Modeling and analysis of thermoelectric energy conversion efficiency in nanostructures.

116 p.

TEST-ANALYSIS MODEL CORRELATION AND CALIBRATION OF A MULTI-COMPONENT PLASTIC PART

The goal of this project is to learn the necessary steps to create a finite element model, which can accurately predict the dynamic response of a Kohler Engines Heavy Duty Air Cleaner (HDAC). This air cleaner is composed of three glass reinforced plastic components and two air filters. Several uncertainties arose in the finite element (FE) model due to the HDAC’s component material properties and assembly conditions. To help understand and mitigate these uncertainties, analytical and experimental modal models were created concurrently to perform a model correlation and calibration. Over the course of the project simple and practical methods were found for future FE model creation. Similarly, an experimental method for the optimal acquisition of experimental modal data was arrived upon. After the model correlation and calibration was performed a validation experiment was used to confirm the FE models predictive capabilities.

From the traditional verification approach to the spatial methods: a study of applicability of the SAL metric in the framework of the WMO project MesoVICT

Increasing in resolution of numerical weather prediction models has allowed more and more realistic forecasts of atmospheric parameters. Due to the growing variability into predicted fields the traditional verification methods are not always able to describe the model ability because they are based on a grid-point-by-grid-point matching between observation and prediction. Recently, new spatial verification methods have been developed with the aim of show the benefit associated to the high resolution forecast. Nested in among of the MesoVICT international project, the initially aim of this work is to compare the newly tecniques remarking advantages and disadvantages. First of all, the MesoVICT basic examples, represented by synthetic precipitation fields, have been examined. Giving an error evaluation in terms of structure, amplitude and localization of the precipitation fields, the SAL method has been studied more thoroughly respect to the others approaches with its implementation in the core cases of the project. The verification procedure has concerned precipitation fields over central Europe: comparisons between the forecasts performed by the 00z COSMO-2 model and the VERA (Vienna Enhanced Resolution Analysis) have been done. The study of these cases has shown some weaknesses of the methodology examined; in particular has been highlighted the presence of a correlation between the optimal domain size and the extention of the precipitation systems. In order to increase ability of SAL, a subdivision of the original domain in three subdomains has been done and the method has been applied again. Some limits have been found in cases in which at least one of the two domains does not show precipitation. The overall results for the subdomains have been summarized on scatter plots. With the aim to identify systematic errors of the model the variability of the three parameters has been studied for each subdomain.

IMPROVED SAMPLE INTRODUCTION SYSTEMS FOR INDUCTIVELY COUPLED PLASMA OPTICAL EMISSION SPECTROMETRY AND THEIR APPLICATION TO THE ANALYSIS OF SIMPLE AND COMPLEX MATRICES

The objective of this thesis is to explore new and improved methods for greater sample introduction efficiency and enhanced analytical performance with inductively coupled plasma optical emission spectrometry (ICP-OES). Three projects are discussed in which the capabilities and applications of ICP-OES are expanded: 1. In the first project, a conventional ultrasonic nebuliser was modified to replace the heater/condenser with an infrared heated pre-evaporation tube. In continuation from previous works with pre-evaporation, the current work investigated the effects of heating with infrared block and rope heaters on two different ICP-OES instruments. Comparisons were made between several methods and setups in which temperatures were varied. By monitoring changes to sensitivity, detection limit, precision, and robustness, and analyzing two certified reference materials, a method with improved sample introduction efficiency and comparable analytical performance to a previous method was established. 2. The second project involved improvements to a previous work in which a multimode sample introduction system (MSIS) was modified by inserting a pre-evaporation tube between the MSIS and torch. The new work focused on applying an infrared heated ceramic rope for pre-evaporation. This research was conducted in all three MSIS modes (nebulisation mode, hydride generation mode, and dual mode) and on two different ICP-OES instruments, and comparisons were made between conventional setups in terms of sensitivity, detection limit, precision, and robustness. By tracking both hydride-forming and non-hydride forming elements, the effects of heating in combination with hydride generation were probed. Finally, optimal methods were validated by analysis of two certified reference materials. 3. A final project was completed in collaboration with ZincNyx Energy Solutions. This project sought to develop a method for the overall analysis of a 12 M KOH zincate fuel, which is used in green energy backup systems. By employing various techniques including flow injection analysis and standard additions, a final procedure was formulated for the verification of K concentration, as well as the measurement of additives (Al, Fe, Mg, In, Si), corrosion products (such C from CO₃²¯), and Zn particles both in and filtered from solution. Furthermore, the effects of exposing the potassium zincate electrolyte fuel to air were assessed.

