Turbulent Pipe Flow - High Resolution Measurements in CICLoPE

The thesis deals with the experimental investigation of turbulent pipe flow at high Reynolds number. Wall-bounded turbulence is an extremely relevant topic for engineering and natural science applications and yet many aspects of the physics are not clear due to the difficulty in performing high Re experiments. To overcome these difficulties the CICLoPE Laboratory was developed, the main element of which is the Long Pipe wind tunnel. The facility is unique in its kind, as thanks to its large scale it delivers a flow quality and resolution that can not be achieved elsewhere at these Reynolds number. Reported here are the results from the first experimental campaign performed in the facility. A first part of the results presented concerns the characterization of this new facility. Flow quality and stability are assessed, particular attention is given to the characterization of pressure drop. The scaling of velocity fluctuations is analysed. The magnitude of the inner peak of the streamwise normal stress shows an increasing trend up to the highest Reynolds number examined, while no outer peak was clearly distinguishable from present measurements. Scaling of coherent motions is investigated via spectral analysis. An inner and outer spectral peaks are identified, with the former scaling in inner units while the latter neither following inner nor outer scaling, and increasing in magnitude with Re. Analysis of the spectra at y+ ≈ 15 shows how the increase of Reynolds normal stress is related to the influence of large scales in the inner wall region. Quadrant analysis was carried out on streamwise and wall-normal velocity fluctuations. The results show the important role in contribution to Reynolds shear stress of highly intermittent and strong events like ejections, that assume an even more intermittent and dominant role with the increase of Reynolds number.

Power flow forecasting and smart charging for electric vehicle charging systems with photovoltaic integration

An essential role in the global energy transition is attributed to Electric Vehicles (EVs) the energy for EV traction can be generated by renewable energy sources (RES), also at a local level through distributed power plants, such as photovoltaic (PV) systems. However, EV integration with electrical systems might not be straightforward. The intermittent RES, combined with the high and uncontrolled aggregate EV charging, require an evolution toward new planning and paradigms of energy systems. In this context, this work aims to provide a practical solution for EV charging integration in electrical systems with RES. A method for predicting the power required by an EV fleet at the charging hub (CH) is developed in this thesis. The proposed forecasting method considers the main parameters on which charging demand depends. The results of the EV charging forecasting method are deeply analyzed under different scenarios. To reduce the EV load intermittency, methods for managing the charging power of EVs are proposed. The main target was to provide Charging Management Systems (CMS) that modulate EV charging to optimize specific performance indicators such as system self-consumption, peak load reduction, and PV exploitation. Controlling the EV charging power to achieve specific optimization goals is also known as Smart Charging (SC). The proposed techniques are applied to real-world scenarios demonstrating performance improvements in using SC strategies. A viable alternative to maximize integration with intermittent RES generation is the integration of energy storage. Battery Energy Storage Systems (BESS) may be a buffer between peak load and RES production. A sizing algorithm for PV+BESS integration in EV charging hubs is provided. The sizing optimization aims to optimize the system's energy and economic performance. The results provide an overview of the optimal size that the PV+BESS plant should have to improve whole system performance in different scenarios.