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.
Heavy fuel oil, as a traditional fuel for large ships, and biodiesel, as a promising zero-carbon fuel for the shipping industry, both have a wide range of applications. To address the current gap in fundamental research, this study employs a constant volume combustion chamber to conduct a comparative investigation of the spray and ignition characteristics of heavy oil and biodiesel. The spray and evaporation characteristics of RME180 are slightly better than those of RMG380, and heavy oil injection is abnormal at low ambient temperature. The spray penetration time of heavy oil and biodiesel is shortened by 0.6 ms when the ambient temperature rises from 300 K to 700 K. The spray area of heavy fuel oil is proportional to the ambient temperature, while that of biodiesel is inversely proportional. The ignition delay of biodiesel is shortened by 1.02 ms and 1.35 ms compared to RMG380 and RME180, respectively. Heavy oil has fewer ignition points, slower flame propagation speed, and a distinct combustion recession phenomenon is observed during the late stage of combustion. The ignition delay of RMG380 is directly proportional to the injection pressure, and the ignition delay of biodiesel increases first and then decreases with the increase of injection pressure. As the injection pressure decreases, the number of ignition points for biodiesel and RMG380 increases, and the difference in ignition characteristics decreases. This research provides both fundamental insights for modeling heavy fuel oil spray and combustion, and practical guidance for optimizing the performance of marine power systems.
Highway–rail grade crossing incidents remain a transportation safety concern, yet prior studies have focused mainly on crash occurrence, severity, and hotspot detection, leaving limited evidence on how injury-or-fatality harm concentrates at crossings with repeated incidents. Using Federal Railroad Administration (FRA) Form 57 records from six U.S. states during 2013–2025, this study quantified repeat-site harm concentration and evaluated leakage-controlled surveillance ranking. The final cohort comprised 8,286 incidents, of which 3,250 involved at least one injury or fatality. Repeat-active crossings, defined as crossings with at least two incidents, accounted for 56.3% of documented harm and exceeded the randomized benchmark (p = 0.013; false-discovery-rate-adjusted p = 0.026). The harm-count Gini coefficient across all observed incident-involved crossings was 0.630, indicating an unequal distribution of documented harm. Leakage-controlled rolling-origin histogram gradient boosting validation yielded a mean area under the precision–recall curve (AUPRC) of 0.721, while unseen-crossing evaluation yielded 0.693; separate leave-one-state-out logistic validation yielded 0.674. Conceptually, the study formalizes repeat-site harm concentration as distinct from incident frequency and separates surveillance prioritization from causal safety mechanisms. The framework supports repeat-site screening and prioritization for further inspection and engineering assessment without implying causal infrastructure deficiencies or intervention effectiveness.
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 mu m, 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.
As one of the high-performance ships, the performance of trimaran is significantly affected by the side hull position. The influence of side-hull positioning on the resistance and seakeeping of trimaran has been widely studied, but the effect on the sailing performance and broaching susceptibility is still not clear enough. In this paper, the autopilot of a trimaran model in waves is simulated by a hybrid method coupling Fully-nonlinear-potential-flow-theory (FNPT) and computational fluid dynamics (CFD), and the effect of the side hull position on the course keeping and broaching susceptibility is studied. The effect of the grid on the computed results is tested first. Then, the autopilot of the trimaran model with different layouts in quartering stern waves is simulated at a relatively high speed that could lead to broaching. By changing the wave steepness, the effect of the layout on the sailing performance and broaching of trimaran is studied. The research results indicate that the arrangement of the side hulls has a significant impact on the vessel's broaching phenomenon and course-keeping performance. Under steep wave conditions, a forward side-hull arrangement increases the vessel's susceptibility to broaching and weakens its course-keeping ability, while a rear side-hull arrangement with a larger transverse spacing demonstrates better directional stability and can effectively reduce the probability of broaching. The systematic analysis in this paper provides new insights for optimizing the stability of trimarans in following quartering seas.