Average-case analysis of power consumption in embedded systems

Power efficiency is one of the most important constraints in the design of embedded systems since such systems are generally driven by batteries with limited energy budget or restricted power supply. In every embedded system, there are one or more processor cores to run the software and interact with the other hardware components of the system. The power consumption of the processor core(s) has an important impact on the total power dissipated in the system. Hence, the processor power optimization is crucial in satisfying the power consumption constraints, and developing low-power embedded systems. A key aspect of research in processor power optimization and management is “power estimation”. Having a fast and accurate method for processor power estimation at design time helps the designer to explore a large space of design possibilities, to make the optimal choices for developing a power efficient processor. Likewise, understanding the processor power dissipation behaviour of a specific software/application is the key for choosing appropriate algorithms in order to write power efficient software. Simulation-based methods for measuring the processor power achieve very high accuracy, but are available only late in the design process, and are often quite slow. Therefore, the need has arisen for faster, higher-level power prediction methods that allow the system designer to explore many alternatives for developing powerefficient hardware and software. The aim of this thesis is to present fast and high-level power models for the prediction of processor power consumption. Power predictability in this work is achieved in two ways: first, using a design method to develop power predictable circuits; second, analysing the power of the functions in the code which repeat during execution, then building the power model based on average number of repetitions. In the first case, a design method called Asynchronous Charge Sharing Logic (ACSL) is used to implement the Arithmetic Logic Unit (ALU) for the 8051 microcontroller. The ACSL circuits are power predictable due to the independency of their power consumption to the input data. Based on this property, a fast prediction method is presented to estimate the power of ALU by analysing the software program, and extracting the number of ALU-related instructions. This method achieves less than 1% error in power estimation and more than 100 times speedup in comparison to conventional simulation-based methods. In the second case, an average-case processor energy model is developed for the Insertion sort algorithm based on the number of comparisons that take place in the execution of the algorithm. The average number of comparisons is calculated using a high level methodology called MOdular Quantitative Analysis (MOQA). The parameters of the energy model are measured for the LEON3 processor core, but the model is general and can be used for any processor. The model has been validated through the power measurement experiments, and offers high accuracy and orders of magnitude speedup over the simulation-based method.

When theories become practice—a metaphorical analysis of adult-education school-leaders’ talk

Marketization has changed the education system. If we say that education is a market, this transforms the understanding of education and influences how people act. In this paper, adult-education school-leaders’ talk is analysed and seven metaphors for education are found: education as administration, market, matching, democracy, policy work, integration and learning. Exploring empirical metaphors provides a rich illustration of coinciding meanings. In line with studies on policy texts, economic metaphors are found to dominate. This should be understood not only as representing liberal ideology, as is often discussed in analyses of policy papers, but also as representing economic theory. In other words, contemporary adult education can be understood as driven by economic theories. The difference and relation between ideology and theory should be further examined since they have an impact on our society and on our everyday lives.

Optimal control of non-linear systems through hybrid cell-mapping/artificial-neural-networks techniques

In this paper, a real-time optimal control technique for non-linear plants is proposed. The control system makes use of the cell-mapping (CM) techniques, widely used for the global analysis of highly non-linear systems. The CM framework is employed for designing approximate optimal controllers via a control variable discretization. Furthermore, CM-based designs can be improved by the use of supervised feedforward artificial neural networks (ANNs), which have proved to be universal and efficient tools for function approximation, providing also very fast responses. The quantitative nature of the approximate CM solutions fits very well with ANNs characteristics. Here, we propose several control architectures which combine, in a different manner, supervised neural networks and CM control algorithms. On the one hand, different CM control laws computed for various target objectives can be employed for training a neural network, explicitly including the target information in the input vectors. This way, tracking problems, in addition to regulation ones, can be addressed in a fast and unified manner, obtaining smooth, averaged and global feedback control laws. On the other hand, adjoining CM and ANNs are also combined into a hybrid architecture to address problems where accuracy and real-time response are critical. Finally, some optimal control problems are solved with the proposed CM, neural and hybrid techniques, illustrating their good performance.

Credit spreads and monetary policy in a small open economy: an analysis of the colombian economy

This paper estimates Bejarano and Charry (2014)’s small open economy with financial frictions model for the Colombian economy using Bayesian estimation techniques. Additionally, I compute the welfare gains of implementing an optimal response to credit spreads into an augmented Taylor rule. The main result is that a reaction to credit spreads does not imply significant welfare gains unless the economic disturbances increases its volatility, like the disruption implied by a financial crisis. Otherwise its impact over the macroeconomic variables is null.

