## 1 Introduction  
Technology developments in the field of aviation require continuous research and improvement of aircraft aerodynamic properties. These tests aim to evaluate aerodynamic forces and moments, static and dynamic stability, and aircraft performance. Information about the aerodynamic characteristics of a specific aircraft allows the pilot to have effective and safe control during flight. This is particularly crucial during critical flight phases, including aerobatic and combat maneuvers, as well as those related to takeoff and landing. For instance, to prevent a stall during landing, the pilot needs to know the minimal velocity and the maximum angle of attack, boundaries that allow for a safe approach. Among relevant research methods, computational fluid dynamics (CFD) simulations are gaining attention due to their low cost and wide range of applications. The CFD method allows to solve issues related to the flow, in particular it enables the simulations of vortices, flow detachment and even the heat transfer and turbulence models became available all at once. Examples of adaptation for this technique can be found in [1; 2; 3; 4]. In the first two papers, the CFD method was applied to the calculation of bioreactor hydrodynamics [1], as well as to analyze interactions between the photovoltaic system and its environment [2]. Furthermore, in [3], a CFD simulation was also used to determine the temperature field in an urban area and, in [4], to perform temperature and pressure field estimation in a dry scroll vacuum pump. There are a number of CFD method applications in the field of aerodynamics [5; 6; 7; 8; 9]. Paper [5] presents the results of airfoil parameter optimization using machine learning methods in conjunction with CFD simulations. The relationship between the lift-to-drag characteristics and the angle of attack for different airfoils was calculated. Thus, the high costs of experiments and simulations were avoided. In [6], the CFD and experimental data were used with an artificial neural network to develop a forecasting model of the wing temperature intended for an anti-icing system. Moreover, paper [7] considers the aerodynamic performance of a vertical-axis wind turbine. In this work, a comparison of two-dimensional (2D) and three-dimensional (3D) CFD models utilizing the unsteady Reynolds-averaged Navier-Stokes (URANS) approach was shown and discussed. Additionally, in [8], a computational fluid dynamics simulation of flow passage along rotor-stator domains of a gas turbine engine was investigated and analyzed. The results obtained were compared with published data in the literature. In [9], the CFD method was used for preliminary aerodynamic analysis of a helicopter's main rotor blade. Like any computational method, CFD requires validation with representative examples. This validation can be achieved using experimental results obtained in flight tests or based on data acquired from wind or hydrodynamic tunnel tests. Among these research methods, tests in hydrodynamic tunnels are gaining more and more recognition due to their speed, low cost, and wide range of applications [10]. Hydrodynamic tunnels, using water as a working medium, offer a number of advantages over classic wind tunnels. Water is about 800 times denser than air under standard conditions. Its dynamic viscosity is 50 times higher and it has a lower mass diffusivity, which facilitates the visualization of flow and the study of complex flow dynamics. Wind and water are both considered fluids. Consequently, both media are subject to the principles of fluid mechanics. When studying models in both wind tunnels and hydrodynamic tunnels, the same similarity criteria and dimensionless numbers are applied, taking into account the fluid density. Therefore, due to its higher density, water is more suitable for flow visualization, which is an advantage over wind tunnels, where the visualization medium disperses and it is harder to show the flow.