Thermal fatigue damage in monofilament reinforced copper for heat sink applications in divertor elements
Source abstract
Abstract
Abstract In fusion reactor systems extreme conditions require materials with high temperature and radiation resistance. The divertor component consists of a plasma facing W plate attached to a Cu heat sink to extract the heat from the nuclear reaction chamber coolant. The Coefficient of Thermal Expansion (CTE) mismatch between the W plate and the Cu heat sink causes interface delamination reducing the long term stability of the divertor. To avert this problem, composites are developed as interlayer materials with a high thermal conducting Cu matrix reinforced with up to 50 vol.% SiC or W monofilaments to increase the mechanical strength and to reduce the CTE mismatch. Thermal stresses are transferred from the macroscopic interface between the components into the bulk of the composite. Oscillating micro stresses may lead to fiber delamination and matrix damage during thermal cycling. Different matrix alloys, fiber materials and interface designs are investigated. In situ neutron diffraction performed during thermal cycling show the effect of bonding strength on the stress amplitudes expected under service conditions. The long term stability is tested by measurements after further ex situ cycling. Thermal fatigue damage and its propagation are visualized by in situ as well as ex situ high resolution synchrotron tomography. The combination of both methods helps to understand the strain induced damage mechanisms. Weak bonding leads to delamination of the fiber–matrix interfaces. Strong bonding causes severe matrix deformation and damage. Fiber cracks originating from sample production cause accumulating thermal fatigue damage during thermal cycling.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Fusion materials and technologies · Nuclear Materials and Properties · Superconducting Materials and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (18)
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