, linearization operation needs to be done. This has been investigated in this thesis work with the help of behavioral modeling and DPD. An essential initial step in designing a linearizer for a PA is to model the PA nonlinearity accurately. Behavioral modeling has been used for PA model for its computational efficiency, which means by relating input and output signals without addressing to the physical analysis of the device. DPD technique has been chosen for linearizing the performance of PA based on their low requirement of resources for implementation. In this thesis, five different PA models with memory effect, based on Volterra series, are studied and compared for three different PAs selected by Ericsson. These PAs are designed for third and fourth generation telecommunication system. Two different signals with different peak to average ratios and different bandwidths have been used as input signals of PA for this study. The main result in this thesis work includes the comparison of all five forward behavioral modeling results for all three PA’s. The results also describe that; two of the given PA’s can be linearized by using the DPD technique within the 3GPP standard regulations for ACPR.

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Attachment of particles to one another due to action of certain inter-particle forces is called as particle agglomeration. It has applications ranging from efficient capture of ultra-fine particles generated in coal-burning boilers to effective discharge of aerosol sprays. Aerosol sprays have their application in asthma relievers, coatings, cleaning agents, air fresheners, personal care products and insecticides. There are several factors that cause particle agglomeration and based on the application, agglomeration or de-agglomeration is desired. These various factors associated with agglomeration include van derWaals forces, capillary forces, electrostatic double-layer forces, effects of turbulence, gravity and brownian motion. It is therefore essential to understand the underlying agglomeration mechanisms involved. It is difficult to perform experiments to quantify certain effects of the inter-particle forces and hence we turn to numerical simulations as an alternative. Simulations can be performed using the various numerical simulation techniques such as molecular dynamics, discrete element method, dissipative particle dynamics or other probabilistic simulation techniques. The main objective of this thesis is to study the geometric characteristics of particle agglomerates using dissipative particle dynamics. In this thesis, agglomeration is simulated using the features of dissipative particle dynamics as the simulation technique. Forces of attraction from the literature are used to modify the form of the conservative force. Agglomeration is simulated and the characteristics of the result ing agglomerates are quantified. Simulations were performed on a sizeable number of particles and we observe agglomeration behavior. A study of the agglomerates resulting from the different types of attractive forces is performed to characterize them methodically. Also as a part of this thesis, a novel, dynamic particle simulation technique was developed by interfacing MATLAB and our computational C program.

Consumer- and pharmaceutical-based products are a major component of the chemical industry. In the personal care industry, formulations often consist of a mixture of surfactants and fatty alcohols. The addition of surfactants aids the stability of the formulation. The formulated product microstructure depends upon the preparation conditions as well as the ingredients. Controlling which microstructures form during the production of a formulated product is important as different microstructures can have wildly different physical properties, making some far more favourable than others. This thesis examines several of the processes undertaken in the manufacture of formulated products. The dissolution of a surfactant in a bulk water phase is examined. This is examined in a number of ways; firstly, the dissolution times of the surfactants are measured using electrical resistance tomography. It is found that the dissolution time varies with the agitation rate, agitator size, and addition method. The dissolution is also examined using dissipative particle dynamics to gain insight into the dissolution on a molecular scale. It is found that the surfactant breaks into wormlike micelles on dissolution. If an oil is added to the initial bulk then the dissolution process is modified so that long cylinders are produced with some spherical micelles. Finally, the break-up rate is predicted using a breakage model based on the agitator shear rate and a network-of-zones model. This produces good results. The production and post-shear processing of a sample formulated product, hair conditioner, is examined. Firstly, the mixing in a vessel is examined with electrical resistance tomography. Problems are encountered when the production method involves the use of distilled water as the conductivity is very low; however, the mixing time of the final product in the vessel can be determined. It is also shown that the majority of the structural changes in the post-shearing process are caused by the in-line rotor-stator mixer. The viscosity of the product increases in a linear fashion with the shear rate, while the conductivity increases as a function of the shear rate and the recycle rate. This allows the monitoring of the post-shearing process to be carried out using electrical resistance tomography. This thesis also looks at the possibility of producing a multiple frequency electrical resistance tomography device to monitor formulated product production; however, it has been shown that the conductivity does not vary with the voltage frequency over a usable range. This meant that no further effort was put into developing this, as it gave no advantage over the traditional single frequency technique. Nevertheless, important advances towards better understanding of mixing processes resulted due to the investigations carried out.

