Development of Physics-based Models and Design Optimization of Power Electronic Conversion Systems

The main objective for physics based modeling of the power converter components is to design the whole converter with respect to physical and operational constraints. Therefore, all the elements and components of the energy conversion system are modeled numerically and combined together to achieve the whole system behavioral model. Previously proposed high frequency (HF) models of power converters are based on circuit models that are only related to the parasitic inner parameters of the power devices and the connections between the components. This dissertation aims to obtain appropriate physics-based models for power conversion systems, which not only can represent the steady state behavior of the components, but also can predict their high frequency characteristics. The developed physics-based model would represent the physical device with a high level of accuracy in predicting its operating condition. The proposed physics-based model enables us to accurately develop components such as; effective EMI filters, switching algorithms and circuit topologies [7]. One of the applications of the developed modeling technique is design of new sets of topologies for high-frequency, high efficiency converters for variable speed drives. The main advantage of the modeling method, presented in this dissertation, is the practical design of an inverter for high power applications with the ability to overcome the blocking voltage limitations of available power semiconductor devices. Another advantage is selection of the best matching topology with inherent reduction of switching losses which can be utilized to improve the overall efficiency. The physics-based modeling approach, in this dissertation, makes it possible to design any power electronic conversion system to meet electromagnetic standards and design constraints. This includes physical characteristics such as; decreasing the size and weight of the package, optimized interactions with the neighboring components and higher power density. In addition, the electromagnetic behaviors and signatures can be evaluated including the study of conducted and radiated EMI interactions in addition to the design of attenuation measures and enclosures.

Leakage Temperature Dependency Aware Real-Time Scheduling for Power and Thermal Optimization

Catering to society’s demand for high performance computing, billions of transistors are now integrated on IC chips to deliver unprecedented performances. With increasing transistor density, the power consumption/density is growing exponentially. The increasing power consumption directly translates to the high chip temperature, which not only raises the packaging/cooling costs, but also degrades the performance/reliability and life span of the computing systems. Moreover, high chip temperature also greatly increases the leakage power consumption, which is becoming more and more significant with the continuous scaling of the transistor size. As the semiconductor industry continues to evolve, power and thermal challenges have become the most critical challenges in the design of new generations of computing systems. In this dissertation, we addressed the power/thermal issues from the system-level perspective. Specifically, we sought to employ real-time scheduling methods to optimize the power/thermal efficiency of the real-time computing systems, with leakage/ temperature dependency taken into consideration. In our research, we first explored the fundamental principles on how to employ dynamic voltage scaling (DVS) techniques to reduce the peak operating temperature when running a real-time application on a single core platform. We further proposed a novel real-time scheduling method, “M-Oscillations” to reduce the peak temperature when scheduling a hard real-time periodic task set. We also developed three checking methods to guarantee the feasibility of a periodic real-time schedule under peak temperature constraint. We further extended our research from single core platform to multi-core platform. We investigated the energy estimation problem on the multi-core platforms and developed a light weight and accurate method to calculate the energy consumption for a given voltage schedule on a multi-core platform. Finally, we concluded the dissertation with elaborated discussions of future extensions of our research.

Meshfree Method for Prediction of Thermal Properties of Porous Ceramic Materials

In the presented thesis work, meshfree method with distance fields is applied to create a novel computational approach which enables inclusion of the realistic geometric models of the microstructure and liberates Finite Element Analysis(FEA) from thedependance on and limitations of meshing of fine microstructural feature such as splats and porosity.Manufacturing processes of ceramics produce materials with complex porosity microstructure.Geometry of pores, their size and location substantially affect macro scale physical properties of the material. Complex structure and geometry of the pores severely limit application of modern Finite Element Analysis methods because they require construction of spatial grids (meshes) that conform to the geometric shape of the structure. As a result, there are virtually no effective tools available for predicting overall mechanical and thermal properties of porous materials based on their microstructure. This thesis is a separate handling and controls of geometric and physical computational models that are seamlessly combined at solution run time. Using the proposedapproach we will determine the effective thermal conductivity tensor of real porous ceramic materials featuring both isotropic and anisotropic thermal properties. This work involved development and implementation of numerical algorithms, data structure, and software.

