A Noise and Vibration Tolerant ResNet for Field Reconstruction with Sparse Sensors (Communications in Computational Physics, 2024)
The aging of nuclear reactors presents a substantial challenge within thefield of nuclear energy. Consequently, there is a critical demand for field reconstruction techniques capable of obtaining comprehensive spatial data about the conditionof nuclear reactors, even when provided with limited observer data. It is worth notingthat prior research has often neglected to account for the impact of noise and changesin sensor states that can occur during actual production scenarios. In this paper, the socalled Noise and Vibration Tolerant ResNet (NVT-ResNet) is proposed to tackle thesechallenges. By introducing noise and vibrations into the training data, NVT-ResNetis able to learn the tolerance thus exhibits robustness for the field reconstruction. Theinfluence of sensor numbers on the model’s performance is also investigated. Numerical results convincingly demonstrate that even with limited sparse sensors exposedto a noise with magnitude of 5% and vibrations, NVT-ResNet consistently achieves areconstruction field of relative $L_2$ error within 1% and relative $L_∞$ error of less than5% in average sense. Additionally, NVT-ResNet exhibits remarkable computationalefficiency, with field reconstruction taking only microseconds. This makes it a viablecandidate for integration into online monitoring systems, thereby enhancing the safetyperformance of nuclear reactors.