Waste Heat Recovery Systems: Numerical and Experimental Analysis of organic Rankine Cycle Solutions

This thesis aims to present the ORC technology, its advantages and related problems. In particular, it provides an analysis of ORC waste heat recovery system in different and innovative scenarios, focusing on cases from the biggest to the lowest scale. Both industrial and residential ORC applications are considered. In both applications, the installation of a subcritical and recuperated ORC system is examined. Moreover, heat recovery is considered in absence of an intermediate heat transfer circuit. This solution allow to improve the recovery efficiency, but requiring safety precautions. Possible integrations of ORC systems with renewable sources are also presented and investigated to improve the non-programmable source exploitation. In particular, the offshore oil and gas sector has been selected as a promising industrial large-scale ORC application. From the design of ORC systems coupled with Gas Turbines (GTs) as topper systems, the dynamic behavior of the GT+ORC innovative combined cycles has been analyzed by developing a dynamic model of all the considered components. The dynamic behavior is caused by integration with a wind farm. The electric and thermal aspects have been examined to identify the advantages related to the waste heat recovery system installation. Moreover, an experimental test rig has been realized to test the performance of a micro-scale ORC prototype. The prototype recovers heat from a low temperature water stream, available for instance in industrial or residential waste heat. In the test bench, various sensors have been installed, an acquisitions system developed in Labview environment to completely analyze the ORC behavior. Data collected in real time and corresponding to the system dynamic behavior have been used to evaluate the system performance based on selected indexes. Moreover, various operational steady-state conditions are identified and operation maps are realized for a completely characterization of the system and to detect the optimal operating conditions.

Methods for Biodata Analysis in different Omic Contexts

The world of Computational Biology and Bioinformatics presently integrates many different expertise, including computer science and electronic engineering. A major aim in Data Science is the development and tuning of specific computational approaches to interpret the complexity of Biology. Molecular biologists and medical doctors heavily rely on an interdisciplinary expert capable of understanding the biological background to apply algorithms for finding optimal solutions to their problems. With this problem-solving orientation, I was involved in two basic research fields: Cancer Genomics and Enzyme Proteomics. For this reason, what I developed and implemented can be considered a general effort to help data analysis both in Cancer Genomics and in Enzyme Proteomics, focusing on enzymes which catalyse all the biochemical reactions in cells. Specifically, as to Cancer Genomics I contributed to the characterization of intratumoral immune microenvironment in gastrointestinal stromal tumours (GISTs) correlating immune cell population levels with tumour subtypes. I was involved in the setup of strategies for the evaluation and standardization of different approaches for fusion transcript detection in sarcomas that can be applied in routine diagnostic. This was part of a coordinated effort of the Sarcoma working group of "Alleanza Contro il Cancro". As to Enzyme Proteomics, I generated a derived database collecting all the human proteins and enzymes which are known to be associated to genetic disease. I curated the data search in freely available databases such as PDB, UniProt, Humsavar, Clinvar and I was responsible of searching, updating, and handling the information content, and computing statistics. I also developed a web server, BENZ, which allows researchers to annotate an enzyme sequence with the corresponding Enzyme Commission number, the important feature fully describing the catalysed reaction. More to this, I greatly contributed to the characterization of the enzyme-genetic disease association, for a better classification of the metabolic genetic diseases.

Chemical looping reforming from methane to syngas: investigation on CeO2 oxygen carrier lifetime and optimal process times

Emissions of CO2 are constantly growing since the beginning of industrial era. Interruption of the production of major emitters sectors (energy and agriculture) is not a viable way and reducing all the emission through carbon capture and storage (CCS) is not economically viable and little publicly accepted, therefore, it becomes fundamentals to take actions like retrofitting already developed infrastructure employing cleanest resources, modify the actual processes limiting the emissions, and reduce the emissions already present through direct air capture. The present thesis will deeply discuss the aspects mentioned in regard to syngas and hydrogen production since they have a central role in the market of energy and chemicals. Among the strategies discussed, greater emphasis is given to the application of looping technologies and to direct air capture processes, as they have been the main point of this work. Particularly, chemical looping methane reforming to syngas was studied with Aspen Plus thermodynamic simulations, thermogravimetric analysis characterization (TGA) and testing in a fixed bed reactor. The process was studied cyclically exploiting the redox properties of a Ce-based oxide oxygen carrier synthetized with a simple forming procedure. The two steps of the looping cycles were studied isothermally at 900 °C and 950° C with a mixture of 10 %CH4 in N2 and of 3% O2 in N2, for carrier reduction and oxidation, respectively. During the stay abroad, in collaboration with the EHT of Zurich, a CO2 capture process in presence of amine solid sorbents was investigated, studying the difference in the performance achievable with the use of contactors of different geometry. The process was studied at two concentrations (382 ppm CO2 in N2 and 5.62% CO2 in N2) and at different flow rates, to understand the dynamics of the adsorption process and to define the mass transfer limiting step.

