Design And Application Of R-T-O Ice And Snow Situation Sensor And Automatic Detection System

Frozen phenomenon and its hazards often occur in the north of the Yellow River, the Heilongjiang river basin, region of China. It is one of the major natural disasters endemic to China. In order to effectively prevent the occurrence of ice disaster, we need to have detailed scientific data of inland river ice and snow, including ice thickness, snow depth, snow layer temperature distribution, stress, etc. At very low temperatures case(below-30°C), manufacturing the new equipment for growth and melting process of ice and snow is a leading subject in that field.This project is a basic research for application which was supported and assisted by the National Natural Science Foundation of China(51279122), completing the design of R-T-O ice and snow detection sensor, which realize synchronization detection of ice inside the equivalent resistance(R), ice vertical temperature gradient(T), the snow layer optical characteristics(O). Cooperating with hydrological bureau of the greater Xing'an mountains in Heilongjiang province and China institute of water resources scientific researchers cooperation, we tested 2014 ~ 2015, 2015 ~ 2016 winter ice and snow situation in MO River. Completing the design of R-T-O ice and snow detection sensor, to automatically obtain data of ice and snow in unattended observation point on river. For the first time by automatically detecting device, we obtained Heilongjiang downstream snow data from 2014 to 2016, and completed analysis of the data of the snow situation. Research contents and research results are summarized as follows:(1) After a review of a large number of references and understanding of domestic and foreign ice detection theory, we determined detection principle of sensor: detection of ice and snow situation based on the differences in physical properties of the medium layers. Through simulation experiment, we summarized threshold variation of different medium layers. Experimental results demonstrated that measurement of physical parameters in different medium layers exist difference, which can determine detection point located in which medium layer by measuring the range of values of the physical quantity, achieving purposes of discrimination of ice thickness and snow depth.(2) According to the range of values of the physical quantity of the medium layer, we conducted design of sensor. In preliminary issue, we designed tentative sensor and made performance testing and data collection from 2014 to 2015 in Mo River. By four months of continuous testing, we obtained a lot of observational data of ice and snow situation and accumulated experience on-site engineering. We optimized the design of the R-T-O sensor to comply with the requirements of the actual project.1) Selected MC901 cast nylon as the new sensor housing material, reducing the weight of sensor and increasing resistance to ice extruded. 2) Increased the cascade function of R-T-O ice and snow detecting sensor. 3) I used FPC technology to change hardware form of R-T-O sensor, which reduce the construction of the sensor itself impact on the test results. 4) Using Pt100 as temperature measurement devices, I proposed and designed multi-point temperature acquisition system. By temperature calibration experiments, we improved the accuracy of temperature measurement. 5) Contrasting with artificial measured data, I summed defects of beam structure, using a reflective design to replace beam structure.(3) Combining with special applications R-T-O ice detecting sensor and the basic process of data collection and transmission, I proposed design scheme of R-T-O ice and snow detection system, including data acquisition circuit, the data transfer circuit, power supply system and insulation system. We made software program and compete joint commissioning of hardware and software program.(4) R-T-O system completed the automatically detection test of the ice situation in the Heilongjiang River site. Get basic data of the snow situation to analysis the data of rivers ice from frozen to growth(the period of the maximum ice thickness), verifying the stability of R-T-O detection theory and the feasibility of sensors. Specially, I analyzed anomaly phenomenon of ice thickness observations in 2015, and survey of local hydrological forecasting staff on-site, verifying the results of the analysis and once again confirmed the performance of the equipment.