Shallow Recurrent Decoder networks are a novel data-driven methodology able to provide accurate state estimation in engineering systems, such as nuclear reactors. This deep learning architecture is a robust technique designed to map the temporal trajectories of a few sparse measures to the full state space, including unobservable fields, which is agnostic to sensor positions and able to handle noisy data through an ensemble strategy, leveraging the short training times and without the need for hyperparameter tuning. The architecture was successfully applied to the Molten Salt Fast Reactor concept; now, this work considers the performance of Shallow Recurrent Decoders on a deployed reactor concept. 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 objectives of this work are therefore: 1) presenting the first application of SHRED to a deployed nuclear reactor (TRIGA Mark II); 2) the integration of hybrid synthetic/experimental data within the SHRED framework; 3) a systematic assessment of SHRED robustness under physically constrained, low-dynamics sensor locations; and 4) a quantitative evaluation of SHRED self-correction capability when model-to-data discrepancies are present. Indeed, this approach is capable of accurately reconstruct every field of interest in real-time, using both synthetic (with an average relative error lower than 4% in euclidean norm) and experimental data (with a RMSE of 1.52 K on the temperature, compared of 1.85 K of the CFD), making it suitable for interpretable monitoring and control purposes in the context of building digital twins for nuclear reactors.
The digitalization of power networks and the integration of distributed generation have increased protection complexity in underground transmission systems, where non-stationary transient faults challenge conventional detection methods. This study presents a multiscale db4 Discrete Wavelet Transform (DWT) energy representation combined with a hybrid Lazy Rule-Guided classifier for fast underground fault detection under Supervisory Control and Data Acquisition (SCADA) latency constraints. Three phase and ground currents from a simulated 12-class underground feeder are denoised, decomposed into level-3 wavelet bands, and aggregated into multiscale energy vectors. The proposed approach is evaluated against classical, metaheuristic, and deep learning benchmarks. It achieves 0.9106 accuracy and 0.9084 macro-F1 with an inference latency of 0.0035 ms per sample. It achieves performance comparable to Random Forest (RF) with 103 - 104 times lower latency than Convolutional Neural Networks (CNN) and Transformer-based models. Robustness analysis under 10 dB additive white Gaussian noise shows limited performance degradation of <= 7% over classification accuracy. The resulting interpretable decision rules support real-time, physics-informed underground cable protection and practical SCADA deployment. With future extension to field SCADA, Phasor Measurement Units (PMU) data, and evaluation under a broader range of noise levels, the model could be deployed in power systems' real-time protection.
Robust design optimisation (RDO) of performance-based seismic design (PBSD) has been established as a rigorous approach for obtaining optimal solutions that are minimally sensitive to record-to-record (RTR) variability of ground motions used in nonlinear response history analysis (NLRHA). However, this method is not able to explicitly account for the suite-to-suite (STS) variability due to the epistemic uncertainties associated with ground motion selection, especially when each suite is selected and scaled based on the requirements of the seismic design codes currently in force. To account for this uncertainty, this paper proposes a novel formulation for robust optimisation, which explicitly accounts for ground motion suite variability. In this method, one objective is to minimise the dispersion in the probability of limit state violation due to STS variability and the other objective is to minimise the economic costs. To increase the computational efficiency of the proposed method, surrogate seismic demand models are developed and applied to assess the probability of limit state violation. The application of the proposed framework is demonstrated using a case study of a typical reinforced concrete (RC) highway bridge with cylindrical piers, and the ability of the RDO framework to account for the STS variability is discussed. Finally, the Pareto-optimal solutions obtained from the proposed approach are compared with those given by the conventional RDO approach.
The increasing demand for durable and efficient corrosion inhibitors has intensified interest in macrocyclic ligands as an emerging class of tunable corrosion inhibitors. Several salient features, including macrocyclic effect, host-guest recognition, preorganized multidentate coordination, extensive surface coverage, and metal-ion complexation within a single molecular framework, make them an attractive and effective class of inhibitors. These properties also make them different from traditional corrosion inhibitors. The present review provides broad coverage of phthalocyanines, crown ethers, porphyrins, azamacrocycles, and calixarenes, with particular focus on their structure-performance relationships, coordination mechanisms, adsorption, and the formation of corrosion-protective films on different substrates, including steel, iron, aluminum alloys, copper, and magnesium in acidic, alkaline, and saline environments. Macrocyclic inhibitors exhibit a broad range of inhibition efficiencies depending on their molecular structure, metallic substrate, and experimental conditions. This review also highlights the roles of donor atoms, molecular geometry, ring or cavity size, metal coordination, substituents, and solubility in the adsorption, coordination, and corrosion inhibition of macrocyclic ligands. This article also discusses their challenges related to poor solubility, synthetic complexity, scalability, environmental consideration, and limited industrial-scale validation under realistic conditions. Lastly, research gaps in macrocyclic-based corrosion inhibition have also been presented with outlines, enabling effective and sustainable design of macrocycle-based corrosion inhibitors.
Low salinity waterflooding (LSWF) has emerged as a cost-effective and environmentally favorable enhanced oil recovery (EOR) technique, attracting extensive research attention worldwide. Various reviews existing in the literature systematically summarize the progress of LSWF, but with a focus on reservoirs outside China. Given the distinct geological characteristics, reservoir heterogeneity, crude oil compositions, and formation water chemistries of Chinese oilfields, a comprehensive and region-specific comprehensive review is still lacking. Here, we present a critical review of current studies on LSWF conducted in China, synthesizing laboratory investigations, numerical simulations, and theoretical analysis. This review is divided into four sections. First, the EOR performance of LSWF in different reservoir types (i.e., sandstone, carbonate, and unconventional oil reservoirs) in China is discussed. Second, important factors affecting LSWF performance, such as salinity, ion types, rock mineral content, oil properties, and reservoir conditions, are analyzed. Third, the fundamental mechanisms proposed to explain LSWF in Chinese reservoirs, including fines migration, wettability alteration, double layer expansion, multicomponent ion exchange, and pH-related effects, are critically discussed. Finally, recent advances in hybrid LSWF technologies combined with other EOR methods, such as chemical flooding, gas injection, and nanofluids, are reviewed. This article provides a detailed literature review to understand the status, challenges, and future research directions of LSWF in China, aiming to support more effective design and field implementation.
