Numerical optimization,modeling and system evaluation of a thermophotovoltaic hybrid panel

Photovoltaic (PV) solar panels generally produce electricity in the 6% to 16% efficiency range, the rest being dissipated in thermal losses. To recover this amount, hybrid photovoltaic thermal systems (PVT) have been devised. These are devices that simultaneously convert solar energy into electricity and heat. It is thus interesting to study the PVT system globally from different point of views in order to evaluate advantages and disadvantages of this technology and its possible uses. In particular in Chapter II, the development of the PVT absorber numerical optimization by a genetic algorithm has been carried out analyzing different internal channel profiles in order to find a right compromise between performance and technical and economical feasibility. Therefore in Chapter III ,thanks to a mobile structure built into the university lab, it has been compared experimentally electrical and thermal output power from PVT panels with separated photovoltaic and solar thermal productions. Collecting a lot of experimental data based on different seasonal conditions (ambient temperature,irradiation, wind...),the aim of this mobile structure has been to evaluate average both thermal and electrical increasing and decreasing efficiency values obtained respect to separate productions through the year. In Chapter IV , new PVT and solar thermal equation based models in steady state conditions have been developed by software Dymola that uses Modelica language. This permits ,in a simplified way respect to previous system modelling softwares, to model and evaluate different concepts about PVT panel regarding its structure before prototyping and measuring it. Chapter V concerns instead the definition of PVT boundary conditions into a HVAC system . This was made trough year simulations by software Polysun in order to finally assess the best solar assisted integrated structure thanks to F_save(solar saving energy)factor. Finally, Chapter VI presents the conclusion and the perspectives of this PhD work.

Optimization of bioenergy solutions at different farm scales

RAF is a bio-energetic descriptive model integrates with MAD model to support Integrated Farm Management. RAF model aimed to enhancing economical, social and environmental sustainability of farm production in terms of energy via convert energy crops and animal manure to biogas and digestate (bio-fertilizers) by anaerobic digestion technologies, growing and breeding practices. The user defines farm structure in terms of present crops, livestock and market prices and RAF model investigates the possibilities of establish on-farm biogas system (different anaerobic digestion technologies proposed for different scales of farms in terms of energy requirements) according to budget and sustainability constraints to reduce the dependence on fossil fuels. The objective function of RAF (Z) is optimizing the total net income of farm (maximizing income and minimizing costs) for whole period which is considered by the analysis. The main results of this study refers to the possibility of enhancing the exploitation of the available Italian potentials of biogas production from on-farm production of energy crops and livestock manure feedstock by using the developed mathematical model RAF integrates with MAD to presents reliable reconcile between farm size, farm structure and on-farm biogas systems technologies applied to support selection, applying and operating of appropriate biogas technology at any farm under Italian conditions.

A numerical methodology for the multi-objective optimization of the DI Diesel engine combustion

DI Diesel engine are widely used both for industrial and automotive applications due to their durability and fuel economy. Nonetheless, increasing environmental concerns force that type of engine to comply with increasingly demanding emission limits, so that, it has become mandatory to develop a robust design methodology of the DI Diesel combustion system focused on reduction of soot and NOx simultaneously while maintaining a reasonable fuel economy. In recent years, genetic algorithms and CFD three-dimensional combustion simulations have been successfully applied to that kind of problem. However, combining GAs optimization with actual CFD three-dimensional combustion simulations can be too onerous since a large number of calculations is usually needed for the genetic algorithm to converge, resulting in a high computational cost and, thus, limiting the suitability of this method for industrial processes. In order to make the optimization process less time-consuming, CFD simulations can be more conveniently used to generate a training set for the learning process of an artificial neural network which, once correctly trained, can be used to forecast the engine outputs as a function of the design parameters during a GA optimization performing a so-called virtual optimization. In the current work, a numerical methodology for the multi-objective virtual optimization of the combustion of an automotive DI Diesel engine, which relies on artificial neural networks and genetic algorithms, was developed.

