# Parametric Analysis Towards the Design of Micro-Scale Wind Turbines: A Machine Learning Approach
## 1 Introduction  
The shift towards renewable energy sources has increased recently in a contemporary world with the high demand of energy. Due to the tendency to deplete fossil fuels and their negative environmental impact, renewable energy has thrived both in research and in real life [1]. Wind energy is among the most advanced renewable energy sources in recent years. A total of 117 GW of wind power has been installed in the year 2023 alone, representing a 50% increase compared to the year 2022 [2]. Research on wind energy is ongoing vastly to cover all the possible aspects of wind power production. Novel methodologies of wind power extraction have been found, for instance, the airborne wind energy systems [3]. The conventional form of extracting wind power is through wind turbines. Improvements to the wind turbine design and optimization of its parameters are covered in the literature to maximize the power output of the turbines [4]. Optimization is also extended to model wind farms whether onshore or offshore [5]. Wind energy development has extended not only to the grid connection, but also to domestic applications [6]. Wind turbines used on rooftops or for electric vehicle charging stations have been studied by many researchers. The key advantage of small-scale turbines for domestic applications is to provide a standalone energy source, despite the grid location or connection [7]. The term micro-scale wind turbine refers to very small wind turbines with a capacity of usually less than 1000 Watts [8]. This size is usually used for off-grid domestic applicationslike charging batteries for home appliances, or for vehicle charging whether in standing charging stations or placed on moving vehicles like recreational cars. To design a wind turbine, different considerations should be taken into account. The turbine should utilize most of the available wind power while maintaining a good structural integrity and less vibrations and noise. The process of designing a wind turbine is complicated and requires intensive calculations and simulations to cover all the design aspects, and ensure its power production efficiency and its structural behavior. Computational Fluid Dynamics (CFDs) are very common in the wind turbine simulation field. They comprise a numerical approach where the flow field, as well as the blade, are discretized into infinitesimal elements and solved for the wind flow governing equations. Despite the high accuracy of CFD simulations, the computational cost of this numerical approach is very high. It may also require a supercomputer to perform a thorough study on a large-scale wind turbine. Usually, CFDs are more suitable for studying the effect of simple changes on a wind turbine to improve its performance; however, they is not the best solution for preliminary designs [9, 10]  
As an alternative to CFD models, there are many software tools that are capable of modeling and simulating wind turbines using deterministic models. Some tools require a license like \"Bladed\" provided by Det Norske Veritas (DNV) Company [11], or \"HAWC2\" provided by the Technical University of Denmark (DTU) [12]. Other software tools are open access like \"OpenFAST\", which is provided by the National Renewable Energy Laboratory (NREL) [13], and \"QBlade\", by the Technical University of Berlin [14]. Most of these tools provide a complete wind turbine aeroelastic analysis. Almost all wind conditions, fixations, designs, and configurations can be simulated on all these tools. The main differences are in the limitations of each software, and the method of simulation. Many researchers and commercial companies have relied on deterministic-model-based software tools in their wind turbine preliminary design stages, proving their credibility and trustworthiness [15, 16]. QBlade was chosen for the study performed in this work, since it is easy to use, and provides a friendly Graphical User Interface (GUI), with accurate results that have been proven in many works in the literature. [PERSON] et al.