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• W4229081573 abstract "In power engineering, analysis of the thermal behavior of steam turbine and development of control strategies depend on online evaluations of the temperature fields of in-service components. Due to the complexity of structure and the variability of operating conditions, an online reconstruction technique fused with proper orthogonal decomposition (POD) and sparse sensor data is described herein to monitor the three-dimensional (3D) transient temperature fields of a steam turbine casing. The POD-based reduced order approach was driven by the high-fidelity data of the steam turbine under several start-up conditions, and the POD was used to extract a reduced set of vectors in the orthogonal basis to capture the spatial distribution modes of 3D temperature field. As a result, the temperature fields of steam turbine casing can be reconstructed using the reduced set of vectors in the orthogonal POD basis and the corresponding coefficients, and then it is feasible to online monitor the global temperature distribution by minimizing deviations between the predicted and measured temperatures. The feasibility and accuracy of the reconstruction approach were demonstrated using two numerical examples in comparison with high fidelity data. Finally, real sensor-measured sequential temperatures were used to dynamically determine the real temperature field of the casing. Notably, the reconstructed temperatures were highly consistent with the sensor-measured temperatures, and the time delay of almost every reconstruction process was less than 0.02 s. This proposed reconstruction approach can provide reliable online monitoring for the transient temperature fields of a 3D complex component based on sparse sensors, and it will provide effective support for the design, operation and maintenance." @default.
• W4229081573 created "2022-05-08" @default.
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• W4229081573 date "2022-05-01" @default.
• W4229081573 modified "2023-09-29" @default.
• W4229081573 title "Online reconstruction of 3D temperature field fused with POD-based reduced order approach and sparse sensor data" @default.
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• W4229081573 doi "https://doi.org/10.1016/j.ijthermalsci.2022.107489" @default.
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