Integrated analysis and design of optimization and up-scaling of inductively coupled plasma synthesis of nanoparticles

This study is focused on radio-frequency inductively coupled thermal plasma (ICP) synthesis of nanoparticles, combining experimental and modelling approaches towards process optimization and industrial scale-up, in the framework of the FP7-NMP SIMBA European project (Scaling-up of ICP technology for continuous production of Metallic nanopowders for Battery Applications). First the state of the art of nanoparticle production through conventional and plasma routes is summarized, then results for the characterization of the plasma source and on the investigation of the nanoparticle synthesis phenomenon, aiming at highlighting fundamental process parameters while adopting a design oriented modelling approach, are presented. In particular, an energy balance of the torch and of the reaction chamber, employing a calorimetric method, is presented, while results for three- and two-dimensional modelling of an ICP system are compared with calorimetric and enthalpy probe measurements to validate the temperature field predicted by the model and used to characterize the ICP system under powder-free conditions. Moreover, results from the modeling of critical phases of ICP synthesis process, such as precursor evaporation, vapour conversion in nanoparticles and nanoparticle growth, are presented, with the aim of providing useful insights both for the design and optimization of the process and on the underlying physical phenomena. Indeed, precursor evaporation, one of the phases holding the highest impact on industrial feasibility of the process, is discussed; by employing models to describe particle trajectories and thermal histories, adapted from the ones originally developed for other plasma technologies or applications, such as DC non-transferred arc torches and powder spherodization, the evaporation of micro-sized Si solid precursor in a laboratory scale ICP system is investigated. Finally, a discussion on the role of thermo-fluid dynamic fields on nano-particle formation is presented, as well as a study on the effect of the reaction chamber geometry on produced nanoparticle characteristics and process yield.

Coordination and Control of Autonomous Mobile Robots Swarms by using Particle Swarm Optimization Algorithm and Consensus Theory

This thesis presents some different techniques designed to drive a swarm of robots in an a-priori unknown environment in order to move the group from a starting area to a final one avoiding obstacles. The presented techniques are based on two different theories used alone or in combination: Swarm Intelligence (SI) and Graph Theory. Both theories are based on the study of interactions between different entities (also called agents or units) in Multi- Agent Systems (MAS). The first one belongs to the Artificial Intelligence context and the second one to the Distributed Systems context. These theories, each one from its own point of view, exploit the emergent behaviour that comes from the interactive work of the entities, in order to achieve a common goal. The features of flexibility and adaptability of the swarm have been exploited with the aim to overcome and to minimize difficulties and problems that can affect one or more units of the group, having minimal impact to the whole group and to the common main target. Another aim of this work is to show the importance of the information shared between the units of the group, such as the communication topology, because it helps to maintain the environmental information, detected by each single agent, updated among the swarm. Swarm Intelligence has been applied to the presented technique, through the Particle Swarm Optimization algorithm (PSO), taking advantage of its features as a navigation system. The Graph Theory has been applied by exploiting Consensus and the application of the agreement protocol with the aim to maintain the units in a desired and controlled formation. This approach has been followed in order to conserve the power of PSO and to control part of its random behaviour with a distributed control algorithm like Consensus.

Whole ship energy optimization: the case study of a chemical tanker

La presente dissertazione investiga la possibilità di ottimizzare l’uso di energia a bordo di una nave per trasporto di prodotti chimici e petrolchimici. Il software sviluppato per questo studio può essere adattato a qualsiasi tipo di nave. Tale foglio di calcolo fornisce la metodologia per stimare vantaggi e miglioramenti energetici, con accuratezza direttamente proporzionale ai dati disponibili sulla configurazione del sistema energetico e sui dispositivi installati a bordo. Lo studio si basa su differenti fasi che permettono la semplificazione del lavoro; nell’introduzione sono indicati i dati necessari per svolgere un’accurata analisi ed è presentata la metodologia adottata. Inizialmente è fornita una spiegazione sul layout dell’impianto, sulle sue caratteristiche e sui principali dispositivi installati a bordo. Vengono dunque trattati separatamente i principali carichi, meccanico, elettrico e termico. In seguito si procede con una selezione delle principali fasi operative della nave: è seguito tale approccio in modo da comprendere meglio la ripartizione della richiesta di potenza a bordo della nave e il suo sfruttamento. Successivamente è svolto un controllo sul dimensionamento del sistema elettrico: ciò aiuta a comprendere se la potenza stimata dai progettisti sia assimilabile a quella effettivamente richiesta sulla nave. Si ottengono in seguito curve di carico meccanico, elettrico e termico in funzione del tempo per tutte le fasi operative considerate: tramite l’uso del software Visual Basic Application (VBA) vengono creati i profili di carico che possono essere gestiti nella successiva fase di ottimizzazione. L’ottimizzazione rappresenta il cuore di questo studio; i profili di potenza ottenuti dalla precedente fase sono gestiti in modo da conseguire un sistema che sia in grado di fornire potenza alla nave nel miglior modo possibile da un punto di vista energetico. Il sistema energetico della nave è modellato e ottimizzato mantenendo lo status quo dei dispositivi di bordo, per i quali sono considerate le configurazioni di “Load following”, “two shifts” e “minimal”. Una successiva investigazione riguarda l’installazione a bordo di un sistema di accumulo di energia termica, così da migliorare lo sfruttamento dell’energia disponibile. Infine, nella conclusione, sono messi a confronto i reali consumi della nave con i risultati ottenuti con e senza l’introduzione del sistema di accumulo termico. Attraverso la configurazione “minimal” è possibile risparmiare circa l’1,49% dell’energia totale consumata durante un anno di attività; tale risparmio è completamente gratuito poiché può essere raggiunto seguendo alcune semplici regole nella gestione dell’energia a bordo. L’introduzione di un sistema di accumulo termico incrementa il risparmio totale fino al 4,67% con un serbatoio in grado di accumulare 110000 kWh di energia termica; tuttavia, in questo caso, è necessario sostenere il costo di installazione del serbatoio. Vengono quindi dibattuti aspetti economici e ambientali in modo da spiegare e rendere chiari i vantaggi che si possono ottenere con l’applicazione di questo studio, in termini di denaro e riduzione di emissioni in atmosfera.

