Quick, efficient, and reliable methods for in-service inspection of rails to ensure the safety of transportation is an open challenge in the railroad industry. It is well known that fatigue cracks are the leading cause of derailments. Furthermore, new high-speed and heavy-load trains have seen increased use, leading to an increase in the loads and number of cycles experienced by a given section of track. Additionally, most methods for inspecting rails require that sections of the track be shut down for inspection. As a result, much industry attention has been paid to the development of nondestructive methods for inspecting whole sections of the track, although a significant gap in inspection needs and capabilities exists, especially with the inspection of rail base. This studied the feasibility of applying Line Scan Thermography (LST) toward detecting defects in the rail base using Finite Element Analysis (FEA) validated by analytical solutions and experiments and simulated the LST inspection in multiple models at speeds up to 40 mph. In the simulations, subsurface fabricated defects were considered to correlate the necessary thermal contrast, amount of energy, and scan speed. The digital twins, when compared to experimental results, showed the same trend. The rail base section model was simulated with 6000 W of heat, and scanning speeds varying from 0.3 mph up to 40 mph with a 150 mm distance showed an exponential decrease in the thermal contrast. However, when the heat power and camera location are changed proportionally to the speed increase, the thermal contrast remains within a change of 1% and 16% for the detectable reflectors. Moreover, the technique was considered feasible if the previous relationship was respected. Further studies regarding this application account for a deeper investigation of this scanning speed and energy relation, developing a Computational Fluid Dynamics model of this problem, and testing samples with surface defects.
| Identifer | oai:union.ndltd.org:siu.edu/oai:opensiuc.lib.siu.edu:theses-4040 |
| Date | 01 December 2022 |
| Creators | Caselato Gandia, Guilherme |
| Publisher | OpenSIUC |
| Source Sets | Southern Illinois University Carbondale |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses |
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