An Improved Particle Impact Model by Accounting for Rate of Strain and Stochastic Rebound

Abstract

Abstract A methodology for informing physics-based impact and deposition models through the use of novel experimental and analysis techniques is presented. Coefficient of restitution (CoR) data were obtained for Arizona Road Dust (ARD), AFRL02 dust, and each component of AFRL02 impacting a Hastelloy X plate at a variety of flow temperatures (295–866 K), surface temperatures (295–1255 K), particle velocities (0–100 m/s), and impact angles (0–90 deg). High speed particle shadow velocimetry (PSV) allowed individual impact data to be obtained for more than 8 million particles overall, corresponding to 20 combinations of particle composition, flow temperature, and surface temperature. The experimental data were applied to an existing physics-based particle impact model to assess its ability to accurately capture the physics of particle impact dynamics. Using the experimental data and model predictions, two improvements to the model were proposed. The first defined a velocity-dependent “effective yield strength,” designed to account for the effects of strain hardening and strain rate during impact. The second introduces statistical spread to the model output, accounting for the effect of randomizing variables such as particle shape and rotation. Both improvements were demonstrated to improve the model predictions significantly. Applying the improved model to the experimental data sets, along with known temperature-dependent material properties such as the elastic modulus and particle density, allowed the temperature dependence of the effective yield strength to be determined. It was found that the effective yield strength is not a function of temperature over the range studied, suggesting that changes in other properties are responsible for differences in rebound behavior. The improved model was incorporated into a computational simulation of an impinging flow to assess the effect of the model improvements on deposition predictions, with the improved model obtaining deposition trends that more closely match what has been observed in previous experiments. The work performed serves as a stepping stone towards further improvement of physics-based impact and deposition models through refinement of other modeled physical processes.