Predicting electrical conductivity in Cu/Nb composites: a combined model-experiment study

TL;DR

This study combines theoretical modeling and experimental validation to predict how microstructural features like layer thickness and grain size influence electrical conductivity in Cu/Nb composites, crucial for high magnetic field applications.

Contribution

It introduces a microstructure-aware predictive model for Cu/Nb conductivity and validates it with experiments, highlighting the impact of interfaces and grain boundaries.

Findings

Interfaces and grain boundaries significantly affect conductivity.

Layer thickness below Cu's mean free path reduces conductivity.

Optimal microstructure requires larger average layer thickness.

Abstract

The generation of high magnetic fields requires materials with high electric conductivity and good strength properties. Cu/Nb composites are considered to be good candidates for this purpose. In this work we aim to predict, from theory, the dependence of electric conductivity on the microstructure, most notably on the layer thickness and grain sizes. We also conducted experiments to calibrate and validate our simulations. Bimetal interfaces and grain boundaries are confirmed to have the largest impact on conductivity in this composite material. In this approach, a distribution of the layer thickness is accounted for in order to better model the experimentally observed microstructure. Because layer thicknesses below the mean free path of Cu significantly degrade the conductivity, an average layer thickness larger than expected may be needed to meet conductivity requirements in order to minimize these smaller layers in the distribution. We also investigate the effect of variations in volume fraction of Nb and temperature on the material's conductivity.