Dynamic modeling and simulation of the airflow cleaning device

The objective of the work is to study the flow behavior and to support the design of air cleaner by dynamic simulation.In a paper printing industry, it is necessary to monitor the quality of paper when the paper is being produced. During the production, the quality of the paper can be monitored by camera. Therefore, it is necessary to keep the camera lens clean as wood particles may fall from the paper and lie on the camera lens. In this work, the behavior of the air flow and effect of the airflow on the particles at different inlet angles are simulated. Geometries of a different inlet angles of single-channel and double-channel case were constructed using ANSYS CFD Software. All the simulations were performed in ANSYS Fluent. The simulation results of single-channel and double-channel case revealed significant differences in the behavior of the flow and the particle velocity. The main conclusion from this work are in following. 1) For the single channel case the best angle was 0 degree because in that case, the air flow can keep 60% of the particles away from the lens which would otherwise stay on lens. 2) For the double channel case, the best solution was found when the angle of the first inlet was 0 degree and the angle of second inlet was 45 degree . In that case, the airflow can keep 91% of particles away from the lens which would otherwise stay on lens.

Real-time simulation of multibody systems with applications for working mobile vehicles

This dissertation describes an approach for developing a real-time simulation for working mobile vehicles based on multibody modeling. The use of multibody modeling allows comprehensive description of the constrained motion of the mechanical systems involved and permits real-time solving of the equations of motion. By carefully selecting the multibody formulation method to be used, it is possible to increase the accuracy of the multibody model while at the same time solving equations of motion in real-time. In this study, a multibody procedure based on semi-recursive and augmented Lagrangian methods for real-time dynamic simulation application is studied in detail. In the semirecursive approach, a velocity transformation matrix is introduced to describe the dependent coordinates into relative (joint) coordinates, which reduces the size of the generalized coordinates. The augmented Lagrangian method is based on usage of global coordinates and, in that method, constraints are accounted using an iterative process. A multibody system can be modelled as either rigid or flexible bodies. When using flexible bodies, the system can be described using a floating frame of reference formulation. In this method, the deformation mode needed can be obtained from the finite element model. As the finite element model typically involves large number of degrees of freedom, reduced number of deformation modes can be obtained by employing model order reduction method such as Guyan reduction, Craig-Bampton method and Krylov subspace as shown in this study The constrained motion of the working mobile vehicles is actuated by the force from the hydraulic actuator. In this study, the hydraulic system is modeled using lumped fluid theory, in which the hydraulic circuit is divided into volumes. In this approach, the pressure wave propagation in the hoses and pipes is neglected. The contact modeling is divided into two stages: contact detection and contact response. Contact detection determines when and where the contact occurs, and contact response provides the force acting at the collision point. The friction between tire and ground is modelled using the LuGre friction model, which describes the frictional force between two surfaces. Typically, the equations of motion are solved in the full matrices format, where the sparsity of the matrices is not considered. Increasing the number of bodies and constraint equations leads to the system matrices becoming large and sparse in structure. To increase the computational efficiency, a technique for solution of sparse matrices is proposed in this dissertation and its implementation demonstrated. To assess the computing efficiency, augmented Lagrangian and semi-recursive methods are implemented employing a sparse matrix technique. From the numerical example, the results show that the proposed approach is applicable and produced appropriate results within the real-time period.

Two-dimensional drift-diffusion simulation of organic solar cells

The aim of this master's thesis is to develop a two-dimensional drift-di usion model, which describes charge transport in organic solar cells. The main bene t of a two-dimensional model compared to a one-dimensional one is the inclusion of the nanoscale morphology of the active layer of a bulk heterojunction solar cell. The developed model was used to study recombination dynamics at the donor-acceptor interface. In some cases, it was possible to determine e ective parameters, which reproduce the results of the two-dimensional model in the one-dimensional case. A summary of the theory of charge transport in semiconductors was presented and discussed in the context of organic materials. Additionally, the normalization and discretization procedures required to nd a numerical solution to the charge transport problem were outlined. The charge transport problem was solved by implementing an iterative scheme called successive over-relaxation. The obtained solution is given as position-dependent electric potential, free charge carrier concentrations and current densities in the active layer. An interfacial layer, separating the pure phases, was introduced in order to describe charge dynamics occurring at the interface between the donor and acceptor. For simplicity, an e ective generation of free charge carriers in the interfacial layer was implemented. The pure phases simply act as transport layers for the photogenerated charges. Langevin recombination was assumed in the two-dimensional model and an analysis of the apparent recombination rate in the one-dimensional case is presented. The recombination rate in a two-dimensional model is seen to e ectively look like reduced Langevin recombination at open circuit. Replicating the J-U curves obtained in the two-dimensional model is, however, not possible by introducing a constant reduction factor in the Langevin recombination rate. The impact of an acceptor domain in the pure donor phase was investigated. Two cases were considered, one where the acceptor domain is isolated and another where it is connected to the bulk of the acceptor. A comparison to the case where no isolated domains exist was done in order to quantify the observed reduction in the photocurrent. The results show that all charges generated at the isolated domain are lost to recombination, but the domain does not have a major impact on charge transport. Trap-assisted recombination at interfacial trap states was investigated, as well as the surface dipole caused by the trapped charges. A theoretical expression for the ideality factor n_id as a function of generation was derived and shown to agree with simulation data. When the theoretical expression was fitted to simulation data, no interface dipole was observed.

