Uncertainty propagation for wind tunnel data post-processing: Application to a test model
Keywords:
Wind tunnel testing, uncertainty propagation, chain method, data post-processing, standard uncertainty, aerodynamic coefficients, flow parametersAbstract
Reliable wind tunnel data reduction requires a clear quantification of the uncertainty associated with each derived flow parameter and aerodynamic coefficient. In a typical post-processing workflow, the primary measurements, such as total pressure, static pressure, total temperature, model incidence, balance loads and pressure-tap readings, are successively used to calculate other variables like Mach number, static temperature, density, speed of sound, airspeed, dynamic pressure, viscosity, Reynolds number and aerodynamic coefficients. This paper presents a structured methodology for estimating the standard uncertainty of these quantities directly within the data-reduction methodology, using the chain (sensitivity) method, i.e. the first-order law of propagation of uncertainty. Rather than propagating the uncertainty of each intermediate quantity as if it were an independent input, every derived quantity is expressed as a function of the independent primary measurements and differentiated through the data-reduction equations by the chain rule, so that the correlations introduced by shared inputs are handled consistently. The procedure is applied point-by-point to a reference wind tunnel test campaign and demonstrated across four flow regimes: subsonic (M = 0.4), transonic (M = 1.05) and supersonic (M = 2.0 and M = 3.5). The resulting framework provides a traceable, reproducible procedure for embedding uncertainty estimation into wind tunnel data processing and can serve as a reference for similar experimental campaigns.
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