Theoretical and experimental study on electron interactions with chlorobenzene: Shape resonances and differential cross sections

In this work, we report theoretical and experimental cross sections for elastic scattering of electrons by chlorobenzene (ClB). The theoretical integral and differential cross sections (DCSs) were obtained with the Schwinger multichannel method implemented with pseudopotentials (SMCPP) and the independent atom method with screening corrected additivity rule (IAM-SCAR). The calculations with the SMCPP method were done in the static-exchange (SE) approximation, for energies above 12 eV, and in the static-exchange plus polarization approximation, for energies up to 12 eV. The calculations with the IAM-SCAR method covered energies up to 500 eV. The experimental differential cross sections were obtained in the high resolution electron energy loss spectrometer VG-SEELS 400, in Lisbon, for electron energies from 8.0 eV to 50 eV and angular range from 7 degrees to 110 degrees. From the present theoretical integral cross section (ICS) we discuss the low-energy shape-resonances present in chlorobenzene and compare our computed resonance spectra with available electron transmission spectroscopy data present in the literature. Since there is no other work in the literature reporting differential cross sections for this molecule, we compare our theoretical and experimental DCSs with experimental data available for the parent molecule benzene. Published by AIP Publishing.

Spanish Rheumatology Society and Hospital Pharmacy Society Consensus on recommendations for biologics optimization in patients with rheumatoid arthritis, ankylosing spondylitis and psoriatic arthritis

The aim of this study was to establish guidelines for the optimization of biologic therapies for health professionals involved in the management of patients with RA, AS and PsA. Recommendations were established via consensus by a panel of experts in rheumatology and hospital pharmacy, based on analysis of available scientific evidence obtained from four systematic reviews and on the clinical experience of panellists. The Delphi method was used to evaluate these recommendations, both between panellists and among a wider group of rheumatologists. Previous concepts concerning better management of RA, AS and PsA were reviewed and, more specifically, guidelines for the optimization of biologic therapies used to treat these diseases were formulated. Recommendations were made with the aim of establishing a plan for when and how to taper biologic treatment in patients with these diseases. The recommendations established herein aim not only to provide advice on how to improve the risk:benefit ratio and efficiency of such treatments, but also to reduce variability in daily clinical practice in the use of biologic therapies for rheumatic diseases

Introducing the Logarithmic finite element method: a geometrically exact planar Bernoulli beam element

We propose a novel finite element formulation that significantly reduces the number of degrees of freedom necessary to obtain reasonably accurate approximations of the low-frequency component of the deformation in boundary-value problems. In contrast to the standard Ritz–Galerkin approach, the shape functions are defined on a Lie algebra—the logarithmic space—of the deformation function. We construct a deformation function based on an interpolation of transformations at the nodes of the finite element. In the case of the geometrically exact planar Bernoulli beam element presented in this work, these transformation functions at the nodes are given as rotations. However, due to an intrinsic coupling between rotational and translational components of the deformation function, the formulation provides for a good approximation of the deflection of the beam, as well as of the resultant forces and moments. As both the translational and the rotational components of the deformation function are defined on the logarithmic space, we propose to refer to the novel approach as the “Logarithmic finite element method”, or “LogFE” method.

Nitrogen use efficiency and optimization of nitrogen fertilizer application for stable yields and high quality of cereals grown in conservation agriculture

