A Practical Methodology for Quantifying the Random and Systematic Components of Unexplained Variance in a Wind Tunnel

Abstract

This paper documents a check standard wind tunnel test conducted in the Langley 0.3-Meter Transonic Cryogenic Tunnel (0.3M TCT) that was designed and analyzed using the Modern Design of Experiments (MDOE). The test designed to partition the unexplained variance of typical wind tunnel data samples into two constituent components, one attributable to ordinary random error, and one attributable to systematic error induced by covariate effects. Covariate effects in wind tunnel testing are discussed, with examples. The impact of systematic (non-random) unexplained variance on the statistical independence of sequential measurements is reviewed. The corresponding correlation among experimental errors is discussed, as is the impact of such correlation on experimental results generally. The specific experiment documented herein was organized as a formal test for the presence of unexplained variance in representative samples of wind tunnel data, in order to quantify the frequency with which such systematic error was detected, and its magnitude relative to ordinary random error. Levels of systematic and random error reported here are representative of those quantified in other facilities, as cited in the references.

Design of experiments: a statistical tool for PIV uncertainty quantification

“…However, they also highlighted that a residual level of variance is unavoidable, whose systematic component is likely to exceed its random component. An accurate assessment of uncertainty requires that systematic variations be taken into account along with the random variations in the data (DeLoach et al 2012). Aeschliman and Oberkampf (1998) first demonstrated how DOE could be applied to measurement UQ by choosing the bias error sources as factors of interest.…”

Design of experiments: a statistical tool for PIV uncertainty quantification

“…Design of Experiments (DOE) is a statistical tool used in many fields of science and engineering to evaluate the systematic effect of input factors on the measurement output. It was first employed for wind tunnel measurements by DeLoach (2000) and was shown to be effective for quantifying the systematic errors in experiment (DeLoach et al 2012). Smith and Oberkampf (2014) stated DOE to be an alternative tool to 2 Methodology DOE refers to the process of planning the experiment in order to collect appropriate data that can be analysed by statistical methods resulting in valid and objective conclusions (Montgomery 2013).…”