Chenghai Xu

ZrB2-CNTs Nanocomposites Fabricated by Spark Plasma Sintering

ZrB2-based nanocomposites with and without carbon nanotubes (CNTs) as reinforcement were prepared at 1600 °C by spark plasma sintering. The effects of CNTs on the microstructure and mechanical properties of nano-ZrB2 matrix composites were studied. The results indicated that adding CNTs can inhibit the abnormal grain growth of ZrB2 grains and improve the fracture toughness of the composites. The toughness mechanisms were crack deflection, crack bridging, debonding, and pull-out of CNTs. The experimental results of the nanograined ZrB2-CNTs composites were compared with those of the micro-grained ZrB2-CNTs composites. Due to the small size and surface effects, the nanograined ZrB2-CNTs composites exhibited stronger mechanical properties: the hardness, flexural strength and fracture toughness were 18.7 ± 0.2 GPa, 1016 ± 75 MPa, and 8.5 ± 0.4 MPa·m1/2, respectively.

Application of CCG Sensors to a High-Temperature Structure Subjected to Thermo-Mechanical Load

This paper presents a simple methodology to perform a high temperature coupled thermo-mechanical test using ultra-high temperature ceramic material specimens (UHTCs), which are equipped with chemical composition gratings sensors (CCGs). The methodology also considers the presence of coupled loading within the response provided by the CCG sensors. The theoretical strain of the UHTCs specimens calculated with this technique shows a maximum relative error of 2.15% between the analytical and experimental data. To further verify the validity of the results from the tests, a Finite Element (FE) model has been developed to simulate the temperature, stress and strain fields within the UHTC structure equipped with the CCG. The results show that the compressive stress exceeds the material strength at the bonding area, and this originates a failure by fracture of the supporting structure in the hot environment. The results related to the strain fields show that the relative error with the experimental data decrease with an increase of temperature. The relative error is less than 15% when the temperature is higher than 200 °C, and only 6.71% at 695 °C.