Numerical and experimental investigation of thermal louvers for space applications

Thermal louvers, using movable or rotating shutters over a radiating surface, have gained a wide acceptance as highly efficient devices for controlling the temperature of a spacecraft. This paper presents a detailed analysis of the performance of a rectangular thermal louver with movable blades. The radiative capacity of the louver, determined by its effective emittance, is calculated for different values of the blades opening angle. Experimental results obtained with a prototype of a spacecraft thermal louver show good agreement with the theoretical values.

Tooth-Coil Permanent Magnet Synchronous Machine Design for Special Applications

This doctoral thesis presents a study on the design of tooth-coil permanent magnet synchronous machines. The electromagnetic properties of concentrated non-overlapping winding permanent magnet synchronous machines, or simply tooth-coil permanent magnet synchronous machines (TC-PMSMs), are studied in details. It is shown that current linkage harmonics play the deterministic role in the behavior of this type of machines. Important contributions are presented as regards of calculation of parameters of TC-PMSMs,particularly the estimation of inductances. The current linkage harmonics essentially define the air-gap harmonic leakage inductance, rotor losses and localized temporal inductance variation. It is proven by FEM analysis that inductance variation caused by the local temporal harmonic saturation results in considerable torque ripple, and can influence on sensorless control capabilities. Example case studies an integrated application of TC-IPMSMs in hybrid off-highway working vehicles. A methodology for increasing the efficiency of working vehicles is introduced. It comprises several approaches – hybridization, working operations optimization, component optimization and integration. As a result of component optimization and integration, a novel integrated electro-hydraulic energy converter (IEHEC) for off-highway working vehicles is designed. The IEHEC can considerably increase the operational efficiency of a hybrid working vehicle. The energy converter consists of an axial-piston hydraulic machine and an integrated TCIPMSM being built on the same shaft. The compact assembly of the electrical and hydraulic machines enhances the ability to find applications for such a device in the mobile environment of working vehicles.Usage of hydraulic fluid, typically used in working actuators, enables direct-immersion oil cooling of designed electrical machine, and further increases the torque- and power- densities of the whole device.

Control of Structures with Cubic and Quadratic Non-Linearities with Time Delay Consideration

This paper studies the effect of time delay on the active non-linear control of dynamically loaded flexible structures. The behavior of non-linear systems under state feedback control, considering a fixed time delay for the control force, is investigated. A control method based on non-linear optimal control, using a tensorial formulation and state feedback control is used. The state equations and the control forces are expressed in polynomial form and a performance index, quadratic in both state vector and control forces, is used. General polynomial representations of the non-linear control law are obtained and implemented for control algorithms up to the fifth order. This methodology is applied to systems with quadratic and cubic non-linearities. Strongly non-linear systems are tested and the effectiveness of the control system including a delay for the application of control forces is discussed. Numerical results indicate that the adopted control algorithm can be efficient for non-linear systems, chiefly in the presence of strong non-linearities but increasing time delay reduces the efficiency of the control system. Numerical results emphasize the importance of considering time delay in the project of active structural control systems.

Proposal of methodology for the modeling and control of manipulators

Industrial applications demand that robots operate in agreement with the position and orientation of their end effector. It is necessary to solve the kinematics inverse problem. This allows the displacement of the joints of the manipulator to be determined, to accomplish a given objective. Complete studies of dynamical control of joint robotics are also necessary. Initially, this article focuses on the implementation of numerical algorithms for the solution of the kinematics inverse problem and the modeling and simulation of dynamic systems. This is done using real time implementation. The modeling and simulation of dynamic systems are performed emphasizing off-line programming. In sequence, a complete study of the control strategies is carried out through the study of several elements of a robotic joint, such as: DC motor, inertia, and gearbox. Finally a trajectory generator, used as input for a generic group of joints, is developed and a proposal of the controller's implementation of joints, using EPLD development system, is presented.

