Data-driven optimization of sparse sensor placement in thermal hydraulic experiments

Abstract: Thermal-Hydraulic (TH) experiments provide valuable insight into the physics of heat and mass transfer and qualified data for code development, calibration and validation. However, measurements are typically collected from sparsely distributed sensors, offering limited coverage over the domain of interest and phenomena of interest. Determination of the spatial configuration of these sensors is crucial and challenging during the pre-test design stage. This paper develops a data-driven framework for optimizing sensor placement in TH experiments, including (i) a sensitivity analysis to construct datasets, (ii) Proper Orthogonal Decomposition (POD) for dimensionality reduction, and (iii) QR factorization with column pivoting to determine optimal sensor configuration under spatial constraints. The framework is demonstrated on a test conducted in the TALL-3D Lead-bismuth eutectic (LBE) loop. In this case, the utilization of optical techniques, such as Particle Image Velocimetry (PIV), are impractical. Thereby the quantification of momentum and energy transport relies heavily on readings from Thermocouples (TCs). The test section was previously instrumented with many TCs determined through a manual process combining simulation results with expert judgement. The proposed framework provides a systematic and automated approach for sensor placement. The resulting TCs exhibit high sensitivity to the variation of uncertain input parameters and enable accurate full field reconstruction while maintaining robustness against measurement noise.