A systematic literature review of surrogate-assisted multidisciplinary coupled optimization of wind turbine blades for enhancing AEP and reducing LCOE in LWSRs with nanotechnology-enhanced materials and nanoscale optimization

Wind turbine blades play a crucial role in capturing wind energy and converting it into mechanical power. Optimizing blade design is especially important in low wind speed regions (LWSRs), where aerodynamic performance, structural integrity, and energy extraction strongly influence turbine efficiency. This systematic literature review (SLR) examines studies published between 2020 and 2025 that address blade design challenges in LWSRs, surrogate modeling within multidisciplinary design optimization (MDO) frameworks, and the integration of fluid–structure interaction (FSI) with techno-economic metrics such as AEP and LCOE. The review followed PRISMA guidelines and searched major databases, including Scopus, Web of Science, ScienceDirect, SpringerLink, and IEEE Xplore. From 3,303 initial records, 30 relevant studies are finally included. Commonly used methods include Kriging, RBF, and NSGA-II. Results show that surrogate models reduce CFD/FEA computational costs by 60–70% with errors below 5%, while NSGA-II achieved up to 6% AEP improvement and 4–7% LCOE reduction. Key gaps remain in real-world validation, limited LCOE–LCA integration, and data quality for surrogate training. Moreover, nanotechnology-based approaches enable nanoscale material enhancement, improved aerodynamic surface properties, and advanced structural performance, contributing to higher energy capture efficiency and reduced levelized cost of energy in wind turbine blades.