On the assessment of a Bayesian validation methodology for data reduction models relevant to shock tube experiments

Experimental raw data provided by measuring instruments often need to be converted into meaningful physical quantities through data reduction modeling processes in order to be useful for comparison with outputs of computer simulations. These processes usually employ mathematical models that have to be properly calibrated and rigorously validated so that their reliability can be clearly assessed. A validation procedure based on a Bayesian approach is applied here to a data reduction model used in shock tube experiments. In these experiments, the raw data, given in terms of photon counts received by an ICCD camera, are post-processed into radiative intensities. Simple mathematical models describing the nonlinear behavior associated with very short opening times (gate widths) of the camera are developed, calibrated, and not invalidated, or invalidated, in this study. The main objective here is to determine the feasibility of the methodology to precisely quantify the uncertainties emanating from the raw data and from the choice of the reduction model. In this analysis of the methodology, shortcomings, suggested improvements, and future research areas are also highlighted. Experimental data collected at the Electric Arc Shock Tube (EAST) facility at the NASA Ames Research Center (ARC) are employed to illustrate the validation procedure.