Flood is a kind of natural disaster that is extremely harmful and occurs frequently. To reduce losses caused by the hazards, it is urgent to monitor the disaster area timely and carry out rescue operations efficiently. However, conventional space observers cannot achieve sufficient spatiotemporal resolution. As spaceborne GNSS-R technique can observe the Earth’s surface with high temporal and spatial resolutions; and it is expected to provide a new solution to the problem of flood hazards. During 19–21 July 2021, Henan province, China, suffered a catastrophic flood and urban waterlogging. In order to test the feasibility of flood disaster monitoring on a daily basis by using GNSS-R observations, the CYGNSS (Cyclone Global Navigation Satellite System) Level 1 Science Data were processed for a few days before and after the flood to obtain surface reflectivity by correcting the analog power. Afterwards, the flood was monitored and mapped daily based on the analysis of changes in surface reflectivity from spaceborne GNSS-R mission. The results were evaluated based on the image from MODIS (Moderate Resolution Imaging Spectroradiometer) data, and compared with the observations of SMAP (Soil Moisture Active Passive) in the same period. The results show that the area with high CYGNSS reflectivity corresponds to the flooded area monitored by MODIS, and it is also in high agreement with SMAP. Moreover, CYGNSS can achieve more detailed mapping and quantification of the inundated area and the duration of the flood, respectively, in line with the specific situation of the flood. Thus, spaceborne GNSS-R technology can be used as a method to monitor floods with high temporal resolution.

In this paper, according to the design parameters of oil-immersed iron core reactor, the thermal network model of windings is established by the thermo-electric analogy method, and the temperature distribution of the windings can be obtained. Meanwhile, a fluid-thermal coupled finite element model is established, the temperature and fluid velocity distribution are extracted, and the simulation results show that the error coefficient of temperature is less than 3% compared with the thermal network model, so the correctness of thermal network model has been verified. Taking the metal conductor usage and loss of windings as the optimization objects, the optimization method based on the particle swarm algorithm and thermal network model is proposed, and the Pareto optimal solutions between the metal conductor usage and loss of windings are given. The optimization results show that the metal conductor usage is reduced by 23.05%, and the loss is reduced by 20.25% compared with the initial design parameters, and the maximum temperature of winding does not exceed the expected value. Thus, the objects of low metal conductor usage and loss of windings are conflicted and cannot be optimized simultaneously; the optimization method has an important guiding significance for the design of oil-immersed iron core.

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