Thermal Heads for Melt Drilling to Subglacial Lakes: Design and Testing.

Antarctic subglacial lakes are often considered suitable analogues to extraterrestrial subglacial aqueous environments. Recently, an environmentally friendly RECoverable Autonomous Sonde (RECAS) was designed at the Polar Research Center of Jilin University (JLU) to sample the water of subglacial lakes without contamination. In this regard, the development of a fast-penetration thermal head is the key issue for RECAS. Two different prototypes were designed and tested at the JLU ice-well to determine the optimal design and operation parameters of the thermal heads. Practical top and bottom thermal heads were then designed based on one of the prototypes, which can penetrate ice at an average rate of 1.88 m/h. The test results for the RECAS thermal heads show that the rate of penetration (ROP) can be 1.80-1.95 m/h in -10°C ice, and the axial load on the thermal head only affects the ROP when it is lower than a specified threshold. The decrease of the ice temperature from -10°C to -30°C leads to a decrease of 17% in the ROP. The bottom thermal head can drill into dirty ice, and a simple collector positioned above the head can collect solid particles suspended in the melted ice. The top thermal head exhibited a long lifetime and stable heating performance after being powered in water for 2 weeks. In addition, the ice temperature near the borehole was monitored to evaluate the range of heat disturbance caused by the thermal head.

Estimation of the Driving Style Based on the Users' Activity and Environment Influence.

New models and methods have been designed to predict the influence of the user's environment and activity information to the driving style in standard automotive environments. For these purposes, an experiment was conducted providing two types of analysis: (i) the evaluation of a self-assessment of the driving style; (ii) the prediction of aggressive driving style based on drivers' activity and environment parameters. Sixty seven h of driving data from 10 drivers were collected for analysis in this study. The new parameters used in the experiment are the car door opening and closing manner, which were applied to improve the prediction accuracy. An Android application called Sensoric was developed to collect low-level smartphone data about the users' activity. The driving style was predicted from the user's environment and activity data collected before driving. The prediction was tested against the actual driving style, calculated from objective driving data. The prediction has shown encouraging results, with precision values ranging from 0.727 up to 0.909 for aggressive driving recognition rate. The obtained results lend support to the hypothesis that user's environment and activity data could be used for the prediction of the aggressive driving style in advance, before the driving starts.