Parameter Optimization of Reduced Fluid Model via Sparse Point Measurements

Abstract

Model order reduction is a modeling method that can facilitate the analysis and the control synthesis of distributed parameter systems, which are governed by partial differential equations. Many techniques have been proposed to further improve the property of the reduced order model based on full state information, which is usually not available in practical applications. In this paper, a reduced-order modeling method based on sparse point measurements is proposed for the benchmark system of flow past a cylinder. This method involves solving the dynamic optimization problems online, whose objective functions measure the differences between the predicted and observed variables on each time horizon. In this method, the sensor placement is also discussed, and we determine the sensor locations depending on a model-free optimization technique. Several numerical examples of different scenarios are simulated not only to illustrate the effectiveness of the proposed method but also to verify the discussion on the sensor placement strategy.