Contributions to the performance analysis of continuum parallel robots

Continuum parallel robots (CPRs) are manipulators employing multiple flexible beams arranged in parallel and connected to a rigid end-effector. CPRs promise higher payload and accuracy than serial CRs while keeping great flexibility. As the risk of injury during accidental contacts between a human and a CPR should be reduced, CPRs may be used in large-scale collaborative tasks or assisted robotic surgery. There exist various CPR designs, but the prototype conception is rarely based on performance considerations, and the CPRs realization in mainly based on intuitions or rigid-link parallel manipulators architectures. This thesis focuses on the performance analysis of CPRs, and the tools needed for such evaluation, such as workspace computation algorithms. In particular, workspace computation strategies for CPRs are essential for the performance assessment, since the CPRs workspace may be used as a performance index or it can serve for optimal-design tools. Two new workspace computation algorithms are proposed in this manuscript, the former focusing on the workspace volume computation and the certification of its numerical results, while the latter aims at computing the workspace boundary only. Due to the elastic nature of CPRs, a key performance indicator for these robots is the stability of their equilibrium configurations. This thesis proposes the experimental validation of the equilibrium stability assessment on a real prototype, demonstrating limitations of some commonly used assumptions. Additionally, a performance index measuring the distance to instability is originally proposed in this manuscript. Differently from the majority of the existing approaches, the clear advantage of the proposed index is a sound physical meaning; accordingly, the index can be used for a more straightforward performance quantification, and to derive robot specifications.

Modeling and Model Predictive Control of an Industrial Hydraulic System

This thesis aims to illustrate the construction of a mathematical model of a hydraulic system, oriented to the design of a model predictive control (MPC) algorithm. The modeling procedure starts with the basic formulation of a piston-servovalve system. The latter is a complex non linear system with some unknown and not measurable effects that constitute a challenging problem for the modeling procedure. The first level of approximation for system parameters is obtained basing on datasheet informations, provided workbench tests and other data from the company. Then, to validate and refine the model, open-loop simulations have been made for data matching with the characteristics obtained from real acquisitions. The final developed set of ODEs captures all the main peculiarities of the system despite some characteristics due to highly varying and unknown hydraulic effects, like the unmodeled resistive elements of the pipes. After an accurate analysis, since the model presents many internal complexities, a simplified version is presented. The latter is used to linearize and discretize correctly the non linear model. Basing on that, a MPC algorithm for reference tracking with linear constraints is implemented. The results obtained show the potential of MPC in this kind of industrial applications, thus a high quality tracking performances while satisfying state and input constraints. The increased robustness and flexibility are evident with respect to the standard control techniques, such as PID controllers, adopted for these systems. The simulations for model validation and the controlled system have been carried out in a Python code environment.

Performance Analysis of Topological Quantum Error Correcting Codes

In the last few years there has been a great development of techniques like quantum computers and quantum communication systems, due to their huge potentialities and the growing number of applications. However, physical qubits experience a lot of nonidealities, like measurement errors and decoherence, that generate failures in the quantum computation. This work shows how it is possible to exploit concepts from classical information in order to realize quantum error-correcting codes, adding some redundancy qubits. In particular, the threshold theorem states that it is possible to lower the percentage of failures in the decoding at will, if the physical error rate is below a given accuracy threshold. The focus will be on codes belonging to the family of the topological codes, like toric, planar and XZZX surface codes. Firstly, they will be compared from a theoretical point of view, in order to show their advantages and disadvantages. The algorithms behind the minimum perfect matching decoder, the most popular for such codes, will be presented. The last section will be dedicated to the analysis of the performances of these topological codes with different error channel models, showing interesting results. In particular, while the error correction capability of surface codes decreases in presence of biased errors, XZZX codes own some intrinsic symmetries that allow them to improve their performances if one kind of error occurs more frequently than the others.

Analysis of human posterior EEG alpha frequency as a function of implicit learning of probabilistic lateralized stimuli in a visual detection task

Alpha oscillatory activity has long been associated with perceptual and cognitive processes related to attention control. The aim of this study is to explore the task-dependent role of alpha frequency in a lateralized visuo-spatial detection task. Specifically, the thesis focuses on consolidating the scientific literature's knowledge about the role of alpha frequency in perceptual accuracy, and deepening the understanding of what determines trial-by-trial fluctuations of alpha parameters and how these fluctuations influence overall task performance. The hypotheses, confirmed empirically, were that different implicit strategies are put in place based on the task context, in order to maximize performance with optimal resource distribution (namely alpha frequency, associated positively with performance): “Lateralization” of the attentive resources towards one hemifield should be associated with higher alpha frequency difference between contralateral and ipsilateral hemisphere; “Distribution” of the attentive resources across hemifields should be associated with lower alpha frequency difference between hemispheres; These strategies, used by the participants according to their brain capabilities, have proven themselves adaptive or maladaptive depending on the different tasks to which they have been set: "Distribution" of the attentive resources seemed to be the best strategy when the distribution probability between hemifields was balanced: i.e. the neutral condition task. "Lateralization" of the attentive resources seemed to be more effective when the distribution probability between hemifields was biased towards one hemifield: i.e., the biased condition task.