Surrogate-based stochastic optimization of horizontal-axis wind turbine composite blades

Mishal Thapa, Samy Missoum

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


In this paper, a framework for stochastic optimization of horizontal-axis wind turbine composite blades is presented. It is well known that the structural responses of the wind turbines (e.g., natural frequency, blade tip displacement) are affected by uncertainties in, for instance, wind conditions and material properties. These uncertainties can have an undesirable impact on the performance and reliability of wind turbine blades, and therefore must be accounted for. However, performing the stochastic optimization of wind turbine blades is challenging because of the computational cost and the need to incorporate several disciplines. To make the stochastic problem tractable, a surrogate-based optimization framework using Kriging and support vector machines with adaptive refinement was developed. The framework is based on blade element momentum theory for aerodynamics coupled with a fully parameterized finite element structural model. The framework is used to find the optimal chord and twist distribution of a composite blade and, notably, the optimal control features such as tip-speed ratio and pitch angle with respect to operating wind speeds. The objective function considered is the ratio of mass to the expected value of the Annual Energy Production subjected to several probabilistic constraints on the blade tip deflection, natural frequencies, and failure indices. Uncertainties in material properties, as well as wind conditions are considered. The results of this industrial application demonstrate that the framework can lead, in a reasonable number of function calls, to an optimal composite blade with higher efficiency and robustness to uncertainty.

Original languageEnglish (US)
Article number41
JournalStructural and Multidisciplinary Optimization
Issue number2
StatePublished - Feb 2022


  • Adaptive sampling
  • Composite wind turbine blades
  • Parametric finite element blade model
  • Stochastic optimization
  • Surrogate modeling

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design
  • Control and Optimization


Dive into the research topics of 'Surrogate-based stochastic optimization of horizontal-axis wind turbine composite blades'. Together they form a unique fingerprint.

Cite this