Mechanical behaviour of wood-plastic composites at cold temperatures for potential application to the manufacturing of wind turbine blades

Renewable energy resources, including wind power, are part of the solution to the global energy problem. Over the past few decades, various types of materials such as wood, aluminium and composites have been used in the manufacturing of wind turbine blades. However, no investigations have been conducted on the application of wood-plastic composites (WPCs) for the production of small rotorblades for wind turbines in northern conditions; characterized by extremely cold temperatures and major winter storms. In order to investigate the application of WPCs in the rotorblades industry, the mechanical behaviour of this material under the operational conditions of a wind turbine should be investigated. In cold climate regions, wind turbines are exposed to severe conditions characterized by temperatures below — 50°C and wind speeds sometimes exceeding 25 m/s. This thesis is mostly divided into two main parts. First, the mechanical behaviour of wood-plastic composites is investigated using the experimental and numerical characterization of the material at cold temperatures. The studies are conducted under the maximum pressure of 18 psi within a temperature range of —50°C to +50°C with 25°C increments. Wood-plastic composite membranes with mass concentration of 20, 30, 40, 50, and 60wt% of wood fibre are tested and high-density polyethylene (HDPE) is used as the thermoplastic matrix of the composites. Second, the structural behaviour of a rotorblade made with WPCs is investigated under the operating conditions of a wind turbine. In this research, the bubble inflation technique is used for experimental and numerical modelling of the behaviour of WPCs. The elastic (Hooke's law) and hyperelastic (neo-Hookean) models, along with the artificial neural networks, are used to characterize the mechanical behaviour of the membranes. The elastic and hyperelastic behaviour of the specimens are modelled in Abaqus with different material constants in order to generate a learning library for