Coopetition as a lead generating mechanism: design, modelling and simulation

The aim of this research is to develop a tool that could allow to organize coopetitional relationships between organizations on the basis of two-sided Internet platform. The main result of current master thesis is a detailed description of the concept of the lead generating internet platform-based coopetition. With the tools of agent-based modelling and simulation, there were obtained results that could be used as a base for suggestion that the developed concept is able to cause a positive effect on some particular industries (e.g. web-design studios market) and potentially can bring some benefits and extra profitability for most companies that operate on this particular industry. Also on the basis of the results it can be assumed that the developed instrument is also able to increase the degree of transparency of the market to which it is applied.

A sequential analysis of therapist scaffolding and child concept formation in narrative therapy

Narrative therapy is a postmodern therapy that takes the position that people create self-narratives to make sense of their experiences. To date, narrative therapy has compiled virtually no quantitative and very little qualitative research, leaving gaps in almost all areas of process and outcome. White (2006a), one of the therapy's founders, has recently utilized Vygotsky's (1934/1987) theories of the zone of proximal development (ZPD) and concept formation to describe the process of change in narrative therapy with children. In collaboration with the child client, the narrative therapist formalizes therapeutic concepts and submits them to increasing levels of generalization to create a ZPD. This study sought to determine whether the child's development proceeds through the stages of concept formation over the course of a session, and whether therapists' utterances scaffold this movement. A sequential analysis was used due to its unique ability to measure dynamic processes in social interactions. Stages of concept formation and scaffolding were coded over time. A hierarchical log-linear analysis was performed on the sequential data to develop a model of therapist scaffolding and child concept development. This was intended to determine what patterns occur and whether the stated intent of narrative therapy matches its actual process. In accordance with narrative therapy theory, the log-linear analysis produced a final model with interactions between therapist and child utterances, and between both therapist and child utterances and time. Specifically, the child and youth participants in therapy tended to respond to therapist scaffolding at the corresponding level of concept formation. Both children and youth and therapists also tended to move away from earlier and toward later stages of White's scaffolding conversations map as the therapy session advanced. These findings provide support for White's contention that narrative therapists promote child development by scaffolding child concept formation in therapy.

Investigations in flow injection analysis (FIA): conventional FIA determination of chloride in water; sequential injection (SI)-FIA determination of mercury in water

Flow injection analysis (FIA) was applied to the determination of both chloride ion and mercury in water. Conventional FIA was employed for the chloride study. Investigations of the Fe3 +/Hg(SCN)2/CI-,450 nm spectrophotometric system for chloride determination led to the discovery of an absorbance in the 250-260 nm region when Hg(SCN)2 and CI- are combined in solution, in the absence of iron(III). Employing an in-house FIA system, absorbance observed at 254 nm exhibited a linear relation from essentially 0 - 2000 Jlg ml- 1 injected chloride. This linear range spanning three orders of magnitude is superior to the Fe3+/Hg(SCN)2/CI- system currently employed by laboratories worldwide. The detection limit obtainable with the proposed method was determin~d to be 0.16 Jlg ml- 1 and the relative standard deviation was determined to be 3.5 % over the concentration range of 0-200 Jig ml- 1. Other halogen ions were found to interfere with chloride determination at 254 nm whereas cations did not interfere. This system was successfully applied to the determination of chloride ion in laboratory water. Sequential injection (SI)-FIA was employed for mercury determination in water with the PSA Galahad mercury amalgamation, and Merlin mercury fluorescence detection systems. Initial mercury in air determinations involved injections of mercury saturated air directly into the Galahad whereas mercury in water determinations involved solution delivery via peristaltic pump to a gas/liquid separator, after reduction by stannous chloride. A series of changes were made to the internal hardware and valving systems of the Galahad mercury preconcentrator. Sequential injection solution delivery replaced the continuous peristaltic pump system and computer control was implemented to control and integrate all aspects of solution delivery, sample preconcentration and signal processing. Detection limits currently obtainable with this system are 0.1 ng ml-1 HgO.

