Cornell AUVSI Competition Entry 2005
This paper presents the 2005 Autonomous Underwater Vehicle (AUV) designed by the Cornell University AUV Team. This year's vehicle builds on the team's previous iterations and maintains the aspects that have proven to be successful in the past, including a dual hull structure, brushless motors, explicit separation of hardware by functionality, and flexible software. Significant improvements made this year include a modular sensor infrastructure, signal and power backplanes, enhanced power management, and improved vehicle control.
Modular hardware infrastructure for autonomous underwater vehicles
Autonomous underwater vehicles (AUVs) are taking on increasingly visible and valuable roles in oceanographic research institutions, the offshore industry, and the military (Podder et al., 2004 and Yongkuan, 2002). AUV research, especially at the university level, is hindered by the lack of flexible and affordable platforms suitable for a wide range of applications. This paper presents an electrical and mechanical infrastructure developed to improve the flexibility and modularity of research AUVs. External devices can be added or replaced with minimal vehicle modification. A power backplane offers built-in monitoring and switching for all electronic devices. A signal backplane allows new electronics to be quickly integrated into the existing system. Easily detachable segments make the entire system mechanically modular.
Prediction of a representative point for rail temperature measurement by considering longitudinal deformation
Monitoring rail temperature is very important for determining the safe running speed of trains and to prevent buckling. In general, the maximum variation of the internal rail temperature can be >7 ℃ depending on the point of measurement. However, there is as yet no sufficient information about how to predict the measurement point to represent the thermal deformation due to temperature distribution. In this study, the authors report a new point, called the representative measurement point, at which the rail temperature can be measured. This point considers the average deformation of the rail through structural analysis by adopting experimental and actual rail temperature data. The authors designed and installed a measurement system similar to an actual rail environment. Using the system, various data were acquired (internal/surface rail temperature and weather data) for 10 months. On the basis of these data, an analysis was done to calculate the average deformation point through thermal analysis. Finally, the representative measurement point was proposed as the position at which the average deformation point converges regardless of weather or seasons. The authors believe that the method described herein is advantageous in that it could be used in a high-accuracy temperature-monitoring system and for predicting thermal deformation and buckling.