Skin repair optimization for the airbus A350XWB cargo door surround

The aim of the thesis is to design and verify a doubler for the Airbus A350XWB cargo door surround. The software used for the design is Catia and the software used for the doubler verification are Patran and Nastran.

Automotive diesel engine transient operation: modeling, optimization and control

Traditionally, the study of internal combustion engines operation has focused on the steady-state performance. However, the daily driving schedule of automotive engines is inherently related to unsteady conditions. There are various operating conditions experienced by (diesel) engines that can be classified as transient. Besides the variation of the engine operating point, in terms of engine speed and torque, also the warm up phase can be considered as a transient condition. Chapter 2 has to do with this thermal transient condition; more precisely the main issue is the performance of a Selective Catalytic Reduction (SCR) system during cold start and warm up phases of the engine. The proposal of the underlying work is to investigate and identify optimal exhaust line heating strategies, to provide a fast activation of the catalytic reactions on SCR. Chapters 3 and 4 focus the attention on the dynamic behavior of the engine, when considering typical driving conditions. The common approach to dynamic optimization involves the solution of a single optimal-control problem. However, this approach requires the availability of models that are valid throughout the whole engine operating range and actuator ranges. In addition, the result of the optimization is meaningful only if the model is very accurate. Chapter 3 proposes a methodology to circumvent those demanding requirements: an iteration between transient measurements to refine a purpose-built model and a dynamic optimization which is constrained to the model validity region. Moreover all numerical methods required to implement this procedure are presented. Chapter 4 proposes an approach to derive a transient feedforward control system in an automated way. It relies on optimal control theory to solve a dynamic optimization problem for fast transients. From the optimal solutions, the relevant information is extracted and stored in maps spanned by the engine speed and the torque gradient.

Temperature Variation Aware Energy Optimization in Heterogeneous MPSoCs

Thermal effects are rapidly gaining importance in nanometer heterogeneous integrated systems. Increased power density, coupled with spatio-temporal variability of chip workload, cause lateral and vertical temperature non-uniformities (variations) in the chip structure. The assumption of an uniform temperature for a large circuit leads to inaccurate determination of key design parameters. To improve design quality, we need precise estimation of temperature at detailed spatial resolution which is very computationally intensive. Consequently, thermal analysis of the designs needs to be done at multiple levels of granularity. To further investigate the flow of chip/package thermal analysis we exploit the Intel Single Chip Cloud Computer (SCC) and propose a methodology for calibration of SCC on-die temperature sensors. We also develop an infrastructure for online monitoring of SCC temperature sensor readings and SCC power consumption. Having the thermal simulation tool in hand, we propose MiMAPT, an approach for analyzing delay, power and temperature in digital integrated circuits. MiMAPT integrates seamlessly into industrial Front-end and Back-end chip design flows. It accounts for temperature non-uniformities and self-heating while performing analysis. Furthermore, we extend the temperature variation aware analysis of designs to 3D MPSoCs with Wide-I/O DRAM. We improve the DRAM refresh power by considering the lateral and vertical temperature variations in the 3D structure and adapting the per-DRAM-bank refresh period accordingly. We develop an advanced virtual platform which models the performance, power, and thermal behavior of a 3D-integrated MPSoC with Wide-I/O DRAMs in detail. Moving towards real-world multi-core heterogeneous SoC designs, a reconfigurable heterogeneous platform (ZYNQ) is exploited to further study the performance and energy efficiency of various CPU-accelerator data sharing methods in heterogeneous hardware architectures. A complete hardware accelerator featuring clusters of OpenRISC CPUs, with dynamic address remapping capability is built and verified on a real hardware.

