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 the artificial neural network. Young's modulus and C1 represent the material constants for elastic and hyperelastic models, respectively.;The optimum material constants are obtained using the neural network. The experimental results are used as the input of the network and the results from the simulations in Abaqus are used to train the neural network. The output of the network is the optimum material constants for different materials at different temperatures.;The results of the neural network are then verified by a set of wind tunnel experiments and computer simulations in Abaqus. For the purpose of the experiments, a rectangular HDPE plate is tested at different temperatures and wind speeds in a wind tunnel. Furthermore, an HDPE plate is modelled in Abaqus with the same dimensions under the same pressure using the optimum material constant relevant to the corresponding temperature. The deformation values obtained in the experiments are compared with the ones attained in Abaqus in order to verify the accuracy of the material constants.;Moreover, the application of the wood-plastic composites is investigated in the rotorblades industry by comparing the material performance with the material requirements of the industry. The operational parameters and conditions require the material to have a high stiffness, low density, and long fatigue life.;Finally, a small rotorblade is modelled in Abaqus to investigate the deformation of a blade made of WPC and aluminium. In addition, in order to introduce the challenges involved in the application of this material in the rotorblades industry, a brief review of the effects of humidity and ice on WPCs and rotorblades is presented at the end of this project.