An efficient offline sensor placement method for flow estimation

We present an efficient method to optimize sensor placement for flow estimation using sensors with time-delay embedding in advection-dominated flows. Our solution allows identifying promising candidates for sensor positions using solely preliminary flow field measurements with non-time-resolved Particle Image Velocimetry (PIV), without introducing physical probes in the flow. Data-driven estimation in advection-dominated flows often exploits time-delay embedding to enrich the sensor information for the reconstruction, i.e. it uses the information embedded in probe time series to provide a more accurate estimation. Optimizing the probe position is the key to improving the accuracy of such estimation. Unfortunately, the cost of performing an online combinatorial search to identify the optimal sensor placement in experiments is often prohibitive. We leverage the principle that, in advection-dominated flows, rows of vectors from PIV fields embed similar information to that of probe time series located at the downstream end of the domain. We propose thus to optimize the sensor placement using the row data from non-time-resolved PIV measurements as a surrogate of the data a real probe would actually capture in time. This optimization is run offline and requires only one preliminary experiment with standard PIV. Once the optimal positions are identified, the probes can be installed and operated simultaneously with the PIV to perform the time-resolved field estimation. We show that the proposed method outperforms equidistant positioning or greedy optimization techniques available in the literature. • We demonstrate an efficient method to locate sensors for flow estimation. • It uses lines of vectors in Particle Image Velocimetry snapshots as surrogates of probes during training. • It does not require real probe placement for the sensor position optimization. • The method is applicable for advection-dominated flows.