## 1 Introduction  
Renewable energy sources are the solution to a sustainable world, meeting contemporary energy needs with fewer negative effects on the environment. Research has been ongoing in the past few decades to improve the usage of renewable energy sources and optimize the power output [1]. Those sources include but are not limited to solar, wind, hydro, tidal, geothermal, and nuclear energy sources [2]. Wind energy is among the fastest-growing renewable energy sources, with gigawatts of new installed capacity annually [3]. This is because it is one of the cleanest sources with minimal environmental impact. Innovative and novel methods of capturing wind power have been developed recently, including airborne wind energy or bladeless turbines [4]. However, the oldest and most traditional method of capturing wind power is the horizontal axis wind turbines (HAWT). Studying wind turbines involves many criteria, including the control algorithms to optimize the power output, optimizing wind farms' locations and layouts, the aerodynamic design of the turbine rotor, and the structural design of the turbine's tower and blades [5]. Among the crucial factors affecting a wind turbine's performance and lifetime is the blade's structural design. Severe deflections of the blades in the upwind turbine configurations can lead to tower strikes, causing catastrophic failures. Blade deflections also affect the power output of a wind turbine since the relative flow angle changes with the blade position. Atmospheric turbulence is one of the significant reasons for severe blade loads and deflections. It also affects the frequency by which the blade deflects and hence induces fatigue loads that shorten the blade's lifetime [6]. The study of turbulence's effect on the blade's structural dynamics has gained wide attention in the past few years. Many approaches are used to model and analyze the effect of turbulence on a wind turbine's structural and power performance. One of the most famous approaches is the computational fluid dynamics (CFD) numerical solution approach. [PERSON] et al. developed a 2-dimensional CFD model to study H-Darrieus turbines under turbulent conditions. They tested many of Reynold's Averaged Navier-Stokes (RANS) turbulence models to study the aerodynamic coefficients of the blade's airfoil. This approach, however, required a rotating ring mesh for the computational domain and an unsteady solver to be able to capture stall dynamics [7]. [PERSON] et al. developed an inverse actuator disc CFD approach to optimize the chord and twist angle distributions over a HAWT blade. The optimized blade improved the annual energy production by 17.64% compared to the original blade design [8]. Although the results of both works were in agreement with the experiments, the computational cost was high. Studies using CFD analysis extend to studying more than one turbine in the same domain or wind farm layouts [9]. [PERSON] et al. studied two wind turbines tandemly arrayed under turbulent wind conditions. To handle the relative motion between rotary and stationary parts of the turbine they had to use a moving grid with a sliding surface. The study aimed to predict the wake profile for the downstream turbine. Again, the model showed very good agreement with the experiments but with a high computational cost [10]. CFD discretizes the flow field around the blade to solve for the aerodynamic loads and turbulent wind conditions. For an aeroelastic analysis, the blade is discretized as well, and a dynamic mesh is required to follow the blade deformations and their effect on the aerodynamic loads. A two-way fluid-structure interaction (FSI) involves integrating turbulent models with dynamic structural models and a substantial computational cost despite their highly accurate results [11]. An alternative to CFD simulations is using deterministic models that can solve for the aerodynamic loads and structural dynamics with much less computational cost. Many software tools use these methods to generate an aeroelastic analysis of wind turbines.