Genetic programming for the RoboCup Rescue Simulation System

The Robocup Rescue Simulation System (RCRSS) is a dynamic system of multi-agent interaction, simulating a large-scale urban disaster scenario. Teams of rescue agents are charged with the tasks of minimizing civilian casualties and infrastructure damage while competing against limitations on time, communication, and awareness. This thesis provides the first known attempt of applying Genetic Programming (GP) to the development of behaviours necessary to perform well in the RCRSS. Specifically, this thesis studies the suitability of GP to evolve the operational behaviours required of each type of rescue agent in the RCRSS. The system developed is evaluated in terms of the consistency with which expected solutions are the target of convergence as well as by comparison to previous competition results. The results indicate that GP is capable of converging to some forms of expected behaviour, but that additional evolution in strategizing behaviours must be performed in order to become competitive. An enhancement to the standard GP algorithm is proposed which is shown to simplify the initial search space allowing evolution to occur much quicker. In addition, two forms of population are employed and compared in terms of their apparent effects on the evolution of control structures for intelligent rescue agents. The first is a single population in which each individual is comprised of three distinct trees for the respective control of three types of agents, the second is a set of three co-evolving subpopulations one for each type of agent. Multiple populations of cooperating individuals appear to achieve higher proficiencies in training, but testing on unseen instances raises the issue of overfitting.

Simulation-Based Finite and Large Sample Tests in Multivariate Regressions.

In the context of multivariate linear regression (MLR) models, it is well known that commonly employed asymptotic test criteria are seriously biased towards overrejection. In this paper, we propose a general method for constructing exact tests of possibly nonlinear hypotheses on the coefficients of MLR systems. For the case of uniform linear hypotheses, we present exact distributional invariance results concerning several standard test criteria. These include Wilks' likelihood ratio (LR) criterion as well as trace and maximum root criteria. The normality assumption is not necessary for most of the results to hold. Implications for inference are two-fold. First, invariance to nuisance parameters entails that the technique of Monte Carlo tests can be applied on all these statistics to obtain exact tests of uniform linear hypotheses. Second, the invariance property of the latter statistic is exploited to derive general nuisance-parameter-free bounds on the distribution of the LR statistic for arbitrary hypotheses. Even though it may be difficult to compute these bounds analytically, they can easily be simulated, hence yielding exact bounds Monte Carlo tests. Illustrative simulation experiments show that the bounds are sufficiently tight to provide conclusive results with a high probability. Our findings illustrate the value of the bounds as a tool to be used in conjunction with more traditional simulation-based test methods (e.g., the parametric bootstrap) which may be applied when the bounds are not conclusive.

Markovian Processes, Two-Sided Autoregressions and Finite-Sample Inference for Stationary and Nonstationary Autoregressive Processes.

In this paper, we develop finite-sample inference procedures for stationary and nonstationary autoregressive (AR) models. The method is based on special properties of Markov processes and a split-sample technique. The results on Markovian processes (intercalary independence and truncation) only require the existence of conditional densities. They are proved for possibly nonstationary and/or non-Gaussian multivariate Markov processes. In the context of a linear regression model with AR(1) errors, we show how these results can be used to simplify the distributional properties of the model by conditioning a subset of the data on the remaining observations. This transformation leads to a new model which has the form of a two-sided autoregression to which standard classical linear regression inference techniques can be applied. We show how to derive tests and confidence sets for the mean and/or autoregressive parameters of the model. We also develop a test on the order of an autoregression. We show that a combination of subsample-based inferences can improve the performance of the procedure. An application to U.S. domestic investment data illustrates the method.

