Energy and Climate

The energy research performed at PSI focuses on processes that can be used in sustainable and safer technologies, ideally with minimal CO2 emissions. The main emphasis is on renewable energy sources. The ESI (Energy System Integration) platform enables research and industry to test solutions for integrating renewables into the existing energy supply. Another focus in this area is the safer use of nuclear energy. These activities are supplemented by analyses giving a comprehensive assessment of energy systems. PSI scientists in the Energy and Environment division study the chemical processes that take place in the atmosphere.

Find out more at: Overview Energy and Climate

tensor_tomography

Macroscopic mapping of microscale fibers in freeform injection molded fiber-reinforced composites using X-ray scattering tensor tomography

Prediction of the mechanical properties dictated by the local microfiber orientation is essential for the performance characterization of fiber-reinforced composites. Typically, tomographic imaging methods that provide fine spatial resolution are employed to investigate various materials' local micro- and nano-architecture in a non-destructive manner. However, conventional imaging techniques are limited by a substantial trade-off between the structure size of interest and the accessible field of view (FOV). Researchers from the TOMCAT beamline at Paul Scherrer Institut, Xnovo Technology ApS, and the Technical University of Denmark have demonstrated the potential of X-ray scattering tensor tomography for industrial applications by characterizing the microstructure of a centimeter-sized industrially relevant freeform injection molding fiber-reinforced composite sample. This emerging technique provides unprecedented access to microstructural information over centimeter-sized sample volumes paving the way towards its potential integration as an invaluable tool, for instance, in the fiber-reinforced-composite (FRC) industry. The obtained fiber orientation and anisotropy information over statistically relevant large volumes can be used to predict the mechanical properties of final products, optimize production parameters, and improve fiber injection molding simulation frameworks. The work is published in Composites Part B: Engineering on 15 March 2022.

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