Objectives: This article investigates the corrosion behavior and mechanical-property changes of S135, G105, and V150 drill pipe materials in a high-temperature-resistant, potassium amino poly-sulfonate drilling fluid, which has good lubrication performance and contains CO₂/H₂S, by applying an 80% yield-limit-load simulation. The results show that the CO₂-corrosion behavior of G105, S135, and V150 drill pipes are obvious under the simulated constant-load-stress-corrosion environments at the wellhead, well-middle, and bottomhole positions. Compared to uncorroded drill pipes, S135 and V150 drill pipes have increased strength and yield ratios, decreased fracture elongation, and increased sensitivity to hydrogen embrittlement under H₂S action, and V150 has a greater risk of stress-hydrogen embrittlement. The strength and yield ratios of G105-material drill pipes decrease, while the fracture elongation increases; the intensity-change amplitude levels are ranked V150 > G105 > S135, and the fracture-elongation-change amplitude is ranked G105 > S135 > V150. The tensile-performance-change amplitude and the SSCC (Sulfide-Stress-Corrosion Cracking) sensitivity of G105 and V150 drill pipes were the highest at the bottomhole. S135 drill pipe materials were most affected by pitting and tensile action at the wellhead, and they had the with the largest SSCC sensitivity.

In large-scale warehouses, mobile robots often face energy shortages during inspection tasks, necessitating multiple charging points. Considering battery limits and multiple charging points makes path planning challenging. This paper presents a two-level solution: (i) local path planning via improved B-RRT* (adaptive Gaussian sampling + dynamic goal bias) to build a path-cost matrix, and (ii) global inspection and charging scheduling under multi-charging-point constraints. We evaluate planning time, total path length (as an energy proxy), and the number of sampling points. Experimental results demonstrate that the improved B-RRT* algorithm achieves an average reduction of 10–15% in path length, 20–30% in computation time, and 15–40% in the number of sampling points compared to the initial B-RRT* and RRT* algorithms across various warehouse environments. For global planning with up to 60 inspection targets and 3–5 charging points, a feasible charging schedule is obtained within 150–360 s on a standard desktop (Ryzen 7 5800H, 16 GB RAM), demonstrating strong practicality and scalability.

With the rapid increase in electric vehicle ownership, fire risks associated with electric vehicles in tunnels have attracted increasing attention. In this study, a numerical model of an electric vehicle fire in a two-lane tunnel with an arched ceiling was established using the Fire Dynamics Simulator. The effects of transverse and longitudinal fire source locations on smoke layer thickness and longitudinal temperature distribution were investigated. Smoke layer thickness was identified using the visibility discontinuity method, and theoretical models incorporating the geometrical characteristics of the arched ceiling were established. The results indicate that fire source location exerts a marked influence on smoke layer development and longitudinal temperature attenuation. The maximum smoke-layer-thickness ratio exceeded unity in all cases and was generally greater downstream than upstream of the fire source. During the quasi-steady stage, the ceiling-temperature rise exhibited exponential decay along the tunnel. Relative to the centerline fire condition, displacement of the fire source to the center of the right lane increased the magnitude of the downstream composite attenuation coefficient by 5.6–11.2%; a further displacement toward the sidewall increased it by 20.6–34.1%. Most predictions from the proposed longitudinal temperature model agreed with the comparison data within ±20%. These findings underscore the need to jointly account for transverse and longitudinal fire-source locations in smoke-control design and evacuation-safety assessment for arched road tunnels.