Abstract
The technique of stochastic estimation is examined as a specific application of linear least squares modelling. Factors that are relevant to the objectives of estimation in fluids, such as the number of sensors, the use of multiple time lags, and the strength of linear correlations, are discussed in the context of a general regression formulation. We consolidate the established findings of several research fields in order to outline clearly the potential pitfalls and reasonable performance expectations of these empirical strategies. Experimental measurements of velocity and fluctuating pressure in the wake of a blunt trailing edge body are used for quantitative illustration of key considerations for model construction and performance evaluation. It is emphasized that estimator accuracy is influenced strongly by the physical relationships among the measured variables, in addition to their correlation with the estimated variable. The evaluation of several performance metrics on an independent test set provides valuable information for the selection of a suitably complex model. In particular, “variance inflation” is interpreted as an indicator of the potential amplification of noise by a stochastic estimator.
Published Version
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