7 resultados para Renewable diesel

em AMS Tesi di Dottorato - Alm@DL - Università di Bologna


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The Department of Mechanical and Civil Engineering (DIMeC) of the University of Modena and Reggio Emilia is developing a new type of small capacity HSDI 2-Stroke Diesel engine (called HSD2), featuring a specifically designed combustion system, aimed to reduce weight, size and manufacturing costs, and improving pollutant emissions at partial load. The present work is focused on the analysis of the combustion and the scavenging process, investigated by means of a version of the KIVA-3V code customized by the University of Chalmers and modified by DIMeC. The customization of the KIVA-3V code includes a detailed combustion chemistry approach, coupled with a comprehensive oxidation mechanism for diesel oil surrogate and the modeling of turbulence/chemistry interaction through the PaSR (Partially Stirred Reactor) model. A four stroke automobile Diesel engine featuring a very close bore size is taken as a reference, for both the numerical models calibration and for a comparison with the 2-Stroke engine. Analysis is carried out trough a comparison between HSD2 and FIAT 1300 MultiJet in several operating conditions, at full and partial load. Such a comparison clearly demonstrates the effectiveness of the two stroke concept in terms of emissions reduction and high power density. However, HSD2 is still a virtual engine, and experimental results are needed to assume the reliability of numerical results.

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Nowadays the development of sustainable polymers, with convenient properties to substitute the traditional petroleum-based materials, is one of the major issues for material science. The utilization of renewable resources as feedstock for biopolyesters is a challenging target.The research work described in the present thesis is strictly connected to these urgent necessities and is focused mainly in finding new biopolymers, in particular biopolyesters, which are obtainable from biomass and characterized by a wide range of properties, in order to potentially substitute polyolefins and aromatic polyesters (for example, poly(ethylene terephthalate))

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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.

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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.

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The present research project focuses its attention on the study of structure-property relations in polymers from renewable sources (bio-based polymers) such as polymers microbially produced, i.e. polyhydrohyalkanoates (PHAs) or chemically synthesized using monomers from renewable sources, i.e. polyammide 11 (PA11). By means of a broad spectrum of experimental techniques, the influence of different modifications on bio-based polymers such as blending with other components, copolymerization with different co-monomers and introduction of branching to yield complex architectures have been investigated. The present work on PHAs focused on the study of the dependence of polymer properties on both the fermentation process conditions (e.g. bacterial strain and carbon substrate used) and the method adopted to recover PHAs from cells. Furthermore, a solvent-free method using an enzyme and chemicals in an aqueous medium, was developed in order to recover PHAs from cells. Such a method allowed to recover PHA granules in their amorphous state, i.e. in native form useful for specific applications (e.g. paper coating). In addition, a commercial PHA was used as polymeric matrix to develop biodegradable and bio-based composites for food packaging applications. Biodegradable, non-toxic, food contact plasticizers and low cost, widely available lignocellulosic fibers (wheat straw fibers) were incorporated in such a polymeric matrix, in order to decrease PHA brittleness and the polymer cost, respectively. As concerns the study of polyamide 11, both the rheological and the solid-state behavior of PA11 star samples with different arm number and length was studied. Introduction of arms in a polymer molecule allows to modulate melt viscosity behavior which is advantageous for industrial applications. Also, several important solid-state properties, in particular mechanical properties, are affected by the presence of branching. Given the importance of using ‘green’ synthetic strategies in polymer chemistry, novel poly(-amino esters), synthesized via enzymatic-catalyzed polymerization, have also been investigated in this work.

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The present thesis is focused on the study of innovative Si-based materials for third generation photovoltaics. In particular, silicon oxi-nitride (SiOxNy) thin films and multilayer of Silicon Rich Carbide (SRC)/Si have been characterized in view of their application in photovoltaics. SiOxNy is a promising material for applications in thin-film solar cells as well as for wafer based silicon solar cells, like silicon heterojunction solar cells. However, many issues relevant to the material properties have not been studied yet, such as the role of the deposition condition and precursor gas concentrations on the optical and electronic properties of the films, the composition and structure of the nanocrystals. The results presented in the thesis aim to clarify the effects of annealing and oxygen incorporation within nc-SiOxNy films on its properties in view of the photovoltaic applications. Silicon nano-crystals (Si NCs) embedded in a dielectric matrix were proposed as absorbers in all-Si multi-junction solar cells due to the quantum confinement capability of Si NCs, that allows a better match to the solar spectrum thanks to the size induced tunability of the band gap. Despite the efficient solar radiation absorption capability of this structure, its charge collection and transport properties has still to be fully demonstrated. The results presented in the thesis aim to the understanding of the transport mechanisms at macroscopic and microscopic scale. Experimental results on SiOxNy thin films and SRC/Si multilayers have been obtained at macroscopical and microscopical level using different characterizations techniques, such as Atomic Force Microscopy, Reflection and Transmission measurements, High Resolution Transmission Electron Microscopy, Energy-Dispersive X-ray spectroscopy and Fourier Transform Infrared Spectroscopy. The deep knowledge and improved understanding of the basic physical properties of these quite complex, multi-phase and multi-component systems, made by nanocrystals and amorphous phases, will contribute to improve the efficiency of Si based solar cells.

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Beside the traditional paradigm of "centralized" power generation, a new concept of "distributed" generation is emerging, in which the same user becomes pro-sumer. During this transition, the Energy Storage Systems (ESS) can provide multiple services and features, which are necessary for a higher quality of the electrical system and for the optimization of non-programmable Renewable Energy Source (RES) power plants. A ESS prototype was designed, developed and integrated into a renewable energy production system in order to create a smart microgrid and consequently manage in an efficient and intelligent way the energy flow as a function of the power demand. The produced energy can be introduced into the grid, supplied to the load directly or stored in batteries. The microgrid is composed by a 7 kW wind turbine (WT) and a 17 kW photovoltaic (PV) plant are part of. The load is given by electrical utilities of a cheese factory. The ESS is composed by the following two subsystems, a Battery Energy Storage System (BESS) and a Power Control System (PCS). With the aim of sizing the ESS, a Remote Grid Analyzer (RGA) was designed, realized and connected to the wind turbine, photovoltaic plant and the switchboard. Afterwards, different electrochemical storage technologies were studied, and taking into account the load requirements present in the cheese factory, the most suitable solution was identified in the high temperatures salt Na-NiCl2 battery technology. The data acquisition from all electrical utilities provided a detailed load analysis, indicating the optimal storage size equal to a 30 kW battery system. Moreover a container was designed and realized to locate the BESS and PCS, meeting all the requirements and safety conditions. Furthermore, a smart control system was implemented in order to handle the different applications of the ESS, such as peak shaving or load levelling.