Sensitivity Analysis of Unsteady Flow Fields and Impact of Measurement Strategy

Abstract

Difficulty of data assimilation arises from a large difference between the sizes of a state vector to be determined, that is, the number of spatiotemporal mesh points of a discretized numerical model and a measurement vector, that is, the amount of measurement data. Flow variables on a large number of mesh points are hardly defined by spatiotemporally limited measurements, which poses an underdetermined problem. In this study we conduct the sensitivity analysis of two- and three-dimensional vortical flow fields within a framework of data assimilation. The impact of measurement strategy, which is evaluated by the sensitivity of the 4D-Var cost function with respect to measurements, is investigated to effectively determine a flow field by limited measurements. The assimilation experiment shows that the error defined by the difference between the reference and assimilated flow fields is reduced by using the sensitivity information to locate the limited number of measurement points. To conduct data assimilation for a long time period, the 4D-Var data assimilation and the sensitivity analysis are repeated with a short assimilation window.

Efficient Sensor Node Selection for Observability Gramian Optimization

“…Recent advances in data science techniques have enabled us to extract reduced-order models from vastly large-scale measurements of complex phenomena [30][31][32][33][34][35][36][37][38][39]. Therefore, the optimized sensor measurement is gaining importance for the reconstruction of complex phenomena from sensor measurements based on the data-driven reduced-order models [40,41], as well as for model-free machine learning [42,43] and data assimilation [44][45][46].…”