Development of nano-meter motion control in Japan

Production machines for next generation LSIs such as 4G-DRAMs and for large liquid crystal displays such as 0.5mx0.5m size, and information equipment such as magnetic hard disks and DVDs must have the positioning accuracy of a nano-meter order. To realize such a high degree of the positioning accuracy, not only precision machine elements and mechanisms but also high precision sensors, actuators and controller design techniques becomes crucial. This paper introduces recent topics of precision positioning and motion control technology in Japan.

Machine vision methods for process measurements in pulping

The papermaking industry has been continuously developing intelligent solutions to characterize the raw materials it uses, to control the manufacturing process in a robust way, and to guarantee the desired quality of the end product. Based on the much improved imaging techniques and image-based analysis methods, it has become possible to look inside the manufacturing pipeline and propose more effective alternatives to human expertise. This study is focused on the development of image analyses methods for the pulping process of papermaking. Pulping starts with wood disintegration and forming the fiber suspension that is subsequently bleached, mixed with additives and chemicals, and finally dried and shipped to the papermaking mills. At each stage of the process it is important to analyze the properties of the raw material to guarantee the product quality. In order to evaluate properties of fibers, the main component of the pulp suspension, a framework for fiber characterization based on microscopic images is proposed in this thesis as the first contribution. The framework allows computation of fiber length and curl index correlating well with the ground truth values. The bubble detection method, the second contribution, was developed in order to estimate the gas volume at the delignification stage of the pulping process based on high-resolution in-line imaging. The gas volume was estimated accurately and the solution enabled just-in-time process termination whereas the accurate estimation of bubble size categories still remained challenging. As the third contribution of the study, optical flow computation was studied and the methods were successfully applied to pulp flow velocity estimation based on double-exposed images. Finally, a framework for classifying dirt particles in dried pulp sheets, including the semisynthetic ground truth generation, feature selection, and performance comparison of the state-of-the-art classification techniques, was proposed as the fourth contribution. The framework was successfully tested on the semisynthetic and real-world pulp sheet images. These four contributions assist in developing an integrated factory-level vision-based process control.

Development of a process for the direct synthesis of hydrogen peroxide in a novel microstructured reactor

Microreactors have proven to be versatile tools for process intensification. Over recent decades, they have increasingly been used for product and process development in chemical industries. Enhanced heat and mass transfer in the reactors due to the extremely high surfacearea- to-volume ratio and interfacial area allow chemical processes to be operated at extreme conditions. Safety is improved by the small holdup volume of the reactors and effective control of pressure and temperature. Hydrogen peroxide is a powerful green oxidant that is used in a wide range of industries. Reduction and auto-oxidation of anthraquinones is currently the main process for hydrogen peroxide production. Direct synthesis is a green alternative and has potential for on-site production. However, there are two limitations: safety concerns because of the explosive gas mixture produced and low selectivity of the process. The aim of this thesis was to develop a process for direct synthesis of hydrogen peroxide utilizing microreactor technology. Experimental and numerical approaches were applied for development of the microreactor. Development of a novel microreactor was commenced by studying the hydrodynamics and mass transfer in prototype microreactor plates. The prototypes were designed and fabricated with the assistance of CFD modeling to optimize the shape and size of the microstructure. Empirical correlations for the mass transfer coefficient were derived. The pressure drop in micro T-mixers was investigated experimentally and numerically. Correlations describing the friction factor for different flow regimes were developed and predicted values were in good agreement with experimental results. Experimental studies were conducted to develop a highly active and selective catalyst with a proper form for the microreactor. Pd catalysts supported on activated carbon cloths were prepared by different treatments during the catalyst preparation. A variety of characterization methods were used for catalyst investigation. The surface chemistry of the support and the oxidation state of the metallic phase in the catalyst play important roles in catalyst activity and selectivity for the direct synthesis. The direct synthesis of hydrogen peroxide was investigated in a bench-scale continuous process using the novel microreactor developed. The microreactor was fabricated based on the hydrodynamic and mass transfer studies and provided a high interfacial area and high mass transfer coefficient. The catalysts were prepared under optimum treatment conditions. The direct synthesis was conducted at various conditions. The thesis represents a step towards a commercially viable direct synthesis. The focus is on the two main challenges: mitigating the safety problem by utilization of microprocess technology and improving the selectivity by catalyst development.