Design and optimization of turbo-expanders for organic rankine cycles

In a world focused on the need to produce energy for a growing population, while reducing atmospheric emissions of carbon dioxide, organic Rankine cycles represent a solution to fulfil this goal. This study focuses on the design and optimization of axial-flow turbines for organic Rankine cycles. From the turbine designer point of view, most of this fluids exhibit some peculiar characteristics, such as small enthalpy drop, low speed of sound, large expansion ratio. A computational model for the prediction of axial-flow turbine performance is developed and validated against experimental data. The model allows to calculate turbine performance within a range of accuracy of ±3%. The design procedure is coupled with an optimization process, performed using a genetic algorithm where the turbine total-to-static efficiency represents the objective function. The computational model is integrated in a wider analysis of thermodynamic cycle units, by providing the turbine optimal design. First, the calculation routine is applied in the context of the Draugen offshore platform, where three heat recovery systems are compared. The turbine performance is investigated for three competing bottoming cycles: organic Rankine cycle (operating cyclopentane), steam Rankine cycle and air bottoming cycle. Findings indicate the air turbine as the most efficient solution (total-to-static efficiency = 0.89), while the cyclopentane turbine results as the most flexible and compact technology (2.45 ton/MW and 0.63 m3/MW). Furthermore, the study shows that, for organic and steam Rankine cycles, the optimal design configurations for the expanders do not coincide with those of the thermodynamic cycles. This suggests the possibility to obtain a more accurate analysis by including the computational model in the simulations of the thermodynamic cycles. Afterwards, the performance analysis is carried out by comparing three organic fluids: cyclopentane, MDM and R245fa. Results suggest MDM as the most effective fluid from the turbine performance viewpoint (total-to-total efficiency = 0.89). On the other hand, cyclopentane guarantees a greater net power output of the organic Rankine cycle (P = 5.35 MW), while R245fa represents the most compact solution (1.63 ton/MW and 0.20 m3/MW). Finally, the influence of the composition of an isopentane/isobutane mixture on both the thermodynamic cycle performance and the expander isentropic efficiency is investigated. Findings show how the mixture composition affects the turbine efficiency and so the cycle performance. Moreover, the analysis demonstrates that the use of binary mixtures leads to an enhancement of the thermodynamic cycle performance.

Many-Core Architectures: Hardware-Software Optimization and Modeling Techniques

During the last few decades an unprecedented technological growth has been at the center of the embedded systems design paramount, with Moore’s Law being the leading factor of this trend. Today in fact an ever increasing number of cores can be integrated on the same die, marking the transition from state-of-the-art multi-core chips to the new many-core design paradigm. Despite the extraordinarily high computing power, the complexity of many-core chips opens the door to several challenges. As a result of the increased silicon density of modern Systems-on-a-Chip (SoC), the design space exploration needed to find the best design has exploded and hardware designers are in fact facing the problem of a huge design space. Virtual Platforms have always been used to enable hardware-software co-design, but today they are facing with the huge complexity of both hardware and software systems. In this thesis two different research works on Virtual Platforms are presented: the first one is intended for the hardware developer, to easily allow complex cycle accurate simulations of many-core SoCs. The second work exploits the parallel computing power of off-the-shelf General Purpose Graphics Processing Units (GPGPUs), with the goal of an increased simulation speed. The term Virtualization can be used in the context of many-core systems not only to refer to the aforementioned hardware emulation tools (Virtual Platforms), but also for two other main purposes: 1) to help the programmer to achieve the maximum possible performance of an application, by hiding the complexity of the underlying hardware. 2) to efficiently exploit the high parallel hardware of many-core chips in environments with multiple active Virtual Machines. This thesis is focused on virtualization techniques with the goal to mitigate, and overtake when possible, some of the challenges introduced by the many-core design paradigm.