This research addresses green communication issues, including energy efficiency, peak-to-average power ratio (PAPR) reduction and power amplifier (PA) linearization. Green communication is expected to be a primary goal in next generation cellular systems because it promises to reduce operating costs. The first key issue is energy efficiency of distributed antenna systems (DASs). The power consumption of high power amplifiers (HPAs) used in wireless communication systems is determined by the transmit power and drain efficiency. For unequal power allocation of orthogonal frequency division multiplexing (OFDM), the drain efficiency of the PA is determined by the PAPR and hence by the power distribution. This research proposes a PAPR-aware energy-efficient resource allocation scheme for joint orthogonal frequency division multiple access (OFDMA)/space division multiple access (SDMA) downlink transmission from DASs. Grouping-based SDMA is applied to exploit the spatial diversity while avoiding performance degradation from correlated channels. The developed scheme considers the impact of both system data rate and effective power consumption on the PAPR during resource allocation. We also present a suboptimal joint subcarrier and power allocation algorithm to facilitate implementation of power-efficient multi-channel wireless communications. By solving Karush-Kuhn-Tucker conditions, a closed-form solution for the power allocation of each remote radio head is obtained. The second key issue is related with PAPR reduction in the massive multiple-input multiple-output (MIMO) systems. The large number of PAs in next generation massive MIMO cellular communication system requires using inexpensive PAs at the base station to keep array cost reasonable. Large-scale multiuser (MU) MIMO systems can provide extra spatial degrees-of-freedom (DoFs) for PAPR reduction. This work applies both recurrent neural network (RNN)- and semidefinite relaxation (SDR)-based schemes for different purposes to reduce PAPR. The highly parallel structure of RNN is proposed in this work to address the issues of scalability and stringent requirements on computational times in PAPR-aware precoding problem. An SDR-based framework is proposed to reduce PAPR that accommodates channel uncertainties and intercell coordination. Both of the proposed structures reduce linearity requirements and enable the use of lower cost RF components for large-scale MU-MIMO-OFDM downlink. The third key issue is digital predistortion (DPD) in the massive MIMO systems. The primary source of nonlinear distortion in wireless transmitters is the PA, which is commonly modeled using polynomials. Conventional DPD schemes use high-order polynomials to accurately approximate and compensate for the nonlinearity of the PA. This is impractical for scaling to tens or hundreds of PAs in massive MIMO systems. This work therefore proposes a scalable DPD method, achieved by exploiting massive DoFs of next generation front ends. We propose a novel indirect learning structure which adapts the channel and PA distortion iteratively by cascading adaptive zero-forcing precoding and DPD. Experimental results show that over 70% of computational complexity is saved for the proposed solution, it is shown that a 3rd order polynomial with the new solution achieves the same performance as the conventional DPD using 11th order polynomial for a 100x10 massive MIMO configuration. / Ph. D.