On the Political Economy of Sequential Reforms

This paper proposes an explanation for why efficient reforms are not carried out when losers have the power to block their implementation, even though compensating them is feasible. We construct a signaling model with two-sided incomplete information in which a government faces the task of sequentially implementing two reforms by bargaining with interest groups. The organization of interest groups is endogenous. Compensations are distortionary and government types differ in the concern about distortions. We show that, when compensations are allowed to be informative about the government’s type, there is a bias against the payment of compensations and the implementation of reforms. This is because paying high compensations today provides incentives for some interest groups to organize and oppose subsequent reforms with the only purpose of receiving a transfer. By paying lower compensations, governments attempt to prevent such interest groups from organizing. However, this comes at the cost of reforms being blocked by interest groups with relatively high losses.

Simulation-Based Finite-Sample Normality Tests in Linear Regressions

In the literature on tests of normality, much concern has been expressed over the problems associated with residual-based procedures. Indeed, the specialized tables of critical points which are needed to perform the tests have been derived for the location-scale model; hence reliance on available significance points in the context of regression models may cause size distortions. We propose a general solution to the problem of controlling the size normality tests for the disturbances of standard linear regression, which is based on using the technique of Monte Carlo tests.

Finite-Sample Inference Methods for Simultaneous Equations and Models with Unobserved and Generated Regressors

We propose finite sample tests and confidence sets for models with unobserved and generated regressors as well as various models estimated by instrumental variables methods. The validity of the procedures is unaffected by the presence of identification problems or \"weak instruments\", so no detection of such problems is required. We study two distinct approaches for various models considered by Pagan (1984). The first one is an instrument substitution method which generalizes an approach proposed by Anderson and Rubin (1949) and Fuller (1987) for different (although related) problems, while the second one is based on splitting the sample. The instrument substitution method uses the instruments directly, instead of generated regressors, in order to test hypotheses about the \"structural parameters\" of interest and build confidence sets. The second approach relies on \"generated regressors\", which allows a gain in degrees of freedom, and a sample split technique. For inference about general possibly nonlinear transformations of model parameters, projection techniques are proposed. A distributional theory is obtained under the assumptions of Gaussian errors and strictly exogenous regressors. We show that the various tests and confidence sets proposed are (locally) \"asymptotically valid\" under much weaker assumptions. The properties of the tests proposed are examined in simulation experiments. In general, they outperform the usual asymptotic inference methods in terms of both reliability and power. Finally, the techniques suggested are applied to a model of Tobin’s q and to a model of academic performance.

Simulation-Based Finite-and Large-sample Inference Methods in Multivariate Regressions and Seemingly Unrelated Regressions

In the context of multivariate regression (MLR) and seemingly unrelated regressions (SURE) models, it is well known that commonly employed asymptotic test criteria are seriously biased towards overrejection. in this paper, we propose finite-and large-sample likelihood-based test procedures for possibly non-linear hypotheses on the coefficients of MLR and SURE systems.

Finite-Sample Diagnostics for Multivariate Regressions with Applications to Linear Asset Pricing Models

In this paper, we propose several finite-sample specification tests for multivariate linear regressions (MLR) with applications to asset pricing models. We focus on departures from the assumption of i.i.d. errors assumption, at univariate and multivariate levels, with Gaussian and non-Gaussian (including Student t) errors. The univariate tests studied extend existing exact procedures by allowing for unspecified parameters in the error distributions (e.g., the degrees of freedom in the case of the Student t distribution). The multivariate tests are based on properly standardized multivariate residuals to ensure invariance to MLR coefficients and error covariances. We consider tests for serial correlation, tests for multivariate GARCH and sign-type tests against general dependencies and asymmetries. The procedures proposed provide exact versions of those applied in Shanken (1990) which consist in combining univariate specification tests. Specifically, we combine tests across equations using the MC test procedure to avoid Bonferroni-type bounds. Since non-Gaussian based tests are not pivotal, we apply the “maximized MC” (MMC) test method [Dufour (2002)], where the MC p-value for the tested hypothesis (which depends on nuisance parameters) is maximized (with respect to these nuisance parameters) to control the test’s significance level. The tests proposed are applied to an asset pricing model with observable risk-free rates, using monthly returns on New York Stock Exchange (NYSE) portfolios over five-year subperiods from 1926-1995. Our empirical results reveal the following. Whereas univariate exact tests indicate significant serial correlation, asymmetries and GARCH in some equations, such effects are much less prevalent once error cross-equation covariances are accounted for. In addition, significant departures from the i.i.d. hypothesis are less evident once we allow for non-Gaussian errors.