Evaluation of Pavement Runoff and Driving Safety on Highway Curve Segment

Runoff depth distribution on the concave and circular curve sections is obtained from a two-dimensional numerical simulating model in order to analyze the temporal and spatial variation of the pavement runoff on the curve section. The two-dimensional model verified by the field data can depict the alignment of pavement more accurately as compared to the empirical equation and a one-dimensional model. The runoff on the concave section and circular curve section is compared for the free water drainage and centerline drainage. Results show that a two-dimensional model is essential for the analysis of the centerline drainage. The runoff depth can be controlled by a reasonable curb height and location interval. The drainage type affects the variation of the runoff depth on the nearside lane, and the maximum water depth can be up to more than 80 mm on the concave section and nearly 60 mm on the circular curve section under centerline drainage. Besides the existing hydroplaning results, the runoff depth difference of the wheel trace should be considered to evaluate driving safety. Sideslip will occur when the depth difference becomes more than 6 mm under condition that the runoff depth is less than the tread depth (7 mm). When the runoff depth is more than the tread depth, sideslip will occur once the depth difference exceeds 4 mm.

Research on Simulation Calculation of the Safety of Tight-Lock Coupler Curve Coupling

Once a train breaks down on a busy railway line, it will affect the whole traffic network. However, when a rescue locomotive is hooked up to the broken train for towing it to the next station, it is common that coupling dislocation occurs, which results in damages to couplers and the driver’s cab. To ensure the safety of the trains during the coupling, it becomes crucial to determine whether they can be linked safely and automatically under different line conditions. In this paper, position and pose of the rescue locomotive and the broken train on the line are calculated by geometric analytical calculation method, which determines the position relation of their couplers. Then a so-called “coupling characteristic triangle” was proposed to determine whether trains can be safely and automatically linked on the railway line. The triangles are constructed by the peak points of the couplers head of the front vehicle and the rear one and border lines of secure coupling area on the couplers. By judging the shape of the triangle, it can directly judge whether their couplers can be connected successfully. The method has been applied to check the safety of the trains during coupling on the Nanchang urban railway Line 4. The results show that the maximum swing angle of the coupler reaches 17.3957° in the straight–curve section with a radius of 325 m. At this time, coupling is most difficult, and trains need to be connected manually through the tractor; all the calculation results are verified in the actual line. By comparing different calculation methods for judging coupling safety, it is shown that the method proposed in this paper is accurate, efficient, and users can judge coupling safety more intuitively.

A simulation based large bus side slip and rollover threshold study in slope-curve section under adverse weathers

To study the side slip and rollover threshold of large bus in slope–curve section under adverse weather, factors that affect the safety of large buses that run in slope–curve section, such as rain, snow, cross-wind environmental factors, and road geometry, were analyzed to obtain the friction coefficient of the road surface under different rainfall and snowfall intensities through field measurements and to determine the six-component force coefficient of wind that acts on large buses through wind tunnel tests. The force analysis of large bus in slope-curve section was carried out, and the mechanical equations of large bus under the limit conditions of sideslip and rollover in slope-curve section were established. TruckSim simulation test platform was used to establish a three-dimensional road model and large bus mechanical model at a design speed of 100 km/h. Input parameters, such as cross-wind speed and road friction coefficient, simulate the impact of wind-rain/snow coupling. Under the combined action of wind-rain/snow, the operation test of large bus in slope-curve section was carried out, and the key parameters and indicators of the sideslip and rollover of large bus in slope-curve section were outputted and analyzed. The sudden change point of lateral acceleration is the judging condition for sideslip of large bus in slope-curve section under different road friction coefficient (0.2–0.7), changing from 0.15m/s2 and stabilizing to 0.52 m/s2, and a 0N vertical reaction force of the inner tire is the critical judging condition for rollover under road friction coefficient0.8, and the operating speed thresholds were proposed under different road friction coefficient. This study is expected to provide theoretical support for the speed limit of large bus in slope-curve section under adverse weather.

Machine Learning as New Approach for Dogleg Severity Prediction

Conventionally offset well studies are performed by individuals where the results depend very much on visual perception, interpretation, and experience. In the specific cases for predicting the dogleg severity (DLS) output, the offset well study will take time proportionate to the volume of input, with the results being averaged out and contain high tolerances. In specific projects, these tolerances are larger than accepted, encouraging the service provider to utilize conservative solutions such as rotary steerable system (RSS) with high DLS capability in order to reduce the residual risks. These solutions can often be more costly in terms of maintenance and may add unnecessary tortuosity to the hole leading to issues during execution. This paper explores the concept of using machine learning (ML) to perform offset well study and defining key parameters affecting the DLS output. This concept consolidates the vast volumes of data that have been acquired while drilling and defines the relationship of each parameter to the final output of DLS. The first analysis reviewed five offset wells and found a multivariable correlation between applied drilling parameters to the DLS output. This correlation was then applied in 6 boreholes (3 multilateral wells), observing consistent DLS output increase by 50% using the same technology and optimal drilling parameters. The second analysis uses the same process to determine a planning DLS limit in a curve section over different formations. This paper demonstrates the potential of ML in offset well studies and beyond to predict behavior and define the relationship in a big data environment.

Numerical prediction of the development of rail wear on high-speed railways

In this paper, a numerical prediction model was established to investigate the development of rail wear on high-speed railways, and a corresponding program was written using Matlab. According to Archard’s material wear theory, the wear depth distribution in the wheel–rail contact patch and along the rail profile was calculated based on a simulation of vehicle–track dynamics and a wheel–rail rolling contact analysis. In the dynamics model, various structural components and the complex nonlinear interactions between components were precisely simulated to ensure consistency with reality. Simulations were then conducted for every possible load case, and dimensionless weight factors were introduced to model the diverse operating conditions of a high-speed railway. An adaptive step algorithm was adopted to iteratively update the rail profile and reduce cumulative deviation or errors, improving the stability and reliability of the numerical model. Finally, a case study was conducted to investigate the development of wear in different track sections on a high-speed railway using the developed model. The results indicated that in the circular curve and transition sections, the side wear of the outer rail was obvious, and the wear of the inner rail was relatively smaller and mostly distributed in the middle of the railhead. The wear of the outer rail was more severe in the circular curve section compared to that in the transition sections. The closer to the rail shoulder, the greater the difference between the wear in the circular curve section and that in the transition section. In the tangent section, the wear of both rails was similarly distributed in the middle of the railhead and far less severe than in either the circular curve or transition sections. The agreement between the calculated results and field observations verified the rationality of the established rail wear model, which shows promise for improving the maintenance planning of high-speed railways and furthering the understanding of the rail wear processes.