Latex polymers serve as binders in a wide range of architectural paints and coatings. A latex is an aqueous colloidal dispersion of polymer particles that when dried above the polymer’s film formation temperature coalesces into a dry polymer film (Dragnevski, Routh, Murray, & Donald, 2010). The other main components of paint include associative thickeners, surfactants, pigments and fillers with the thickener being the primary area of focus for this study. The relatively simple system of latex, associative thickener and surfactant has been studied extensively. These studies have shown the mechanism of thickening for the associative thickener, and surfactant effects on both latex and thickener; however, there are few studies conducted for a fully-formulated system. The introduction of pigments, fillers, coalescing aids, functional amines, and other additives greatly increases the difficulty of research in this area. The addition of many additives ultimately affects the stability and physical properties of the end-product. Phase separation of the paints, also called syneresis, is a major concern of paint formulators because paints need to be as stable when left sitting in a paint-can for an extended period of time. The goal of this project is to essentially probe the areas of phase separation for some hydrophobically modified ethoxylated urethane (HEUR) thickened paint systems that are very similar to commercially used paint formulations. The probing of these phase separated regions includes the careful preparation of each paint sample, physical property testing, as well as new experimental development in the area of syneresis, rheology, followed by statistical analysis of data. Dispersion phase diagrams (DPDs) were first reported by Kostansek (2003) in a simple system of HEUR thickener, surfactant, and latex. They are a plot of the three possible dispersion states for an associative thickened system. These states include bridging flocculation which occurs at low levels of HEUR in which 50% or less of the latex particle surface is covered by the associative thickener. The second state is a good dispersion, which does not show any signs of flocculation. The third state is a mode of flocculation called depletion flocculation that occurs when the particle surfaces of the system are covered mostly with surfactant. The free associative polymer in the system is excluded from the free space in between particles, and the latex particles form aggregates (Otsubo, 1995). The three dispersion phases are then plotted with wt% HEUR on the continuous phase versus wt% surfactant on the continuous phase. The ideal end product for this project would be to use various combinations of latex, surfactant, and associative thickeners (ATs) to create multiple DPDs, which then could be used to troubleshoot formulations and samples in which flocculation is present. Each formulation was made using a thickening package of two non-ionic HEURs: a low-shear and high-shear thickener. Surfactant additions were made after the HEUR in small incremental amounts. Each DPD would consist of one surfactant, the previously stated combination of HEURs, and an all-acrylic latex. Three different surfactants were used in the study: two non-ionic surfactants, and an anionic surfactant. The first non-ionic surfactant was not studied in full as the other two surfactants due to time constraints. Two different all-acrylic latexes were used which varied in the particle size. The first latex studied, Acrylic-A, has an average particle size of 105 nm, and the second latex was Acrylic-B with 150 nm particle size. The TiO2 used in each DPD was surface treated and used in powder form. By the end of the project, 4 full-scale DPDs were made with the following combinations: Acrylic-A and a non-ionic surfactant, Acrylic-A and an anionic surfactant, Acrylic-B and a non-ionic surfactant, and Acrylic-B and an anionic surfactant. From these DPDs the mechanistic interactions of various components of the system could be made. The DPDs could also be used to troubleshoot problematic paints and even hypothesize new formulations.

La simulation des matériaux demande une compréhension de leur comportement à de nombreuses échelles de temps et d’espace. Ces différentes échelles requièrent des méthodes de simulations différentes, qui se basent sur des approximations différentes et donnent accès à différentes propriétés. La simulation multiéchelle est une approche qui regroupe l’utilisation de ces différentes méthodes, ainsi que des relations qui les unissent. Des développements plus récents ont permis la mise au point de méthodes mésoscopiques, comblant le trou entre les simulations atomistiques (< 10 nm) et les milieux continus (>mm). La dynamique de particules dissipatives (DPD) est une telle méthode, qui présente de nombreux avantages théoriques et pratiques en comparaison avec d’autres méthodes mésoscopiques. La DPD est une méthode modélisant la matière par des particules molles, s’inspirant de l’équation de Langevin. La dynamique des particules est gérée par trois forces : une force conservative, une force dissipative et une force aléatoire. La force conservative naît des interactions effectives moyennes à l’échelle méso, alors que la force dissipative et la force aléatoire sont d’origine statistique. Différentes formulations et contributions à la force conservative sont présentées, permettant notamment la simulation de polymères enchevêtrés et de systèmes chargés. Les contraintes auxquelles les forces statistiques sont soumises, ainsi que