Reflectance indices as a diagnostic tool for weed control performed by multipurpose equipment in precision agriculture

Several tools of precision agriculture have been developed for specific uses. However, this specificity may hinder the implementation of precision agriculture due to an increasing in costs and operational complexity. The use of vegetation index sensors which are traditionally developed for crop fertilization, for site-specific weed management can provide multiple utilizations of these sensors and result in the optimization of precision agriculture. The aim of this study was to evaluate the relationship between reflectance indices of weeds obtained by the GreenSeekerTM sensor and conventional parameters used for weed interference quantification. Two experiments were conducted with soybean and corn by establishing a gradient of weed interference through the use of pre- and post-emergence herbicides. The weed quantification was evaluated by the normalized difference vegetation index (NDVI) and the ratio of red to near infrared (Red/NIR) obtained using the GreenSeekerTM sensor, the visual weed control, the weed dry matter, and digital photographs, which supplied information about the leaf area coverage proportions of weed and straw. The weed leaf coverage obtained using digital photography was highly associated with the NDVI (r = 0.78) and the Red/NIR (r = -0.74). The weed dry matter also positively correlated with the NDVI obtained in 1 m linear (r = 0.66). The results indicated that the GreenSeekerTM sensor originally used for crop fertilization could also be used to obtain reflectance indices in the area between rows of crops to support decision-making programs for weed control.

Business intelligence and management control

The purpose of this study is to explore the possibilities of utilizing business intelligence (BI)systems in management control (MC). The topic of this study is explored trough four researchquestions. Firstly, what kind of management control systems (MCS) use or could use the data and information enabled by the BI system? Secondly, how the BI system is or could be utilized? Thirdly, has BI system enabled new forms of control or changed old ones? The fourth and final research question is whether the BI system supports some forms of control that the literature has not thought of, or is the BI system not used for some forms of control the literature suggests it should be used? The study is conducted as an extensive case study. Three different organizations were interviewed for the study. For the theoretical basis of the study, central theories in the field of management control are introduced. The term business intelligence is discussed in detail and the mechanisms for governance of business intelligence are presented. A literature analysis of the uses of BI for management control is introduced. The theoretical part of the study ends in the construction of a framework for business intelligence in management control. In the empirical part of the study the case organizations, their BI systems, and the ways they utilize these systems for management control are presented. The main findings of the study are that BI systems can be utilized in the fields suggested in the literature, namely in planning, cybernetic, reward, boundary, and interactive control. The systems are used both as the data or information feeders and directly as the tools. Using BI systems has also enabled entirely new forms of control in the studied organizations, most significantly in the area of interactive control. They have also changed the old control systems by making the information more readily available to the whole organization. No evidence of the BI systems being used for forms of control that the literature had not suggested was found. The systems were mostly used for cybernetic control and interactive control, whereas the support for other types of control was not as prevalent. The main contribution of the study to the existing literature is the insight provided into how BI systems, both theoretically and empirically, are used for management control. The framework for business intelligence in management control presented in the study can also be utilized in further studies about the subject.

