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.

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.

Physical models for numerical simulation of Si-based nanoscale FETs and sensors

To continuously improve the performance of metal-oxide-semiconductor field-effect-transistors (MOSFETs), innovative device architectures, gate stack engineering and mobility enhancement techniques are under investigation. In this framework, new physics-based models for Technology Computer-Aided-Design (TCAD) simulation tools are needed to accurately predict the performance of upcoming nanoscale devices and to provide guidelines for their optimization. In this thesis, advanced physically-based mobility models for ultrathin body (UTB) devices with either planar or vertical architectures such as single-gate silicon-on-insulator (SOI) field-effect transistors (FETs), double-gate FETs, FinFETs and silicon nanowire FETs, integrating strain technology and high-κ gate stacks are presented. The effective mobility of the two-dimensional electron/hole gas in a UTB FETs channel is calculated taking into account its tensorial nature and the quantization effects. All the scattering events relevant for thin silicon films and for high-κ dielectrics and metal gates have been addressed and modeled for UTB FETs on differently oriented substrates. The effects of mechanical stress on (100) and (110) silicon band structures have been modeled for a generic stress configuration. Performance will also derive from heterogeneity, coming from the increasing diversity of functions integrated on complementary metal-oxide-semiconductor (CMOS) platforms. For example, new architectural concepts are of interest not only to extend the FET scaling process, but also to develop innovative sensor applications. Benefiting from properties like large surface-to-volume ratio and extreme sensitivity to surface modifications, silicon-nanowire-based sensors are gaining special attention in research. In this thesis, a comprehensive analysis of the physical effects playing a role in the detection of gas molecules is carried out by TCAD simulations combined with interface characterization techniques. The complex interaction of charge transport in silicon nanowires of different dimensions with interface trap states and remote charges is addressed to correctly reproduce experimental results of recently fabricated gas nanosensors.

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.