| DigiWind

Material Characterisation for Numerical Modelling

Course Overview

This course trains learners in the application and analysis of a wide range of experimental material characterisation methods, providing valuable input properties for numerical modelling. The course focuses on the mechanical performance and manufacturing parameters of materials used in wind turbine components, including both metallic materials for structural parts and composites for blade structures. Through video demonstrations and complementary data files, learners will develop and apply Python-based tools to characterise key material properties. A core emphasis of the course is on directly linking high-quality experimental testing to numerical modelling, ensuring learners understand good practices not only for generating reliable material input data but also for designing experiments that reciprocally inform modelling efforts.

Course Highlights

Placeholder Text

MAIN GOAL

The course features a comprehensive library of different test methods tailored to various modelling tasks, covering both manufacturing and structural modelling of wind turbine materials. Measurement techniques addressed include permeability testing of fabrics, cure determination for adhesive and resin materials, elastic-plastic behavior analysis of metallic parts, stress-strain testing for adhesives and resins, viscoelastic material characterization, tensile and compressive testing of composites, single fiber testing for Weibull strength analysis, fatigue testing of composites, and cohesive law determination for adhesives, among others.

In addition to an overall introduction to Python-based data analysis, each measurement technique is presented as a standalone module, allowing for independent learning and flexible course progression. The course is designed to evolve over time, with not all measurement techniques covered in the initial offering. This modular approach enables different elements to be combined in various ways, depending on the specific focus of each course iteration and the needs of the learners.

Learning Outcomes

After completion of this course, you will be able to:

+ Specify appropriate test procedures to extract parameters for targeted numerical models.
+ Apply and develop Python scripts to analyse raw experimental data using scientific programming and data engineering techniques.
+ Assess and prepare high-quality input data for robust numerical analyses and simulations.
+ Validate numerical models by comparing them with experimental measurements.