@Article{CiCP-35-5,
author = {Li, Han and Lu, Jialiang and Ji, Hongjun and Lizhan, Hong and Helin, Gong},
title = {A Noise and Vibration Tolerant ResNet for Field Reconstruction with Sparse Sensors},
journal = {Communications in Computational Physics},
year = {2024},
volume = {35},
number = {5},
pages = {1287--1308},
abstract = {<p style="text-align: justify;">The aging of nuclear reactors presents a substantial challenge within the
field of nuclear energy. Consequently, there is a critical demand for field reconstruction techniques capable of obtaining comprehensive spatial data about the condition
of nuclear reactors, even when provided with limited observer data. It is worth noting
that prior research has often neglected to account for the impact of noise and changes
in sensor states that can occur during actual production scenarios. In this paper, the so
called Noise and Vibration Tolerant ResNet (NVT-ResNet) is proposed to tackle these
challenges. By introducing noise and vibrations into the training data, NVT-ResNet
is able to learn the tolerance thus exhibits robustness for the field reconstruction. The
influence of sensor numbers on the model’s performance is also investigated. Numerical results convincingly demonstrate that even with limited sparse sensors exposed
to a noise with magnitude of 5% and vibrations, NVT-ResNet consistently achieves a
reconstruction field of relative $L_2$ error within 1% and relative $L_∞$ error of less than
5% in average sense. Additionally, NVT-ResNet exhibits remarkable computational
efficiency, with field reconstruction taking only microseconds. This makes it a viable
candidate for integration into online monitoring systems, thereby enhancing the safety
performance of nuclear reactors.</p>},
issn = {1991-7120},
doi = {https://doi.org/10.4208/cicp.OA-2023-0252},
url = {https://global-sci.com/article/90955/a-noise-and-vibration-tolerant-resnet-for-field-reconstruction-with-sparse-sensors}
}