Quantifying Surface Regression Rate in a Model Solid-Fuel Ramjet

The present work details combined experimental and computational investigations of the fuel surface regression rate within a small-scale optically accessible solid fuel ramjet (SFRJ) combustor. Photogrammetric reconstruction of postburn fuel grains was used to quantify two-dimensional surface regression rates of the fuel grains, and uncertainty was calculated using the Monte Carlo method. Regression rates were also measured for solid fuel burns that were quenched directly after ignition to quantify the transient regression rates during piloted combustion. Results demonstrated significant variation in the steady phase regression rate from the total regression rate after accounting for the ignition correction. The largest sources of uncertainty associated with the photogrammetric reconstruction process were identified through comparison between sample standard error and calculated standard uncertainty. Accompanying three-dimensional Reynolds-averaged Navier–Stokes (RANS) and detached-eddy simulations (DES) of the model SFRJ combustor were conducted to compare with experimental regression rates. The DES simulations showed superior accuracy in the prediction of surface regression structure in comparison to RANS, owing to the former’s ability to resolve the turbulent flow processes. The numerical models showed satisfactory agreement in spanwise averaged regression rate prediction, highlighting the importance of proper boundary-condition specification.