In most agroecosystems, nitrogen (N) is the most important nutrient limiting plant growth. One management strategy that affects N cycling and N use efficiency (NUE) is conservation agriculture (CA), an agricultural system based on a combination of minimum tillage, crop residue retention and crop rotation. Available results on the optimization of NUE in CA are inconsistent and studies that cover all three components of CA are scarce. Presently, CA is promoted in the Yaqui Valley in Northern Mexico, the country´s major wheat-producing area in which from 1968 to 1995, fertilizer application rates for the cultivation of irrigated durum wheat (Triticum durum L.) at 6 t ha-1 increased from 80 to 250 kg ha-1, demonstrating the high intensification potential in this region. Given major knowledge gaps on N availability in CA this thesis summarizes the current knowledge of N management in CA and provides insights in the effects of tillage practice, residue management and crop rotation on wheat grain quality and N cycling. Major aims of the study were to identify N fertilizer application strategies that improve N use efficiency and reduce N immobilization in CA with the ultimate goal to stabilize cereal yields, maintain grain quality, minimize N losses into the environment and reduce farmers’ input costs. Soil physical and chemical properties in CA were measured and compared with those in conventional systems and permanent beds with residue burning focusing on their relationship to plant N uptake and N cycling in the soil and how they are affected by tillage and N fertilizer timing, method and doses. For N fertilizer management, we analyzed how placement, time and amount of N fertilizer influenced yield and quality parameters of durum and bread wheat in CA systems. Overall, grain quality parameters, in particular grain protein concentration decreased with zero-tillage and increasing amount of residues left on the field compared with conventional systems. The second part of the dissertation provides an overview of applied methodologies to measure NUE and its components. We evaluated the methodology of ion exchange resin cartridges under irrigated, intensive agricultural cropping systems on Vertisols to measure nitrate leaching losses which through drainage channels ultimately end up in the Sea of Cortez where they lead to algae blooming. A throughout analysis of N inputs and outputs was conducted to calculate N balances in three different tillage-straw systems. As fertilizer inputs are high, N balances were positive in all treatments indicating the risk of N leaching or volatilization during or in subsequent cropping seasons and during heavy rain fall in summer. Contrary to common belief, we did not find negative effects of residue burning on soil nutrient status, yield or N uptake. A labeled fertilizer experiment with urea 15N was implemented in micro-plots to measure N fertilizer recovery and the effects of residual fertilizer N in the soil from summer maize on the following winter crop wheat. Obtained N fertilizer recovery rates for maize grain were with an average of 11% very low for all treatments.

Modeling, Component Selection and Optimization of Servo-controlled Automatic Machinery

A servo-controlled automatic machine can perform tasks that involve synchronized actuation of a significant number of servo-axes, namely one degree-of-freedom (DoF) electromechanical actuators. Each servo-axis comprises a servo-motor, a mechanical transmission and an end-effector, and is responsible for generating the desired motion profile and providing the power required to achieve the overall task. The design of a such a machine must involve a detailed study from a mechatronic viewpoint, due to its electric and mechanical nature. The first objective of this thesis is the development of an overarching electromechanical model for a servo-axis. Every loss source is taken into account, be it mechanical or electrical. The mechanical transmission is modeled by means of a sequence of lumped-parameter blocks. The electric model of the motor and the inverter takes into account winding losses, iron losses and controller switching losses. No experimental characterizations are needed to implement the electric model, since the parameters are inferred from the data available in commercial catalogs. With the global model at disposal, a second objective of this work is to perform the optimization analysis, in particular, the selection of the motor-reducer unit. The optimal transmission ratios that minimize several objective functions are found. An optimization process is carried out and repeated for each candidate motor. Then, we present a novel method where the discrete set of available motor is extended to a continuous domain, by fitting manufacturer data. The problem becomes a two-dimensional nonlinear optimization subject to nonlinear constraints, and the solution gives the optimal choice for the motor-reducer system. The presented electromechanical model, along with the implementation of optimization algorithms, forms a complete and powerful simulation tool for servo-controlled automatic machines. The tool allows for determining a wide range of electric and mechanical parameters and the behavior of the system in different operating conditions.

Advanced decision-support methods for the design, control, and optimization of perishable products life cycle

In the last decades, global food supply chains had to deal with the increasing awareness of the stakeholders and consumers about safety, quality, and sustainability. In order to address these new challenges for food supply chain systems, an integrated approach to design, control, and optimize product life cycle is required. Therefore, it is essential to introduce new models, methods, and decision-support platforms tailored to perishable products. This thesis aims to provide novel practice-ready decision-support models and methods to optimize the logistics of food items with an integrated and interdisciplinary approach. It proposes a comprehensive review of the main peculiarities of perishable products and the environmental stresses accelerating their quality decay. Then, it focuses on top-down strategies to optimize the supply chain system from the strategical to the operational decision level. Based on the criticality of the environmental conditions, the dissertation evaluates the main long-term logistics investment strategies to preserve products quality. Several models and methods are proposed to optimize the logistics decisions to enhance the sustainability of the supply chain system while guaranteeing adequate food preservation. The models and methods proposed in this dissertation promote a climate-driven approach integrating climate conditions and their consequences on the quality decay of products in innovative models supporting the logistics decisions. Given the uncertain nature of the environmental stresses affecting the product life cycle, an original stochastic model and solving method are proposed to support practitioners in controlling and optimizing the supply chain systems when facing uncertain scenarios. The application of the proposed decision-support methods to real case studies proved their effectiveness in increasing the sustainability of the perishable product life cycle. The dissertation also presents an industry application of a global food supply chain system, further demonstrating how the proposed models and tools can be integrated to provide significant savings and sustainability improvements.