Modal testing and numerical modeling of the dynamic properties of layered sheet-steel structure

Recently, due to the increasing total construction and transportation cost and difficulties associated with handling massive structural components or assemblies, there has been increasing financial pressure to reduce structural weight. Furthermore, advances in material technology coupled with continuing advances in design tools and techniques have encouraged engineers to vary and combine materials, offering new opportunities to reduce the weight of mechanical structures. These new lower mass systems, however, are more susceptible to inherent imbalances, a weakness that can result in higher shock and harmonic resonances which leads to poor structural dynamic performances. The objective of this thesis is the modeling of layered sheet steel elements, to accurately predict dynamic performance. During the development of the layered sheet steel model, the numerical modeling approach, the Finite Element Analysis and the Experimental Modal Analysis are applied in building a modal model of the layered sheet steel elements. Furthermore, in view of getting a better understanding of the dynamic behavior of layered sheet steel, several binding methods have been studied to understand and demonstrate how a binding method affects the dynamic behavior of layered sheet steel elements when compared to single homogeneous steel plate. Based on the developed layered sheet steel model, the dynamic behavior of a lightweight wheel structure to be used as the structure for the stator of an outer rotor Direct-Drive Permanent Magnet Synchronous Generator designed for high-power wind turbines is studied.

Functional analysis of newly discovered growth control genes: experimental approaches

A large number of DNA sequences corresponding to human and animal transcripts have been filed in data banks, as cDNAs or ESTs (expression sequence tags). However, the actual function of their corresponding gene products is still largely unknown. Several of these genes may play a role in regulation of important biological processes such as cell division, differentiation, malignant transformation and oncogenesis. Elucidation of gene function is based on 2 main approaches, namely, overexpression and expression interference, which respectively mimick or suppress a given phenotype. The currently available tools and experimental approaches to gene functional analysis and the most recent advances in mass cDNA screening by functional analysis are discussed.

Adaptive Markov chain Monte Carlo and Bayesian filtering for state space models

This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.

Control of Robot by Means of Human Thought

Brain computer interface (BCI) is a kind of human machine interface, which provides a new interaction method between human and computer or other equipment. The most significant characteristic of BCI system is that its control input is brain electrical activities acquired from the brain instead of traditional input such as hands or eyes. BCI technique has rapidly developed during last two decades and it has mainly worked as an auxiliary technique to help the disable people improve their life qualities. With the appearance of low cost novel electrical devices such as EMOTIV, BCI technique has been applied to the general public through many useful applications including video gaming, virtual reality and virtual keyboard. The purpose of this research is to be familiar with EMOTIV EPOC system and make use of it to build an EEG based BCI system for controlling an industrial manipulator by means of human thought. To build a BCI system, an acquisition program based on EMOTIV EPOC system is designed and a MFC based dialog that works as an operation panel is presented. Furthermore, the inverse kinematics of RV-3SB industrial robot was solved. In the last part of this research, the designed BCI system with human thought input is examined and the results indicate that the system is running smoothly and displays clearly the motion type and the incremental displacement of the motion.

Design and Drive-Cycle Based Analysis of Direct-Driven Permanent Magnet Synchronous Machine for a Small Urban Use Electric Vehicle

A direct-driven permanent magnet synchronous machine for a small urban use electric vehicle is presented. The measured performance of the machine at the test bench as well as the performance over the modified New European Drive Cycle will be given. The effect of optimal current components, maximizing the efficiency and taking into account the iron loss, is compared with the simple id=0 – control. The machine currents and losses during the drive cycle are calculated and compared with each other.

Improving reporting and internal control with process thinking - case study in a multinational corporation

The aim of this Master’s Thesis is to find applicable methods from process management literature for improving reporting and internal control in a multinational corporation. The method of analysis is qualitative and the research is conducted as a case study. Empirical data collection is carried out through interviews and participating observation. The theoretical framework is built around reporting and guidance between parent company and subsidiary, searching for means to improve them from process thinking and applicable frameworks. In the thesis, the process of intercompany reporting in the case company is modelled, and its weak points, risks, and development targets are identified. The framework of critical success factors in process improvement is utilized in assessing the development targets. Also internal control is analyzed with the tools of process thinking. As a result of this thesis, suggestions for actions improving the reporting process and internal control are made to the case company, the most essential of which are ensuring top management’s awareness and commitment to improvement, creating guidelines and tools for internal control and creating and implementing improved intercompany reporting process.