Evaluating Experimental Data Requirements for Spatially Varying Turbine Flowfields Through Uncertainty Analysis

Abstract

Experimental data from two cold-airflow turbine tests were evaluated. The two tests had different, relatively high spatial variations in the turbine exit flowfields. The objective of the research was to evaluate data requirements, including the averaging techniques, the number of measurements (spatial resolution), and the types of measurements needed, to obtain high-quality performance and code validation data for these types of flowfields. Uncertainty analysis methodology was used, and new uncertainty techniques were developed for the data studies. The results were then used to establish guidelines for planning future tests of other turbines. The guidelines set forth a quantified method for determining data requirements to meet specified test goals. Implementation of these guidelines will improve the quality of experimental results while decreasing time and cost for experimental studies of highly nonuniform turbine flowfields.