Precise modeling of electric vehicle (EV) energy consumption is fundamental to the efficient design and management of modern transportation systems. While physics-based models offer superior interpretability, they often struggle with limited adaptability to dynamic driving conditions and heterogeneous vehicle platforms. To bridge this gap, achieving real-time and accurate calibration of physical model parameters becomes essential. This paper proposes a novel two-stage Bayesian optimization framework that integrates Contextual Bayesian Optimization (CBO) and Transfer Bayesian Optimization (TBO). In the first stage, the CBO module learns a context-aware mapping between operating conditions and physical parameters within a source domain. In the second stage, the TBO module leverages the learned prior knowledge to achieve rapid adaptation to a target vehicle domain with minimal data requirements. We evaluate the proposed framework using real-world datasets from BMW i3 and Tesla Model 3. Experimental results demonstrate that the proposed framework achieves a per-second WMAPE of 17.27% in cross-condition scenarios. For cross-vehicle transfer, the primary out-of-sample evaluation on the held-out 70% of the Tesla trips yields a WMAPE of 24.82% and a total energy error of 12.65%. The CBO results further demonstrate rapid convergence under a limited online evaluation budget. This research provides a scalable and sample-efficient solution for high-fidelity energy modeling across diverse driving conditions and vehicle platforms.
High-manganese steel (HMS) is widely used in wear-resistant components, but its poor machinability and dependence on casting increase the difficulty of same-material repair. Laser cladding provides a promising route for HMS repair; however, the load-dependent evolution of work-hardening mechanisms in Fe–Mn HMS coatings remains insufficiently understood. In this study, a Fe–Mn HMS coating was fabricated by laser cladding using self-prepared high-purity Fe–Mn powder. Tribological tests were conducted under a wide load range of 25–300 N. Compared with the cast HMS substrate, the coating exhibited an 18.5% reduction in average grain size and a higher initial geometrically necessary dislocation (GND) density, resulting in a distinct work-hardening pathway and a wider stable hardening window. Under low-to-intermediate loads, the coating developed a dense nanocrystalline surface layer with a maximum thickness of approximately about 49 μm, whereas the substrate was mainly strengthened by the twinning-induced plasticity (TWIP) effect. Under heavy loads, both materials were dominated by TWIP-related feather-like twins, but the coating retained a higher twin density and lower wear loss. The strengthening mechanisms are therefore governed by the coupling and competition among grain-refinement strengthening, nanocrystalline layer formation, and the TWIP effect. These findings clarify the dynamic wear-resistance mechanism of laser-cladding HMS coatings and provide guidance for same-material repair of HMS wear-resistant components.
Water pollution and shortages are critical global challenges, and the green synthesis of nanomaterials provides a sustainable solution for water purification, targeting the removal of dyes, heavy metals, and bacteria. Utilizing agricultural waste (e.g., cladosiphon, bamboo, cane stalks), this approach offers eco-friendly, cost-effective alternatives to conventional methods. Recent advancements in green synthesis techniques have led to the development of carbon-based nanomaterials like carbon dots, quantum dots, nanofibers, nanotubes, and metal oxides such as ZnO, TiO2, and IONPs. A bibliometric analysis of 270 relevant articles (2015-2024) reveals a significant rise in publications, peaking at 57 articles in 2023, with India leading the field with 89 publications. CVarious methods have shown impressive pollution reduction: CNTs at 0.05 g/L removed 99.8% of Methylene Blue (MB), while TiO2 eliminated 100% of RR250 dye in 60 min. Green-synthesized nanomaterials, like iron and silver, also demonstrate potential, with iron nanoparticles achieving 100% Cr (VI) removal and silver from Acacia ehrenbergiana reducing RhB dye by 96%. This review emphasizes the novelty of hybrid synthesis methods that combine green chemistry with traditional techniques, offering a useful solution for addressing challenges in scalability. By integrating these approaches, the potential of green-synthesized nanomaterials for large-scale water treatment is significantly enhanced. Future studies will focus on optimising these synthesis processes through AI-driven techniques and incorporating life cycle assessments (LCA) to evaluate the long-term environmental impact and commercial feasibility of these materials, ensuring their broader applicability and sustainability.
The fifth edition handbook of FMEA introduced action priority (AP) to address issues caused by difference in the weightings of risk factors severity (S), occurrence (O), and detection (D). However, it omitted the underlying design theory of AP, limiting interpretability and scenario-specific adaptation. This study proposes a flexible AP design methodology incorporating customizable risk factor weights and adjustable failure mode risk levels. First, a risk matrix for O and D is constructed for different S risk levels, and a risk index based on factor-weighted distance is defined. A risk utility function then models the risk preference differences, generating a stratified distribution of S risk levels, which determines failure mode risk levels and corresponding AP assignments. Applying this method with weights of 0.5, 0.3, 0.2 for S, O, D, and a Type-A index distribution yields results fully consistent with the handbook. Furthermore, an enhanced AP is proposed, subdividing the three-category AP (High, Medium, and Low) into 125 distinct levels. Finally, the method is developed to process failure mode assessment in aviation manufacturing. A case study shows that a refined five-category AP (Extremely High to Extremely Low) improves discrimination among failure modes within the same category and enhances practical applicability.
Localized scouring at monopile foundations is one of the major technical challenges hindering offshore wind projects. Scour, by lowering the structural capacity, is a direct threat to turbine safety and the long-term performance. A review of the latest developments in this area is presented in this paper. It first looks at how the combined action of waves and currents affects the pile flow field and explains in detail the formation of horseshoe and wake vortices and how they move the sediment. Next, the article goes into seabed liquefaction due to waves and its effect on scour. Then, it considers the impact of localized scour on the deformation and dynamic response of monopiles. At last, it gives an account of the main methods of scour monitoring such as those using mechanical, acoustic, optical, electromagnetic waves, and vibration frequency techniques, and it also gives a short description of the makeup and working of scour monitoring systems for single-pile foundations in offshore wind farms. The purpose of this paper is to provide technical support for the scour monitoring of offshore wind power single pile foundation and ensure the safe and stable operation of wind power facilities.
