Modeling and simulating of reservoir operation using the artificial neural network, support vector regression, deep learning algorithm

Reservoirs and dams are vital human-built infrastructures that play essential roles in flood control, hydroelectric power generation, water supply, navigation, and other functions. The realization of those functions requires efficient reservoir operation, and the effective controls on the outflow from a reservoir or dam. Over the last decade, artificial intelligence (AI) techniques have become increasingly popular in the field of streamflow forecasts, reservoir operation planning and scheduling approaches. In this study, three AI models, namely, the backpropagation (BP) neural network, support vector regression (SVR) technique, and long short-term memory (LSTM) model, are employed to simulate reservoir operation at monthly, daily, and hourly time scales, using approximately 30 years of historical reservoir operation records. This study aims to summarize the influence of the parameter settings on model performance and to explore the applicability of the LSTM model to reservoir operation simulation. The results show the following: (1) for the BP neural network and LSTM model, the effects of the number of maximum iterations on model performance should be prioritized; for the SVR model, the simulation performance is directly related to the selection of the kernel function, and sigmoid and RBF kernel functions should be prioritized; (2) the BP neural network and SVR are suitable for the model to learn the operation rules of a reservoir from a small amount of data; and (3) the LSTM model is able to effectively reduce the time consumption and memory storage required by other AI models, and demonstrate good capability in simulating low-flow conditions and the outflow curve for the peak operation period

Performance of Photosensors in the PandaX-I Experiment

We report the long term performance of the photosensors, 143 one-inch R8520-406 and 37 three-inch R11410-MOD photomultipliers from Hamamatsu, in the first phase of the PandaX dual-phase xenon dark matter experiment. This is the first time that a significant number of R11410 photomultiplier tubes were operated in liquid xenon for an extended period, providing important guidance to the future large xenon-based dark matter experiments.Comment: v3 as accepted by JINST with modifications based on reviewers' comment

Prospects of cold dark matter searches with an ultra-low-energy germanium detector

The report describes the research program on the development of ultra-low-energy germanium detectors, with emphasis on WIMP dark matter searches. A threshold of 100 eV is achieved with a 20 g detector array, providing a unique probe to the low-mas WIMP. Present data at a surface laboratory is expected to give rise to comparable sensitivities with the existing limits at the $\rm{5 - 10 GeV}$ WIMP-mass range. The projected parameter space to be probed with a full-scale, kilogram mass-range experiment is presented. Such a detector would also allow the studies of neutrino-nucleus coherent scattering and neutrino magnetic moments.Comment: 3 pages, 4 figures, Proceedings of TAUP-2007 Conferenc

Numerical analysis of integrated forming process of diagonal rolling and piercing of flange nuts

In this paper, Simufact FE software is used to establish a simulation model of three-roll diagonal roll piercing integrated forming flange nut blanks, elaborate its process principle, analyze its forming process through numerical simulation. The law of load change, equivalent plastic strain distribution and wall thickness uniformity during the piercing process and diameter reduction process were investigated, and verify the feasibility of this forming process for manufacturing flange nut blanks