Tetti verdi: Analisi sperimentale e Modellazione numerica

I tetti verdi rappresentano, sempre più frequentemente, una tecnologia idonea alla mitigazione alle problematiche connesse all’ urbanizzazione, tuttavia la conoscenza delle prestazioni dei GR estensivi in clima sub-Mediterraneo è ancora limitata. La presente ricerca è supportata da 15 mesi di analisi sperimentali su due GR situati presso la Scuola di Ingegneria di Bologna. Inizialmente vengono comparate, tra loro e rispetto a una superficie di riferimento (RR), le prestazioni idrologiche ed energetiche dei due GR, caratterizzati da vegetazione a Sedum (SR) e a erbe native perenni (NR). Entrambi riducono i volumi defluiti e le temperature superficiali. Il NR si dimostra migliore del SR sia in campo idrologico che termico, la fisiologia della vegetazione del NR determina l'apertura diurna degli stomi e conseguentemente una maggiore evapotraspirazione (ET). Successivamente si sono studiate la variazioni giornaliere di umidità nel substrato del SR riscontrando che la loro ampiezza è influenzata dalla temperatura, dall’umidità iniziale e dalla fase vegetativa. Queste sono state simulate mediante un modello idrologico basato sull'equazione di bilancio idrico e su due modelli convenzionali per la stima della ET potenziale combinati con una funzione di estrazione dell’ umidità dal suolo. Sono stati proposti dei coefficienti di correzione, ottenuti per calibrazione, per considerare le differenze tra la coltura di riferimento e le colture nei GR durante le fasi di crescita. Infine, con l’ausilio di un modello implementato in SWMM 5.1. 007 utilizzando il modulo Low Impact Development (LID) durante simulazioni in continuo (12 mesi) si sono valutate le prestazioni in termini di ritenzione dei plot SR e RR. Il modello, calibrato e validato, mostra di essere in grado di riprodurre in modo soddisfacente i volumi defluiti dai due plot. Il modello, a seguito di una dettagliata calibrazione, potrebbe supportare Ingegneri e Amministrazioni nella valutazioni dei vantaggi derivanti dall'utilizzo dei GR.

Exact and heuristic algorithms for the integrated planning of multi-energy systems

The aim of this thesis is to present exact and heuristic algorithms for the integrated planning of multi-energy systems. The idea is to disaggregate the energy system, starting first with its core the Central Energy System, and then to proceed towards the Decentral part. Therefore, a mathematical model for the generation expansion operations to optimize the performance of a Central Energy System system is first proposed. To ensure that the proposed generation operations are compatible with the network, some extensions of the existing network are considered as well. All these decisions are evaluated both from an economic viewpoint and from an environmental perspective, as specific constraints related to greenhouse gases emissions are imposed in the formulation. Then, the thesis presents an optimization model for solar organic Rankine cycle in the context of transactive energy trading. In this study, the impact that this technology can have on the peer-to-peer trading application in renewable based community microgrids is inspected. Here the consumer becomes a prosumer and engages actively in virtual trading with other prosumers at the distribution system level. Moreover, there is an investigation of how different technological parameters of the solar Organic Rankine Cycle may affect the final solution. Finally, the thesis introduces a tactical optimization model for the maintenance operations’ scheduling phase of a Combined Heat and Power plant. Specifically, two types of cleaning operations are considered, i.e., online cleaning and offline cleaning. Furthermore, a piecewise linear representation of the electric efficiency variation curve is included. Given the challenge of solving the tactical management model, a heuristic algorithm is proposed. The heuristic works by solving the daily operational production scheduling problem, based on the final consumer’s demand and on the electricity prices. The aggregate information from the operational problem is used to derive maintenance decisions at a tactical level.

Model predictive control in thermal management of multiprocessor systems-on-chip

MultiProcessor Systems-on-Chip (MPSoC) are the core of nowadays and next generation computing platforms. Their relevance in the global market continuously increase, occupying an important role both in everydaylife products (e.g. smartphones, tablets, laptops, cars) and in strategical market sectors as aviation, defense, robotics, medicine. Despite of the incredible performance improvements in the recent years processors manufacturers have had to deal with issues, commonly called “Walls”, that have hindered the processors development. After the famous “Power Wall”, that limited the maximum frequency of a single core and marked the birth of the modern multiprocessors system-on-chip, the “Thermal Wall” and the “Utilization Wall” are the actual key limiter for performance improvements. The former concerns the damaging effects of the high temperature on the chip caused by the large power densities dissipation, whereas the second refers to the impossibility of fully exploiting the computing power of the processor due to the limitations on power and temperature budgets. In this thesis we faced these challenges by developing efficient and reliable solutions able to maximize performance while limiting the maximum temperature below a fixed critical threshold and saving energy. This has been possible by exploiting the Model Predictive Controller (MPC) paradigm that solves an optimization problem subject to constraints in order to find the optimal control decisions for the future interval. A fully-distributedMPC-based thermal controller with a far lower complexity respect to a centralized one has been developed. The control feasibility and interesting properties for the simplification of the control design has been proved by studying a partial differential equation thermal model. Finally, the controller has been efficiently included in more complex control schemes able to minimize energy consumption and deal with mixed-criticalities tasks

Development and Validation of a Model-Based Combustion Controller for Water Injection Management

This manuscript reports the overall development of a Ph.D. research project during the “Mechanics and advanced engineering sciences” course at the Department of Industrial Engineering of the University of Bologna. The project is focused on the development of a combustion control system for an innovative Spark Ignited engine layout. In details, the controller is oriented to manage a prototypal engine equipped with a Port Water Injection system. The water injection technology allows an increment of combustion efficiency due to the knock mitigation effect that permits to keep the combustion phasing closer to the optimal position with respect to the traditional layout. At the beginning of the project, the effects and the possible benefits achievable by water injection have been investigated by a focused experimental campaign. Then the data obtained by combustion analysis have been processed to design a control-oriented combustion model. The model identifies the correlation between Spark Advance, combustion phasing and injected water mass, and two different strategies are presented, both based on an analytic and semi-empirical approach and therefore compatible with a real-time application. The model has been implemented in a combustion controller that manages water injection to reach the best achievable combustion efficiency while keeping knock levels under a pre-established threshold. Three different versions of the algorithm are described in detail. This controller has been designed and pre-calibrated in a software-in-the-loop environment and later an experimental validation has been performed with a rapid control prototyping approach to highlight the performance of the system on real set-up. To further make the strategy implementable on an onboard application, an estimation algorithm of combustion phasing, necessary for the controller, has been developed during the last phase of the PhD Course, based on accelerometric signals.