Extraction of One-Dimensional Flow Properties from Multidimensional Data Sets

Abstract

The engineering design and analysis of air-breathing propulsion systems relies heavily on zero- or one-dimensional properties (e.g., thrust, total pressure recovery, mixing, and combustion efficiency, etc.) for figures of merit. The extraction of these parameters from experimental data sets and/or multidimensional computational data sets is therefore an important aspect of the design process. A variety of methods exist for extracting performance measures from multidimensional data sets. Some of the information contained in the multidimensional flow is inevitably lost when any one-dimensionalization technique is applied. Hence, the unique assumptions associated with a given approach may result in one-dimensional properties that are significantly different than those extracted using alternative approaches. The purpose of this effort is to examine some of the more popular methods used for the extraction of performance measures from multidimensional data sets, reveal the strengths and weaknesses of each approach, and highlight various numerical issues that result when mapping data from a multidimensional space to a space of one dimension.