Distributed Large-scale Mixed-Integer Optimization with Application to Energy and Multi-robot Networks

Several decision and control tasks in cyber-physical networks can be formulated as large- scale optimization problems with coupling constraints. In these "constraint-coupled" problems, each agent is associated to a local decision variable, subject to individual constraints. This thesis explores the use of primal decomposition techniques to develop tailored distributed algorithms for this challenging set-up over graphs. We first develop a distributed scheme for convex problems over random time-varying graphs with non-uniform edge probabilities. The approach is then extended to unknown cost functions estimated online. Subsequently, we consider Mixed-Integer Linear Programs (MILPs), which are of great interest in smart grid control and cooperative robotics. We propose a distributed methodological framework to compute a feasible solution to the original MILP, with guaranteed suboptimality bounds, and extend it to general nonconvex problems. Monte Carlo simulations highlight that the approach represents a substantial breakthrough with respect to the state of the art, thus representing a valuable solution for new toolboxes addressing large-scale MILPs. We then propose a distributed Benders decomposition algorithm for asynchronous unreliable networks. The framework has been then used as starting point to develop distributed methodologies for a microgrid optimal control scenario. We develop an ad-hoc distributed strategy for a stochastic set-up with renewable energy sources, and show a case study with samples generated using Generative Adversarial Networks (GANs). We then introduce a software toolbox named ChoiRbot, based on the novel Robot Operating System 2, and show how it facilitates simulations and experiments in distributed multi-robot scenarios. Finally, we consider a Pickup-and-Delivery Vehicle Routing Problem for which we design a distributed method inspired to the approach of general MILPs, and show the efficacy through simulations and experiments in ChoiRbot with ground and aerial robots.

Study and experimental characterization of the laser welding process for the manufacturing of electric powertrain components in the automotive industry

The PhD project that will be presented in this thesis is focused on the study and optimization of the production process for the manufacturing of electrical powertrain components in the automotive field using the laser beam welding process (LBW). The objective is to define, through experimental activities, an optimized process condition for applications in the electrical field that can be generalized, that is, which guarantees its reproducibility as the types of connections vary and which represents the basis for extending the method to future applications in e-mobility sector. The work developed along two lines of research, the convergence of which made it possible to create prototypes of battery modules based on different types of lithium-ion cells and stator windings for electric motors. On the one hand, the different welding configurations involving the production of batteries based on pouch cells and therefore the welding of aluminum and copper in dissimilar configuration were studied, while for the prismatic cells only one configuration was analyzed. On the other hand, the welding of pure copper hairpins with rectangular shape in edge joint configuration was studied for the production of stator windings. The experimental tests carried out have demonstrated the feasibility of using the LBW process for the production of electric powertrain components entirely designed and developed internally as the types of materials and welding configurations vary; the methodologies required for the characterization methods, necessary for the end-of-line tests, for the evaluation of the properties of the different joint configurations and components (battery and electric motor) were also defined with the aim of obtaining the best performance. The entire doctorate program was conducted in collaboration with Ferrari Auto S.p.A. and the direct industrial application of the issues addressed has been faced.

Methods for integrating machine learning and constrained optimization

In the framework of industrial problems, the application of Constrained Optimization is known to have overall very good modeling capability and performance and stands as one of the most powerful, explored, and exploited tool to address prescriptive tasks. The number of applications is huge, ranging from logistics to transportation, packing, production, telecommunication, scheduling, and much more. The main reason behind this success is to be found in the remarkable effort put in the last decades by the OR community to develop realistic models and devise exact or approximate methods to solve the largest variety of constrained or combinatorial optimization problems, together with the spread of computational power and easily accessible OR software and resources. On the other hand, the technological advancements lead to a data wealth never seen before and increasingly push towards methods able to extract useful knowledge from them; among the data-driven methods, Machine Learning techniques appear to be one of the most promising, thanks to its successes in domains like Image Recognition, Natural Language Processes and playing games, but also the amount of research involved. The purpose of the present research is to study how Machine Learning and Constrained Optimization can be used together to achieve systems able to leverage the strengths of both methods: this would open the way to exploiting decades of research on resolution techniques for COPs and constructing models able to adapt and learn from available data. In the first part of this work, we survey the existing techniques and classify them according to the type, method, or scope of the integration; subsequently, we introduce a novel and general algorithm devised to inject knowledge into learning models through constraints, Moving Target. In the last part of the thesis, two applications stemming from real-world projects and done in collaboration with Optit will be presented.

