The importance of Indoor Air Quality (IAQ) has been highlighted by the COVID-19 pandemic, particularly due to the possibility of long-distance airborne transmission. Consequently, assessment of ventilation rates and estimation of infection risk has become a matter of the utmost importance. In this paper, a naturally ventilated elementary school classroom is studied, where carbon dioxide (${\mathrm{CO}}_2$) concentrations were measured during five months. Ventilation rates are calculated via a fully-mixed box model and the airborne risk of infection for SARS-CoV-2 is assessed. Risk results are found to steadily decline from winter to spring. Furthermore, analytical simulations for different scenarios are conducted. It is shown that periodic ventilation significantly reduces the transmission risk, even if it occurs only during very reduced time spans. The results show that periodic ventilation is a useful strategy for reducing the risk of any airborne transmitted disease. It is particularly well-suited for naturally ventilated environments in cold weathers, as it allows for a compromise between IAQ and thermal comfort, and does not require any modification to existing buildings.

Recently, there has been an increasing interest in the academic community as well as in the industrial sector about the operation and control of a wind farm at a farm level. This new paradigm has different purposes, from maximizing power production through wake modulation to active power control. The former has been extensively analyzed with numerical or physical modelling, finding in general an improvement in the power production by yawing the wind turbine rotors. The aim of the present paper is to compare both approaches, by taking into account results from state of the art numerical simulations and wind tunnel campaigns. A case with three model wind turbines subject to an atmospheric boundary layer like inflow condition will be studied.

As the size of wind turbines increases and their hub heights become higher, which partially explains the vertiginous increase of wind power worldwide in the last decade, the interaction of wind turbines with the atmospheric boundary layer (ABL) and between each other is becoming more complex. There are different approaches to model and compute the aerodynamic loads, and hence the power production, on wind turbines subject to ABL inflow conditions ranging from the classical Blade Element Momentum (BEM) method to Computational Fluid Dynamic (CFD) approaches. Also, modern multi-MW wind turbines have a torque controller and a collective pitch controller to manage power output, particularly in maximizing power production or when it is required to down-regulate their production. In this work the results of a validated numerical method, based on a Large Eddy Simulation-Actuator Line Model framework, was applied to simulate a real 7.7 MNW onshore wind farm on Uruguay under different wind conditions, and hence operational situations are shown with the aim to assess the capability of this approach to model actual wind farm dynamics. A description of the implementation of these controllers in the CFD solver Caffa3d, presenting the methodology applied to obtain the controller parameters, is included. For validation, the simulation results were compared with 1 Hz data obtained from the Supervisory Control and Data Acquisition System of the wind farm, focusing on the temporal evolution of the following variables: Wind velocity, rotor angular speed, pitch angle, and electric power. In addition to this, simulations applying active power control at the wind turbine level are presented under different de-rate signals, both constant and time-varying, and were subject to different wind speed profiles and wind directions where there was interaction between wind turbines and their wakes.

Wind energy has become cost competitive in recent years for several reasons. Among them, wind turbines have become more efficient, increasing its size, both rotor diameter and tower height. This growth in size makes the prediction of the wind flow through wind turbines more challenging. To avoid the computational cost related to resolve the blade boundary layer as well as the atmospheric boundary layer, actuator models have been proposed in the past few years. Among them, the Actuator Line Model (ALM) has shown to reproduce with reasonable accuracy the wind flow in the wake of a wind turbine with moderately computational cost. However, its use to simulate the flow through wind farms requires a spatial resolution and a time step that makes it unaffordable in some cases. The present paper aims to assess the ALM with coarser resolution and larger time step than what is generally recommended, taking into account an atmospheric sheared and turbulent inflow condition and comparing the results with the Actuator Disk Model with Rotation (ADM-R) and experimental data. To accomplish this, a well known wind tunnel campaign is considered as validation case.

This paper presents a new approach based on the optimization of the blade pitching strategy of offshore wind turbines in order to maximize the global energy output considering the Gaussian wake model and including the effect of added turbulence. A genetic algorithm is proposed as an optimization tool in the process of finding the optimal setting of the wind turbines, which aims to determine the individual pitch of each turbine so that the overall losses due to the wake effect are minimised. The integration of the Gaussian model, including the added turbulence effect, for the evaluation of the wakes provides a step forward in the development of strategies for optimal operation of offshore wind farms, as it is one of the state-of-the-art analytical wake models that allow the evaluation of the energy output of the project in a more reliable way. The proposed methodology has been tested through the execution of a set of test cases that show the ability of the proposed tool to maximize the energy production of offshore wind farms, as well as highlights the importance of considering the effect of added turbulence in the evaluation of the wake.