leurs impacts sur les dynamiques, sont ensuite discutés. La présentation de la DPD se termine par des considérations sur les effets numériques particuliers à la DPD. La puissance de la DPD est démontrée par la simulation de polymères arborescents. Les polymères arborescents sont des macromolécules hyperbranchées obtenues par une séquence de réactions de greffage de chaînes polymères. La structure qu’adoptent ces molécules n’est pas connue avec certitude. Des expériences ont permis aux chercheurs de proposer un modèle en loi de puissance pour le profil de densité radiale. Or, cette propriété n’est accessible qu’indirectement aux méthodes expérimentales, alors qu’elle peut être obtenue directement des travaux de simulation. La masse énorme de ces composés, ainsi que leur topologie complexe, impossible à réduire à un modèle plus simple, empêche toute simulation par des méthodes microscopiques traditionnelles. L’utilisation de méthodes mésoscopiques s’impose donc. Les polymères arborescents de génération 2 (d’une masse de l’ordre de 3,2×103 kDa) en solution (5 %) peuvent être simulés explicitement grâce à la DPD, et ce, en un temps acceptable. Les propriétés du solvant peuvent être ajustées, notamment leur qualité et leur masse moléculaire. Le profil de densité radiale moyen simulé correspond plutôt bien au modèle en loi de puissance proposé. L’analyse des données expérimentale suppose une symétrie sphérique des molécules individuelles qui s’avère être erronée. L’anisotropie des macromolécules est étudiée et s’avère être hautement variable. Des fonctions de distribution radiale ainsi que les patrons de diffusion de neutrons associés ont été obtenus. Ces derniers pourront être comparés directement aux résultats expérimentaux lorsque ces derniers seront disponibles. L’utilisation de la DPD est riche en possibilités. Elle est facilement étendue à diverses classes de matériaux. Par sa nature dynamique et ses propriétés, la DPD donne accès à certaines classes de phénomènes inaccessibles aux autres méthodes de simulation mésoscopique. Notamment, la DPD permet naturellement la simulation dans l’état stationnaire, tel que démontré par la simulation de la structure du Nafion c sous cisaillement. De plus, le comportement hydrodynamique devrait permettre la simulation à l’échelle mésoscopique de la transition vitreuse ou à tout le moins, d’une transition lui ressemblant. De plus, la DPD peut être étendue afin d’effectuer la simulation dans d’autres ensembles thermodynamiques, qui donnent accès à d’autres propriétés d’intérêt pour les matériaux (conductivité thermique, propriétés mécaniques). Les versions actuelles de la DPD, bien que versatiles, ne permettent pas encore de reproduire quantitativement les propriétés des matériaux. Différents succès, obstacles et pistes de réflexion sont présentés. Le perfectionnement de la DPD fournit à la fois un prétexte et un banc d’essai de choix pour tenter de comprendre les questions fondamentales suscitées par le coarse-graining et l’échelle méso en elle-même.

This work presents the mathematical and computational aspects of a smooth dissipative particle dynamics with dynamic virtual particle allocation method (SDPD-DV) for modeling and simulation of mesoscopic fluids in wall-bounded domains. The SDPD-DV method is realized with fluid particles, boundary particles and dynamically allocated virtual particles near solid boundaries. The physical domain in SDPD-DV contains external and internal solid boundaries, periodic inlets and outlets, and the fluid region. The solid boundaries of the domain are represented with boundary particles which have an assigned position, wall velocity, and temperature upon initialization. The fluid domain is discretized with fluid particles placed in a global index. The algorithm for nearest neighbor particle search is based on a combination of the linked-cell and Verlet-list approaches and utilizes large rectangular cells for computational efficiency. The density model of a fluid particle in the proximity of a solid boundary includes the contribution from the virtual particles in its truncated support domain. The thermodynamic properties of a virtual particle are identical to those of the corresponding fluid particle. A periodic boundary particle allocation method is used at periodic inlets and outlets. Models for the conservative and dissipative forces on a fluid particle in the proximity of a solid boundary are presented and include the contributions of the virtual particles in its truncated support domain. The integration of the fluid particle position and momentum equations is accomplished with an implementation of the velocity-Verlet algorithm. The integration is supplemented by a bounce-forward algorithm in cases where the virtual particle force model is not able to prevent particle penetration. The integration of the entropy equation is based on the Runge-Kutta scheme. In isothermal simulations, the pressure of a fluid particle is obtained by an artificial compressibility formulation for liquids and the ideal gas law for compressible fluids. Sampling methods used for particle properties and transport coefficients in SDPD-DV are presented. The self-diffusion coefficient is obtained by an implementation of the generalized Einstein and the Green-Kubo relations. Field properties are obtained by sampling SDPD-DV outputs on a post-processing grid that allows harnessing the particle information on desired spatio-temporal scales. The isothermal (without the entropy equation) SDPD-DV method is verified and validated with