Distributed optimization and games over networks: a system theoretical perspective

Several decision and control tasks involve networks of cyber-physical systems that need to be coordinated and controlled according to a fully-distributed paradigm involving only local communications without any central unit. This thesis focuses on distributed optimization and games over networks from a system theoretical perspective. In the addressed frameworks, we consider agents communicating only with neighbors and running distributed algorithms with optimization-oriented goals. The distinctive feature of this thesis is to interpret these algorithms as dynamical systems and, thus, to resort to powerful system theoretical tools for both their analysis and design. We first address the so-called consensus optimization setup. In this context, we provide an original system theoretical analysis of the well-known Gradient Tracking algorithm in the general case of nonconvex objective functions. Then, inspired by this method, we provide and study a series of extensions to improve the performance and to deal with more challenging settings like, e.g., the derivative-free framework or the online one. Subsequently, we tackle the recently emerged framework named distributed aggregative optimization. For this setup, we develop and analyze novel schemes to handle (i) online instances of the problem, (ii) ``personalized'' optimization frameworks, and (iii) feedback optimization settings. Finally, we adopt a system theoretical approach to address aggregative games over networks both in the presence or absence of linear coupling constraints among the decision variables of the players. In this context, we design and inspect novel fully-distributed algorithms, based on tracking mechanisms, that outperform state-of-the-art methods in finding the Nash equilibrium of the game.

Optimization of aqueous Zn-ion batteries

MnHCF was synthesized by simple co-precipitation method. In this work we investigate the electrochemical behavior of manganese hexacyanoferrate in zinc sulfate (ZnSO4), ZnSO4+MnSO4 and zinc triflate (Zn(OTF)2) aqueous electrolytes. Electrochemical tests were performed by both El-cell which is designed for reflection investigation and coin cell. In cyclic voltammetry curves, we observed redox peaks of both Fe3+/2+ and Mn3+/2+ pairs. The results based on current shows that the capacity of battery is controlled by diffusion process in aqueous electrolyte system. MnHCF undergoes severe dissolution and zinc displacement during cycling. Compared to ZnSO4, anions of Zn (OTF)2 electrolyte are strongly adsorbed on the electrolyte surface, in turn hindering the water oxidation reaction and reducing the decomposition of MnHCF. The MnHCF/Zn battery using 3M Zn (OTF)2 delivers a specific capacity of 41 mAhg-1 at 50 mAg-1 while by using 3M ZnSO4+1M MnSO4 the specific capacity reaches to 400 mAhg-1 for the pure sample and around 250 mAhg-1 for the MnHCF+A. Our results suggest that the anions in the aqueous electrolyte are of great importance to optimize the electrochemical performance of metal hexacyanoferrates. The pre-addition of MnSO4 into ZnSO4 solution is capable of easing the Mn2+ dissolution from the cathode.

Axis control optimization: autotuning techniques and frequency response analysis of industrial mechatronic systems

In the field of industrial automation, there is an increasing need to use optimal control systems that have low tracking errors and low power and energy consumption. The motors we are dealing with are mainly Permanent Magnet Synchronous Motors (PMSMs), controlled by 3 different types of controllers: a position controller, a speed controller, and a current controller. In this thesis, therefore, we are going to act on the gains of the first two controllers by going to find, through the TwinCAT 3 software, what might be the best set of parameters. To do this, starting with the default parameters recommended by TwinCAT, two main methods were used and then compared: the method of Ziegler and Nichols, which is a tabular method, and advanced tuning, an auto-tuning software method of TwinCAT. Therefore, in order to analyse which set of parameters was the best,several experiments were performed for each case, using the Motion Control Function Blocks. Moreover, some machines, such as large robotic arms, have vibration problems. To analyse them in detail, it was necessary to use the Bode Plot tool, which, through Bode plots, highlights in which frequencies there are resonance and anti-resonance peaks. This tool also makes it easier to figure out which and where to apply filters to improve control.