Bench testing of hybrid ems prototype

Aim: To test the levitation performance of a hybrid EMS prototype. Materials and Methods: a levitation test setup with a 18 mm thick steel rail was constructed on a basis of the certified test bench 1958U-10-1 for measurement in the range up to 100 kN. The attractive force was investigated by varying the air gap size and coil current. Measured data were compared with parametric simulations. Results: Experimental and numerical results agreed with the accuracy required for practical application. Conclusions: A prototype of hybrid EMS (HEMS) for maglev transport has been designed, built, and tested at JSC NIIEFA. The HEMS concept has an advantage of reduced power loss and low stray field. The bench testing has proved good levitation performance and low power consumption of the proposed design. The measured data were used to check design solutions and verify 3D numerical models of the magnets. The comparison demonstrated a good match between measurements and simulations.

REINFORCEMENT UNDER LOAD OF DEFORMED COMPRESSED STEEL RAIL TRUSS

The results of the survey of steel trusses covering six industrial buildings are presented. In particular, the presence of various forms of defects of compressed rods was revealed: general bends in the plane and out of the plane of the truss, local perishes of the shelves - grinding and grinding. The necessity of strengthening the rods for further operation of the structures is determined. The proposed technical solutions for strengthening deformed rods are presented. When developing technical solutions, we tried to achieve, if possible, compensation for the damaged part of the cross-section (for locally damaged rods) and bringing the axis of the repaired rod to the design position (for rods with common bends). To confi rm the eff ectiveness of the proposed solutions, experimental studies were conducted. A brief analysis of the results is given.

Instrumentation Results of New Manual for Assessing Safety Hardware Crash Tested Barriers for William P. Lane, Jr. Bridge

Two new bridge barriers were crash tested in accordance with AASHTO Manual for Assessing Safety Hardware (MASH) guidelines for future use on the William P. Lane Bridge over the Chesapeake Bay: (1) a combination barrier consisting of a reinforced concrete parapet with a top steel rail evaluated for Test Level 4 (TL-4); and (2) a combination barrier consisting of a steel parapet with a top steel rail evaluated for test levels TL-4 and TL-5. For the first test configuration, the reinforced concrete barrier was attached to a representative overhang deck slab using anchor rods. In the vicinity of the vehicle impact points, load cells were installed to measure forces in anchor bolts, and strain gauges were attached to reinforcing bars to resolve measured strain data into forces through the overhang deck slab. In the second test configuration, the steel barrier was supported by evenly spaced representative floorbeams using a bolted base plate connection. Strain gauges were attached to elements of the barrier at support locations adjacent to the vehicle impact point to evaluate force transfer through the barrier system into the base plate connections. Linear potentiometers were installed to measure lateral dynamic deflection of the barrier near the vehicle impact region. This paper presents the analysis results of the force, strain, and displacement data measured in the barrier and deck structural components during crash load testing.

Deep Learning-Based Acoustic Emission Scheme for Nondestructive Localization of Cracks in Train Rails under a Load

This research proposes a nondestructive single-sensor acoustic emission (AE) scheme for the detection and localization of cracks in steel rail under loads. In the operation, AE signals were captured by the AE sensor and converted into digital signal data by AE data acquisition module. The digital data were denoised to remove ambient and wheel/rail contact noises, and the denoised data were processed and classified to localize cracks in the steel rail using a deep learning algorithmic model. The AE signals of pencil lead break at the head, web, and foot of steel rail were used to train and test the algorithmic model. In training and testing the algorithm, the AE signals were divided into two groupings (150 and 300 AE signals) and the classification accuracy compared. The deep learning-based AE scheme was also implemented onsite to detect cracks in the steel rail. The total accuracy (average F1 score) under the first and second groupings were 86.6% and 96.6%, and that of the onsite experiment was 77.33%. The novelty of this research lies in the use of a single AE sensor and AE signal-based deep learning algorithm to efficiently detect and localize cracks in the steel rail, unlike existing AE crack-localization technology that relies on two or more sensors and human interpretation.