基于浅层循环解码器的受约束感知与可靠状态估计：应用于TRIGA Mark II反应堆

浅层循环解码器网络是一种新型的数据驱动方法，能够在工程系统中（如核反应堆）提供准确的状态估计。该深度学习架构是一种稳健的技术，旨在将少量稀疏测量值的时序轨迹映射到完整的状态空间，包括不可观测场，其特征对传感器位置不敏感，能够通过集成策略处理噪声数据，同时具有短训练时间且无需调参。ollowing its application to a novel reactor concept, this work investigates the performance of Shallow Recurrent Decoders when applied to a real system. The underlying model is represented by a fluid dynamics model of the TRIGA Mark II research reactor; the architecture will use both synthetic temperature data coming from the numerical model and leveraging experimental temperature data recorded during a previous campaign. The objective of this work is, therefore, two-fold: 1) assessing if the architecture can reconstruct the full state of the system (temperature, velocity, pressure, turbulence quantities) given sparse data located in specific, low-dynamics channels and 2) assessing the correction capabilities of the architecture (that is, given a discrepancy between model and data, assessing if sparse measurements can provide some correction to the architecture output). As will be shown, the accurate reconstruction of every characteristic field, using both synthetic and experimental data, in real-time makes this approach suitable for interpretable monitoring and control purposes in the framework of a reactor digital twin.