Citation: García-Gutiérrez, A.; Gonzalo, J.; López, D.; Delgado, A. Advances in CFD Modeling of Urban Wind Applied to Aerial Mobility. Fluids 2022, 7, 246. https://doi.org/ 10.3390/fluids7070246 Academic Editor: Mesbah Uddin Received: 21 June 2022 Accepted: 15 July 2022 Published: 18 July 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). fluids Review Advances in CFD Modeling of Urban Wind Applied to Aerial Mobility Adrián García-Gutiérrez * , Jesús Gonzalo, Deibi López and Adrián Delgado Aerospace Engineering Area, University of León, Campus dde Vegazana s/n, 24071 León, Spain; jgonzalo@unileon.es (J.G.); dlopr@unileon.es (D.L.); adelm@unileon.es (A.D.) * Correspondence: a.gutierrez@unileon.es; Tel.: +34-9872-93685 Abstract: The feasibility, safety, and efficiency of a drone mission in an urban environment are heavily influenced by atmospheric conditions. However, numerical meteorological models cannot cope with fine-grained grids capturing urban geometries; they are typically tuned for best resolutions ranging from 1 to 10 km. To enable urban air mobility, new now-casting techniques are being developed based on different techniques, such as data assimilation, variational analysis, machine-learning algorithms, and time series analysis. Most of these methods require generating an urban wind field database using CFD codes coupled with the mesoscale models. The quality and accuracy of that database determines the accuracy of the now-casting techniques. This review describes the latest advances in CFD simulations applied to urban wind and the alternatives that exist for the coupling with the mesoscale model. First, the distinct turbulence models are introduced, analyzing their advantages and limitations. Secondly, a study of the meshing is introduced, exploring how it has to be adapted to the characteristics of the urban environment. Then, the several alternatives for the definition of the boundary conditions and the interpolation methods for the initial conditions are described. As a key step, the available order reduction methods applicable to the models are presented, so the size and operability of the wind database can be reduced as much as possible. Finally, the data assimilation techniques and the model validation are presented. Keywords: urban CFD; urban wind database; now-casting 1. Introduction The use of UAVs in urban areas is becoming a growing trend [1]. This is due to numerous applications varying from civil security to parcel delivery. In spite of this considerable interest, the flight of drones in cities is a genuine challenge due to the high turbulence levels and the drastic changes in wind patterns experienced by the aircraft [2]. This is further aggravated given that the small size of the aircraft increases its vulnerability to weather. To guarantee the safety of these missions, it is essential to know, as precisely as possible, the wind and turbulence conditions that the aircraft are likely to undergo [3]. Traditionally, mesoscale models have been the tools used to numerically predict the weather [4]. These algorithms implement full-physical models, including long- and shortwave radiation, surface interaction, planetary boundary layer and, of course, water vapor and rain behavior. Nevertheless, these models have best resolutions in the range of 1–1.5 km at best, which is insufficient to accurately represent the relevant scales for the operation of UAVs [5]. CFD simulations are typically used to obtain the required resolution of this application [6]. Usually, the initial and boundary conditions are interpolated from the results produced by a coarser mesoscale prediction, making it necessary to have a proper procedure for the coupling of both models. Though reliable results are obtained, the high cost, both in time and resources, makes this technique unfeasible for real-time now-casting as per today. Given the previous, current studies are focused on refining the predictions (mainly wind fields) made with mesoscale models using other auxiliary numerical techniques, such Fluids 2022, 7, 246. https://doi.org/10.3390/fluids7070246 https://www.mdpi.com/journal/fluids