This study presents an aerostructural optimization process for wind turbine blades aimed at enhancing the turbine’s performance. The optimization framework integrates DAFoam as the computational fluid dynamics (CFD) solver, TACS as the finite element method (FEM) solver, Mphys for fluid–structure coupling, and SNOPT as the optimizer within the OpenMDAO framework. The objective is to simultaneously increase the torque generated by the wind turbine while decreasing the mass of the blade, thereby improving its efficiency. The design variables in this optimization process are the blade shape and panel thickness. The aerodynamic objective function is torque, a key performance indicator for wind turbine efficiency. The structural objective function is the blade mass, as reducing mass is essential to minimize material and manufacturing costs. The optimization process utilizes the integrated capabilities of DAFoam, TACS, Mphys, and SNOPT to iteratively evaluate and modify the blade shape and panel thickness. The OpenMDAO framework facilitates seamless communication between the solvers and the optimizer, ensuring a well-coordinated, efficient optimization process. The results of the optimization show a 6.78% increase in torque, which indicates a significant improvement in the wind turbine’s energy production capacity. Additionally, a 4.22% decrease in blade mass demonstrates a successful reduction in material usage without compromising structural integrity. These findings highlight the potential of the proposed aerostructural optimization process to enhance the performance and cost-effectiveness of wind turbine blades, contributing to the advancement of sustainable energy solutions. This work represents the first attempt to implement DAFoam for wind turbine aerostructural design optimization.

This study examines the inclusion of uneven terrains in wind farm layout optimization by the integration of digital elevation model (DEM) data, Gaussian wake modeling, and terrain-adaptive hub heights within a computationally efficient techno-economic workflow.The framework transitions from idealized flat-terrain benchmarks to topographically constrained environments. A key technical feature is the incorporation of terrain-dependent hub heights which preserves the optimization problem's dimensionality by linking hub elevation directly to local ground height. Annual energy production (AEP) is maximized under multi-directional wind conditions using gradient-based optimization approaches, while internal cable infrastructure is evaluated via a minimum spanning tree (MST) representation and quantified through a levelized cost of energy (LCOE) formulation. The process is applied to the mountainous Greek islet of Agios Georgios demonstrating turbine placement trends that favor high-yield, ridge-aligned corridors. Sensitivity analysis shows that while higher turbine density increases gross AEP, non-linear infrastructure scaling and cumulative wake losses lead to diminishing economic returns. For the examined islet application, a compact 9-turbine (45 MW) configuration reaches an output of 172 GWh/year at 31 €/MWh, achieving a specific yield 27\% higher than the currently deployed 73.2 MW wind farm. The results highlight that in complex terrain, optimal wind farm configuration is governed by the interplay between aerodynamic performance, geometric feasibility, and infrastructure scaling. This approach provides a robust and extendable methodology for the high-fidelity screening of wind farm layouts in mountainous and island environments.

Wind farm layout optimization (WFLO) often utilizes engineering wake models where wake decay coefficients serve as a proxy for atmospheric stability. However, the sensitivity of optimal topologies to these stability assumptions and their impact on electrical infrastructure remain largely unquantified. This study employs a Gaussian wake framework and gradient-based optimization to evaluate high-density turbine layouts across three atmospheric stability regimes, statistically validated across 10 random initializations. Results indicate that mean optimized AEP ranges from 293.35 to 348.08 GWh across stability regimes, while optimization yields divergent geometric strategies, most notably a perimeter-loading logic under stable conditions. Cross-evaluation reveals a 3.5% energy penalty in stability-blind designs. Notably, internal cable lengths varied by less than 2% across configurations despite significant layout reconfiguration, suggesting that aerodynamic wake mitigation can be decoupled from infrastructure planning in space-constrained domains. Stable conditions further require approximately 17% more iterations to converge, indicating a more complex objective landscape. These findings provide a controlled benchmark for stability-aware layout frameworks.