Small-Scale Turbines (SSTs) are among the most important energy-extraction-enabling technologies in domestic power production systems. However, owing to centrifugal forces, the high rotating speed of SSTs causes excessive strains in the aerofoil portions of the turbine blades. In this paper, a structural performance analysis is provided by combining Finite Element Methods (FEM) with Computational Fluid Dynamics (CFD). The primary objective was to examine the mechanical stresses of a Small-Scale Radial Turbine (SSRT) constructed utilizing 3D printing technology and a novel plastic material, RGD 525, to construct a SSRT model experimentally. After introducing a suitable turbine aerodynamics model, the turbine assembly and related loads were translated to a structural model. Subsequently, a structural analysis was conducted under various loading situations to determine the influence of different rotational speed values and blade shapes on the stress distribution and displacement. Maximum von Mises and maximum main stresses are significantly affected by both the rotor rotational speed and the working fluid input temperature, according to the findings of this research. The maximum permitted deformation, on the other hand, was more influenced by rotational speed, while the maximum allowable fatigue life was more influenced by rotating speed and fluid intake temperature. Also, the region of the tip shroud in the rotor had greater deflection values of 21% of the blade tip width.

The growing demand for renewable energy has amplified the need for efficient and reliable wind turbine technologies, where understanding aerodynamic performance and aeroelastic behavior plays a critical role. In this study, a high-fidelity computational fluid dynamics (CFD) model was developed to analyze the aerodynamic loads and structural responses of a 2 kW horizontal-axis wind turbine, while an artificial neural network (ANN) was trained using CFD-generated data to predict power output and aeroelastic characteristics. The work combines ANN predictions and CFD simulations to determine the feasibility of machine learning as a surrogate model, which is much less expensive in terms of computational costs and time, with no negative effects on the accuracy. Findings indicate ANN predictions are closely comparable to CFD results with under 5–7% deviation at optimal blade pitch angles, which was shown to be very reliable in capturing nonlinear aerodynamic trends at different wind speeds and blade pitch angles. In addition, the obtained result emphasizes the example of the trade-off between aerodynamic efficiency and structural safety, where the largest power coefficient (0.42) was achieved at $\left(0^{°}\right)$ pitch and the tip deflections were reduced by almost 60% as the pitch was raised to $\left(5^{°}\right)$. Such results substantiate the usefulness of ANN-based methods in the rapid aerodynamic and aeroelastic simulation of wind turbines and provide a prospective direction for effectively designed wind power generation and optimization.