It is necessary to measure accurately the rolling noise generated by the friction between wheels and rails in railway transport systems. Although many systems have recently been developed to measure the surface roughness of wheels and rails, there exist large deviations in measurements between each system whose measuring mechanism is based on a single sensor. To correct the structural problems in existing systems, we developed an automatic mobile measurement platform, named the Automatic Rail Checker (ARCer), which measures the acoustic roughness of a longitudinal railhead profile maintaining a constant speed. In addition, a new chord offset synchronization algorithm has been developed. This uses three displacement sensors to improve the measuring accuracy of the acoustic roughness of a longitudinal railhead profile, thereby minimizing the limitations of mobile platform measurement systems and measurement deviation. We then verified the accuracy of the measurement system and the algorithm through field tests on rails with different surface wear conditions.

Contact and friction between wheel and rail during train operation is the main cause of the rolling noise for which railways are known. Therefore, it is necessary to accurately measure the surface roughness of wheels and rails to monitor railway noise and predict noise around tracks. Conventional systems developed to measure surface roughness have large deviations in measured values or low repeatability. The recently developed automatic mobile measurement platform known as Auto Rail Checker (ARCer) uses three displacement sensors to reduce measurement deviation and increase the accuracy of existing systems. This paper proposes enhancing the chord offset synchronization algorithm applied to the existing ARCer for high measurement precision with only two displacement sensors. As a result, when the two sensor-based measurement algorithm was applied, the spectrum level at $\lambda$ = 0.314 m, the wavelength amplification associated with wheel diameter, was reduced to at least 6 dB in comparison with that of the three sensors based algorithm. We also verified the accuracy of the proposed batch algorithm through a field test on an operating rail track with a corrugated rail surface.

In recent years, a novel skid-steered duct-cleaning mobile platform was developed to remove dust accumulated on the inner surface of an air-ventilation duct with its rolling brushes. During the cleaning process, the irregular brushing pressure acting on the upper arm makes it difficult to control the platform through the duct path. In fact, the repulsive external force due to the brushing pressure is not directly measurable or computable because of the nonlinear deformation of the brush. In addition, dynamic uncertainties in platform motion can occur during reciprocating motion of the upper arm. Therefore, a model-based trajectory-tracking controller is required to control the mobile cleaning platform by considering irregular external forces. The robustness of the developed controller based on the adaptable PD(Proportional-Derivative)-backstepping method has been proposed and evaluated through numerical analysis and experiments. For the turning motion in a narrow space, a skid-steered platform model considering wheel slippage has been also implemented. The result shows that tracking control can be successfully achieved under various conditions of frequencies in brushing-arm motion and torque limitation of the traction motors.

This study aims to establish fundamental steps for developing an optimal methodology to enhance experimental probe calibration. Multihole pressure probes have been extensively used to study the airflow around airfoils, wings, and other surfaces in the field of fluid dynamics. Five-hole probes are broadly used in wind tunnel testing and aerodynamic survey to collect data on velocity, pressure distribution, and flow characteristics. These measurements help in understanding the aerodynamic performance of aircrafts, planes, and aerospace vehicles to improve efficiency and lower environmental costs; however, the calibration process for these probes conventionally demands considerable effort, time, and cost. The purpose of this study is to leverage advanced instrumentation and measurement techniques to ensure optimal calibration of probes, thereby minimizing data uncertainty and maximizing accuracy. The automated calibration facility for data acquisition ensures 0.2–0.9 repeatability coefficients. A computational study suggested an optimal stand-off distance (SOD) from a calibration jet nozzle to read accurate measurements by investigating the flow field around the probe surface and nozzle. To shorten calibration time, experiments for finding an optimized measurement incremental step were implemented and examined by comparing the uncertainty. Experimental calibration maps were generated using nondimensional pressure coefficients to describe flow characteristics within predetermined flow conditions. The experimental calibration map was assessed by quantitative comparison and compared to a high-resolution numerical calibration map. This methodology is expected to facilitate exploration of numerical and experimental calibrations in subsonic flow regimes by obtaining great insight into advanced calibration from the present study.