Optimization of purification procedures in organic semiconductor synthesis for improving the performances of organic field-effect transistors

The aim of the research activity focused on the investigation of the correlation between the degree of purity in terms of chemical dopants in organic small molecule semiconductors and their electrical and optoelectronic performances once introduced as active material in devices. The first step of the work was addressed to the study of the electrical performances variation of two commercial organic semiconductors after being processed by means of thermal sublimation process. In particular, the p-type 2,2′′′-Dihexyl-2,2′:5′,2′′:5′′,2′′′-quaterthiophene (DH4T) semiconductor and the n-type 2,2′′′- Perfluoro-Dihexyl-2,2′:5′,2′′:5′′,2′′′-quaterthiophene (DFH4T) semiconductor underwent several sublimation cycles, with consequent improvement of the electrical performances in terms of charge mobility and threshold voltage, highlighting the benefits brought by this treatment to the electric properties of the discussed semiconductors in OFET devices by the removal of residual impurities. The second step consisted in the provision of a metal-free synthesis of DH4T, which was successfully prepared without organometallic reagents or catalysts in collaboration with Dr. Manuela Melucci from ISOF-CNR Institute in Bologna. Indeed the experimental work demonstrated that those compounds are responsible for the electrical degradation by intentionally doping the semiconductor obtained by metal-free method by Tetrakis(triphenylphosphine)palladium(0) (Pd(PPh3)4) and Tributyltin chloride (Bu3SnCl), as well as with an organic impurity, like 5-hexyl-2,2':5',2''-terthiophene (HexT3) at, in different concentrations (1, 5 and 10% w/w). After completing the entire evaluation process loop, from fabricating OFET devices by vacuum sublimation with implemented intentionally-doped batches to the final electrical characterization in inherent-atmosphere conditions, commercial DH4T, metal-free DH4T and the intentionally-doped DH4T were systematically compared. Indeed, the fabrication of OFET based on doped DH4T clearly pointed out that the vacuum sublimation is still an inherent and efficient purification method for crude semiconductors, but also a reliable way to fabricate high performing devices.

Assessment and optimization of chemical industrial processes from a life cycle perspective

During the PhD program in chemistry, curriculum in environmental chemistry, at the University of Bologna the sustainability of industry was investigated through the application of the LCA methodology. The efforts were focused on the chemical sector in order to investigate reactions dealing with the Green Chemistry and Green Engineering principles, evaluating their sustainability in comparison with traditional pathways by a life cycle perspective. The environmental benefits associated with a reduction in the synthesis steps and the use of renewable feedstock were assessed through a holistic approach selecting two case studies with high relevance from an industrial point of view: the synthesis of acrylonitrile and the production of acrolein. The current approach wants to represent a standardized application of LCA methodology to the chemical sector, which could be extended to several case studies, and also an improvement of the current databases, since the lack of data to fill the inventories of the chemical productions represent a huge limitation, difficult to overcome and that can affects negatively the results of the studies. Results emerged from the analyses confirms that the sustainability in the chemical sector should be evaluated from a cradle-to-gate approach, considering all the stages and flows involved in each pathways in order to avoid shifting the environmental burdens from a steps to another. Moreover, if possible, LCA should be supported by other tools able to investigate the other two dimensions of sustainability represented by the social and economic issues.

Mathematical Optimization Applied to Thermal and Electrical Energy Systems

This Thesis aims at building and discussing mathematical models applications focused on Energy problems, both on the thermal and electrical side. The objective is to show how mathematical programming techniques developed within Operational Research can give useful answers in the Energy Sector, how they can provide tools to support decision making processes of Companies operating in the Energy production and distribution and how they can be successfully used to make simulations and sensitivity analyses to better understand the state of the art and convenience of a particular technology by comparing it with the available alternatives. The first part discusses the fundamental mathematical background followed by a comprehensive literature review about mathematical modelling in the Energy Sector. The second part presents mathematical models for the District Heating strategic network design and incremental network design. The objective is the selection of an optimal set of new users to be connected to an existing thermal network, maximizing revenues, minimizing infrastructure and operational costs and taking into account the main technical requirements of the real world application. Results on real and randomly generated benchmark networks are discussed with particular attention to instances characterized by big networks dimensions. The third part is devoted to the development of linear programming models for optimal battery operation in off-grid solar power schemes, with consideration of battery degradation. The key contribution of this work is the inclusion of battery degradation costs in the optimisation models. As available data on relating degradation costs to the nature of charge/discharge cycles are limited, we concentrate on investigating the sensitivity of operational patterns to the degradation cost structure. The objective is to investigate the combination of battery costs and performance at which such systems become economic. We also investigate how the system design should change when battery degradation is taken into account.