simulations in bounded and periodic domains that cover the hydrodynamic and mesoscopic regimes. Verification is achieved with SDPD-DV simulations of transient, Poiseuille, body-force driven flow of liquid water between plates separated. The velocity profiles from the SDPD-DV simulations are in very good agreement with analytical estimates and the field density fluctuation near solid boundaries is shown to be below 5%. Additional verification involves SDPD-DV simulations of transient, planar, Couette liquid water flow. The top plate is moving and separated from the bottom stationary plate. The numerical results are in very good agreement with the analytical solutions. Additional SDPD-DV verification is accomplished with the simulation of a body-force driven, low-Reynolds number flow of water over a cylinder of radius R=0.02m. The SDPD-DV field velocity and pressure are compared with those obtained by FLUENT. An extensive set of SDPD-DV simulations of liquid water and gaseous nitrogen in mesoscopic periodic domains is presented. For the SDPD-DV simulations of liquid water the mass of the fluid particles is varied between 1.24 and 3.3e-7 real molecular masses and their corresponding size is between 1.08 and 323 physical length scales. For SDPD-DV simulations of gaseous nitrogen the mass of the fluid particles is varied between 6.37e3and 6.37e6 real molecular masses and their corresponding size is between 2.2e2 and 2.2e3 physical length scales. The equilibrium states are obtained and show that the particle speeds scale inversely with particle mass (or size) and that the translational temperature is scale-free. The self-diffusion coefficient for liquid water is obtained through the mean-square displacement and the velocity auto-correlation methods for the range of fluid particle masses (or sizes) considered. Various analytical expressions for the self-diffusivity of the SDPD fluid are developed in analogy to the real fluid. The numerical results are in very good agreement with the SDPD-fluid analytical expressions. The numerical self-diffusivity is shown to be scale dependent. For fluid particles approaching asymptotically the mass of the real particle the self-diffusivity is shown to approach the experimental value. The Schmidt numbers obtained from the SDPD-DV simulations are within the range expected for liquid water. The SDPD-DV method (with entropy) is verified and validated with simulations with an extensive set of simulations of gaseous nitrogen in mesoscopic, periodic domains in equilibrium. The simulations of N2(g) are performed in rectangular domains. The self-diffusion coefficient for N2(g) at equilibrium states is obtained through the mean-square displacement for the range of fluid particle masses (or sizes) considered. The numerical self-diffusion is shown to be scale dependent. The simulations show that self-diffusion decreases with increasing mass ratio. For a given mass ratio, increasing the smoothing length, increases the self-diffusion coefficient. The shear viscosity obtained from SDPD-DV is shown to be scale free and in good agreement with the real value. We examine also the effects of timestep in SDPD-DV simulations by examining thermodynamic parameters at equilibrium. These results show that the time step can lead to a significant error depending on the fluid particle mass and smoothing length. Fluctuations in thermodynamic variables obtained from SDPD-DV are compared with analytical estimates. Additional verification involves SDPD-DV simulations of steady planar thermal Couette flow of N2(g). The top plate at temperature T1 =330K is moving at Vxw =30m/s and is separated by 10-4 m from the bottom stationary plate at T2=300K. The SDPD-DV velocity and temperature fields are in excellent agreement with those obtained by FLUENT.

This master’s thesis deals with integrating simulations using Agilents Electronic Design Automation tool ADS with customized Matlab scripts, for solving complex analog and digital radio architectures. In particular, it addresses predistortion, realized in the digital domain, of power amplifiers, modeled in the analog domain. The former is implemented in Matlab while the latter is implemented in ADS. Two versions of integrating the two systems have been tested: 1) Iterative approach on sample basis and 2) Scheduled batch solution by matrix inversion. The concept has been tested on two different PA configurations: 1) a standard class-AB PA and 2) a Doherty PA architecture. Evaluation has also been done on ADS ability to correctly simulate memory effects in PAs and on the actual DPD-algorithms ability to compensate for these memory effects.  An integrated simulation environment for ADS and Matlab was successfully established within the work of this thesis. Matlab scripts, used in predistortion algorithms in the digital domain, could interact directly with component-based PA models, in an enclosed simulation system.  The simulation results show that sample basis method is the most accurate, fast and reliable method to linearize a PA. The PA1 proved to be easier than the DPA to linearize, except for when being close to saturation where better IMD-suppression was achieved with the DPA.  ADS is clearly able to simulate memory effects in the analog domain. At low gain-levels the applied compensating memory-algorithms showed a great improvement to the linearization of the output signal of the PA. At higher gain-levels though, the compensation for memory effects lost their efficiency because the non-linearity of the PA became too significant.