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.
Reduced chemical reaction mechanisms for ethanol and biodiesel surrogates (methyl-butanoate and methyldecanoate) are developed using element flux analysis and genetic algorithm optimization. The optimized mechanisms are validated against detailed mechanisms and experimental data over a wide range of temperatures, pressures, and equivalence ratios. Quantitative comparisons of ignition delay, peak temperature, and selected species show that the optimized mechanisms reproduce detailed-model predictions within 1-5% for ethanol and 5-15% for methyl-butanoate, while non-optimized reduced mechanisms exhibit significantly larger deviations. The methodology is further assessed in a lattice Boltzmann reactive flow test and a single-cylinder HCCI engine simulation, demonstrating improved agreement in pressure profile evolution with substantially reduced computational cost. The present work focuses on mechanism development and validation; full threedimensional CFD engine simulations and detailed emissions validation are not included and are considered future work. The reduced mechanisms and parameters are provided in the Supplementary Material to support reproducibility.
Underground hydrogen storage (UHS) is emerging as a critical enabling technology for large-scale integration of renewable energy. In this context, the wettability of H-2/brine/rock systems directly impacts flow behavior and distribution in the storage medium, influencing hydrogen trapping (and thus withdrawal) potential. Experimental contact-angle datasets for H-2/brine/rock systems remain sparse and demonstrate variability across lithologies, brine chemistry, and pressure-temperature conditions, limiting the development of transferable predictive tools for screening storage formations. Here, we compile 931 historical laboratory contact angle datasets spanning 12 lithologies (sandstone, carbonate, shale, basalt, coal, evaporite, and other mineral substrates) across a broad range of pressures (0.1-30 MPa), temperatures (293-353 K), and salinity conditions (0-23.3 wt% brine). Equivalent/meta-stable (thermodynamic) contact angles are standardized using Tadmor's correlation when advancing/receding angles are available, and a unified machine-learning workflow is implemented, including multivariate imputation, multicollinearity diagnostics, and systematic benchmarking of linear, tree-based, boosting, and neural-network models. The optimized CatBoost model achieves near-experimental predictive skill on a held-out test set (R-2 approximate to 0.96; RMSE approximate to 4.1 degrees), while also enabling explainable interpretation via SHAP and partial dependence analysis. Results suggest that surface roughness and organic content (TOC), together with pressure, aging fluid carbon number, lithology, and temperature, dominate hydrogen wettability responses, whereas individual ionic species contribute primarily through interaction-amplified effects. Mapping continuous predictions to wettability classes yields similar to 88% exact wettability state classification accuracy and 100% accuracy within +/- 1 class, supporting rapid UHS formation screening. The resulting framework provides a robust, generalizable, and interpretable tool for estimating H-2 wettability across geosystems and de-risking early-stage UHS design.
